WO2024129928A2

WO2024129928A2 - Methylation markers for cervical cancer detection and surveillance

Info

Publication number: WO2024129928A2
Application number: PCT/US2023/083935
Authority: WO
Inventors: Saraswati V. Sukumar; Mary Jo Fackler; Deyin XING
Original assignee: The Johns Hopkins University
Priority date: 2022-12-13
Filing date: 2023-12-13
Publication date: 2024-06-20
Also published as: WO2024129928A8; WO2024129928A3

Abstract

A methylated gene marker panel is identified that can distinguish between biopsies and PAP smears of normal cervix from premalignant and malignant cervical cancer. In addition, a panel of markers that distinguish between normal, low-grade, and high grade squamous intraepithelial lesions, and invasive squamous cell carcinoma of the cervix allows for the choice of an appropriate treatment.

Description

METHYLATION MARKERS FOR CERVICAL CANCER DETECTION AND

SURVEILLANCE

The present application claims the benefits of priority from 1) U.S. provisional application no. 63/432,182 filed December 13, 2022; 2) U.S. provisional application no. 63/464,516 filed May 5, 2023; and 3) U.S. provisional application no. 63/522,972 filed June 23, 2023, all of which are incorporated herein by reference in their entirety.

BACKGROUND

[0001] Cervical cancer is the second most common cancer among women in underdeveloped countries and the third leading cause of cancer death in women (1). In 2022 year, an estimated 14,100 women in the United States will be diagnosed with invasive cervical cancer. Worldwide, an estimated 604,127 women were diagnosed with cervical cancer in 2020. Incidence rates of cervical cancer dropped by more than 50% from the mid-1970s to the mid-2000s due in part to an increase in screening, which can find cervical changes before they turn cancerous (1). From 2009 to 2018, incidence rates generally remained the same. It is estimated that 4,280 deaths from this disease will occur in the United States this year. Cervical cancer worldwide is most often diagnosed between the ages of 35 and 44. The average age of diagnosis in the United States is 50; about 20% of cervical cancers are diagnosed after age 65 (1).

[0002] Multiple studies have established HPV as the major etiologic agent in the pathogenesis of cervical dysplasia and carcinoma. HPV16 and HPV18 are high-risk genotypes found in over 70% of high- grade squamous intraepithelial lesions (HSILs) and cervical invasive squamous cell carcinomas (ISCC) (1). Low-grade squamous intraepithelial lesions (LSIL), which represent transient HPV infections that are cleared within two to five years, have a low but recognized risk of malignancy. Progression of LSIL to HSIL has been reported between 6-13.6%. HSILs, on the other hand, are associated with persistent infection and a greater risk of progression to invasive cancer, especially if the persistent infection is a high-risk genotype such as HPV16 and/or HPV 18 (2). Around 60% of women with HSIL cytology will have at least CIN 2 on biopsy, with approximately 2% showing invasive cancer, though the latter is more likely in older women. Women over 30 years of age have an 8% 5-year risk of cervical cancer after a diagnosis of HSIL (2).

SUMMARY [0003] Embodiments of the disclosure are directed to treatment of cervical cancer by detection of biomarkers that distinguish between normal, low-grade, and high grade squamous intraepithelial lesions, and cervical tumors in FFPE tissues and in PAP smears in women.

[0004] Accordingly, in certain aspects, a method of treating a subject suspected of having cancer comprises diagnosing the subject as having cancer, wherein diagnosis comprises determining a methylation profile of biomarkers obtained from a sample from the subject; and, administering to the subject a therapy to treat the cancer.

[0005] In certain embodiments, the biomarkers comprise one or more of ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2, HIST1H3C and combinations thereof.

[0006] In certain embodiments, the biomarkers comprise a panel of biomarkers comprising two or more of ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2, and HIST1H3C. The panel includes a plurality of the biomarkers, such as any 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 or 12 of ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2, and HIST1H3C. Preferred biomarkers panels for use in the present methods and kits include at least 5, 6, 7, 8, 9 or 10 ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2, and HIST1H3C.

[0007] In certain embodiments, the biomarkers are selected from a panel of biomarkers consisting of ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2, and HIST1H3C.

[0008] In certain embodiments, the biomarkers include or consist of ZNF671, EDNRB, TMEFF2, FMN2, MOS and TBXT.

[0009] In certain embodiments, the biomarkers include or consist of ZNF671, EDNRB, FMN2, MOS and TBXT.

[0010] In certain embodiments, the cumulative methylation of biomarkers ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2, HIST1H3C, or combinations thereof, from the subject having cancer is above a threshold value as compared to the cumulative methylation of the biomarkers from healthy subjects, i.e. subjects that have been evaluated as free from cancer, such as the cancer being assessed including cervical cancer. In certain aspects, the cumulative methylation of the biomarkers is above the threshold value. [0011] In certain embodiments, the cumulative methylation of biomarkers ZNF671, EDNRB, FMN2, MOS and TBXT from the subject having cancer is above a threshold value as compared to the cumulative methylation of the biomarkers from healthy subjects, i.e. subjects that have been evaluated as free from cancer, such as the cancer being assessed including cervical cancer. In certain aspects, the cumulative methylation of the biomarkers is above the threshold value.

[0012] In certain embodiments, the magnitude of methylation and/or frequency of methylation of each of the biomarkers ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, or combinations thereof, from the subject having cancer is above a threshold value as compared to each of the biomarkers from healthy subjects, i.e. subjects that have been evaluated as free from cancer, such as the cancer being assessed including cervical cancer. In certain aspects, the magnitude of methylation and/or frequency of methylation of each of the biomarkers is above the threshold value.

[0013] In certain embodiments, the magnitude of methylation and/or frequency of methylation of each of the biomarkers ZNF671, EDNRB, FMN2, MOS and TBXT from the subject having cancer is above a threshold value as compared to each of the biomarkers from healthy subjects, i.e. subjects that have been evaluated as free from cancer, such as the cancer being assessed including cervical cancer. In certain aspects, the magnitude of methylation and/or frequency of methylation of each of the biomarkers is above the threshold value.

[0014] In certain embodiments, the cancer comprises cervical cancer, uterine cancer, or ovarian cancer.

[0015] In certain embodiments, the cancer is cervical cancer.

[0016] In certain embodiments, the present markers can identify cancer including high-risk cervical lesions, irrespective of HPV status. Such identification can be very important for women with varied HPV status, such as subjects with transient hrHPV infections, hrHPV-negative, positive for HPV subtypes of unknown significance, and women who have been vaccinated against commonly oncogenic hrHPV. The present assays and methods of treatment that include detecting methylation of a panel of genes may play a critical role in detecting high grade lesions and cancers in the postvaccination era.

[0017] In certain embodiments, the subject may have a transient HPV infection, or is HPV-negative, and the present markers can assess cervical cancer risk or status.

[0018] In certain embodiments, the present methods and assays may include use of the present markers to determine or assess progression from low risk to high risk lesions (squamous intraepithelial lesions) and thus provide the ability to intervene in disease progression and early treatment of cervical cancer. The methods and assays may include surveillance of HPV-positive and HPV-negative women to determine or assess progression from low risk to high risk lesions.

[0019] In certain embodiments, the therapy for treating cancer comprises: a surgical therapy, a locally applied chemical, chemotherapy, radiation therapy, cryotherapy, hyperthermia treatment, phototherapy, radioablation therapy, hormonal therapy, immunotherapy, small molecule therapy, receptor kinase inhibitor therapy, anti-angiogenic therapy, cytokine therapy, gene therapy, a biological therapy or combinations thereof or other method of treatment of cervical cancer (in other words our markers are treatment-agnostic).

[0020] In certain embodiments, the sample comprises: whole blood, serum, plasma, saliva, buccal swab, cervical pap smears, urine, bladder washing, uterine washing, sputum, lymphatic fluid, cerebrospinal fluid, ascites fluid, cystic fluid, fine needle aspiration, a tissue sample from one or both ovaries, uterus, cervix, frozen tissues, formalin-fixed, paraffin-embedded (FFPE) tissues or metastatic tumor tissue or lymph nodes of the subject.

[0021] In another aspect, a method of treating cervical cancer, comprises obtaining a sample from a subject; determining a methylation profile of a group of biomarkers obtained from a subject’s sample, wherein the biomarkers comprise ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, or combinations thereof, wherein if the methylation profile of the biomarkers is diagnostic of cervical cancer or pre-cancer, the subject is administered a therapy; thereby treating the cervical lesion.

[0022] In another aspect, a method of treating cervical cancer, comprises obtaining a sample from a subject; determining a methylation profile of a group of biomarkers obtained from a subject’s sample, wherein the biomarkers comprise ZNF671, EDNRB, FMN2, MOS, TBXT, or combinations thereof, wherein if the methylation profile of the biomarkers is diagnostic of cervical cancer or pre-cancer, the subject is administered a therapy; thereby treating the cervical lesion.

[0023] In certain embodiments of such methods, the methylation profile is a measure of the magnitude of methylation and frequency of methylation of each of the biomarkers.

[0024] In certain embodiments of such methods, the amount of methylation and/or frequency of methylation of each of the biomarkers ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, or combinations thereof, from the subject having cancer is above a threshold value as compared to each of the biomarkers from healthy subjects, i.e. subjects that have been evaluated as free from cancer, such as the cancer being assessed including cervical cancer. [0025] In certain embodiments of such methods, the amount of methylation and/or frequency of methylation of each of the biomarkers ZNF671, EDNRB, FMN2, MOS, TBXT, or combinations thereof, from the subject having cancer is above a threshold value as compared to each of the biomarkers from healthy subjects, i.e. subjects that have been evaluated as free from cancer, such as the cancer being assessed including cervical cancer.

[0026] In certain embodiments of such methods, the sample comprises: whole blood, serum, plasma, saliva, buccal swab, cervical pap smears, stool, urine, bladder washing, uterine washing, sputum, lymphatic fluid, cerebrospinal fluid, ascites fluid, cystic fluid, fine needle aspiration, a tissue sample from one or both ovaries, uterus, cervix, or lymph node, frozen tissues, formalin-fixed, paraffin-embedded (FFPE) tissues or metastatic tumor tissue, at any site, of the subject.

[0027] In certain embodiments of such methods, the therapy for treating cancer comprises: a surgical therapy, a locally applied chemical, chemotherapy, radiation therapy, cryotherapy, hyperthermia treatment, phototherapy, radioablation therapy, hormonal therapy, immunotherapy, small molecule therapy, receptor kinase inhibitor therapy, anti -angiogenic therapy, cytokine therapy, gene therapy, a biological therapy or combinations thereof or other any newly developed method of treatment of cervical cancer.

[0028] In certain systems, a suitable threshold level of methylation is first determined for a biomarker or multiple (a panel) of markers. A suitable threshold level can be determined from measurements of the biomarker methylation in multiple individuals from a test group, e.g. one or more subjects that are known or believed to be cancer-free, such as free from cervical cancer cohort. The median methylation of the biomarker in said multiple methylation measurements in normal or benign samples is taken as the suitable threshold value.

[0029] In certain systems, one or more biomarkers as disclosed herein can be compared as follows to a threshold level suitably determined as described above: the one or more biomarkers are compared to a respective threshold level, for example the methylation level of the biomarker(s) from a test sample can be evaluated for being above the determined threshold level. For instance, biornarker(s) from a test sample can be above a respective threshold level at a value that is at least about 101%, 102%, 103% (i.e a value that is about 3% or more of the threshold value above the threshold value), 105% (i.e. a value that is about 5% or more of the threshold value above the threshold value), 1 10% (i.e a value that is about 10% or more of the threshold value above the threshold value), 120% (i.e. a value that is about 20% or more of the threshold value above the threshold value), 130% (i.e. a value that is about 30% or more above the threshold value), 140% (i e. a value that is about 40% or more above the threshold value), 150% (i ,e. a value that is about 50% or more above the threshold value), 160% (i.e. a value that is about 60% or more above the threshold value), 170% (i .e. a value that is about 70% or more of the threshold value above the threshold value), 180% (i.e. a value that is about 80% or more above the threshold value), 190% (i.e. a value that is about 90% or more above tire threshold value) or 200% (i.e. a value that is about 100% or more above the threshold value).

[0030] In another aspect, a method of distinguishing between and treating of invasive squamous cell carcinomas (ISCC), high grade squamous intraepithelial lesions (HSIL) and low- grade squamous intraepithelial lesions (LSIL), comprises determining a methylation profile of biomarkers obtained from a sample from the subject; and, administering to the subject a therapy to treat the ISCC, HSIL or LSIL.

[0031] In certain embodiments of such methods, the biomarkers comprise ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2, HIST1H3C or combinations thereof.

[0032] In certain embodiments of such methods, the biomarkers consist of ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2 and HIST1H3C. In certain embodiments, the methylation profile is a measure of the magnitude of methylation and frequency of methylation of each of the biomarkers, or of combinations thereof.

[0033] In certain embodiments of such methods, the magnitude of methylation and frequency of methylation of each of the biomarkers ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, or combinations thereof, from the subject having ISCC or HSIL or LSIL is above a threshold value as compared to biomarkers from healthy subjects and varies with the method of analysis and tissue/fluid used.

[0034] In certain embodiments of such methods, the biomarkers comprise or consist of a panel of biomarkers comprising: ZNF671, EDNRB, TMEFF2, FMN2, MOS and TBXT.

[0035] In certain embodiments of such methods, the biomarkers are selected from a panel of biomarkers consisting of ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2, and HIST1H3C.

[0036] In certain embodiments of such methods, the biomarkers consist of ZNF671, EDNRB, TMEFF2, FMN2, MOS and TBXT. [0037] In certain embodiments of such methods, the biomarkers consist of ZNF671, EDNRB, FMN2, MOS and TBXT.

[0038] In certain embodiments of such methods, the cumulative methylation of biomarkers ZNF671, EDNRB, TMEFF2, FMN2, MOS and TBXT from the subject having ISCC or HSIL or LSIL is above a threshold value as compared to each of the biomarkers from healthy subjects.

[0039] In certain embodiments of such methods, the cumulative methylation of biomarkers ZNF671, EDNRB, FMN2, MOS and TBXT from the subject having ISCC or HSIL or LSIL is above a threshold value as compared to each of the biomarkers from healthy subjects.

[0040] In certain embodiments of such methods, the therapy for treating cancer comprises: a surgical therapy, a locally applied chemical, chemotherapy, radiation therapy, cryotherapy, hyperthermia treatment, phototherapy, radioablation therapy, hormonal therapy, immunotherapy, small molecule therapy, receptor kinase inhibitor therapy, anti -angiogenic therapy, cytokine therapy, gene therapy, a biological therapy or combinations thereof or other method of treatment of cervical cancer.

[0041] In another aspect, the methylation of each of the biomarkers in a panel comprising ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2, and HIST1H3C can be weighted differently and unique thresholds (e.g. normal vs cancer) can be derived for individual markers. In a further aspect, the methylation of each of the biomarkers in a panel comprising or consisting of ZNF671, EDNRB, FMN2, MOS and TBXT can be weighted differently and unique thresholds (e.g. normal vs cancer) can be derived for individual markers. In some embodiments the methylation of a biomarker marker in the sample comprises determining the methylation of one biomarker. In some embodiments, the methylation of biomarkers in the sample comprises determining the collective extent or cumulative methylation of a plurality of biomarkers.

[0042] Any method can be utilized in determining the methylation, cumulative or otherwise, of a panel of biomarkers. Examples, include without limitation QM-MSP assays, cMethDNA assays, DNA methylation arrays, whole genome bisulfite sequencing (WGBS), bisulfite sequencing, reduced representation bisulfite sequencing (RRBS), Array or Bead Hybridization, CpG island arrays, serial analysis of gene expression (SAGE), Pyrosequencing, Methylation-sensitive single-nucleotide primer extension (Ms-SNuPE), Methylation-sensitive single stranded conformation analysis (MS-SSCA), and High resolution melting analysis (HRM) and the like. A review of various techniques which are also applicable here, is provided by S. Kurdyukov and Martyn Bullock, Biology 2016, 5, 3; doi: 10.3390/biology5010003, incorporated herein by reference in its entirety.

[0043] In some embodiments, the methods comprise generating a standard curve for the unmethylated target by using standards. The standard curve is constructed from at least two points and relates the real- time Ct (cycle threshold) value for unmethylated DNA to known quantitative standards. Then, a second standard curve is constructed from at least two points and relates the real-time Ct value for methylated DNA to known quantitative standards. Next, the test sample Ct values are determined for the methylated and unmethylated targets and the genomic equivalents of DNA are calculated from the standard curves produced by the first two steps.

[0044] Methylation can be calculated by various methods depending on the assays used. In some embodiments, the methods comprise using reference DNAs, and may involve generating a standard curve or otherwise comparing the methylated target DNA to the reference DNA. In some embodiments, for example, quantitating methylated target DNA may comprise comparing the Ct of methylated target DNA in a sample to the Ct of a reference gene in the sample. In some embodiments the reference gene may be endogenous to the test sample, such as ACTB, or otherwise may be spiked into the sample. In some embodiments methylated target DNA may be directly quantitated without reference to another molecular entity.

[0045] In another aspect, a kit for diagnosing and treating cervical cancer, comprises one or more assay components to determine methylation profiles of a group of biomarkers comprising or consisting of ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2, HIST1H3C or combinations thereof.

[0046] In a further aspect, a kit for diagnosing and treating cervical cancer, comprises one or more assay components to determine methylation profiles of a group of biomarkers comprising or consisting of ZNF671, EDNRB, FMN2, MOS and TBXT.

[0047] The present kits also may comprise instructions (e.g. written instructions or electronic record) for use of the kit in a method as disclosed herein.

[0048] In certain embodiments, the one or assay components of a kit are for a quantitative multiplex- methylation-specific PCR (QM-MSP) assay or a cMethDNA assay.

[0049] In other embodiments, the one or assay components of a kit are for a whole genome bisulfite sequencing (Ziller M.J., Hansen K.D., Meissner A., Aryee M.J. Coverage recommendations for methylation analysis by whole-genome bisulfite sequencing. Nat. Methods. 2015; 12:230-232. doi: 10.1038/nmeth.3152. Johnson M.D., Mueller M., Game L., Aitman T.J. Single nucleotide analysis of cytosine methylation by whole-genome shotgun bisulfite sequencing. Curr. Protoc. Mol. Biol. 2012 doi: 10.1002/0471142727. mb2123 s99), that is DNA sequencing using bisulfite- converted, MBD-protein enriched DNA , or RRBS (Meissner A , Gnirke A., Bell G.W., Ramsahoye B., Lander E.S., Jaenisch R. Reduced representation bisulfite sequencing for comparative high-resolution DNA methylation analysis. Nucleic Acids Res. 2005;33:5868-5877. doi: 10.1093/nar/gki901) where enrichment of CpG-rich regions is first achieved by isolation of short fragments after MspI digestion that recognizes CCGG sites followed by sequencing or by pyrosequencing. Methylated DNA fractions of the genome, usually obtained by immunoprecipitation, or DNA following bisulfite treatment, could be used for hybridization with microarrays (Marabita F., el al. An evaluation of analysis pipelines for DNA methylation profiling using the Illumina humanmethylation450 beadchip platform. Epigenetics. 2013;8:333-346. doi: 10.4161/epi.24008), or the technique of serial analysis of gene expression (SAGE) has been adapted for this purpose and is known as methylation-specific digital karyotyping (Hu M., Yao J., Polyak K. Methylation-specific digital karyotyping. Nat. Protoc. 2006; 1 : 1621-1636. doi : 10.1038/nprot.2006 278)

[0050] As used to herein, the following marker designations are used interchangeably

Zinc Finger Protein 671 and ZNF671 are used interchangeably;

Endothelin receptor type B and EDNRB are used interchangeably;

Transmembrane Protein With EGF-Like And Two Follistatin-Like Domains and TMEFF2 are used interchangeably;

Formin 2 and FMN2 are used interchangeably;

MOS Proto-Oncogene, Serine/Threonine Kinase and MOS are used interchangeably;

T-Box Transcription Factor T and TBXT are used interchangeably;

Growth-arrest-specific 7 and GAS7C are used interchangeably;

Myelin and lymphocyte protein and MAL are used interchangeably;

Collagen Type VI Alpha 2 Chain and COL6A2 are used interchangeably;

Transmembrane 6 Superfamily Member 1 and TM6SF1 are used interchangeably;

Ras Protein Specific Guanine Nucleotide Releasing Factor 2 and RASGRF2 are used interchangeably; H3 Clustered Histone 3 and HIST1H3C are used interchangeably.

[0051] In preferred embodiments, the biomarkers comprise one or more of:

Zinc Finger Protein 671 (ZNF671), as may be available: HGNC: 26279 NCBI Entrez Gene: 79891 Ensembl: ENSG00000083814 UniProtKB/Swiss-Prot: Q8TAW3),

Endothelin receptor type B (EDNRB), as may be available: HGNC: 3180 NCBI Entrez Gene: 1910 Ensembl: ENSG00000136160 OMIM®: 131244 UniProtKB/Swiss-Prot: P24530);

Transmembrane Protein With EGF-Like And Two Follistatin-Like Domains ((TMEFF2), as may be available: HGNC: 11867 NCBI Entrez Gene: 23671 Ensembl: ENSG00000144339 OMIM®: 605734 UniProtKB/Swiss-Prot: Q9UIK5);

Formin 2 (FMN2), as may be available: HGNC: 14074 NCBI Entrez Gene: 56776 Ensembl: ENSG00000155816 OMIM®: 606373 UniProtKB/Swiss-Prot: Q9NZ56);

MOS Proto-Oncogene, Serine/Threonine Kinase (MOS), as may be available: HGNC: 7199 NCBI Entrez Gene: 4342 Ensembl: ENSG00000172680 OMIM®: 190060 UniProtKB/Swiss-Prot: P00540);

T-Box Transcription Factor T (TBXT), as may be available: HGNC: 11515 NCBI Entrez Gene: 6862 Ensembl: ENSG00000164458 OMIM®: 601397 UniProtKB/Swiss-Prot: 015178);

Growth-arrest-specific 7 (GAS7C), as may be available: Ensembl:ENSG00000007237 MIM:603127;

AllianceGenome:HGNC :4169);

Myelin and lymphocyte protein (MAL), as may be available: HGNC: 6817 NCBI Entrez Gene: 4118 Ensembl: ENSG00000172005 OMIM®: 188860 UniProtKB/Swiss-Prot: P21145);

Collagen Type VI Alpha 2 Chain (COL6A2), as may be available: HGNC: 2212 NCBI Entrez Gene: 1292 Ensembl: ENSG00000142173 OMIM®: 120240 UniProtKB/Swiss-Prot: P12110);

Transmembrane 6 Superfamily Member 1 (TM6SF1), as may be available: HGNC: 11860 NCBI Entrez Gene: 53346 Ensembl: ENSG00000136404 OMIM®: 606562 UniProtKB/Swiss-Prot: Q9BZW5);

Ras Protein Specific Guanine Nucleotide Releasing Factor 2 (RASGRF2), as may be available: HGNC: 9876 NCBI Entrez Gene: 5924 Ensembl: ENSG00000113319 OMIM®: 606614 UniProtKB/Swiss-Prot: 014827);

H3 Clustered Histone 3 (HIST1H3C), as may be available: HGNC: 4768 NCBI Entrez Gene: 8352 Ensembl: ENSG00000287080 OMIM®: 602812 UniProtKB/Swiss-Prot: P68431). [0052] Definitions

[0053] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. Unless specifically defined otherwise, all technical and scientific terms used herein shall be taken to have the same meaning as commonly understood by one of ordinary skill in the art (e.g., molecular genetics, chemistry, and biochemistry).

[0054] As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. Furthermore, to the extent that the terms “including”, “includes”, “having”, “has”, “with”, or variants thereof are used in either the detailed description and/or the claims, such terms are intended to be inclusive in a manner similar to the term “comprising.”

[0055] The term “about” or “approximately” means within an acceptable error range for the particular value as determined by one of ordinary skill in the art, which will depend in part on how the value is measured or determined, i.e., the limitations of the measurement system. For example, “about” can mean within 1 or more than 1 standard deviation, per the practice in the art. Alternatively, “about” can mean a range of up to 20%, up to 10%, up to 5%, or up to 1% of a given value or range. Alternatively, particularly with respect to biological systems or processes, the term can mean within an order of magnitude within 5-fold, and also within 2-fold, of a value. Where particular values are described in the application and claims, unless otherwise stated the term “about” meaning within an acceptable error range for the particular value should be assumed.

[0056] As used in this specification and the appended claims, the term “or” is generally employed in its sense including “and/or” unless the content clearly dictates otherwise.

[0057] The terms “assaying”, “measuring” and “determining” are used interchangeably throughout and refer to methods which include obtaining or providing a patient sample and/or detecting the level and/or methylation of a biomarker(s) in a sample. In one embodiment, the terms refer to obtaining or providing a patient sample and detecting the level and/or methylation of one or more biomarkers in the sample. In another embodiment, the terms “measuring” and “determining” mean detecting the level and/or methylation of one or more biomarkers in a patient sample. The term “measuring” is also used interchangeably throughout with the term “detecting.” In certain embodiments, the term is also used interchangeably with the term “quantitating.”

[0058] The term “biomarker” means a distinctive biological or biologically derived indicator of a process, event or condition. Biomarkers can be used in methods of diagnosis, e.g. clinical screening, and prognosis assessment; and in monitoring the results of therapy, for identifying patients most likely to respond to a particular therapeutic treatment, as well as in drug screening and development. Biomarkers and uses thereof are valuable for identification of new drug treatments and for discovery of new targets for drug treatment. In addition, the term “biomarker” also includes the isoforms and/or post- translationally modified forms of the biomarkers embodied herein. The present disclosure contemplates the detection, measurement, quantification, determination and the like of both unmodified and modified molecule, e.g., methylation. In certain embodiments modifications include methylation, glycosylation, citrullination, phosphorylation, oxidation or other post-translational modification of proteins/polypeptides/peptides. In certain embodiments, it is understood that reference to the detection, measurement, determination, and the like, of a biomarker refers detection of the gene/polynucleotide/oligonucleotide or protein/polypeptide/peptide (modified and/or unmodified). As discussed above, in certain embodiments, the biomarkers comprise Zinc Finger Protein 671 ((ZNF671) HGNC: 26279 NCBI Entrez Gene: 79891 Ensembl: ENSG00000083814 UniProtKB/Swiss-Prot: Q8TAW3), Endothelin receptor type B ((EDNRB) HGNC: 3180 NCBI Entrez Gene: 1910 Ensembl: ENSG00000136160 OMIM®: 131244 UniProtKB/Swiss-Prot: P24530), Transmembrane Protein With EGF-Like And Two Follistatin-Like Domains ((TMEFF2), HGNC: 11867 NCBI Entrez Gene: 23671 Ensembl: ENSG00000144339 OMIM®: 605734 UniProtKB/Swiss-Prot: Q9UIK5), Formin 2 ((FMN2) HGNC: 14074 NCBI Entrez Gene: 56776 Ensembl: ENSG00000155816 OMIM®: 606373 UniProtKB/Swiss-Prot: Q9NZ56), MOS Proto-Oncogene, Serine/Threonine Kinase ((MOS) HGNC: 7199 NCBI Entrez Gene: 4342 Ensembl: ENSG00000172680 OMIM®: 190060 UniProtKB/Swiss-Prot: P00540), T-Box Transcription Factor T ((TBXT) HGNC: 11515 NCBI Entrez Gene: 6862 Ensembl: ENSG00000164458 OMIM®: 601397 UniProtKB/Swiss-Prot: 015178), Growth-arrest-specific 7 ((GAS7C), Ensembl :ENSG00000007237 MIM:603127; AllianceGenome:HGNC:4169), Myelin and lymphocyte protein ((MAL), HGNC: 6817 NCBI Entrez Gene: 4118 Ensembl: ENSG00000172005 OMIM®: 188860 UniProtKB/Swiss-Prot: P21145), Collagen Type VI Alpha 2 Chain ((COL6A2), HGNC: 2212 NCBI Entrez Gene: 1292 Ensembl: ENSG00000142173 OMIM®: 120240

UniProtKB/Swiss-Prot: P12110), Transmembrane 6 Superfamily Member 1 ((TM6SF1), HGNC: 11860 NCBI Entrez Gene: 53346 Ensembl: ENSG00000136404 OMIM®: 606562 UniProtKB/Swiss-Prot: Q9BZW5), Ras Protein Specific Guanine Nucleotide Releasing Factor 2 ((RASGRF2), HGNC: 9876 NCBI Entrez Gene: 5924 Ensembl: ENSG00000113319 OMIM®: 606614 UniProtKB/Swiss-Prot: 014827), H3 Clustered Histone 3 ((HIST1H3C), HGNC: 4768 NCBI Entrez Gene: 8352 Ensembl: ENSG00000287080 OMIM®: 602812 UniProtKB/Swiss-Prot: P68431). [0059] As used herein, the terms “comprising,” “comprise” or “comprised,” and variations thereof, in reference to defined or described elements of an item, composition, apparatus, method, process, system, etc. are meant to be inclusive or open ended, permitting additional elements, thereby indicating that the defined or described item, composition, apparatus, method, process, system, etc. includes those specified elements— or, as appropriate, equivalents thereof— and that other elements can be included and still fall within the scope/definition of the defined item, composition, apparatus, method, process, system, etc.

[0060] “Diagnostic” or “diagnosed” means identifying the presence or nature of a pathologic condition. Diagnostic methods differ in their sensitivity and specificity. The “sensitivity” of a diagnostic assay is the percentage of diseased individuals who test positive (percent of “true positives”). Diseased individuals not detected by the assay are “false negatives.” Subjects who are not diseased and who test negative in the assay, are termed “true negatives.” The “specificity” of a diagnostic assay is 1 minus the false positive rate, where the “false positive” rate is defined as the proportion of those without the disease who test positive. While a particular diagnostic method may not provide a definitive diagnosis of a condition, it suffices if the method provides a positive indication that aids in diagnosis. The term “predisposition” as used herein means that a subject does not currently present with the dysfunction but is liable to be affected by the dysfunction in time. Methods of diagnosis according to the disclosure are useful to confirm the existence of a dysfunction, or predisposition thereto. Methods of diagnosis are also useful in methods for assessment of clinical screening, prognosis, choice of therapy, evaluation of therapeutic benefit, i.e., for drug screening and drug development.

[0061] As used herein, the terms “comparing” or “comparison” refers to making an assessment of how the proportion, level and/or methylation or cellular localization of one or more biomarkers in a sample from a patient relates to the proportion, level and/or methylation or cellular localization of the corresponding one or more biomarkers in a standard or control sample. For example, “comparing” may refer to assessing whether the proportion, level, and/or methylation or cellular localization of one or more biomarkers in a sample from a patient is the same as, more or less than, or different from the proportion, level and/or methylation or cellular localization of the corresponding one or more biomarkers in standard or control sample. More specifically, the term may refer to assessing whether the proportion, level and/or methylation or cellular localization of one or more biomarkers in a sample from a patient is the same as, more or less than, different from or otherwise corresponds (or not) to the proportion, level and/or methylation, or cellular localization of predefined biomarker methylation levels/ratios that correspond to, for example, a patient having cervical cancer, not having cervical cancer, is responding to treatment for cervical cancer, is not responding to treatment for cervical cancer, is/is not likely to respond to a particular cervical cancer treatment, or having/not having another disease or condition. In a specific embodiment, the term “comparing” refers to assessing whether the level and/or methylation of one or more biomarkers of the present disclosure in a sample from a patient is the same as, more or less than, different from other otherwise correspond (or not) to methylation levels/ratios of the same biomarkers in a control sample (e.g., predefined levels/ratios that correlate to uninfected individuals etc.).

[0062] The terms “differential methylation”, “differential methylation status” or “differential methylation level” indicate a difference in the methylation status and/or methylation level when comparing two or more samples, groups of samples, biomarkers or genomic loci.

[0063] As used herein, the term “kit” refers to any delivery system for delivering materials. In the context of cell sampling devices, such delivery systems include systems that allow for the storage, transport, delivery, or use of devices and/or for processing samples obtained with devices (e.g., drinkable solutions, lubricants, or anesthetics for use of a swallowable device, sample stabilizing reagents; sample processing reagents such as particles, buffers, denaturants, oligonucleotides, filters, assay reaction components, etc. in the appropriate containers) and/or supporting materials (e.g., sample processing or sample storage vessels, written instructions for performing a procedure, etc.) from one location to another. For example, kits include one or more enclosures (e.g., boxes) containing the relevant sampling device and reagents and/or supporting materials. As used herein, the term “fragmented kit” refers to a delivery system comprising two or more separate containers that each contains a subportion of the total kit components. The containers may be delivered to the intended recipient together or separately. For example, a first container may contain materials for sample collection and a buffer, while a second container contains capture oligonucleotides and denaturant. The term “fragmented kit” is intended to encompass kits containing Analyte specific reagents (ASR's) regulated under section 520(e) of the Federal Food, Drug, and Cosmetic Act, but are not limited thereto. Indeed, any delivery system comprising two or more separate containers that each contains a subportion of the total kit components are included in the term “fragmented kit.” In contrast, a “combined kit” refers to a delivery system containing all of the components for sample collection, processing, and assaying in a single container (e.g., in a single box housing each of the desired components). The term “kit” includes both fragmented and combined kits.

[0064] As used herein, “methylation” refers to nucleic acid or amino acid methylation. In vitro amplified DNA is usually unmethylated because typical in vitro DNA amplification methods do not retain the methylation pattern of the amplification template. However, “unmethylated DNA” or “methylated DNA” can also refer to amplified DNA whose original template was unmethylated or methylated, respectively.

[0065] Accordingly, as used herein a “methylated nucleotide” or a “methylated nucleotide base” or a “methylated amino acid” or “methylated peptide” refers to the presence of a methyl moiety on a nucleotide base or amino acid, where the methyl moiety is not present in a recognized typical nucleotide base or amino acid. For example, cytosine does not contain a methyl moiety on its pyrimidine ring, but 5- methylcytosine contains a methyl moiety at position 5 of its pyrimidine ring. Therefore, cytosine is not a methylated nucleotide and 5-methylcytosine is a methylated nucleotide. In another example, thymine contains a methyl moiety at position 5 of its pyrimidine ring; however, for purposes herein, thymine is not considered a methylated nucleotide when present in DNA since thymine is a typical nucleotide base of DNA.

[0066] As used herein, a “methylated nucleic acid molecule” refers to a nucleic acid molecule, e.g. polynucleotide, oligonucleotide, that contains one or more methylated nucleotides.

[0067] As used herein, a “methylated biomarker” refers to either a methylated nucleic acid sequence (e.g. gene, polynucleotide or oligonucleotide) or a methylated amino acid sequence (e.g. protein, polypeptide, oligopeptide).

[0068] As used herein, a “methylation state”, “methylation profile”, and “methylation status” of a nucleic acid or amino acid molecule refers to the presence of absence of one or more methylated nucleotide bases in the nucleic acid molecule or amino acids in a peptide. For example, a nucleic acid molecule containing a methylated cytosine is considered methylated (e.g., the methylation state of the nucleic acid molecule is methylated). Protein methylation is perhaps most common at lysine and arginine residues. However, there are many other sites for such modification in proteins including histidine, glutamate, glutamine, asparagine, D-aspartatel/L-isoaspartate, cysteine, N-terminal, and C-terminal residues. A nucleic acid molecule or peptide that does not contain any methylated nucleotides or amino acid residues is considered unmethylated.

[0069] The methylation state of a particular nucleic acid sequence (e.g., a gene biomarker or DNA region as described herein) can indicate the methylation state of every base in the sequence or can indicate the methylation state of a subset of the bases (e.g., of one or more cytosines) within the sequence, or can indicate information regarding regional methylation density within the sequence with or without providing precise information of the locations within the sequence the methylation occurs. The methylation state of a particular peptide can be identified by, for example, methylation-specific antibodies, mapping of post-translational modifications by mass spectrometry, and radioactive labeling to characterize methylation on target proteins. See, also Carlson SM, Gozani O. Emerging technologies to map the protein methylome. J Mol Biol. 2014 Oct 9;426(20):3350-62. doi: 10.1016/j.jmb.2014.04.024. Epub 2014 May 5. PMID: 24805349; PMCID: PMC4177301. Sebastian KapelL Magnus E Jakobsson, Large-scale identification of protein histidine methylation in human cells, NAR Genomics and Bioinformatics, Volume 3, Issue 2, June 2021, lqab045, doi.org/10.1093/nargab/lqab045.

[0070] The methylation status can optionally be represented or indicated by a “methylation value” (e.g., representing a methylation frequency, fraction, ratio, percent, etc.) A methylation value can be generated, for example, by quantifying the amount of intact nucleic acid present following restriction digestion with a methylation dependent restriction enzyme or by comparing amplification profiles after bisulfite reaction or by comparing sequences of bisulfite-treated and untreated nucleic acids. Accordingly, a value, e.g., a methylation value, represents the methylation status and can thus be used as a quantitative indicator of methylation status across multiple copies of a locus. This is of particular use when it is desirable to compare the methylation status of a sequence in a sample to a threshold or reference value.

[0071] As used herein, “cumulative methylation” of a panel of genes, is calculated as the sum of methylation of the panel of genes.

[0072] The calculation of methylation can vary depending on the assays used. For example, for an individual serum sample, cMethDNA calculations are as follows: methylation index = [Methylated TARGETgene copies/(Methylated TARGETgene + STDgene) copies] (100); and cumulative methylation index (CMI) = the sum of all methylation index values within the gene panel. For an individual sample, QM-MSP calculations: % methylation (%M) = [methylated TARGETgene copies/(methylated TARGETgene /unmethylated TARGETgene) copies](100); CMI = the sum of all %M values within the panel (Mary Jo Fackler et al., Cancer Res,' 74(8) 2160-74, April 15, 2014). Other methods for calculating quantitation of DNA methylation in a panel of biomarkers include the method described in Bradley M. Downs et al. Clin Cancer Res. 2019 November 01; 25(21): 6357-6367. doi: 10.1158/1078-0432.CCR-18- 3277. Briefly, Ct values were obtained for methylated targets and ACTB reference (Ct = the cycle threshold at which signal fluorescence exceeds background). For calculating % methylation, the A Ct (Ct Gene - Ct ACTB) value of each target gene was extrapolated from historical standard curves of mixtures of methylated and unmethylated DNA ranging from 100% to 3% methylation. This enabled quantitation of cumulative methylation (CM), which is the sum of % methylation for all genes in the marker panel. Mary Jo Fackler et al. Cancer Res Commun. 2022 June; 2(6):391-401. doi: 10.1158/2767-9764. crc-22- 0133 described a liquid biopsy method. Briefly, Ct values were obtained for methylated targets and ACTB reference. For calculating methylation, a novel algorithm was used wherein gene methylation (M) = [1/ACt (Ct gene-Ct ACTB)]*1200. This enabled quantitation of cumulative methylation (CM), which is the sum of the individual methylation for all genes in the marker panel.

[0073] As used herein, “methylation frequency” or “methylation percent (%)” refer to the number of instances in which a molecule or locus is methylated relative to the number of instances the molecule or locus is unmethylated. % M = 100 x [no. of copies of methylated DNA/(no. of copies of methylated + unmethylated DNA)]. The sum of unmethylated plus methylated DNA (U + M) is used as an approximation of the total number of copies present of a target gene.

[0074] As used herein, “methylation state” describes the state of methylation of a nucleic acid (e.g., a genomic sequence) or amino acid (e.g., a protein sequence). In addition, the methylation state refers to the characteristics of a nucleic acid segment at a particular genomic locus relevant to methylation. Such characteristics include, but are not limited to, whether any of the cytosine (C) residues within this DNA sequence are methylated, the location of methylated C residue(s), the frequency or percentage of methylated C throughout any particular region of a nucleic acid, and allelic differences in methylation due to, e.g., difference in the origin of the alleles. The terms “methylation state”, “methylation profile”, and “methylation status” also refer to the relative concentration, absolute concentration, or pattern of methylated C or unmethylated C throughout any particular region of a nucleic acid in a biological sample. For example, if the cytosine (C) residue(s) within a nucleic acid sequence are methylated it may be referred to as “hypermethylated” or having “increased methylation”, whereas if the cytosine (C) residue(s) within a DNA sequence are not methylated it may be referred to as “hypomethylated” or having “decreased methylation”. Likewise, if the cytosine (C) residue(s) within a nucleic acid sequence are methylated as compared to another nucleic acid sequence (e.g., from a different region or from a different individual, etc.) that sequence is considered hypermethylated or having increased methylation compared to the other nucleic acid sequence. Alternatively, if the cytosine (C) residue(s) within a DNA sequence are not methylated as compared to another nucleic acid sequence (e.g., from a different region or from a different individual, etc.) that sequence is considered hypomethylated or having decreased methylation compared to the other nucleic acid or amino acid sequences. Additionally, the term “methylation pattern” as used herein refers to the collective sites of methylated and unmethylated nucleotides over a region of a nucleic acid or peptide. Two nucleic acids or amino acids may have the same or similar methylation frequency or methylation percent but have different methylation patterns when the number of methylated and unmethylated nucleotides are the same or similar throughout the region but the locations of methylated and unmethylated nucleotides are different. Sequences are said to be “differentially methylated” or as having a “difference in methylation” or having a “different methylation state” when they differ in the extent (e.g., one has increased or decreased methylation relative to the other), frequency, or pattern of methylation. The term “differential methylation” refers to a difference in the level or pattern of nucleic acid methylation or amino acid methylation in a cervical cancer positive sample as compared with the level or pattern of nucleic acid or amino acid methylation in a cervical cancer negative sample. It may also refer to the difference in levels or patterns between patients who have recurrence of cervical cancer after surgery versus patients who do not have recurrence. Differential methylation and specific levels or patterns of DNA or protein methylation are prognostic and predictive biomarkers, e.g., once the correct cut-off or predictive characteristics have been defined.

[0075] Methylation state frequency can be used to describe a population of individuals or a sample from a single individual. For example, a nucleotide locus having a methylation state frequency of 50% is methylated in 50% of instances and unmethylated in 50% of instances. Such a frequency can be used, for example, to describe the degree to which a nucleotide locus or nucleic acid region is methylated in a population of individuals or a collection of nucleic acids. Thus, when methylation in a first population or pool of nucleic acid molecules is different from methylation in a second population or pool of nucleic acid molecules, the methylation state frequency of the first population or pool will be different from the methylation state frequency of the second population or pool. Such a frequency also can be used, for example, to describe the degree to which a nucleotide locus or nucleic acid region is methylated in a single individual. For example, such a frequency can be used to describe the degree to which a group of cells from a tissue sample are methylated or unmethylated at a nucleotide locus or nucleic acid region.

[0076] The term “one or more of’ refers to combinations of various biomarkers. The term encompasses 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40 . . . N, where “N” is the total number of biomarkers in the particular embodiment. The term also encompasses at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 15, 16, 17, at least 18, at least 19, at least 20, at least 21, at least 22, at least 23, at least 24, at least 25, at least 26, at least 27, at least 28, at least 29, at least 30, at least 31, at least 32, at least 33, at least 34, at least 35, at least 36, at least 37, at least 38, at least 39, at least 40 . . . N. It is understood that the recitation of biomarkers herein includes the phrase “one or more of’ the biomarkers and, in particular, includes the “at least 1, at least 2, at least 3” and so forth language in each recited embodiment of a biomarker panel.

[0077] The terms “sample,” “patient sample,” “biological sample,” and the like, encompass a variety of sample types obtained from a patient, individual, or subject and can be used in a diagnostic, prognostic and/or monitoring assay. The patient sample may be obtained from a healthy subject, a diseased patient or a patient having associated symptoms of cervical cancer. In particular embodiments, a “sample” (e.g., a test sample) from a subject refers to a sample that might be expected to contain elevated levels and/or methylation of the protein biomarkers of the disclosure in a subject having cervical cancer. In certain embodiments, a sample that is “provided” can be obtained by the person (or machine) conducting the assay, or it can have been obtained by another, and transferred to the person (or machine) carrying out the assay.

[0078] As used herein, the “sensitivity” of a given biomarker (or set of biomarkers used together) refers to the percentage of samples that report a DNA or protein methylation value above a threshold value that distinguishes between neoplastic and non-neoplastic samples. In some embodiments, a positive is defined as a histology-confirmed cervical cancer that reports a DNA or protein methylation value above a threshold value (e.g., the range associated with disease), and a false negative is defined as a histology- confirmed cervical cancer that reports a DNA or protein methylation value below the threshold value (e.g., the range associated with no disease). The value of sensitivity, therefore, reflects the probability that a DNA or protein methylation measurement for a given biomarker obtained from a known diseased sample will be in the range of disease-associated measurements. As defined here, the clinical relevance of the calculated sensitivity value represents an estimation of the probability that a given biomarker would detect the presence of a clinical condition when applied to a subject with that condition.

[0079] As used herein, the “specificity” of a given biomarker (or set of biomarkers used together) refers to the percentage of non- cervical cancer samples, including ovarian and uterine cancers that report a DNA or protein methylation value below a threshold value that distinguishes between cervical cancer and non- cervical cancer samples. In some embodiments, a negative is defined as a histology-confirmed non- cervical cancer sample that reports a DNA or protein methylation value below the threshold value (e.g., the range associated with no disease) and a false positive is defined as a histology-confirmed non- cervical cancer sample that reports a DNA or protein methylation value above the threshold value (e.g., the range associated with disease). The value of specificity, therefore, reflects the probability that a DNA or protein methylation measurement for a given biomarker obtained from a known non- cervical cancer sample will be in the range of non-disease associated measurements. As defined here, the clinical relevance of the calculated specificity value represents an estimation of the probability that a given biomarker would detect the absence of a clinical condition when applied to a patient without that condition.

[0080] Where any nucleotide or amino acid sequence is specifically referred to by a Swiss Prot. or GENBANK Accession number, the sequence is incorporated herein by reference. Information associated with the accession number, such as identification of signal peptide, extracellular domain, transmembrane domain, promoter sequence and translation start, is also incorporated herein in its entirety by reference.

[0081] Ranges: throughout this disclosure, various aspects of the disclosure can be presented in a range format. It should be understood that the description in range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the disclosure. Accordingly, the description of a range should be considered to have specifically disclosed all the possible subranges as well as individual numerical values within that range. For example, description of a range such as from 1 to 6 should be considered to have specifically disclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numbers within that range, for example, 1, 2, 2.7, 3, 4, 5, 5.3, and 6. This applies regardless of the breadth of the range.

[0082] Any compositions or methods provided herein can be combined with one or more of any of the other compositions and methods provided herein.

BRIEF DESCRIPTION OF THE DRAWINGS

[0083] The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawings will be provided by the Office upon request and payment of the necessary fee.

[0084] FIG. l is a series of plots demonstrating that invasive squamous cell carcinomas (ISCC) and high grade squamous intraepithelial lesions (HSIL) showed high levels of cumulative methylation in the 12 genes, low- grade squamous intraepithelial lesions (LSIL) show higher than normal methylation in a small subset, and adjacent normal tissues have very low to no detectable methylation.

[0085] FIG. 2 is a series of graphs demonstrating the extent (% M) and frequency of methylation of each gene in the panel. [0086] FIG. 3 is a series of plots and a histogram demonstrating that that both ISCC and HSIL contained high levels of methylation in the six genes, LSILs had significant detectable cumulative methylation in some samples, and normal showed very little methylation in the panel of six genes examined by QM-MSP.

[0087] FIG. 4 is a graph demonstrating results from archival PAP smears. PAP smears from ISCC displayed the highest levels of cumulative methylation, HSILs contained a range of methylation, and normal cervical smears (NEIL) showed low to no methylation.

[0088] FIG. 5 is a bar chart depicting Bar Chart of Region-Specific Incidence and Mortality Age- Standardized Rates for Cancers of the Cervix in 2018. Rates are shown in descending order of the world (W) age-standardized rate, and the highest national age-standardized rates for incidence and mortality are superimposed. Source: GLOBOCAN 2018.

[0089] FIG. 6A shows detection of cervical cancer of HPV-negative samples using the present markers; and FIG. 6B shows graphically DNA methylation analysis of cervical cancer samples using the present markers (Example 2).

[0090] FIG. 7 shows samples used for technical validation of the 5-marker panel. Technical validation of the 5-marker panel was performed using Quantitative Multiplex -Methylation Specific PCR (QM-MSP) on archival formalin fixed paraffin embedded (FFPE)-tissue and cervical smear samples. SCC- Squamous cell carcinoma; CIN2/3- Cervical intraepithelial neoplasia 2/3; CIN1- Cervical intraepithelial neoplasia 1; HSIL- High grade intraepithelial lesion, LSIL-low grade intraepithelial lesion; CIN2- Cervical intraepithelial neoplasia 2.

[0091] FIG 8 (includes FIGS. 8A-8C). Marker discovery in The Cancer Genome Atlas-Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (TCGA-CESC) and Uterine Corpus Endometrial Carcinoma (TCGA-UCEC) databases. FIG. 8A. Principal component analysis of 485,000 probes shows a clear visual separation of cervical cancer (orange/red) and normal (green/pink) tissue samples. FIGS. 8B, 8C. Histogram plots of Cumulative [3-Methylation in the indicated numbers of carcinomas and normal samples is shown (FIG. 8B) for 14 markers and, (FIG. 8C) for the final 5 markers in two histological subtypes of cervical carcinoma- squamous cell carcinoma (SCC, N=254) and adenocarcinoma (AC, N=53). Also, in (FIG. 8C), Mann-Whitney box plots show Cumulative [3- Methylation in SCC and AC samples for the five-marker panel indicating a lower, but not statistically significant difference in methylation (P = 0.139) between the two histological subtypes. In the next panel, results of Receiver Operator Curve Area Under the Curve (ROC AUC) analysis are shown. The sensitivity and specificity were based on the 95th percentile of Cumulative P-Methylation in normal samples (dotted line, histogram). FIG. 13 contains additional details of the marker selection process. Tables 1 and 2 below provide additional probe information. Abbreviations: SCC, squamous cell carcinoma; AC, adenocarcinoma.

[0092] FIG. 9 (includes FIGS. 9A-9C) Technical validation of the 5-marker panel in archival tissue. Quantitative Multiplex-Methylation Specific PCR (QM-MSP) was performed on FFPE tissue sections from FIG. 9A. the United States (U.S.) (N = 63), FIG 9B. South Africa (S. Africa) (N = 69) and FIG. 9C. Vietnam (N = 120). The histogram bar indicates the magnitude of Cumulative Methylation-5 (CM-5) (Y-axis) in each sample (X-axis). Box and whisker plots in A, B and C show comparisons of CM-5 between groups as indicated. Receiver Operator Curve Area Under the Curve (ROC AUC) results are shown. Sensitivity and specificity were based on the 95th percentile of CM-5 in normal samples in each region (dotted line in histogram). Performance of individual markers from U.S. samples is shown in Figure 16. SCC- Squamous cell carcinoma; CIN2/3- Cervical intraepithelial neoplasia 2/3; CIN1- Cervical intraepithelial neoplasia 1.

[0093] FIG. 10 (includes FIGS. 10A-10C). Detection of cervical cancer and high-grade lesions in cervical smears. Quantitative Multiplex-Methylation Specific PCR (QM-MSP) was performed on cervical smears (N = 244 total) from the U.S. (N = 77), Vietnam (N =117), and S. Africa (N = 50) and data was pooled for analyses. FIG. 10A. Histogram indicates the magnitude of Cumulative Methylation-5 (CM- 5) (Y-axis) for each sample (X-axis). FIG. 10B: Box and whisker plot shows comparison of CM-5 in samples of cervical smears from normal (N), low grade squamous intraepithelial lesion (LSIL), high grade squamous intraepithelial lesion (HSIL) and squamous cell carcinoma (SCC) (P < 0.0001, Mann- Whitney). FIG. 10C. Receiver Operator Curve Area Under the Curve (ROC AUC) results are shown. Sensitivity and specificity were based on the 95th percentile of CM-5 in normal samples (dotted line in histogram).

[0094] FIG. 11 (includes FIGS. 11A-11D). Paired tissue and cervical smear analysis. Ninety-two samples of paired tissue (T) and cervical smears (CS) from Vietnam were tested. Histograms indicate the Cumulative Methylation-5 (CM-5) levels obtained by QM-MSP in patients diagnosed with FIG. 11A: squamous cell carcinoma (SCC); FIG. 11B: high grade squamous intraepithelial lesion (HSIL); FIG. 11C: low grade squamous intraepithelial lesion (LSIL); or FIG. 11D: Benign lesion. Data was compiled from samples shown in FIG. 9C and FIG. 18B. [0095] FIG. 12 (includes FIGS. 12A-12B). Human papilloma virus (HPV)-positive and HPV-negative cervical carcinomas are highly methylated for the 5-marker panel. Histograms and box plots of Cumulative P Methylation in the 5-marker panel in HPV-positive and HPV-negative carcinomas in FIG. 12A: The Cancer Genome Atlas-Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (TCGA-CESC) and Uterine Corpus Endometrial Carcinoma (UCEC) datasets; 17/307 primary tumors were HPV-negative; FIG. 12B: The Genomic Spatial Event (GSE) GSE68339 database; 20/268 SCC were HPV-negative.

[0096] FIG. 13 Marker discovery workflow. Array data from cervical tumor and normal cervical and uterine samples from TCGA (CESC and UCEC, respectively) were analyzed. Among 485,000 total Cytosine-phosphate-Guanidine (CpG) probes, 4534 probes were differentially methylated in cervical cancer. These were found to be highly methylated in cancer but not in normal. In a stepwise manner, shown in the figure, five that displayed the lowest beta methylation at the highest frequency (80%) in normal were selected for further evaluation by Quantitative Multiplex Methylation Specific PCR (QM- MSP) in samples from United States, South Africa and Vietnam. Tissue samples were used for initial screening of the markers. Cervical smear samples were used to validate the markers. Methylation in Human Papilloma Virus (HPV)-positive and HPV-negative tumors was evaluated in TCGA and GSE68339 databases. Tables 1 and 2 below contain specific probe Identification (ID) information, location, tumor/normal ratio of methylation and functional characteristics of CpG sites in regions represented in the 5 -gene panel.

[0097] FIG. 14 (includes FIGS. 14A-14D): Validation of the 5-marker panel using external databases. Histograms indicate cumulative [3-methylation (Y-axis) for the 5-marker panel in each sample (X-axis). The height of each colored segment represents the intensity of the beta methylation signal in each of the 5 markers. FIG. 14A: TCGA cervical cancer 450K Illumina array platform was used for marker discovery. FIG. 14B: GSE68339, FIG. 14C: GSE211668 and FIG. 14D: GSE143752 data were analyzed to validate TCGA data presented in A. FIG. 13 contains additional details of the marker selection process.

[0098] FIG. 15 (includes FIGS. 15A-15B): Association between methylation of the five CpG markers and gene expression. The Cancer Genome Atlas (TCGA) cervical cancer 450K array data and RNA sequencing data were plotted for each of the markers in normal (N = 48, 45 uteri, 3 cervix) and tumor (N = 307). The data are presented as box and whiskers plots with Mann-Whitney statistics. FIG. 15A: Significantly higher methylation was observed in tumor compared to normal (P < 0.0001) for all five markers. FIG. 15B: Analysis of RNA sequencing data for the same markers showed low expression levels for ZNF671, EDNRB and FMN2 in tumors compared to normal. For two markers, TBXT and MOS, such a correlation between high methylation and low expression was not observed. Expression was low in both normal and tumor samples.

[0099] FIG. 16: Contribution of individual markers of the 5-marker panel to detect cervical neoplasia. Quantitative Multiplex Methylation Specific PCR (QM-MSP) data of macrodissected cervical formalin fixed paraffin embedded (FFPE) tissue (United States samples, N = 63) shown in FIG. 9A, was evaluated to assess the performance of each marker in progressive stages of neoplasia. Histogram plots show the magnitude of methylation (Y-axis) in each sample (X-axis). Below each histogram the corresponding box and whiskers plot indicates significantly higher methylation in SCC and CIN3 compared to CIN1 and normal cervix (P < 0.0001, Mann Whitney).

[00100] FIG. 17 (includes FIGS. 17A-17C).Methylation of the 5-marker panel in Cervical Intraepithelial Neoplasm 2 (CIN2) lesions. Quantitative Multiplex Methylation Specific PCR (QM-MSP) analysis was performed on an independent set of macrodissected tissue sections from the United States (N = 20), Vietnam (N = 6), and South Africa (N = 15). FIG 17A. Histogram shows the Cumulative Methylation of the 5-marker panel (Y-axis) for each sample (X-axis). The height of each colored segment indicates the percent methylation of each individual marker. FIG. 17B. Box and whisker plot shows significantly higher methylation in CIN2 compared to normal tissue (P= 0.0002). FIG. 17C. Receiver Operating Characteristic (ROC) and area under the curve analysis (AUC) was performed. Sensitivity and specificity were based on the 95th percentile of cumulative methylation in normal samples (N = 14), which included 11 normal samples from the United States presented in FIG. 9A

[00101] FIG. 18 (includes FIGS. 18A-18C) Detection of cervical cancer and high-grade lesions in cervical smears in samples from United States, Vietnam and South Africa. The Quantitative Multiplex Methylation Specific PCR (QM-MSP) data for cervical smears from FIG. 18A: United States (N = 77), FIG. 18B: Vietnam (N = 117), and FIG. 18C: South Africa (N = 50) were analyzed separately by region (data was shown as pooled analysis in main Figure 4). The histogram bar height indicates the magnitude of cumulative methylation (Y-axis) in each sample (X-axis). The size of each colored segment indicates the percent methylation for each marker. Receiver Operating Characteristic (ROC) analyses show high sensitivity, specificity and Area Under the Curve (AUC) to detect HSIL and SCC compared to normal at a threshold based on the 95th percentile of cumulative methylation in normal in United States and Vietnam (dotted line in histogram). In cervical SCC smears from South Africa a sensitivity of 84% was achieved. Specificity was not assessed since normal cervical smear samples were not provided. Performance of individual markers in samples from the United States is shown in FIG. 19.

[00102] FIG. 19 The 5-marker panel is highly methylated in cervical smears from patients with high grade squamous intraepithelial lesion (HSIL) and squamous cell carcinoma (SCC). Histogram analysis shows the magnitude of methylation in individual markers of the 5-marker panel obtained by Quantitative Multiplex Methylation Specific PCR (QM-MSP) in cervical smear samples from the United States (N = 77). Magnitude of single marker and cumulative methylation is shown by the bar height (percent methylation; Y-axis) for each sample (X-axis). Receiver Operating Characteristic (ROC) area under the curve (AUC) analyses shown below each histogram indicate the discriminatory ability of the marker (HSIL and SCC vs. normal), which ranged from 0.861 to 0.933, P < 0.0001.

[00103] FIG. 20 (includes FIGS. 20A-20E): Association between DNA methylation and age in normal/benign tissue. Linear regression analysis of the effect of age on DNA methylation levels in our 5- marker panel is shown. Results are shown as plots for FIG. 20A normal uterine tissue from TCGA and normal/benign cervical samples from our sites in the FIG. 20B U.S, FIG. 20C, Vietnam and FIG. 20D South Africa and FIG. 20E. Age Coefficient is the change in DNA methylation level associated with a one-year increase in age. For example, in Vietnam, the average healthy 20-y ear-old has a CM level of 8.17 units in cervical tissue versus a healthy 70-year-old who has a CM level of 10.72 units, reflecting a change of 2.53 units over 50 years.

DETAILED DESCRIPTION

[00104] Currently used technologies for cervical cancer detection do not accurately distinguish between benign and cervical cancer. For example, the Pap smear which is being used as a screening tool for cervical cancer can effectively detect and lead to treatment of pre-cancerous lesions. However, the Pap smear is limited by a low sensitivity (55%) for detection of high-grade cervical lesions and a high number of false-negative results. Because of the high false negative rate of Pap Smear, it is not ideally suitable for early screening of cervical cancers (3).

[00105] The development of cervical cancer is linked to persistent infection with at least one of 13 types of human papillomavirus (HPV) (4). HPV infections are very common, yet cervical cancer and HSIL, its immediate precancerous precursor, are relatively uncommon. Approximately 90% of incident HPV infections become undetectable using standard test methods within a few years (5), whereas persistent infections are significantly associated with progression to CIN3 lesions (6, 7) of which approximately 30% will progress to invasive cancer over three decades (8). LSILs have a low (9-16%) but recognized risk of progression to HSIL and <1% chance of developing ISCC. Although current vaccines for HPV16/18 hold great promise, the predominant mechanism for cervical cancer prevention for the foreseeable future will continue to be screening and treating women with precancerous lesions.

[00106] The new cervical cancer screening guidelines in the United States recommend HPV co-testing among women 30 years and older (9). Although more sensitive than PAP smear cytology, HPV testing has only modest specificity and positive predictive value for detection of precancer and cannot distinguish infections that will resolve from those that will progress (7). Thus, an important question is how to triage HPV-positive women. This public health need warrants further research on the mechanisms of development and the validation of novel biomarkers associated with HPV-induced and HPV-unrelated transformation and progression from normal to precancer, and from LSIL to invasive cancer. HPV testing with a PAP cytology triage or a triage with an independent test using molecular markers may be a better approach for future cervical cancer screening.

[00107] Development of a simple molecular cervical cancer detection test is particularly awaited because such a test may be more easily implemented in underserved areas of developed countries and in less developed countries. These tests could be performed in vaginal self- or clinically collected specimens, thus leading to considerable reduction in cervical cancer deaths worldwide.

[00108] Genetic and epigenetic changes in the host and/or viral genome affect the outcome of high risk- HPV infection and identifying these key molecular changes is likely to reveal biomarkers for use in cancer prevention programs. Global DNA hypomethylation and site-specific hypermethylation result in genomic instability and transcriptional gene inactivation, respectively, both of which are associated with cancer. Epigenetic silencing of tumor suppressor genes by promoter hypermethylation is commonly observed in human cancers and appears to drive transformation of precancerous tissue. DNA methylation could serve as a marker for early detection of cancer and as a means of assessing response to therapy and the prognosis of cancer patients. One published study tested five promising genes (SOX1, PAX1, LMX1A, NKX6-1 and WT1) by using methylation- specific polymerase chain reaction, bisulfite sequencing, and reverse transcription polymerase chain reaction (10). Methylation of these 5 genes in cervical cancer tissues was significantly higher (81.5, 94.4, 89.9, 80.4, and 77.8 respectively) than that in normal cervical tissues (2.2, 0, 6.7, 11.9, and 11.1 , respectively; P < 0.0001). With the marker panel the sensitivity and specificity for the diagnosis of ISCC using HPV testing were 82 and 54%, respectively. The sensitivity and specificity for the diagnosis of HSIL/ISCC using HPV testing were 66 and 64%, respectively. PAX1 conferred the best performance with sensitivity of 86% and specificity of 82% for ISCC and with sensitivity of 54% and specificity of 99% for HSIL/ISCC [10],

[00109] Another study identified 2,044 differentially methylated probes in tumor and normal samples and selected the five genes containing CpG islands in the promoter region-GGTLA4, FKBP6, ZNF516, SAP130 and INTS1 to study using methylation specific (MSP) and quantitative MSP PCR. ZNF516 demonstrated higher methylation frequencies and levels in cancer when compared with normal tissue. Promoter methylation of ZNF516 showed sensitivity of 90% and specificity of 95% in the validation cohort but much lower sensitivity of 60% and high specificity at 100% in the prevalence cohort. Thus, ZNF516 as a single gene may achieve high predictive power but is inconsistent in its performance, and is yet to be validated in larger cohorts of independent samples (11). Methylation of 26 genes: APC, CADM1, CCND2, CDH13, CDKN2A, CTNNB1, DAPK1, DPYS, EDNRB, EPB41L3, ESRI, GSTP1, HIN1, JAM3, LMX1, MAL, MDR1, PAX1, PTGS2, RARB, RASSF1, SLIT2, SOX1, SPARC, TERT and TWIST1 was measured by pyrosequencing in cytology specimens from a pilot set of women with normal or cervical intraepithelial neoplasia grade 3 (CIN3) histology. Six genes were selected for testing in a colposcopy referral study comprising 799 women. Three of six genes, EPB41L3, DPYS and MAL, were further tested in a second colposcopy referral study, comprising 884 women. Their results were quite modest, methylation values were low for most genes, and the best gene EPB41L3 provided an AUC of 0.69 in the validation set (12). However, the lack of sensitivity and specificity of candidate markers selected from the literature was a major problem in achieving the goal of identifying “high risk” markers in benign lesions. None of these findings have been validated in independent prospective cohorts. Currently, there is no marker or marker panel that can accurately distinguish between benign and cervical cancer, which leads us to consider that the full potential of methylation markers has not yet been explored in cervical cancer screening.

[00110] Accordingly, there is a need to identify markers that can differentially distinguish between benign and cervical cancer.

[00111] As a result of vaccination against high risk HPV in many parts of the world, a sizeable proportion of women who are adolescent or early adult have developed a sustained strongly protective vaccine-induced immune response against the vaccine-targeted oncogenic hrHPVs, 16/18/31/45. The reduction over time of cervical cancer due to common high-risk HPV will require the development of tests that have reliable cancer detection ability irrespective of the HPV status. A triage test will be required to distinguish hrHPV-positive (nonvaccinated and vaccinated) women with clinically relevant cervical lesions from those with transient infections. Methylation markers may provide the solution.

[00112] A few studies have demonstrated the potential of methylation markers to detect both squamous cell carcinoma (ISCC) and CIN3 lesions in HPV+ and HPV negative women. Kelly et al., Br J Cancer 2019; 121 :954-65. A combination of FAM19A4/miR124-2 methylation measured by a quantitative methylation specific PCR assay in 519 samples were positive in 98.3% of cancer cases (95% CI: 96.7- 99.2). Interestingly hrHPV-negative cases (N=19) based on rigorous testing showed an equally high methylation assay positivity rate of 94.7% (18/19; 95% CI: 74.0-99.9) (Vink et al., Int J Cancer 2020;147: 1215-21) presenting an additional advantage of identifying cancer in addition to HPV testing alone. The S5 DNA-methylation classifier, which tests for methylation on the host tumor suppressor gene EPB41L3 and viral late genes (LI and L2) of HPV16, HPV18, HPV31 and HPV33, could accurately identify cases of CIN2/3 and cancer from those with CIN1 or normal cytology. A total of 521 out of 544 women with any cancer type yielded a sensitivity of 95.77% (95%CI 92.39- 97.40). The hrHPV-negative cancer group also tested S5 positive (25/26) at a sensitivity of 96.15 (95% CI: 94.38-98.25). Banila et al., Int J Cancer 2022;150:290-302; Louvanto et al., Clin Infect Dis 2020;70:2582-90.

[00113] Biomarkers

[00114] In certain embodiments, a method of diagnosing cervical cancer comprises detecting biomarkers comprising ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2, HIST1H3C and combinations thereof. In certain embodiments, the biomarkers consist of ZNF671, EDNRB, TMEFF2, FMN2, MOS and TBXT. In certain embodiments, the biomarkers are methylated. In certain embodiments, the extent of methylation and frequency of methylation of each of the biomarkers ZNF671, EDNRB, TMEFF2, FMN2, MOS and TBXT from the subject having cancer is above a threshold value as compared to each of the biomarkers from healthy subjects, i.e. subjects that have been evaluated as free from cancer, such as the cancer being assessed including cervical cancer.

[00115] In certain embodiments, a method of distinguishing between and treating of invasive squamous cell carcinomas (ISCC), high grade squamous intraepithelial lesions (HSIL) and low- grade squamous lesions (LSIL), comprises determining a methylation profile of biomarkers. In certain embodiments, the biomarkers comprise ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2, HIST1H3C or combinations thereof. In certain embodiments, the biomarker panel consists of ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2 and HIST1H3C.

[00116] In certain embodiments, the biomarkers comprise a panel of biomarkers comprising: ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2, HIST1H3C or combinations thereof. In certain embodiments, the biomarkers are selected from a panel of biomarkers consisting of ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2, HIST1H3C and combinations thereof. In certain embodiments, the biomarkers consist of ZNF671, EDNRB, TMEFF2, FMN2, MOS and TBXT. In certain embodiments, the cumulative methylation of biomarkers ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2, HIST1H3C, or combinations thereof, from the subject having cancer is above a threshold value as compared to the cumulative methylation of the biomarkers from healthy subjects, i.e. subjects that have been evaluated as free from cancer, such as the cancer being assessed including cervical cancer.

[00117] In certain embodiments, the biomarkers comprise ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2, HIST1H3C or combinations thereof. In certain embodiments, the biomarkers consist of ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2, HIST1H3C and combinations thereof. In certain embodiments, the methylation profile is a measure of the magnitude of methylation and frequency of methylation of each of the biomarkers, or of combinations thereof. In certain embodiments, the magnitude of methylation and frequency of methylation of each of the biomarkers ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, or combinations thereof, from the subject having ISCC or HSIL or LSIL is above a threshold value as compared to biomarkers from healthy subjects and varies with the method of analysis and tissue/fluid used.

[00118] In another aspect, the methylation of each of the panel of biomarkers comprising ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2, HIST1H3C or combinations thereof, can be weighted differently and unique thresholds (e.g. normal vs cancer) can be derived for individual markers.

[00119] Zinc Finger Protein 671 ((ZNF671) HGNC: 26279 NCBI Entrez Gene: 79891 Ensembl: ENSG00000083814 UniProtKB/Swiss-Prot: Q8TAW3). Zinc finger (ZF) protein 671 (ZNF671) is a member of the KRAB-ZF (KRAB-ZFP) family of mammalian transcriptional repressors (Jian Zhang et al. Front. Oncol., 07 May 2019 Sec. Cancer Molecular Targets and Therapeutics doi.org/10.3389/fonc.2019.00342; Witzgall R, et al. Proc Natl Acad Sci USA. (1994) 91 :4514-18; Margolin JF, et al. Proc Natl Acad Sci USA. (1994) 91 :4509-13; Urrutia R. Genome Biol. (2003) 4:231. doi: 10.1186/gb-2003-4- 10-2314- 6). Through recruitment of KRAB-associated protein-1 and other co- repressors, KRAB-ZFPs can regulate cell differentiation, proliferation, apoptosis, tumor suppression, and neoplastic transformation (Cheng Y, et al. Cancer Res. (2010) 70:6516-26. doi: 10.1158/0008- 5472. CAN-09-4566; Friedman JR, et al. Genes Dev. (1996) 10:2067-78; Moosmann P, et al. Nucleic Acids Res. (1996) 24:4859-67; Zheng L, et al. Mol Cell. (2000) 6:757-68; Zhang J, et al. J Exp Clin Cancer Res. (2017) 36: 147. doi: 10.1186/sl3046-017-0621-2).

[00120] Endothelin receptor type B ((EDNRB) HGNC: 3180 NCBI Entrez Gene: 1910 Ensembl: ENSG00000136160 OMIM®: 131244 UniProtKB/Swiss-Prot: P24530). The protein encoded by this gene is a G protein-coupled receptor which activates a phosphatidylinositol-calcium second messenger system. Its ligand, endothelin, consists of a family of three potent vasoactive peptides: ET1, ET2, and ET3. Studies suggest that the multigenic disorder, Hirschsprung disease type 2, is due to mutations in the endothelin receptor type B gene. Alternative splicing and the use of alternative promoters results in multiple transcript variants.

[00121] Diseases associated with EDNRB include Waardenburg Syndrome, Type 4A and Abed Syndrome. Among its related pathways are Class A/l (Rhodopsin-like receptors) and GPCR downstream signaling. Gene Ontology (GO) annotations related to this gene include G protein-coupled receptor activity and type 1 angiotensin receptor binding. A paralog of this gene is EDNRA.

[00122] Transmembrane Protein With EGF-Like And Two Folli statin-Like Domains (TMEFF2), HGNC: 11867 NCBI Entrez Gene: 23671 Ensembl: ENSG00000144339 OMIM®: 605734 UniProtKB/Swiss- Prot: Q9UIK5). This gene encodes a member of the tomoregulin family of transmembrane proteins. This protein has been shown to function as both an oncogene and a tumor suppressor depending on the cellular context and may regulate prostate cancer cell invasion. Multiple soluble forms of this protein have been identified that arise from both an alternative splice variant and ectodomain shedding. Additionally, this gene has been found to be hypermethylated in multiple cancer types. Alternative splicing results in multiple transcript variants. [00123] Diseases associated with TMEFF2 include Colorectal Cancer and Prostate Cancer. Among its related pathways are Validated targets of C-MYC transcriptional repression. A paralog of this gene is TMEFF1.

[00124] Formin 2 ((FMN2) HGNC: 14074 NCBI Entrez Gene: 56776 Ensembl: ENSG00000155816 OMIM®: 606373 UniProtKB/Swiss-Prot: Q9NZ56). This gene is a member of the formin homology protein family. The encoded protein is thought to have essential roles in organization of the actin cytoskeleton and in cell polarity. This protein mediates the formation of an actin mesh that positions the spindle during oogenesis and also regulates the formation of actin filaments in the nucleus. This protein also forms a perinuclear actin/focal-adhesion system that regulates the shape and position of the nucleus during cell migration. Mutations in this gene have been associated with infertility and also with an autosomal recessive form of intellectual disability (MRT47). Alternatively spliced transcript variants have been identified.

[00125] Diseases associated with FMN2 include Intellectual Developmental Disorder, Autosomal Recessive 47 and Autosomal Recessive Non-Syndromic Intellectual Disability. Gene Ontology (GO) annotations related to this gene include actin binding. An important paralog of this gene is INF2.

[00126] MOS Proto-Oncogene, Serine /Threonine Kinase ((MOS) HGNC: 7199 NCBI Entrez Gene: 4342 Ensembl: ENSG00000172680 OMIM®: 190060 UniProtKB/Swiss-Prot: P00540). MOS is a serine/threonine kinase that activates the MAP kinase cascade through direct phosphorylation of the MAP kinase activator MEK (MAP2K1; MIM 176872) (Prasad et al., 2008 [PubMed 18246541]). Diseases associated with MOS include Sarcoma. Among its related pathways are Pre-implantation embryo and Regulation of actin cytoskeleton. Gene Ontology (GO) annotations related to this gene include transferase activity, transferring phosphorus-containing groups and protein tyrosine kinase activity. A paralog of this gene is MAP3K9.

[00127] T-Box Transcription Factor T ((TBXT) HGNC: 11515 NCBI Entrez Gene: 6862 Ensembl: ENSG00000164458 OMIM®: 601397 UniProtKB/Swiss-Prot: 015178). The protein encoded by this gene is an embryonic nuclear transcription factor that binds to a specific DNA element, the palindromic T-site. It binds through a region in its N-terminus, called the T-box, and effects transcription of genes required for mesoderm formation and differentiation. The protein is localized to notochord-derived cells. Variation in this gene was associated with susceptibility to neural tube defects and chordoma. A mutation in this gene was found in a family with sacral agenesis with vertebral anomalies. Diseases associated with TBXT include Sacral Agenesis With Vertebral Anomalies and Neural Tube Defects. Among its related pathways are Gastrulation and Nervous system development. A paralog of this gene is TBX19.

[00128] Growth-arrest-specific 7 ((GAS7C), Ensembl :ENSG00000007237 MIM:603127; AllianceGenome:HGNC:4169). Growth-arrest-specific 7 (GAS7) belongs to a group of adaptor proteins that coordinate the actin cytoskeleton. Among human GAS7 isoforms, only GAS7C possesses a Src homology 3 domain. GAS7C acts as a migration suppressor and GAS7C overexpression reduces lung cancer migration, whereas GAS7C knockdown enhances cancer cell migration. Ectopically overexpressed GAS7C binds tightly with N-WASP thus inactivates the fibronectin/integrin/FAK pathway, which in turn leads to the suppression of F-actin dynamics. In addition, overexpression of GAS7C sequesters hnRNP U and thus decreases the level of |3-catenin protein via the P-TrCP ubiquitin-degradation pathway (Tseng RC, et al. Oncotarget. 2015 Dec 29;6(42):44207-21. doi: 10.18632/oncotarget.6229. PMID: 26506240; PMCID: PMC4792552).

[00129] Myelin and lymphocyte protein ((MAL), HGNC: 6817 NCBI Entrez Gene: 4118 Ensembl: ENSG00000172005 OMIM®: 188860 UniProtKB/Swiss-Prot: P21145). The protein encoded by this gene is a highly hydrophobic integral membrane protein belonging to the MAL family of proteolipids. The protein has been localized to the endoplasmic reticulum of T-cells and is a candidate linker protein in T-cell signal transduction. In addition, this proteolipid is localized in compact myelin of cells in the nervous system and has been implicated in myelin biogenesis and/or function. The protein plays a role in the formation, stabilization and maintenance of glycosphingolipid-enriched membrane microdomains. Down-regulation of this gene has been associated with a variety of human epithelial malignancies. Alternative splicing produces four transcript variants which vary from each other by the presence or absence of alternatively spliced exons 2 and 3. MAL (Mai, T Cell Differentiation Protein) is a Protein Coding gene. Diseases associated with MAL include Immunodeficiency 66 and Metachromatic Leukodystrophy. Gene Ontology (GO) annotations related to this gene include lipid binding and peptidase activator activity involved in apoptotic process. A paralog of this gene is MALL.

[00130] Collagen Type VI Alpha 2 Chain ((COL6A2\ HGNC: 2212 NCBI Entrez Gene: 1292 Ensembl: ENSG00000142173 OMIM®: 120240 UniProtKB/Swiss-Prot: P12110) This gene encodes one of the three alpha chains of type VI collagen, a beaded filament collagen found in most connective tissues. The product of this gene contains several domains similar to von Willebrand Factor type A domains. These domains have been shown to bind extracellular matrix proteins, an interaction that explains the importance of this collagen in organizing matrix components. Mutations in this gene are associated with Bethlem myopathy and Ullrich scleroatonic muscular dystrophy. Three transcript variants have been identified for this gene. Diseases associated with COL6A2 include Myosclerosis, Autosomal Recessive and Ullrich Congenital Muscular Dystrophy 1. Among its related pathways are Collagen chain trimerization and Integrin Pathway. A paralog of this gene is COL4A5.

[00131 ] Transmembrane 6 Superfamily Member 1 ((TM6SFT), HGNC: 11860 NCBI Entrez Gene: 53346 Ensembl: ENSG00000136404 OMIM®: 606562 UniProtKB/Swiss-Prot: Q9BZW5). A nonsynonymous, loss of function variant (rs58542926, E167K) located in the gene encoding TM6SF2 was identified in multiple genetic association studies as significantly correlating with increased risk for non-alcoholic fatty liver disease (NAFLD) and decreased risk for hyperlipidemia. Given the pivotal role that lipoproteins play at the juncture of these two conditions, it was hypothesized that the ER-membrane spanning TM6SF2 protein regulates the degree of lipidation of VLDL particles synthesized in the liver. Further findings suggest that TM6SF2 may impact cholesterol localization within ER subdomains, which regulate expression levels of cholesterol synthesis genes and activities of ER lipid-raft associated enzymes (Gibeley, Sarah B. (2022) Investigating the Role of TM6SF2 in Lipid Metabolism. doi.org/10.7916/bnxm-t563).

[00132] Ras Protein Specific Guanine Nucleotide Releasing Factor 2 ((RASGRF2), HGNC: 9876 NCBI Entrez Gene: 5924 Ensembl: ENSG00000113319 OMIM®: 606614 UniProtKB/Swiss-Prot: 014827) RAS GTPases cycle between an inactive GDP -bound state and an active GTP -bound state. This gene encodes a calcium-regulated nucleotide exchange factor activating both RAS and RAS-related protein, RAC1, through the exchange of bound GDP for GTP, thereby, coordinating the signaling of distinct mitogen-activated protein kinase pathways. (Ruiz S, et al. (2007). “RasGRF2, a guanosine nucleotide exchange factor for Ras GTPases, participates in T-cell signaling responses”. Mol. Cell. Biol. 27 (23): 8127-42. doi: 10.1128/MCB.00912-07. PMC 2169177. PMID 17923690).

[00133] H3 Clustered Histone 3 ((HIST1 H3C\ HGNC: 4768 NCBI Entrez Gene: 8352 Ensembl: ENSG00000287080 OMIM®: 602812 UniProtKB/Swiss-Prot: P68431) Histones are basic nuclear proteins that are responsible for the nucleosome structure of the chromosomal fiber in eukaryotes. Two molecules of each of the four core histones (H2A, H2B, H3, and H4) form an octamer, around which approximately 146 bp of DNA is wrapped in repeating units, called nucleosomes. The linker histone, Hl, interacts with linker DNA between nucleosomes, and functions in the compaction of chromatin into higher order structures. This gene is found in the large histone gene cluster on chromosome 6, is intronless and encodes a member of the histone H3 family. Transcripts from this gene lack poly A tails, instead containing a palindromic termination element (Yang L, et al. (2002). Oncogene. 21 (1): 148-52. doi: 10.1038/sj. one.1204998. PMID 11791185. Nielsen PR, et al. (2002). Nature. 416 (6876): 103-7. doi: 10.1038/nature722. PMID 11882902. S2CID 4423019).

[00134] DNA Methylation

[00135] DNA methylation is an important regulator of gene transcription and is one of the most studied epigenetic modifications (Lister R, et al. Human DNA methylomes at base resolution show widespread epigenomic differences. Nature. 2009; 462 (7271): 315-322. doi: 10.1038/nature08514). The methylated cytosines are almost exclusively located in CpG dinucleotide sequences (Illingworth RS, Bird AP. CpG Islands-¹ a rough guide’ FEES Lett. 2009; 583(11): 1713-1720. doi: 10. 1016/j.febs1et.2009.04.012). CpGs are uniformly distributed across the genome, and some of them are concentrated in short regions named CpG islands. Methylation in CpG islands within gene promoters usually leads to gene silencing.

Association of altered DNA methylation patterns of the promoter CpG islands with the expression profile of cancer genes has been found in many tumor types (Esteller M. Epigenetics in cancer. N Engl J Med. 2008;358(11 ): 1148—1 159. doi: 10.1056/NEJMra072067; Hitchins MP, et al. Dominantly inherited constitutional epigenetic silencing of MLH1 in a cancer-affected family is linked to a single nucleotide variant within the 5’ UTR. Cancer Cell. 2011,20(2):200-213. doi : 10.1016/j .ccr.2011 .07.003, Network CGAR et al. Integrated genomic analyses of ovarian carcinoma. Nature. 2011;474(7353):609-615. doi: 10.1038, hiaturel 0166). Aberrant hypomethylation may induce genome instability and overexpression of oncogenes, while hypermethylation in promoter regions of tumor suppressor genes may perturb cell cycle regulation, apoptosis and DNA repair, and result in malignant cellular transformation (Irizarry RA, et al. The human colon cancer methylome shows similar hypo-and hypermethylation at conserved tissue- specific CpG island shores. Nat Genet. 2009;41(2): 178-186. doi: 10.1038/ng.298). DNA methylation patterns can be measured genome- wide with microarrays.

[00136] Protein Methylation

[00137] Post translational modification of proteins is a vital process that is subjected to epigenetic modification and maintains cellular machinery like transcription, translation, and cellular signaling. The activation or phosphorylation of protein kinases are known substrates of methylation. Like protein phosphorylation, protein methylation also plays a key role in the regulation of cell signaling pathways, cell proliferation, and cell differentiation. Apart from transcription factors, membrane receptors are also subjected to methylation and demethylation. [00138] Protein methylation can occur on arginine (R), lysine (K), histidine (H), and carboxyl groups. Members of the histone family, including H2A, H2B, H3, and H4, are well-known methylated proteins and are generally methylated on lysine and arginine residues. Methylated histones can change chromatin structure, thereby modulating gene expression. Non-histone methylated proteins have also been reported to regulate cellular processes. Protein lysine methyltransferases (PKMTs) and protein arginine methyltransferases (PRMTs) are representative methyltransferase families. PKMTs generate three types of methylated lysine: monomethyl, dimethyl, and trimethyl lysine. In comparison, three different forms of methylated arginine are generated by PRMTs: monomethyl arginine, asymmetric dimethyl arginine, and symmetric dimethyl arginine (Kim, E.; Ahuja, A.; Kim, M.-Y.; Cho, J.Y. DNA or Protein Methylation- Dependent Regulation of Activator Protein- 1 Function. Cells 2021, 70, 461. doi.org/10.3390/cellsl0020461).

[00139] Detection of Methylated Biomarkers

[00140] DNA methylation may be detected by any methylation or hemi-methylation assay, such as for example, by methylation-specific PCR, whole genome bisulfite sequence, the HELP assay and other methods including methylation-sensitive restriction endonucleases, ChlP-on-chip assays, restriction landmark genomic scanning, COBRA, Ms-SNuPE, methylated DNA immunoprecipitation (MeDip), pyrosequencing, molecular break light assay for DNA adenine methyltransferase activity, methyl sensitive Southern blotting, methylCpG binding proteins, mass spectrometry, HPLC, and reduced representation bisulfite sequencing. In some embodiments, the DNA methylation is detected in a methylation assay utilizing next-generation sequencing. For example, methylated DNA may be detected by massive parallel sequencing with bisulfite conversion, e.g., whole-genome bisulfite sequencing or reduced representation bisulfite sequencing. The methylated DNA can also be detected by microarray, such as a genome-wide microarray. These methods may or may not require pre-treatment of sample DNA to convert unmethylated cytosine to uracil.

[00141] The detection and identification of methylated biomarkers is discussed in the examples section which follows. Briefly, a panel of 12 methylated markers, potentially highly methylated markers in cervical cancer, was identified by searching methylome databases and verifying the choice in TCGA databases. The QM-MSP assay was designed to enable accurate and absolute quantitative detection of methylation in a select panel of genes (up to 12) in a single FFPE section of a core biopsy, or a small aliquot of cells from Pap smear and displays high level of sensitivity of detecting methylated copies in a vast excess of normal copies of DNA (1 :10,000). [00142] Quantitative Multiplex-MSP (QM-MSP) Assay: The QM-MSP has been used to coamplify many genes from quantities of sample previously used for just one gene. This technique combines multiplex PCR and Q-MSP in such a way that a panel of genes can be coamplified in tissues derived from different sources, including those from ductal lavage, endoscopy, and fine-needle aspirates, in which the amount of DNA is limiting, as well as in larger samples, such as formalin-fixed, paraffin-embedded sections of core biopsies. See, Fackler MJ et al., Hypermethylated genes as biomarkers of cancer in women with pathologic nipple discharge. Clin Cancer Res. 2009 Jun 1 ; 15(1 l):3802-l 1. doi: 10.1158/1078-0432. CCR- 08-1981. Epub 2009 May 26. PMID: 19470737. See also Fackler MJ, et al., Quantitative multiplex methylation-specific PCR assay for the detection of promoter hypermethylation in multiple genes in breast cancer. Cancer Res. 2004 Jul l;64(13):4442-52. doi: 10.1158/0008-5472.CAN- 03-3341. PMID: 15231653. Both references are incorporated herein in their entirety.

[00143] This technique can be used to define the extent of gene promoter hypermethylation in normal tissues on a gene-by-gene basis and provides the ability to discriminate between normal/benign and malignant tissues. Briefly, The QM-MSP procedure required two sequential PCR reactions. In the first PCR reaction (the multiplex step), sodium bisulfite-treated DNA is added to a reaction buffer which includes deoxynucleotide triphosphates, Platinum Taq (Invitrogen) and forward and reverse primers. The PCR products are diluted in water and stored at -20°C. For the second round (the Q-MSP step), diluted PCR product from the first PCR reaction 1 is used directly or after further dilution. The diluted DNA is added to the Q-MSP reaction buffer containing deoxynucleotide triphosphates, Platinum DNA Taq Polymerase (Invitrogen), two primers (forward and reverse) and 200 nM labeled probe. The reaction is carried out in a 96-well reaction plate in an ABI Prism 7900HT Sequence Detector (Applied Biosystems). For each gene included in the reaction plate, the following are used to create standard curves and to provide controls: (a) serially diluted stock multiplexed DNA to establish a standard curve; (Z>) 40,000 copy (40 K) standards; (c) no-template control; and (d) a known DNA (“1% M” control) to ensure consistency among runs. In addition, 100% methylated DNA, 0% methylated DNA (HSD), and a sample lacking template DNA from the first PCR reaction (diluted 1:5) are present as controls. All of the samples are analyzed with primer sets for both methylated and unmethylated DNA.

[00144] Calculation of Percentage of Methylation. The relative amount of methylation in each unknown sample was calculated as % M = 100 x [no. of copies of methylated DNA/(no. of copies of methylated + unmethylated DNA)]. The sum of unmethylated plus methylated DNA (U + M) is used as an approximation of the total number of copies present of a target gene. To determine the number of copies of methylated and unmethylated DNA, sample DNA is mixed with Q-MSP reaction buffer after the multiplex reaction, the mixture is assayed with methylated primers and unmethylated primers (in separate wells) in the Q-MSP reaction, and then the CT (CT is defined as the cycle in which the signal exceeds the background) is determined for each. Using the ABI Prism SDS 2.0 software supplied by Applied Biosystems (Foster City, CA) with the 7900 HT Sequence Detector, the number of copies of methylated and unmethylated DNA is extrapolated from the respective standard curves, using the sample CT and applying the absolute quantification method according to the manufacturer’s directions. Only values falling within the range covered by the standard curve (usually 100-10,000,000 copies) were accepted.

[00145] cMethDNA for liquid biopsies

[00146] For each gene, cMethDNA requires: 1) A standard (STDgene) to operate as a gene- specific reference DNA. This has 5’ and 3’ sequences (~20 bp each “external” sequences) homologous to the TARGETgene which flank a short internal non-human DNA sequence (i.e. 140-300 bp of lambda phage DNA). This cassette is packaged into a plasmid (e.g. pCR2.1; Life Technologies); 2) A forward and reverse “external” primer pair used for multiplex PCR capable of hybridizing to the external 5’ and 3’ sequences of the TARGETgene/STDgene; 3) A forward and reverse primer pair used for real-time PCR capable of hybridizing to sequences located internally relative to external sequences, and which are specific to the methylated TARGETgene; 4) A pair of forward and reverse internal primers used for real- time PCR which are capable of specifically hybridizing to the reference STDgene; 4) Probes for TARGETgene and STDgene internal sequences, labeled in distinguishable colors (e g., 6FAM/TAMRA or VIC/TAMRA; used for two-color real-time PCR).

[00147] cMethDNA assay primers/probes are designed to overlap or lie within 100 bases of the differentially methylated loci identified by methylome array. The cMethDNA methylation- specific target gene primers (two) and probe (one) can jointly contain about 9-1 I CpG dinucleotides (ranging from 7-12) depending on the desired melting temperatures (I'm ) of the primers/probe (calculated as C or G = 4, and A or T = 2 Tm units) and the density of CG dinucleotides in the region; independent C residues (about 8; ranging from 6-10) can also be present to ensure selective hybridization only to sodium bisulfite- converted DNA.

[00148] Standards: For individual genes the standard is designed so that STD and endogenous gene amplicons resulting from Step 1 and Step 2 PCR reactions would be the same size. Forward and reverse cloning primers encompassed 5’ and 3’ external primer sequences (~20 bp) fused to lambda phage sequences (predicted after sodium bisulfite conversion; ~20 bp). Each STDgene is designated to have a unique phage sequence to eliminate cross- reactivity between standards. PCR-mediated cloning can be performed and sequences verified by restriction digestion, as well as DNA sequencing. Plasmid copy number was determined by OD260 (Nanodrop, Thermo Scientific, Wilmington, DE), considering the molecular mass of the recombinant plasmid using online OligoCalc, software (Northwestern University, b asi c . northwe stern . edu/bi otool s/OligoC al c . html ) .

[00149] Calculation'. For an individual semm sample, cMethDNA calculation were as follows: methylation index = [Methylated TARGETgene copies/ (Methylated TARGETgene + STDgene) copies] (100), and cumulative methylation index (CMI) ^;;; the sum of all methylation index values within the gene panel. Serum samples can be assayed in duplicate and then results averaged. For an individual sample, QM-MSP calculations: % methylation (%M) " [methylated TARGETgene copies/(methylated TARGETgene + unmethylated TARGETgene) copies](100); CMI = the sum of all %M values within the panel. Any method can be utilized in determining the methylation of a panel of biomarkers. Examples, include without limitation QM-MSP assays, cMethDNA assays, DNA methylation arrays, whole genome bisulfite sequencing (WGBS), bisulfite sequencing, reduced representation bisulfite sequencing (RRBS), Array or Bead Hybridization, CpG island arrays, serial analysis of gene expression (SAGE), Pyrosequencing, Methylation-sensitive single-nucleotide primer extension (Ms-SNuPE), Methylation- sensitive single stranded conformation analysis (MS-SSCA), and High resolution melting analysis (HRM) and the like. A review of various techniques which are also applicable here, is provided by S. Kurdyukov and Martyn Bullock, Biology 2016, 5, 3; doi: 10.3390/biology5010003, incorporated herein by reference in its entirety.

[00150] Infinium HumanMethylation450 BeadChip datasets: Other methods for determining methylation of biomarkers includes the Infinium HumanMethylation450 BeadChip datasets. The HumanMethylation450 BeadChip leverages the Illumina Infinium assay as a DNA analysis platform, for comprehensive, coverage and high-throughput compatible with large sample size, epigenome-wide association studies. By combining Infinium I and Infinium II assay chemistry technologies, the BeadChip provides coverage of 99% of Re/Seq genes, 96% of CpG islands. The Infinium I assay employs two probes per CpG locus: one “unmethylated” and one “methylated” query probe. The 3' terminus of each probe is designed to match either the protected cytosine (methylated design) or the thymine base resulting from bisulfite conversion and whole-genome amplification (unmethylated design). Probe designs for Infinium I assays are based on the assumption that methylation is regionally correlated within a 50 bp span and, thus, underlying CpG sites are treated as in phase with the 'methylated' (C) or 'unmethylated' (T) query sites.

[00151] The Infinium II assay design requires only one probe per locus. The 3' terminus of the probe complements the base directly upstream of the query site while a single base extension results in the addition of a labeled G or A base, complementary to either the 'methylated' C or 'unmethylated' T. A single, 50-mer probe is used to determine methylation state, making an “all-or-none” approach inapplicable. However, underlying CpG sites may be represented by “degenerate” R-bases. Illumina determined that Infinium II probes can have up to three underlying CpG sites within the 50-mer probe sequence (i.e., 27 possible combinations overall) without compromising data quality. This feature enables the methylation status at a query site to be assessed independently of assumptions on the status of neighboring CpG sites. Further, the requirement for only a single bead type enables increased capacity for the number of CpG sites that can be queried (Dedeurwaerder S, et al. A comprehensive overview of Infinium HumanMethylation450 data processing. Brief Bioinform. 2014 Nov; 15 (6): 929-41. doi: 10.1093/bib/bbt054. Epub 2013 Aug 29. PMID: 23990268; PMCID: PMC4239800).

[0092] The markers described herein find use in a variety of methylation detection assays. One method for analyzing a nucleic acid for the presence of 5-methylcytosine is based upon the bisulfite method described by Frommer, et al. for the detection of 5-methylcytosines in DNA (Frommer et al. (1992) Proc. Natl. Acad. Sci. USA 89: 1827-31 explicitly incorporated herein by reference in its entirety for all purposes) or variations thereof. The bisulfite method of mapping 5-methylcytosines is based on the observation that cytosine, but not 5-methylcytosine, reacts with hydrogen sulfite ion (also known as bisulfite). The reaction is usually performed according to the following steps: first, cytosine reacts with hydrogen sulfite to form a sulfonated cytosine. Next, spontaneous deamination of the sulfonated reaction intermediate results in a sulfonated uracil. Finally, the sulfonated uracil is desulphonated under alkaline conditions to form uracil. Detection is possible because uracil base pairs with adenine (thus behaving like thymine), whereas 5-methylcytosine base pairs with guanine (thus behaving like cytosine). This makes the discrimination of methylated cytosines from non-methylated cytosines possible by, e.g., bisulfite genomic sequencing (Grigg G, & Clark S, Bioessays (1994) 16: 431-36; Grigg G, DNA Seq. (1996) 6: 189-98), methylation-specific PCR (MSP) as is disclosed, e.g., in U.S. Pat. No. 5,786,146, or using an assay comprising sequence-specific probe cleavage, e.g., a QuARTS flap endonuclease assay (see, e.g., Zou et al. (2010) “Sensitive quantification of methylated markers with a novel methylation specific technology” Clin Chem 56: A199; and in U.S. Pat. Nos. 8,361,720; 8,715,937; 8,916,344; and 9,212,392. [0093] Some conventional technologies are related to methods comprising enclosing the DNA to be analyzed in an agarose matrix, thereby preventing the diffusion and renaturation of the DNA (bisulfite only reacts with single-stranded DNA) and replacing precipitation and purification steps with a fast dialysis (Olek A, et al. (1996) “A modified and improved method for bisulfite based cytosine methylation analysis” Nucleic Acids Res. 24: 5064-6). It is thus possible to analyze individual cells for methylation status, illustrating the utility and sensitivity of the method. An overview of conventional methods for detecting 5-methylcytosine is provided by Rein, T., et al. (1998) Nucleic Acids Res. 26: 2255.

[0094] The bisulfite technique typically involves amplifying short, specific fragments of a known nucleic acid subsequent to a bisulfite treatment, then either assaying the product by sequencing (Olek & Walter (1997) Nat. Genet. 17: 275-6) or a primer extension reaction (Gonzalgo & Jones (1997) Nucleic Acids Res. 25: 2529-31; WO 95/00669; U.S. Pat. No. 6,251,594) to analyze individual cytosine positions. Some methods use enzymatic digestion (Xiong & Laird (1997) Nucleic Acids Res. 25: 2532-4). Detection by hybridization has also been described in the art (Olek et al., WO 99/28498). Additionally, use of the bisulfite technique for methylation detection with respect to individual genes has been described (Grigg & Clark (1994) Bioessays 16: 431-6; Zeschnigk et al. (1997) Hum Mol Genet. 6: 387-95; Fed et al. (1994) Nucleic Acids Res. 22: 695; Martin et al. (1995) Gene 157: 261-4; WO 9746705; WO 9515373).

[0095] Whole genome bisulfite sequencing (WGBS) is similar to whole genome sequencing, except for one detail: bisulfite conversion. It is the most comprehensive of all existing methods. The only limitations are the cost and difficulties in the analysis of NGS data. As already mentioned above, non- methylated cytosines become thymines after bisulfite treatment, and the DNA composed of just three bases is very difficult to assemble. Another limitation that existed until recently is that a considerable amount of DNA was required for WGBS, but modification of the protocol that postponed the adaptor ligation step till after bisulfite treatment allowed performing WGBS routinely from ~30 ng of DNA and, in some cases, even from as little as 125 pg (Miura, F.; Ito, T. Highly sensitive targeted methylome sequencing by post-bisulfite adaptor tagging. DNARes. 2014, 22, 13-18) However, since only a small fraction of the genome has the potential to be differentially methylated, WGBS is normally not required. Sequencing of the 5 mC- enriched fraction of the genome is not only a less expensive approach, but it also allows one to increase the sequencing coverage and, therefore, precision in revealing differentially-methylated regions. Sequencing could be done using any existing NGS platform; Illumina and Life Technologies both offer kits for such analysis. [0096] Both limitations of WGBS are alleviated in reduced representation bisulfite sequencing (RRBS), where only a fraction of the genome is sequenced. In RRBS, enrichment of CpG-rich regions is achieved by isolation of short fragments after MspI digestion that recognizes CCGG sites (and it cut both methylated and unmethylated sites). It ensures isolation of -85% of CpG islands in the human genome. Then, the same bisulfite conversion and library preparation is performed as for WGBS. The RRBS procedure normally requires ~1 pg of DNA. It could be performed with only 100 ng of DNA, but it needs to be pure enough for successful MspI digestion. Amplification of bisulfite-treated DNA for NGS is not without problems; therefore, it is important to find the most recent procedure, such as described by Chatterjee, A. et al. (Technical considerations for reduced representation bisulfite sequencing with multiplexed libraries. J. Biomed. Biotechnol. 2012, 2012, 741542). Enrichment for CpG-rich regions or specific regions of interest could be performed before NGS. Such enrichment could precede bisulfite conversion and be achieved by hybridization with immobilized oligonucleotides (so-called bait sequences). Such kits are commercially available (e.g., SureSelect Human Methyl-Seq from Agilent). Hybridization for enrichment could be done after bisulfite conversion using the SeqCap Epi CpGiant Enrichment Kit from Roche. Customized versions of these kits are available that allow enrichment for a small fraction of the genome that contains only the region(s) of interest. This approach is called targeted bisulfite sequencing.

[0097] Array or Bead Hybridization. Methylated DNA fractions of the genome, usually obtained by immunoprecipitation, could be used for hybridization with microarrays. Currently available examples of such arrays include: the Human CpG Island Microarray Kit (Agilent), the GeneChip Human Promoter 1.OR Array and the GeneChip Human Tiling 2. OR Array Set (Affymetrix).

[0098] The search for differentially-methylated regions using bisulfite-converted DNA could be done with the use of different techniques. Some of them are easier to perform and analyze than others, because only a fraction of the genome is used. The most pronounced functional effect of DNA methylation occurs within gene promoter regions, enhancer regulatory elements and 3^ untranslated regions (3 ^ UTRs). Assays that focus on these specific regions, such as the Infinium HumanMethylation450 Bead Chip array by Illumina, can save time and money. The array can detect from -500 ng of input DNA the methylation status of 485,000 individual CpG in 99% of known genes, including miRNA promoters, 5 UTR, 3 UTR, coding regions (-17 CpG per gene) and island shores (regions -2 kb upstream of the CpG islands). [0092] The experimental design is an adaptation of the Illumina GoldenGate high throughput single nucleotide polymorphism (SNP) system (Bibikova, M. et al. Methods Mol. Biol. 2009, 507,149-163). Briefly, bisulfite-treated genomic DNA is mixed with assay oligos, one of which is complimentary to uracil (converted from original unmethylated cytosine), and another is complimentary to the cytosine of the methylated (and therefore protected from conversion) site. Following hybridization, primers are extended and ligated to locus-specific oligos to create a template for universal PCR. Finally, labelled PCR primers are used to create detectable products that are immobilized to bar-coded beads, and the signal is measured. The ratio between two types of beads for each locus (individual CpG) is an indicator of its methylation level.

[0093] Methyl-Sensitive Cut Counting: Endonuclease Digestion Followed by Sequencing. As an alternative to sequencing a substantial amount of methylated (or unmethylated) DNA, snippets can be generated from these regions and map them back to the genome after sequencing. Moreover, coverage in NGS could be good enough to quantify the methylation level for particular loci. The technique of serial analysis of gene expression (SAGE) has been adapted for this purpose and is known as methylation- specific digital karyotyping (Hu, M.; Yao, J.; Polyak, K. Methylation-specific digital karyotyping. Nat. Protoc. 2006, 1, 1621-1636), as well as a similar technique, called methyl -sensitive cut counting (MSCC) (Ball, M.P., et al. Targeted and genome-scale strategies reveal gene-body methylation signatures in human cells. Nat. Biotechnol. 2009, 27, 361-368. Suzuki, M. et al. Optimized design and data analysis of tag-based cytosine methylation assays. Genome Biol. 2010, 11, R360).

[0094] Various methylation assay procedures can be used in conjunction with bisulfite treatment. These assays allow for determination of the methylation state of one or a plurality of CpG dinucleotides (e.g., CpG islands) within a nucleic acid sequence. Such assays involve, among other techniques, sequencing of bisulfite-treated nucleic acid, PCR (for sequence-specific amplification), Southern blot analysis, and use of methylation-sensitive restriction enzymes. For example, genomic sequencing has been simplified for analysis of methylation patterns and 5 -methyl cytosine distributions by using bisulfite treatment (Frommer et al. (1992) Proc. Natl. Acad. Sci. USA 89: 1827-1831). Additionally, restriction enzyme digestion of PCR products amplified from bisulfite-converted DNA finds use in assessing methylation state, e.g., as described by Sadri & Hornsby (1997) Nucl. Acids Res. 24: 5058-5059 or as embodied in the method known as COBRA (Combined Bisulfite Restriction Analysis) (Xiong & Laird (1997) Nucleic Acids Res. 25: 2532-2534). MSP (methylation-specific PCR) allows for assessing the methylation status of virtually any group of CpG sites within a CpG island, independent of the use of methylation-sensitive restriction enzymes (Herman et al. Proc. Natl. Acad. Sci. USA 93:9821-9826, 1996; U.S. Pat. No. 5,786, 146). Briefly, DNA is modified by sodium bisulfite, which converts unmethylated, but not methylated cytosines, to uracil, and the products are subsequently amplified with primers specific for methylated versus unmethylated DNA. MSP requires only small quantities of DNA, is sensitive to 0.1% methylated alleles of a given CpG island locus, and can be performed on DNA extracted from paraffin-embedded samples. Typical reagents (e.g., as might be found in a typical MSP-based kit) for MSP analysis may include, but are not limited to: methylated and unmethylated PCR primers for specific loci (e.g., specific genes, markers, regions of genes, regions of markers, bisulfite treated DNA sequence, CpG island, etc.); optimized PCR buffers and deoxynucleotides, and specific probes. Various methods are described by Kurdyukov and Bullock (Biology 5:3, 2016), incorporated herein in its entirety.

[0095] In some embodiments, a quantitative allele-specific real-time target and signal amplification (QuARTS) assay is used to evaluate methylation state. Three reactions sequentially occur in each QuARTS assay, including amplification (reaction 1) and target probe cleavage (reaction 2) in the primary reaction; and FRET cleavage and fluorescent signal generation (reaction 3) in the secondary reaction. When target nucleic acid is amplified with specific primers, a specific detection probe with a flap sequence loosely binds to the amplicon. The presence of the specific invasive oligonucleotide at the target binding site causes a 5' nuclease, e.g., a FEN-1 endonuclease, to release the flap sequence by cutting between the detection probe and the flap sequence. The flap sequence is complementary to a non-hairpin portion of a corresponding FRET cassette. Accordingly, the flap sequence functions as an invasive oligonucleotide on the FRET cassette and effects a cleavage between the FRET cassette fluorophore and a quencher, which produces a fluorescent signal. The cleavage reaction can cut multiple probes per target and thus release multiple fluorophore per flap, providing exponential signal amplification. QuARTS can detect multiple targets in a single reaction well by using FRET cassettes with different dyes. See, e.g., in Zou et al. (2010) “Sensitive quantification of methylated markers with a novel methylation specific technology” Clin Chem 56: A199), and U.S. Pat. Nos. 8,361,720; 8,715,937; 8,916,344; and 9,212,392, each of which is incorporated herein by reference for all purposes.

[0096] Other methods can also be used for analyzing the DNA methylation of specific genes/regions of interest, after, for example, bisulfite conversion. These include bead arrays, PCR and sequencing, pyrosequencing, methylation specific PCR, PCR with high resolution melting, COLD-PCR for detection of unmethylated islands. [0097] Methods which do not require bisulfite conversion are described in Kurdyukov and Bullock (Biology 5:3, 2016).

[0098] Calculation of Methylation

[0099] The calculation of methylation can vary depending on the assays used. For example, for an individual serum sample, cMethDNA calculations are as follows: methylation index = [Methylated TARGETgene copies/(Methylated TARGETgene + STDgene) copies] (100); and cumulative methylation index (CMI) = the sum of all methylation index values within the gene panel. Serum samples can be assayed in duplicate and then results are averaged. For an individual sample, QM-MSP calculations: % methylation (%M) = [methylated TARGETgene copies/(methylated TARGETgene /unmethylated TARGETgene) copies](100); CMI = the sum of all %M values within the panel (Mary Jo Fackler et al., Cancer Res- 74(8) 2160-74, April 15, 2014).

[00100] Other methods for calculating quantitation of DNA methylation in a panel of biomarkers include the method described in Bradley M. Downs et al. Clin Cancer Res. 2019 November 01; 25(21): 6357- 6367. doi: 10.1158/1078-0432. CCR-18-3277. Briefly, Ct values were obtained using the real-time machine software for methylated targets and ACTB reference (Ct = the cycle threshold at which signal fluorescence exceeds background). For calculating % methylation, the A Ct (Ct Gene - Ct ACTB) value of each target gene was extrapolated from historical standard curves of mixtures of methylated and unmethylated DNA ranging from 100% to 3% methylation. This enabled quantitation of cumulative methylation (CM), which is the sum of % methylation for all genes in the marker panel.

[00101] For a liquid biopsy method, (Mary Jo Fackler et al., Cancer Res Commun. 2022 June ; 2(6): 391-401. doi: 10.1158/2767-9764. crc-22-0133 incorporated herein by reference in its entirety), the method of calculating cumulative methylation (CM) includes an algorithm. Briefly, Step 1 : Real-time machine software assigns the Ct at the end of the run; the user assigns Ct = 45 if no signals were detectable during the run; A Ct (Ct gene - Ct ACTB) is calculated to normalize all results to the ACTB reference DNA. If some samples have negative A Ct (Ct gene - Ct ACTB) for a gene, all samples are transformed by adding a constant value to give positive integers for that gene. Step 2: If A Ct (Ct gene - Ct ACTB) is higher than the historical replicate median of 300 copies + 13 A Ct units, the user adjusts to A Ct (Ct gene - Ct ACTB) = 0, thereby removing signals from the analysis that are too low to quantitate (less than 0.04 copies of target). Step 3: Gene methylation (M) = [1 / A Ct (Ct gene - Ct ACTB)] * 1200. This is a robust transformation intended to raise the methylation values from baseline and increase the assay dynamic range. Step 4: Calculate cumulative methylation as follows, where CM = sum of M in the 9 gene-panel.

[00102] Evaluating Methylation in a Subject

[00103] Provided herein are methods of identifying a subject with cervical cancer. For example, DNA methylation of ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2, HIST1H3C and combinations thereof, can be determined, for example, by measuring the methylated nucleic acid molecule by using probes or primers that can specifically hybridize to such sequences or the complementary strand thereof. Similarly, presence ofZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2, HIST1H3C can be determined, for example by using antibodies or fragments thereof that can specifically bind to such a protein. For example, assays such as immunohistochemical assays, ELISA’s etc., can be utilized to measure modulation of expression of markers, levels of protein in a subject’s sample, e.g., tumor tissue, or lowered levels of the protein in the blood (plasma or serum).

[00104] Methylation of ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2, HIST1H3C can be determined by measuring methylation of a nucleic acid molecule, for example by using probes or primers that can specifically hybridize to such sequences or the complementary strand thereof (for example primers or probes for bisulfite sequencing or conversion or pyrosequencing).

[00105] In certain embodiments, the methods herein include comparing the presence and/or methylation of biomarkers from a subject suspected of having cervical cancer, with biomarkers from a healthy subject or a subject that does not have cervical cancer as determined by any of one or more diagnostic methods such as a Pap Test, human papillomavirus (HPV) typing test, or colposcopy. The presence of biomarkers or the methylation of biomarkers can be detected using a variety of methods, including the methods described in the examples section which follows.

[00106] DNA methylation can also be determined, for example, for DNA encoding each of ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2, HIST1H3C in a sample. Exemplary methods of detecting DNA methylation in a sample include bisulfite sequencing or conversion, pyrosequencing, HPLC-UV, LC-MS/MS, ELISA-based methods, and array or bead hybridization. In one example, the VeraCode Methylation technology from Illumina is used. For a review of such methods see Kurdyukov and Bullock (Biology 5:3, 2016). Thus, in some examples, samples, for example, tissue samples taken from the cervix (or DNA isolated from such samples) are contacted with bisulfate and can also be subjected to amplification and sequencing.

[00107] Diagnostic Assays

[00108] The present disclosure provides methods and compositions for detecting methylation profiles of cells that are correlated with a disease and can be used to identify subjects with high probability of having or developing the disease. Detection of an alteration relative to a normal, reference sample can be used as a diagnostic indicator of a disease (e.g., cervical cancer). In some embodiments, altered methylation of a particular gene is correlated with a particular disease.

[00109] The present disclosure also features diagnostic assays for the detection of a disease or the propensity to develop such a condition. In one embodiment, the level of methylation is measured on at least two separate occasions and an increase in the level is an indication of disease progression. The level of methylation in a cell of a subject having a disease or condition or susceptible to develop the disease or condition may be higher relative to the level of methylation in a normal control.

[00110] In certain embodiments, the cumulative methylation of biomarkers from the subject having cancer is above a threshold value as compared to the cumulative methylation of the biomarkers from healthy subjects, e.g. subject determined not to be suffering from cancer such as cervical cancer as determined by any of one or more diagnostic methods such as in the case of cervical cancer a Pap Test, human papillomavirus (HPV) typing test, or colposcopy.

[00111] In certain embodiments, the cumulative methylation of biomarkers from the subject having ISCC or HSIL is above a threshold value as compared to each of the biomarkers from healthy subjects or from subjects with LSIL.

[00112] In certain embodiments, the cumulative methylation of biomarkers from a subject having LSIL is above the threshold value as compared to each of the biomarkers from healthy subjects. (Note: these may represent cases that need to be followed up carefully since they may be more prone to progression to higher levels of HSIL or ISCC.

[00152] In certain embodiments, the methylation biomarkers can be weighted differently and unique thresholds (e.g. normal vs cancer) can be derived for individual markers.

[00113] In certain embodiments, the level of methylation is determined in response to a treatment, wherein a decrease in methylation is indicative of the therapy’s effectiveness. [00114] The diagnostic methods described herein can be used to provide a diagnosis individually or to confirm the results of another diagnostic method. Additionally, the methods described herein can be used with any other diagnostic method described herein for a more accurate diagnosis of the presence or severity of a disease.

[00115] A methylation profile may be obtained from a subject sample and compared to a reference profile obtained from a reference population, enabling classifying the subject as belonging to or not belonging to the reference population. The correlation of a methylation profile to a disease diagnosis may consider the presence or absence of methylation in test and control samples. The correlation may consider both factors when making a disease status determination.

[00116] The disclosure also provides for methods where methylation profiles are measured before and after subject management. In these cases, the methods are used to monitor the status of cervical cancer, e.g., a response to treatment, or progression of the disease.

[00117] The methylation profiles generated using the methods of the present disclosure have uses other than just diagnostic. In some embodiments, they can be used in monitoring responses to therapy. In another embodiment, the profiles can be used to study the regulatory regions of a gene associated with a disease. In some embodiments, the methylation profiles generated by the methods disclosed herein are useful in determining the status or stage of a subject's disease. A methylation profile generated for a subject sample using the methods described herein is compared with the methylation profile of a control sample, wherein differences in the levels or amounts of methylation distinguishes disease status from disease-free status. The techniques can be adjusted, as is well understood in the art, to increase the sensitivity or specificity of the diagnostic assay.

[00118] While methylation of a particular region or gene in the genome can be a useful diagnostic, in some instances, a combination of methylated genes or regions provides greater predictive value than a methylation profile of a single gene or region. Detection of the presence or absence of methylation at a plurality of genes or regions in a sample can decrease false positives and false negative diagnoses, while increasing the occurrence of true positives and true negatives.

[00119] Kits and Compositions for Detecting and Characterizing Methylation

[00120] In another embodiment, kits and compositions are provided that advantageously allow for the detection of methylation in a subject sample. In one embodiment, the kit includes a composition comprising reagents for performing an amplification reaction and/or a bisulfate conversion, including adapters. In some embodiments, the reagents include hemi-methylated adapters, a buffer, Msp I or other methylation insensitive restriction enzyme that cuts at cytosines, and/or a polymerase. A non-exhaustive list of methylation insensitive restriction enzyme includes, but is not limited to, Msp I, Sea I, Bam HI, Hind III, Not I, and Spe I. In some embodiments, the kit comprises a sterile container which contains the amplification reaction reagents; such containers can be boxes, ampoules, bottles, vials, tubes, bags, pouches, blister-packs, or other suitable container forms known in the art. Such containers can be made of plastic, glass, laminated paper, metal foil, or other materials suitable for holding amplification reagents.

[00121] In another embodiment, the kit includes a composition comprising reagents for performing a sequencing reaction, including nucleic molecules that can specifically bind to an adapter as described above. The reagents, in some embodiments, include nucleotides, labeled nucleotides, a buffer, and any other reagent necessary for performing a next-generation sequencing reaction (e.g., on the Illumina platform) or QM-MSP assays. In some embodiments, the kit comprises a sterile container which contains the amplification reaction reagents; such containers are described above. In some embodiments, the kit comprises compositions for amplification and sequencing as described above. Kits may also include instructions for performing the reactions.

[00122] In certain embodiments, the kits include arrays comprising a solid or semi-solid support. In one example, the array includes, probes, primers, peptides etc. (such as an oligonucleotide or antibody) that can detect ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2, HIST1H3C, and any combination thereof. The oligonucleotide probes or primers can further include one or more detectable labels, to permit detection of hybridization signals between the probe and target sequence ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2, HIST1H3C and any combination thereof. In certain embodiments, the probes, primers or peptides detect methylated biomarkers comprising ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2, HIST1H3C, and any combination thereof.

[00123] The solid support of the array can be formed from an organic polymer. Suitable materials for the solid support include, but are not limited to: polypropylene, polyethylene, polybutylene, polyisobutylene, polybutadiene, polyisoprene, polyvinylpyrrolidine, polytetrafluroethylene, polyvinylidene difluroide, polyfluoroethylene-propylene, polyethylenevinyl alcohol, polymethylpentene, polycholorotrifluoroethylene, polysulfornes, hydroxylated biaxially oriented polypropylene, aminated biaxially oriented polypropylene, thiolated biaxially oriented polypropylene, etyleneacrylic acid, thylene methacrylic acid, and blends of copolymers thereof (see U.S. Pat. No. 5,985,567). [00124] In one example, the solid support surface is polypropylene. In another example, a surface activated organic polymer is used as the solid support surface. One example of a surface activated organic polymer is a polypropylene material aminated via radio frequency plasma discharge. Such materials are easily utilized for the attachment of nucleotide molecules. The amine groups on the activated organic polymers are reactive with nucleotide molecules such that the nucleotide molecules can be bound to the polymers. Other reactive groups can also be used, such as carboxylated, hydroxylated, thiolated, or active ester groups.

[00125] Array Formats'. A wide variety of array formats can be employed. One example includes a linear array of oligonucleotide bands, generally referred to in the art as a dipstick. Another suitable format includes a two-dimensional pattern of discrete cells (such as 4096 squares in a 64 by 64 array). Other array formats including, but not limited to slot (rectangular) and circular arrays are equally suitable for use. In some examples, the array is a multi-well plate. In one example, the array is formed on a polymer medium, which is a thread, membrane or film. An example of an organic polymer medium is a polypropylene sheet having a thickness on the order of about 1 mil. (0.001 inch) to about 20 mil., although the thickness of the film is not critical and can be varied over a fairly broad range. The array can include biaxially oriented polypropylene (BOPP) films, which in addition to their durability, exhibit a low background fluorescence.

[00126] The array formats can be included in a variety of different types of formats. A "format" includes any format to which probes, primers or antibodies can be affixed, such as microtiter plates (e.g., multi- well plates), test tubes, inorganic sheets, dipsticks, and the like. For example, when the solid support is a polypropylene thread, one or more polypropylene threads can be affixed to a plastic dipstick-type device; polypropylene membranes can be affixed to glass slides.

[00127] The arrays of can be prepared by a variety of approaches. In one example, oligonucleotide or protein sequences are synthesized separately and then attached to a solid support (see U.S. Pat. No. 6,013,789). In another example, sequences are synthesized directly onto the support to provide the desired array (see U.S. Pat. No. 5,554,501). Suitable methods for covalently coupling oligonucleotides and proteins to a solid support and for directly synthesizing the oligonucleotides or proteins onto the support are describe in Matson et al., Anal. Biochem. 217:306-10, 1994. In one example, the oligonucleotides are synthesized onto the support using chemical techniques for preparing oligonucleotides on solid supports (such as see PCT applications WO 85/01051 and WO 89/10977, or U.S. Pat. No. 5,554,501). [00128] The oligonucleotides can be bound to the polypropylene support by either the 3' end of the oligonucleotide or by the 5' end of the oligonucleotide. In one example, the oligonucleotides are bound to the solid support by the 3' end. In general, the internal complementarity of an oligonucleotide probe in the region of the 3' end and the 5' end determines binding to the support.

[00129] In certain embodiments, the oligonucleotide probes on the array include one or more labels, that permit detection of oligonucleotide probe:target sequence hybridization complexes.

[00130] Detecting Protein Expression'. Antibodies specific for cervical cancer biomarkers, such as methylated or unmethylated ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2, HIST1H3C can be used for protein detection and quantification, for example using an immunoassay method, such as those presented in Harlow and Lane (Antibodies, A Laboratory Manual, CSHL, NewYork, 1988).

[00131] Exemplary immunoassay formats include ELISA, Western blot, and RIA assays. Thus, protein levels of ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2, HIST1H3C in a subject’s sample can be evaluated using these methods.

Immunohistochemical techniques can also be utilized protein detection and quantification. General guidance regarding such techniques can be found in Bancroft and Stevens (Theory and Practice of Histological Techniques, Churchill Livingstone, 1982) and Ausubel et al. (Current Protocols in Molecular Biology, John Wiley & Sons, New York, 1998).

[00132] To quantify proteins, a biological sample of a subject that includes cellular proteins can be used. Quantification of biomarkers, such as ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2, HIST1H3C proteins can be achieved by immunoassay methods. The amounts and/or methylation levels of biomarkers from subject’s samples, such as ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2, HIST1H3C protein in the subject’s sample can be compared to levels and/or methylation of these biomarkers to a control population. A significant increase or decrease in the amount can be evaluated using statistical methods.

[00133] Quantitative spectroscopic approaches, such as SELDI, can be used to analyze expression of biomarker proteins from subjects’ samples, such as ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2, HIST1H3C in a sample. In one example, surface- enhanced laser desorption-ionization time-of-flight (SELDI-TOF) mass spectrometry is used to detect protein expression, for example by using the ProteinChip™ (Ciphergen Biosystems, Palo Alto, Calif.). Such methods are well known in the art (for example see U.S. Pat. Nos. 5,719,060;6,897,072; and 6,881,586). SELDI is a solid phase method for desorption in which the analyte is presented to the energy stream on a surface that enhances analyte capture or desorption.

[00134] The surface chemistry allows the bound analytes to be retained and unbound materials to be washed away. Subsequently, analytes bound to the surface can be desorbed and analyzed by any of several means, for example using mass spectrometry. When the analyte is ionized in the process of desorption, such as in laser desorption/ionization mass spectrometry, the detector can be an ion detector. Mass spectrometers generally include means for determining the time-of-flight of desorbed ions. This information is converted to mass. However, one need not determine the mass of desorbed ions to resolve and detect them: the fact that ionized analytes strike the detector at different times provides detection and resolution of them. Alternatively, the analyte can be detectably labeled (for example with a fluorophore or radioactive isotope). In these cases, the detector can be a fluorescence or radioactivity detector. A plurality of detection means can be implemented in series to fully interrogate the analyte components and function associated with retained molecules at each location in the array.

[00135] Therefore, in one example, the chromatographic surface includes antibodies that specifically bind methylated or unmethylated ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2, HIST1H3C. In certain embodiments, the antibodies specifically bind to one or methylated ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2, HIST1H3C.

[00136] In another example, antibodies are immobilized onto the surface using a bacterial Fc binding support. The chromatographic surface is incubated with a sample, such as a sample of a lung or colon tumor. The antigens present in the sample can recognize the antibodies on the chromatographic surface. The unbound proteins and mass spectrometric interfering compounds are washed away and the proteins that are retained on the chromatographic surface are analyzed and detected by SELDI-TOF. The MS profile from the sample can be then compared using differential protein expression mapping, whereby relative expression levels of proteins at specific molecular weights are compared by a variety of statistical techniques and bioinformatic software systems.

[00137] Methylated proteins can also be detected by radiolabeling with tritium, or by binding to fluorescent broad-specificity antibodies against methylated lysine. A review of various techniques can also be found at Carlson SM, Gozani O. Emerging technologies to map the protein methylome. J Mol Biol. 2014 Oct 9;426(20):3350-62. doi: 10.1016/j.jmb.2014.04.024. Epub 2014 May 5. PMID: 24805349;

PMCID: PMC4177301, incorporated herein by reference in its entirety.

[00138] Combination Therapies

[00139] In certain aspects, a method of treating a subject suspected of having cancer, comprises diagnosing the subject as having cancer, wherein diagnosis comprises determining a methylation profile of biomarkers obtained from a sample from the subject; and, administering to the subject a therapy to treat the cancer. In certain embodiments, the biomarkers comprise ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2, HIST1H3C and combinations thereof. In certain embodiments, the biomarkers consist of ZNF671, EDNRB, TMEFF2, FMN2, MOS and TBXT. In certain embodiments, the percent methylation and frequency of methylation of each of the biomarkers ZNF671, EDNRB, TMEFF2, FMN2, MOS and TBXT from the subject having cancer is above a threshold value as compared to each of the biomarkers from healthy subject or a subject that does not have cervical cancer as determined by any of one or more diagnostic methods such as a Pap Test, human papillomavirus (HPV) typing test, or colposcopy.

[00140] In certain embodiments, the cancer comprises cervical cancer, uterine cancer, ovarian cancer. In certain embodiments, the cancer is cervical cancer. In certain embodiments, the therapy for treating cancer comprises: a surgical therapy, a locally applied chemical, chemotherapy, radiation therapy, cryotherapy, hyperthermia treatment, phototherapy, radioablation therapy, hormonal therapy, immunotherapy, small molecule therapy, receptor kinase inhibitor therapy, anti-angiogenic therapy, cytokine therapy, gene therapy, a biological therapy or combinations thereof. In certain embodiments, the sample comprises: serum, whole blood, blood plasma, saliva, buccal swab, cervical pap smears, stool, urine, bladder washing, uterine washing, sputum, lymphatic fluid, cerebrospinal fluid, a tissue sample from one or both ovaries, frozen tissues, formalin-fixed, paraffin-embedded (FFPE) tissues or metastatic tumor tissue of the subject.

[00141] In another aspect, a method of treating cervical cancer, comprises obtaining a sample from a subject; determining a methylation profile of a group of biomarkers obtained from a subject’s sample, wherein the biomarkers consist of ZNF671, EDNRB, TMEFF2, FMN2, MOS and TBXT wherein if the methylation profile of the biomarkers is diagnostic of cervical cancer, the subject is administered a therapy; thereby treating the cervical cancer. In certain embodiments, the methylation profile is a measure of percent methylation and frequency of methylation of each of the biomarkers. In certain embodiments, the percent methylation and frequency of methylation of each of the biomarkers ZNF671, EDNRB, TMEFF2, FMN2, MOS and TBXT from the subject having cancer is above a threshold value as compared to each of the biomarkers from healthy subjects e.g. subject determined not to be suffering from cancer such as cervical cancer as determined by any of one or more diagnostic methods. In certain embodiments, the sample comprises: serum, whole blood, blood plasma, saliva, buccal swab, cervical pap smears, stool, urine, bladder washing, uterine washing, sputum, lymphatic fluid, cerebrospinal fluid, a tissue sample from one or both ovaries, frozen tissues, formalin-fixed, paraffin-embedded (FFPE) tissues or metastatic tumor tissue of the subject. In certain embodiments, the therapy for treating cancer comprises: a surgical therapy, a locally applied chemical, chemotherapy, radiation therapy, cryotherapy, hyperthermia treatment, phototherapy, radioablation therapy, hormonal therapy, immunotherapy, small molecule therapy, receptor kinase inhibitor therapy, anti-angiogenic therapy, cytokine therapy, gene therapy, a biological therapy or combinations thereof and (please add from previous section in all places mentioning therapy).

[00142] In another aspect, a method of distinguishing between and treating of invasive squamous cell carcinomas (ISCC), high grade intraepithelial lesions (HSIL) and low- grade intraepithelial lesions (LSIL), comprises determining a methylation profile of biomarkers obtained from a sample from the subject; and, administering to the subject a therapy to treat the ISCC, HSIL or LSIL. In certain embodiments, the biomarkers comprise ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2, HIST1H3C or combinations thereof. In certain embodiments, the biomarkers consist of ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2 and HIST1H3C. In certain embodiments, the methylation profile is a measure of percent methylation and frequency of methylation of each of the biomarkers. In certain embodiments, the percent methylation and frequency of methylation of each of the biomarkers ZNF671, EDNRB, TMEFF2, FMN2, MOS and TBXT from the subject having ISCC or HSIL or LSIL is above a threshold value as compared to each of the biomarkers from healthy subjects. In certain embodiments, the therapy for treating cancer comprises: a surgical therapy, a locally applied chemical, chemotherapy, radiation therapy, cryotherapy, hyperthermia treatment, phototherapy, radioablation therapy, hormonal therapy, immunotherapy, small molecule therapy, receptor kinase inhibitor therapy, anti-angiogenic therapy, cytokine therapy, gene therapy, a biological therapy or combinations thereof.

[00143] Chemotherapies. Cancer therapies in general also include a variety of combination therapies with resection and/or chemical and radiation based treatments. Combination chemotherapies include, for example, cisplatin (CDDP), carboplatin, procarbazine, mechlorethamine, cyclophosphamide, camptothecin, ifosfamide, melphalan, chlorambucil, busulfan, nitrosurea, dactinomycin, daunorubicin, doxorubicin, bleomycin, plicomycin, mitomycin, etoposide (VP 16), tamoxifen, raloxifene, estrogen receptor binding agents, taxol, gemcitabien, navelbine, famesyl-protein transferase inhibitors, transplatinum, 5 -fluorouracil, vincristine, vinblastine and methotrexate, Temazolomide (an aqueous form of DTIC), or any analog or derivative variant of the foregoing. The combination of chemotherapy with biological therapy is known as biochemotherapy. The chemotherapy may also be administered at low, continuous doses which is known as metronomic chemotherapy.

[00144] Yet further combination chemotherapies include, for example, alkylating agents such as thiotepa and cyclosphosphamide; alkyl sulfonates such as busulfan, improsulfan and piposulfan; aziridines such as benzodopa, carboquone, meturedopa, and uredopa; ethylenimines and methylamelamines including altretamine, triethylenemelamine, trietylenephosphoramide, triethiylenethiophosphoramide and trimethylolomelamine; acetogenins (especially bullatacin and bullatacinone); a camptothecin (including the synthetic analogue topotecan); bryostatin; cally statin; CC-1065 (including its adozelesin, carzelesin and bizelesin synthetic analogues); cryptophycins (particularly cryptophycin 1 and cryptophycin 8); dolastatin; duocarmycin (including the synthetic analogues, KW-2189 and CB1-TM1); eleutherobin; pancrati statin; a sarcodictyin; spongistatin; nitrogen mustards such as chlorambucil, chlornaphazine, cholophosphamide, estramustine, ifosfamide, mechlorethamine, mechlorethamine oxide hydrochloride, melphalan, novembichin, phenesterine, prednimustine, trofosfamide, uracil mustard; nitrosureas such as carmustine, chlorozotocin, fotemustine, lomustine, nimustine, and ranimnustine; antibiotics such as the enediyne antibiotics (e.g., calicheamicin, especially calicheamicin gammall and calicheamicin omegall; dynemicin, including dynemicin A; bisphosphonates, such as clodronate; an esperamicin; as well as neocarzinostatin chromophore and related chromoprotein enediyne antiobiotic chromophores, aclacinomysins, actinomycin, authrarnycin, azaserine, bleomycins, cactinomycin, carabicin, carminomycin, carzinophilin, chromomycinis, dactinomycin, daunorubicin, detorubicin, 6-diazo-5-oxo-L- norleucine, doxorubicin (including morpholino-doxorubicin, cyanomorpholino-doxorubicin, 2-pyrrolino- doxorubicin and deoxydoxorubicin), epirubicin, esorubicin, idarubicin, marcellomycin, mitomycins such as mitomycin C, mycophenolic acid, nogalarnycin, olivomycins, peplomycin, potfiromycin, puromycin, quelamycin, rodorubicin, streptonigrin, streptozocin, tubercidin, ubenimex, zinostatin, zorubicin; anti- metabolites such as methotrexate and 5 -fluorouracil (5-FU); folic acid analogues such as denopterin, pteropterin, trimetrexate; purine analogs such as fludarabine, 6-mercaptopurine, thiamiprine, thioguanine; pyrimidine analogs such as ancitabine, azacitidine, 6-azauridine, carmofur, cytarabine, dideoxyuridine, doxifluridine, enocitabine, floxuridine; androgens such as calusterone, dromostanolone propionate, epitiostanol, mepitiostane, testolactone; anti-adrenals such as mitotane, trilostane; folic acid replenisher such as frolinic acid; aceglatone; aldophosphamide glycoside; aminolevulinic acid; eniluracil; amsacrine; bestrabucil; bisantrene; edatraxate; defofamine; demecolcine; diaziquone; elformithine; elliptinium acetate; an epothilone; etoglucid; gallium nitrate; hydroxyurea; lentinan; lonidainine; maytansinoids such as maytansine and ansamitocins; mitoguazone; mitoxantrone; mopidanmol; nitraerine; pentostatin; phenamet; pirarubicin; losoxantrone; podophyllinic acid; 2-ethylhydrazide; procarbazine; PSK polysaccharide complex; razoxane; rhizoxin; sizofiran; spirogermanium; tenuazonic acid; triaziquone; 2,2',2"-trichlorotriethylamine; trichothecenes (especially T-2 toxin, verracurin A, roridin A and anguidine); urethan; vindesine; dacarbazine; mannomustine; mitobronitol; mitolactol; pipobroman; gacytosine; arabinoside (“Ara-C”); cyclophosphamide; taxoids, e.g., paclitaxel and docetaxel gemcitabine; 6-thioguanine; mercaptopurine; platinum coordination complexes such as cisplatin, oxaliplatin and carboplatin; vinblastine; platinum; etoposide (VP- 16); ifosfamide; mitoxantrone; vincristine; vinorelbine; novantrone; teniposide; edatrexate; daunomycin; aminopterin; xeloda; ibandronate; irinotecan (e.g., CPT-11); topoisomerase inhibitor RFS 2000; difluorometlhylomithine (DMFO); retinoids such as retinoic acid; capecitabine; carboplatin, procarbazine, plicomycin, gemcitabien, navelbine, farnesyl-protein transferase inhibitors, transplatinum; and pharmaceutically acceptable salts, acids or derivatives of any of the above. In certain embodiments, the compositions provided herein may be used in combination with histone deacetylase inhibitors. In certain embodiments, the compositions provided herein may be used in combination with gefitinib. In other embodiments, the present embodiments may be practiced in combination with Gleevec (e.g., from about 400 to about 800 mg/day of Gleevec may be administered to a patient). In certain embodiments, one or more chemotherapeutic may be used in combination with the compositions provided herein.

[00145] Radiotherapy. Other factors that cause DNA damage and have been used extensively include what are commonly known as y-rays, X-rays, and/or the directed delivery of radioisotopes to tumor cells. Other forms of DNA damaging factors are also known such as microwaves and UV-irradiation. It is most likely that all of these factors effect a broad range of damage on DNA, on the precursors of DNA, on the replication and repair of DNA, and on the assembly and maintenance of chromosomes. Dosage ranges for X-rays range from daily doses of 50 to 200 roentgens for prolonged periods of time (3 to 4 wk), to single doses of 2000 to 6000 roentgens. Dosage ranges for radioisotopes vary widely, and depend on the half- life of the isotope, the strength and type of radiation emitted, and the uptake by the neoplastic cells.

[00146] Immunotherapy. Immunotherapeutics, generally, rely on the use of immune effector cells and molecules to target and destroy cancer cells. The immune effector may be, for example, an antibody specific for some marker on the surface of a tumor cell. The antibody alone may serve as an effector of therapy or it may recruit other cells to actually effect cell killing. The antibody also may be conjugated to a drug or toxin (chemotherapeutic, radionuclide, ricin A chain, cholera toxin, pertussis toxin, etc.) and serve merely as a targeting agent. Alternatively, the effector may be a lymphocyte carrying a surface molecule that interacts, either directly or indirectly, with a tumor cell target. Various effector cells include cytotoxic T cells and NK cells as well as genetically engineered variants of these cell types modified to express chimeric antigen receptors. Mda-7 gene transfer to tumor cells causes tumor cell death and apoptosis. The apoptotic tumor cells are scavenged by reticuloendothelial cells including dendritic cells and macrophages and presented to the immune system to generate anti-tumor immunity.

[00147] The immunotherapy may be an antibody, such as part of a polyclonal antibody preparation, or may be a monoclonal antibody. The antibody may be a humanized antibody, a chimeric antibody, an antibody fragment, a bispecific antibody or a single chain antibody. An antibody as disclosed herein includes an antibody fragment, such as, but not limited to, Fab, Fab' and F(ab')2, Fd, single-chain Fvs (scFv), single-chain antibodies, disulfide-linked Fvs (sdfv) and fragments including either a VL or VH domain. In some aspects, the antibody or fragment thereof specifically binds epidermal growth factor receptor (EGFR1, Erb-Bl), HER2/neu (Erb-B2), CD20, Vascular endothelial growth factor (VEGF), insulin-like growth factor receptor (IGF-1R), TRAIL-receptor, epithelial cell adhesion molecule, carcino- embryonic antigen, Prostate-specific membrane antigen, Mucin-1, CD30, CD33, or CD40.

[00148] Examples of monoclonal antibodies that may be used in combination with the compositions provided herein include, without limitation, trastuzumab (anti-HER2/neu antibody); Pertuzumab (anti- HER2 mAb); cetuximab (chimeric monoclonal antibody to epidermal growth factor receptor EGFR); panitumumab (anti-EGFR antibody); nimotuzumab (anti-EGFR antibody); Zalutumumab (anti-EGFR mAb); Necitumumab (anti-EGFR mAb); MDX-210 (humanized anti-HER-2 bispecific antibody); MDX- 210 (humanized anti-HER-2 bispecific antibody); MDX-447 (humanized anti-EGF receptor bispecific antibody); Rituximab (chimeric murine/human anti-CD20 mAb); Obinutuzumab (anti-CD20 mAb); Ofatumumab (anti-CD20 mAb); Tositumumab-1131 (anti-CD20 mAb); Ibritumomab tiuxetan (anti-CD20 mAb); Bevacizumab (anti-VEGF mAb); Ramucirumab (anti-VEGFR2 mAb); Ranibizumab (anti-VEGF mAb); Aflibercept (extracellular domains of VEGFR1 and VEGFR2 fused to IgGl Fc); AMG386 (angiopoietin-1 and -2 binding peptide fused to IgGl Fc); Dalotuzumab (anti-IGF-lR mAb);

Gemtuzumab ozogamicin (anti-CD33 mAb); Alemtuzumab (anti-Campath- 1/CD52 mAb); Brentuximab vedotin (anti-CD30 mAb); Catumaxomab (bispecific mAb that targets epithelial cell adhesion molecule and CD3); Naptumomab (anti-5T4 mAb); Girentuximab (anti -Carbonic anhydrase ix); or Farletuzumab (anti-folate receptor). Other examples include antibodies such as Panorex™. (17-1A) (murine monoclonal antibody); Panorex (@ (17-1A) (chimeric murine monoclonal antibody); BEC2 (ami-idiotypic mAb, mimics the GD epitope) (with BCG); Oncolym (Lym-1 monoclonal antibody); SMART M195 Ab, humanized 13' 1 LYM-1 (Oncolym), Ovarex (B43.13, anti-idiotypic mouse mAb); 3622W94 mAb that binds to EGP40 (17-1A) pancarcinoma antigen on adenocarcinomas; Zenapax (SMART Anti-Tac (IL-2 receptor); SMART Ml 95 Ab, humanized Ab, humanized); NovoMAb-G2 (pancarcinoma specific Ab); TNT (chimeric mAb to histone antigens); TNT (chimeric mAb to histone antigens); Gliomab-H (Monoclonals-Humanized Abs); GNL250 Mab; EMD-72000 (chimeric-EGF antagonist); LymphoCide (humanized IL.L.2 antibody); and MDX-260 bispecific, targets GD-2, ANA Ab, SMART IDIO Ab, SMART ABL 364 Ab or ImmuRAIT-CEA. Examples of antibodies include those disclosed in U.S. Pat. No. 5,736,167, U.S. Pat. No. 7,060,808, and U.S. Pat. No. 5,821,337.

[00149] Further examples of antibodies include Zanulimumab (anti-CD4 mAb), Keliximab (anti-CD4 mAb); Ipilimumab (MDX-101; anti-CTLA-4 mAb); Tremilimumab (anti-CTLA-4 mAb); (Daclizumab (anti-CD25/IL-2R mAb); Basiliximab (anti-CD25/IL-2R mAb); MDX-1106 (anti-PDl mAb); antibody to GITR; GC1008 (anti-TGF-0 antibody); metelimumab/CAT-192 (anti-TGF-0 antibody); lerdelimumab/CAT-152 (anti-TGF-0 antibody); ID11 (anti-TGF-P antibody); Denosumab (anti-RANKL mAb); BMS-663513 (humanized anti-4-lBB mAb); SGN-40 (humanized anti-CD40 mAb); CP870,893 (human anti-CD40 mAb); Infliximab (chimeric anti-TNF mAb; Adalimumab (human anti-TNF mAb); Certolizumab (humanized Fab anti-TNF); Golimumab (anti-TNF); Etanercept (Extracellular domain of TNFR fused to IgGl Fc); Belatacept (Extracellular domain of CTLA-4 fused to Fc); Abatacept (Extracellular domain of CTLA-4 fused to Fc); Belimumab (anti-B Lymphocyte stimulator); Muromonab- CD3 (anti-CD3 mAb); Otelixizumab (anti-CD3 mAb); Teplizumab (anti-CD3 mAb); Tocilizumab (anti- IL6R mAb); REGN88 (anti-IL6R mAb); Ustekinumab (anti-IL- 12/23 mAb); Briakinumab (anti-IL- 12/23 mAb); Natalizumab (anti-a4 integrin); Vedolizumab (anti-a4 07 integrin mAb); T1 h (anti-CD6 mAb); Epratuzumab (anti-CD22 mAb); Efalizumab (anti-CDl la mAb); and Atacicept (extracellular domain of transmembrane activator and calcium-modulating ligand interactor fused with Fc). [00150] Adoptive Immunotherapy . In adoptive immunotherapy, the patient's circulating lymphocytes, or tumor infiltrated lymphocytes, are isolated in vitro, activated by lymphokines such as IL-2 or transduced with genes for tumor necrosis, and readministered. To achieve this, one would administer to an animal, or human patient, an immunologically effective amount of activated lymphocytes in combination with an adjuvant-incorporated antigenic peptide composition as described herein. The activated lymphocytes will most preferably be the patient's own cells that were earlier isolated from a blood or tumor sample and activated (or “expanded”) in vitro. This form of immunotherapy has produced several cases of regression of melanoma and renal carcinoma, but the percentage of responders were few compared to those who did not respond. More recently, higher response rates have been observed when such adoptive immune cellular therapies have incorporated genetically engineered T cells that express chimeric antigen receptors (CAR) termed CAR T cell therapy. Similarly, natural killer cells both autologous and allogenic have been isolated, expanded and genetically modified to express receptors or ligands to facilitate their binding and killing of tumor cells.

[00151] Other Therapies. It is contemplated that other agents may be used in combination with the compositions provided herein to improve the therapeutic efficacy of treatment. These additional agents include immunomodulatory agents, agents that affect the upregulation of cell surface receptors and GAP junctions, cytostatic and differentiation agents, inhibitors of cell adhesion, or agents that increase the sensitivity of the hyperproliferative cells to apoptotic inducers Immunomodulatory agents include tumor necrosis factor; interferon alpha, beta, and gamma; IL-2 and other cytokines; F42K and other cytokine analogs; or MIP-1, MIP-lbeta, MCP-1, RANTES, and other chemokines. It is further contemplated that the upregulation of cell surface receptors or their ligands such as Fas/Fas ligand, DR4 or DR5/TRA1L would potentiate the apoptotic inducing abilities of the compositions provided herein by establishment of an autocrine or paracrine effect on hyperproliferative cells. Increases intercellular signaling by elevating the number of GAP junctions would increase the anti-hyperproliferative effects on the neighboring hyperproliferative cell population. In other embodiments, cytostatic or differentiation agents can be used in combination with the compositions provided herein to improve the anti-hyerproliferative efficacy of the treatments. Inhibitors of cell adhesion are contemplated to improve the efficacy of the present disclosure. Examples of cell adhesion inhibitors are focal adhesion kinase (FAKs) inhibitors and Lovastatin. It is further contemplated that other agents that increase the sensitivity of a hyperproliferative cell to apoptosis, such as the antibody c225, could be used in combination with the compositions provided herein to improve the treatment efficacy. [00152] In further embodiments, the other agents may be one or more oncolytic viruses, such as an oncolytic viruses engineered to express a gene other than p53 and/or IL24, such as a cytokine. Examples of oncolytic viruses include adenoviruses, adeno-associated viruses, retroviruses, lentiviruses, herpes viruses, pox viruses, vaccinia viruses, vesicular stomatitis viruses, polio viruses, Newcastle's Disease viruses, Epstein-Barr viruses, influenza viruses and reoviruses. In a particular embodiment, the other agent is talimogene laherparepvec (T-VEC) which is an oncolytic herpes simplex virus genetically engineered to express GM-CSF. Talimogene laherparepvec, HSV-1 [strain JS 1 ] ICP34.5-/ICP47-/hGM- CSF, is an intratumorally delivered oncolytic immunotherapy comprising an immune-enhanced HSV-1 that selectively replicates in solid tumors. (Lui et al., Gene Therapy, 10:292-303, 2003; U.S. Pat. No. 7,223,593 and U.S. Pat. No. 7,537,924; incorporated herein by reference). In October 2015, the US FDA approved T-VEC, under the brand name IMLYGIC™., for the treatment of melanoma in patients with inoperable tumors. The characteristics and methods of administration of T-VEC are described in, for example, the IMLYGIC™ package insert (Amgen, 2015) and U.S. Patent Publication No.

US2015/0202290; both incorporated herein by reference. For example, talimogene laherparepvec is typically administered by intratumoral injection into injectable cutaneous, subcutaneous, and nodal tumors at a dose of up to 4.0 ml of 10. sup.6 plaque forming unit/mL (PFU/mL) at day 1 of week 1 followed by a dose of up to 4.0 ml of 10⁸ PFU/mL at day 1 of week 4, and every 2 weeks (± 3 days) thereafter. The recommended volume of talimogene laherparepvec to be injected into the tumor(s) is dependent on the size of the tumor(s) and should be determined according to the injection volume guideline. While T-VEC has demonstrated clinical activity in melanoma patients, many cancer patients either do not respond or cease responding to T-VEC treatment. In one embodiment, the p53 and/or MDA- 7 nucleic acids and the at least one immune checkpoint inhibitor may be administered after, during or before T-VEC therapy, such as to reverse treatment resistance. Exemplary oncolytic viruses include, but are not limited to, Ad5-yCD/mutTKSR39rep-hIL12, Cavatak™, CG0070, DNX-2401, G207, HF 10, IMLYGIC™, JX-594, MG1-MA3, MV-NIS, OBP-301, Reolysin™, Toca 511, Oncorine, and RIGVIR. Other exemplary oncolytic viruses are described, for example, in International Patent Publication Nos. WO20 15/027163, WO2014/138314, W02014/047350, and WO2016/009017; all incorporated herein by reference.

[00153] In certain embodiments, hormonal therapy may also be used in conjunction with the present embodiments or in combination with any other cancer therapy previously described. The use of hormones may be employed in the treatment of certain cancers such as breast, prostate, ovarian, or cervical cancer to lower the level or block the effects of certain hormones such as testosterone or estrogen. This treatment is often used in combination with at least one other cancer therapy as a treatment option or to reduce the risk of metastases.

[00154] In some aspects, the additional anti-cancer agent is a protein kinase inhibitor or a monoclonal antibody that inhibits receptors involved in protein kinase or growth factor signaling pathways such as an EGFR, VEGFR, AKT, Erbl, Erb2, ErbB, Syk, Bcr-Abl, JAK, Src, GSK-3, PI3K, Ras, Raf, MAPK, MAPKK, mTOR, c-Kit, eph receptor or BRAF inhibitors. Nonlimiting examples of protein kinase or growth factor signaling pathways inhibitors include Afatinib, Axitinib, Bevacizumab, Bosutinib, Cetuximab, Crizotinib, Dasatinib, Erlotinib, Fostamatinib, Gefitinib, Imatinib, Lapatinib, Lenvatinib, Mubritinib, Nilotinib, Panitumumab, Pazopanib, Pegaptanib, Ranibizumab, Ruxolitinib, Saracatinib, Sorafenib, Sunitinib, Trastuzumab, Vandetanib, AP23451, Vemurafenib, MK-2206, GSK690693, A- 443654, VQD-002, Miltefosine, Perifosine, CAL101, PX-866, LY294002, rapamycin, temsirolimus, everolimus, ridaforolimus, Alvocidib, Genistein, Selumetinib, AZD-6244, Vatalanib, P1446A-05, AG- 024322, ZD1839, P276-00, GW572016 or a mixture thereof.

[00155] In certain embodiments, the anti-cancer agent is a checkpoint inhibitor. The term “checkpoint inhibitor” means a group of molecules on the cell surface of CD4⁺ and/or CD8⁺ T cells that fine-tune immune responses by down-modulating or inhibiting an anti-tumor immune response. Immune checkpoint proteins are well known in the art and include, without limitation, CTLA-4, PD-1, VISTA, B7-H2, B7-H3, PD-L1, B7-H4, B7-H6, 2B4, ICOS, HVEM, PD-L2, CD160, gp49B, PIR-B, KIR family receptors, TIM-1, TIM-3, TIM-4, LAG-3, BTLA, SIRPalpha (CD47), CD48, 2B4 (CD244), B7.1, B7.2, ILT-2, ILT-4, TIGIT, and A2aR (see, for example, WO 2012/177624). “Anti-immune checkpoint inhibitor therapy” refers to the use of agents that inhibit immune checkpoint inhibitors. Inhibition of one or more immune checkpoint inhibitors can block or otherwise neutralize inhibitory signaling to thereby upregulate an immune response in order to more efficaciously treat cancer. Exemplary agents useful for inhibiting immune checkpoint inhibitors include antibodies, small molecules, peptides, peptidomimetics, natural ligands, and derivatives of natural ligands, that can either bind and/or inactivate or inhibit immune checkpoint proteins, or fragments thereof; as well as RNA interference, antisense, nucleic acid aptamers, etc. that can downregulate the expression and/or activity of immune checkpoint inhibitor nucleic acids, or fragments thereof. Exemplary agents for upregulating an immune response include antibodies against one or more immune checkpoint inhibitor proteins block the interaction between the proteins and its natural receptor(s); a non-activating form of one or more immune checkpoint inhibitor proteins (e.g., a dominant negative polypeptide); small molecules or peptides that block the interaction between one or more immune checkpoint inhibitor proteins and its natural receptor(s); fusion proteins (e.g. the extracellular portion of an immune checkpoint inhibition protein fused to the Fe portion of an antibody or immunoglobulin) that bind to its natural receptor(s); nucleic acid molecules that block immune checkpoint inhibitor nucleic acid transcription or translation; and the like. Such agents can directly block the interaction between the one or more immune checkpoint inhibitors and its natural receptor(s) (e.g., antibodies) to prevent inhibitory signaling and upregulate an immune response. Alternatively, agents can indirectly block the interaction between one or more immune checkpoint proteins and its natural receptor(s) to prevent inhibitory signaling and upregulate an immune response. For example, a soluble version of an immune checkpoint protein ligand such as a stabilized extracellular domain can binding to its receptor to indirectly reduce the effective concentration of the receptor to bind to an appropriate ligand. In one embodiment, anti-PD-1 antibodies, anti-PD-Ll antibodies, and anti-CTLA-4 antibodies, either alone or used in combination.

[00156] In some embodiments, the anti-cancer agent is an angiogenesis inhibitor. For example, an angiogenesis inhibitor may include a VEGF antagonist, e.g., an antagonist of VEGF-A such as bevacizumab (also known as AVASTIN™, Genentech); and an angiopoietin 2 antagonist (also known as Ang2) such as MEDI3617. In some embodiments, the angiogenesis inhibitor may include an antibody.

[00157] In some embodiments, the anti-cancer agent is an antineoplastic agent. For example, an antineoplastic agent may include an agent targeting CSF-1R (also known as M-CSFR or CD115) such as anti-CSF-lR (also known as IMC-CS4); an interferon, e.g., interferon alpha or interferon gamma, such as Roferon-A (also known as recombinant Interferon alpha-2a); GM-CSF (also known as recombinant human granulocyte macrophage colony stimulating factor, rhu GM-CSF, sargramostim, or Leukine™); IL-2 (also known as aldesleukin or Proleukin™); IL-12; and an antibody targeting CD20 such as obinutuzumab (also known as GAI 01 or Gazyva™) or rituximab.

[00158] In some embodiments, the anti-cancer agent is a cancer vaccine. For example, a cancer vaccine may include a peptide cancer vaccine, which in some embodiments is a personalized peptide vaccine. In some embodiments the peptide cancer vaccine is a multivalent long peptide vaccine, a multi-peptide vaccine, a peptide cocktail vaccine, a hybrid peptide vaccine, or a peptide-pulsed dendritic cell vaccine (see, e.g., Yamada et al., Cancer Sci, 104: 14-21, 2013). [00159] In some embodiments, the anti-cancer agent is an adjuvant. Any substance that enhances an anti- cancer immune response, such as against a cancer-related antigen, or aids in the presentation of a cancer antigen to a component of the immune system may be considered an anti-cancer adjuvant of the present disclosure.

[00160] In certain embodiments, the anti-cancer agents described herein are administered systemically, intravenously, subcutaneously, intramuscularly, intraperitoneally, intravesically, or by instillation. The anti-cancer agents can be administered as part of a dosing regimen.

[00161] A “dosing regimen” (or “therapeutic regimen”), as that term is used herein, is a set of unit doses (typically more than one) that are administered individually to a subject, typically separated by periods of time. In some embodiments, a given therapeutic agent has a recommended dosing regimen, which may involve one or more doses. In some embodiments, a dosing regimen comprises a plurality of doses each of which are separated from one another by a time period of the same length; in some embodiments, a dosing regimen comprises a plurality of doses and at least two different time periods separating individual doses. In some embodiments, a dosing regimen is or has been correlated with a desired therapeutic outcome, when administered across a population of patients.

[00162] Those of ordinary skill in the art will appreciate that a dose which will be therapeutically effective for the treatment of cancer in a given patient may depend, at least to some extent, on the nature and extent of cancer, and can be determined by standard clinical techniques. In some embodiments, one or more in vitro or in vivo assays may optionally be employed to help identify optimal dosage ranges. In some embodiments, a particular dose to be employed in the treatment of a given individual may depend on the route of administration, the extent of cancer, and/or one or more other factors deemed relevant in the judgment of a practitioner in light of patient's circumstances. In some embodiments, effective doses may be extrapolated from dose-response curves derived from in vitro or animal model test systems (e g., as described by the U.S. Department of Health and Human Services, Food and Drug Administration, and Center for Drug Evaluation and Research in “Guidance for Industry: Estimating Maximum Safe Starting Dose in Initial Clinical Trials for Therapeutics in Adult Healthy Volunteers”, Pharmacology and Toxicology, July 2005.

[00163] It will be appreciated that a loading dose and maintenance dose amounts, intervals, and duration of treatment may be determined by any available method, such as those exemplified herein and those known in the art. In some embodiments, a loading dose amount is about 0.01-1 mg/kg, about 0.01-5 mg/kg, about 0.01-10 mg/kg, about 0.1-10 mg/kg, about 0.1-20 mg/kg, about 0.1-25 mg/kg, about 0.1-30 mg/kg, about 0.1-5 mg/kg, about 0.1-2 mg/kg, about 0.1-1 mg/kg, or about 0.1-0.5 mg/kg body weight. In some embodiments, a maintenance dose amount is about 0-10 mg/kg, about 0-5 mg/kg, about 0-2 mg/kg, about 0-1 mg/kg, about 0-0.5 mg/kg, about 0-0.4 mg/kg, about 0-0.3 mg/kg, about 0-0.2 mg/kg, about 0- 0.1 mg/kg body weight. In some embodiments, a loading dose is administered to an individual at regular intervals for a given period of time (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or more months) and/or a given number of doses (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30 or more doses), followed by maintenance dosing. In some embodiments, a maintenance dose ranges from 0-2 mg/kg, about 0-1.5 mg/kg, about 0- 1.0 mg/kg, about 0-0.75 mg/kg, about 0-0.5 mg/kg, about 0-0.4 mg/kg, about 0-0.3 mg/kg, about 0-0.2 mg/kg, or about 0-0.1 mg/kg body weight. In some embodiments, a maintenance dose is about 0.01, 0.02, 0.04, 0.06, 0.08, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.2, 1.4, 1.6, 1.8, or 2.0 mg/kg body weight. In some embodiments, maintenance dosing is administered for 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or more months. In some embodiments, maintenance dosing is administered for 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more years. In some embodiments, maintenance dosing is administered indefinitely (e.g., for lifetime).

[00164] The following examples are put forth so as to provide those of ordinary skill in the art with a complete disclosure and description of how to make and use the assay, screening, and therapeutic methods of the invention, and are not intended to limit the scope of what the inventors regard as their invention.

[00165] Examples

[00166J Example 1 : Rapid detection of cervical cancer using an automated assay methylated gene marker.

[00168] A sensitive, quantitative gene methylation detection assay. The QM-MSP assay was designed to enable accurate and absolute quantitative detection of methylation in a select panel of genes (up to 12) in a single FFPE section of a core biopsy, or a small aliquot of cells from Pap smear and displays high level of sensitivity of detecting methylated copies in a vast excess of normal copies of DNA (1 : 10,000) called QM-MSP [13, 14], A novel cervical cancer detection assay was developed to identify a methylated gene marker panel. A panel of 12 known and newly identified markers were identified that distinguish between normal, low-grade, and high grade squamous intraepithelial lesions, and cervical tumors in FFPE tissues and in PAP smears in women.

[00169] Data: A panel of 12 methylated markers, potentially highly methylated markers in cervical cancer, was identified by searching methylome databases and verifying the choice in TCGA databases. Macrodissected FFPE tissues of invasive squamous cell carcinomas (ISCC, N= 12), high grade intraepithelial lesions (HSIL, N=19) and low- grade intraepithelial lesions (LSIL, N=23) and adjacent normal (N=l 1) were tested by QM-MSP (FIG. 1). As seen in FIG. 1, ISCC, HSIL amd some HSILs showed high levels of cumulative methylation in the 12 genes, while the majority of LSIL and adjacent normal tissues have low to no detectable methylation. The difference in methylation is shown in boxplots and Mann Whitney analysis indicated that there were no significant differences in methylation between invasive ISCC, and HSILs. Most LSILs are considered benign disease, and similar to normal samples had no detectable methylation, while a small number had a detectable low level methylation, suggesting the possibility that higher than normal methylation may denote “higher than normal risk” of progression. This finding suggests that the panel of genes can detect both preneoplastic and neoplastic lesions with a high level of accuracy and may have potential not only in detection but also in surveillance of those with LSILs to detect any changes suspicious of malignancy.

[00170] Examining the extent (%M) and frequency of methylation of each gene in the panel, it was concluded that six of the 12 genes may provide coverage for almost all the cancers (sensitivity) while containing low or no methylation in the normal and benign samples (FIG. 2).

[00171] Using this more refined panel, the cumulative methylation was calculated in these six genes in the tumor, HSIL, LSIL and normal samples. As seen in FIG. 3, the histogram shows that both ISCC and HSIL contained high levels of methylation in the six genes, LSILs had significant detectable methylation in some samples, and normal showed very little methylation in the panel of six genes. This data presented as box plots shows the median and range of methylation in each sample set, with significant differences between ISCC and HSIL and between HSIL and LSIL. Receiver Operator Characteristic- AUC is shown for the ISCC and HSIL vs LSILs and also for ISCC/HSIL versus adjacent normal. AUC of 0.99 to 1.00 were achieved in these comparisons suggesting that the assay has the potential to distinguish between “need to be resected” ISCC compared to benign LSILs. Whether the LSILs subset with methylation higher than adjacent normal tissue represents lesions at higher risk of progressing to HSIL or ISCC is a question that will be studied.

[00172] For cervical cancer screening, PAP tests are routinely used for diagnosis by cytologists. Therefore, it was investigated whether the markers that identify cancer in cervical tissues would also identify malignant, preneoplastic and benign cells in PAP smears. A total of 77 archival PAP smears were examined by QM-MSP for the panel of methylated genes. Here again, the preliminary data shows that the 2 PAP smears from ISCC displayed the highest levels of methylation, HSILs contained a range of methylation, while normal cervical smears (NEIL) showed low to no methylation (FIG. 4). Currently, nearly 150 samples from Vietnam, both FFPE tissues and PAP smears representing various stages of progression of cervical cancer have been examined, with similar results. Similar sample sets of FFPE tissues and PAP smears from South Africa are currently being examined. Examining samples from these two disparate ethnic groups will determine if the same panel of genes/markers will detect cancer with equal accuracy in the different ethnicities (Caucasian, Asian and African).

[00173] Trimble et al. (15) showed that around 30% of HSIL regress spontaneously, those infected with HPV16 being less likely to regress. On the other hand, the majority of LSIL ultimately regresses. That is why these lesions are generally not treated and a follow-up PAP test is performed. The need for markers is at two junctures: how to identify the minority of LSILs that progress to HSIL or ISCC? And second, which are the HSILs that have potential to progress to carcinoma (70%) versus those that will regress (30%). However, at the present time, there are no specific markers of progression from LSIL to HSIL or from HSIL to carcinoma. It is proposed herein, that among the 12 markers identified in this study, are smaller panels of genes that can detect both high grade preneoplasia and invasive squamous cell carcinoma with a high level of accuracy and identify precancers that will progress from those that will spontaneously regress.

[00174] Example 2: Cervical carcinogenesis diagnosis

[00175] We observed a high level of accuracy achieved in identifying cancer using our panel of 12 methylation markers (see Example 1 above, the 12 markers being: ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2, and HIST1H3C) in PAP smears (N=244 from the US, South Africa and Vietnam) (AUC: 1.00, sensitivity 100%, specificity 100%) and in FFPE tissues (N=83, AUC: 0.927, sensitivity 87%, specificity 87%).

[00176] Based on these observations, we investigated whether this panel of 12 methylation markers could assess risk for cervical carcinogenesis, irrespective of the presence of highly oncogenic HPV types.

[00177] We analyzed the TCGA public database to determine if marker-panel methylation correlated with presence of cancer in this subgroup. Among 309 cervical cancer samples analyzed using Illumina’s Infinium HumanMethylation450 BeadChip which assayed the methylation status of 485,000 genomic CpG loci, there were 17 cases of HPV-negative cervical cancer. The results showed that 15/17 HPV-negative cancers were positive for methylation. Methylation markers were positive frequently in tumors of several histological types: 7/8 squamous cell carcinoma, 4/4 adenocarcinoma, 3/4 mucinous serous carcinoma, 1/1 complex epithelial neoplasm (Figure 6A). In FIG. 6A, analysis of TCGA’s Illumina’s Infinium HumanMethylation450 BeadChip data (N=309) on HPV -negative cervical malignancies (N=17) for the presence of a 7-gene subset of our 12-gene panel showed positive methylation (white versus darker shades of blue) in 15/17 samples.

[00178J To provide further support for this finding, we correlated quantitative methylation of our panel of 12 methylation markers (ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2, and HIST1H3C) with known HPV status on FFPE specimens (N=34) and in PAP smears (N=29) from Vietnam in our study. Our results showed that marker methylation prevalence was similar (P> 0.1 to P> 0.5) among the HPV- positive and HPV-negative tumors (Figure 6B). In FIG. 6B, DNA methylation of cervical cancer FFPE tissue (N=34) and PAP smears (N=29) in samples from Vietnam with known HPV status was correlated with HPV status. Mann Whitney analysis of the data on SCC and CIN2/3 separately and together shows that methylation markers were positive in a large majority irrespective of HPV status.

[00179] These findings of methylation markers that accurately identify cancer and high- risk cervical lesions, irrespective of HPV status, are very important for women with varied HPV status. These include those with transient hrHPV infections, hrHPV-negative, positive for HPV subtypes of unknown significance, and women who have been vaccinated against commonly oncogenic hrHPV. Assays based on detecting methylation of a panel of genes may play a critical role in detecting high grade lesions and cancers in the postvaccination era.

[00180] The present markers could help identify women (including all women) with a high short-term risk of progression to cancer who need immediate treatment and could reduce colposcopy referrals for example by 30% to 50%, therefore significantly improving cost- effectiveness to allow identification of women with a true risk of cancer.

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15. Trimble CL, Piantadosi S, Gravitt P, Ronnett B, Pizer E, Elko A, Wilgus B, Yutzy W, Daniel R, Shah K, et al. Spontaneous regression of high-grade cervical dysplasia: effects of human papillomavirus type and HLA phenotype. Clin Cancer Res 2005; 11 :4717 - 23; http://dx.doi.org/10.1158/1078-0432.CCR-04-2599; PMID: 16000566. [00181] Example 3: Study with 5-marker panel (FA/7v2, EDNRB. ZNF671. TBXT and MOS)

[00182] Marker discovery was performed in TCGA-CESC Infinium Methylation 450K Array database, and the selected 5 -gene panel was validated in three other public datasets. The panel was technically validated using Quantitative Multiplex -Methylation Specific PCR (QM-MSP) in tissue sections (N ^:::: 293) and cervical smears (N ^::: 244) from the U S., S. Africa, and Vietnam. The gene panel consisted of FMN2, EDNRB, ZNF671, TEXT and. MOS. Cervical tissue samples from U.S., South Africa, and Vietnam showed highly significant differential methylation in squamous cell carcinoma (SCC) with 100% sensitivity, 91 -93% specificity, and a Receiver Operating Characteristic Area under the curve (ROC AUC) = 1.000 [CI 1.000 to 1.000], and cervical intraepithelial neoplasia 2/3 (CIN2/3) with 55-100% sensitivity, 91-96% specificity, and a ROC AUC ranging from 0.793 [CI 0.681 to 0.905] to 1.000 [CI 1.000 to 1.000] compared to normal. In cervical smears, the marker panel detected SCC at 87% sensitivity, 95% specificity, and ROC AUC= 0.925 [CI 0.878 to 0.974], and high-grade intraepithelial lesion (HSIL) at 74% sensitivity, 95% specificity, and a ROC AUC ^::: 0.907 [CI 0.851 to 0.964] in an analysis of pooled data from the three countries. Similar to Human papilloma virus (HPV)-positive, HPV-negative cervical carcinomas were frequently hypermethylated for these markers.

[00183] This 5-marker panel detected SCC and CIN2/.3 in tissue and cervical smears with a high level of sensitivity and specificity. Molecular tests with the ability to rapidly detect high-risk CIN3+ lesions will lead to timely treatmem for those in need and (missing words) prevent unnecessary procedures in women with low-risk lesions throughout the world.

[00184] We targeted a sample size of at least N=25 per diagnosis, in each of three countries, the U.S., Vietnam and South Africa, selected to control the precision of the confidence intervals on sensitivity/specificity. Specifically, with sensitivity/specificity above 90%, as we observed in marker discovery, those values can be estimated to within 15% percentage points (based on 90% lower confidence bound).

[00185] Tissues and cervical smears were obtained and tested following approval by The Johns Hopkins Institutional Review Board (Approval No. IRB00241118 / CIR00095880), Johns Hopkins Hospital, Baltimore, U.S., the Ethics Review Board of National Health Laboratory Services (Approval No. Approval No. Ml 911125), Johannesburg, S. Africa and the Ethics Committee of the Vietnam Hanoi Medical University (Approval No. 4400/QD-DHYHN), Hanoi, Vietnam. These sources are heretofore referred to as U.S., S. Africa or SA, and Vietnam).

Sample collection

[00186] The inclusion criteria used for this study were that the surgically removed tissue samples were from newly diagnosed patients, and were histologically confirmed cases of normal/benign, CIN1, CIN2, CIN3 and invasive cancer. Histological confirmation of cytology diagnosis on smears of benign/normal, low grade intraepithelial lesions (LSIL), HSIL and invasive cancer was preferred. Clinical history should be available for review, where available. Samples should be from patients more than 18 years of age. Exclusion criterion was that normal/benign samples should not be from women with a history of abnormal PAP smear.

[00187] Archival formalin-fixed paraffin embedded (FFPE, N = 293) tissues and cervical smears (N = 244) (FIG. 7) were obtained from women (age >20 yr) in the U.S., S. Africa or SA, and Vietnam who underwent diagnostic cervical procedures for suspicious lesions in the cervix or curative treatment for cervical carcinoma and high-grade squamous intraepithelial lesion (HSIL). Histopathology of hematoxylin and eosin-stained tissue sections confirmed the diagnosis, classified as follows: squamous cell carcinoma (SCC), adenocarcinoma (AC) cervical intraepithelial neoplasia grade 3 (CIN3), cervical intraepithelial neoplasia grade 2 (CIN2), cervical intraepithelial neoplasia grade 1 (CIN1) and benign [18], Three sections from each sample block were obtained from the three institutions for technical evaluation of array markers. Macrodissected or whole sections of cervical tissue samples from a minimal distance of 1 cm from the tumor were used as a source of normal tissue.

[00188] Cytology diagnoses on the cervical smear samples were confirmed by histopathology where indicated (abnormal cytology and HPV-16/18 positive, or with severe cervical ectropion), except for cervical smear samples from South Africa for which histopathology was not accessed. Cervical smear cytology classification was performed according to the Bethesda system [19] as follows: squamous cell carcinoma (SCC), high grade squamous intraepithelial lesion (HSIL), low grade squamous intraepithelial lesion (LSIL), and negative for intraepithelial lesion (NIEL . Cervical smears (up to 4 slides) were obtained from the three institutions for marker validation. Paired cervical tissue and smears from the same patient were available for a total of 92 cases and controls from Vietnam. Demographic data was collected from the registry or from the medical records at the respective institutions.

Public datasets used for marker selection and validation

[00189] The Cancer Genome Atlas (TCGA) Illumina Infinium HumanMethylation450 BeadChip array (450K) data collection of Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (TCGA-CESC) consisted of 307 primary cervical carcinomas, 2 metastatic cervical carcinomas, and 3 normal cervical tissues adjacent to tumor. In the TCGA-CESC database, histological types and number of cervical cancers were: primary SCC, N = 254, AC, N = 53, normal cervical tissue (N = 3). Methylation data for normal uterus (N = 45) from TCGA- UCEC (Uterine Corpus Endometrial Carcinoma) database provided additional controls. To validate the selected markers, we used three datasets deposited in the NCBI’s GEO repository. GSE68339 [21] includes DNA methylation profiles from 270 cervical carcinomas and GSE211668 [22] consists of profiles from 62 cervical carcinomas and 19 normal cervical tissue samples, both on the 450K array. The third set GSE143752 [23] conducted on an Illumina Infinium 850K HumanMethylation array (850K) included 42 CIN3, 40 CIN2, 50 CIN1, 54 normal cervical tissues.

Genomic DNA extraction, bisulfite conversion, and quantitative multiplex-methylation specific PCR (QM-MSP)

[00190] Formalin-fixed paraffin embedded tissue sections (one to two 8 pm) were deparaffinized by two 10 min incubation in fresh xylene. With a sterile flat razor blade, the tissue was scraped into digestion buffer [10 mM Tris, 150 mM NaCl, 2 mM EDTA, 0.5% SDS pH 8.5, 100 pg/ml Salmon Sperm DNA (Thermo Fisher Scientific, Carlsbad CA), 60 pg/ml proteinase K (Millipore Sigma, Burlington, MA)]. The sample was incubated at 52° C overnight, heat- inactivated. DNA conversion was performed using sodium bisulfite according to manufacturer’s directions (EZ DNA Methylation, Zymo Research, Irvine, CA). QM-MSP was performed as previously published [24-26], In brief, this assay is a quantitative nested-methylati on-specific PCR method that consists of two PCR reactions: PCR #1, 35 cycles consisted of pre- amplification of the region of interest using methylation independent primers for each of five markers. PCR #2, consisted of 40 cycles of real-time PCR, to quantify the amplicons PCR #1.

[00191] Cumulative methylation (CM) was expressed as the sum of % M for all markers in the 5 -marker panel.

Correlation between methylation and mRNA expression

[00192] RNA-seq data in the TCGA-CESC and UCEC databases (described above) was downloaded from Broad Institute of MIT & Harvard (Firehose, https://gdac.broadinstitute.org/). TCGA-CESC, -UCEC 450K methylation array data and RNA-seq data were compared for each of the five methylation markers.

Ability of the marker panel to detect HPV-positive and HPV-negative cervical carcinoma

[00193] To assess whether the 5-marker panel could detect HPV-positive and HPV-negative cancer, TCGA-CESC and -UCEC array datasets were used. Among the 307 primary cancers, there were 17 HPV-negative tumors. The second, the Genomic Spatial Event (GSE), GSE68339 array dataset [21] was provided by Dr. Lyng and Dr. Fjeldbo (Oslo, Norway), who also provided HPV status information for 270 SCC; among these 20 were HPV-negative tumors.

Correlation between methylation and age

[00194] To estimate the effect of age on DNA methylation levels in our 5-marker panel, we fit linear regression models and calculated Pearson correlation coefficients using |3-methylation and age data from normal cervix from TCGA-CESC and normal uterus from TCGA-UCEC.

Correlation between CM-5 methylation and age was also performed in normal/benign samples from the U.S., South Africa and Vietnam.

Statistical analysis

[00195] Database analyses were performed using The Partek® Genomics Suite® software Version 6.6 (Partek Inc., Chesterfield, MO) and the R statistical software suite (https://cran.r- project.org/). Figures were generated using GraphPad Prism (GraphPad Software version 10, La Jolla CA). Cumulative stacked histograms and box and whiskers plots were used to display the QM-MSP results expressed as CM-5. Assay performance was reported as areas under the ROC curve as well as sensitivity and specificity. In order to make results more directly comparable, we chose thresholds for distinguishing disease from normal to control sensitivity at 95%, and reported the corresponding specificity. Specifically, we calculated the 95^th percentile of cumulative methylation in control normal or benign samples for each comparison using the quantile function in GraphPad PRISM as the threshold. ROC metrics were calculated in GraphPad PRISM, which uses a normal approximation by Gagnon [27], Confidence intervals for sensitivity and specificity are estimated by the Clopper method [28], This study is reported as per the STARD 2015 reporting guideline for diagnostic accuracy studies (SI Checklist).

RESULTS

Study Design and Workflow

[00196] The study design and workflow for discovery and validation of methylation markers is shown in FIG. 13. First, we conducted an in silico analysis of the TCGA-CESC, -UCEC array data collection and identified candidate markers, which we technically validated in external 450K datasets. Next, we confirmed these findings by testing the selected markers using QM- MSP on tissues and cervical smears from the U.S, Vietnam, and S. Africa (FIG. 7). Finally, we evaluated the association between methylation and expression, and methylation and HPV status.

Marker Discovery

[00197] The TCGA-CESC and UCEC array datasets were used to interrogate 307 cervical cancers, and 48 normal tissues in order to identify markers of cervical carcinoma (FIG. 8A). Principal component analysis revealed clear separation between cervical carcinoma and normal tissues (FIG. 8A). For discovery of cervical tumor-specific markers, the array probes were serially filtered in several steps as described in FIG. 13. The 14 top candidate cervical cancer markers were evaluated as shown in the histogram of cumulative P-methylation in the tumors (FIG. 8B). To arrive at the 5 CpG probes from the top 14 candidate markers selected in this study, 1) we further refined probe selection for achieving a high level of specificity by eliminating the remaining nine probes which were found to have beta methylation values higher than 0.05 units among the 48 normal samples. Data was thus reduced to 5 CpG probes that clearly distinguished SCC and adenocarcinoma from normal tissues (P < 0.0001, Mann- Whitney (FIG. 8C). Descriptive statistics for the 5 markers are provided in Table 1 below.

[00198] In the TCGA databases examined, we observed significant cumulative methylation (CM) levels of the 5-marker panel in squamous cell carcinoma (SCC, N = 254) and also in the rarer histological type, adenocarcinomas (AC, N = 53) compared to normal tissues in the TCGA- CESC database. Interestingly, CM-5 levels were not significantly different (P = 0.1319) between SCC and AC (FIG. 8C). This suggested this marker panel could be broadly useful among different types of cervical carcinoma. The selected probes recognize CpG sites in FMN2, EDNRB, ZNF671, TBXT anti MOS, as summarized in Table 1 below. Table 2 below provides probe index (ID), gene name, location and function.

[00199] The TCGA-CESC methylation profile of the 5-marker panel was examined in three other publicly available datasets (FIG. 14). The analysis confirmed that the selected markers showed high levels of tumor-specific methylation, and low levels of methylation in normal tissues in two databases, GSE68339 [21], and GSE21168 [22], Intermediate levels of methylation were present in cervical intraepithelial neoplasia that increased with grade in GSE143752 [29],

Association between methylation of the five CpG markers and gene expression

[00200] CpG methylation can lead to gene silencing and subsequent loss of tumor suppressor function [8], To determine if there was a relationship between gain in methylation in the 5- marker panel and loss of gene expression, we plotted TCGA-CESC methylation (FIG. 15 A) and RNA-seq expression data (FIG. 15B) for the same CESC cases. Methylation of all 5 CpG markers was significantly higher in tumor than normal (FIG. 15 A). By RNA-seq, ZNF671, EDNRB, and FMN2 showed a high level of expression in normal and low expression in tumors. In contrast, gene expression was low in both normal and tumor samples for TBXT and MOS (FIG. 15B). Thus, we observed a loss of expression for ZNF671, EDNRB, and FMN2, but not for TBXT and MOS.

Analytical validation of 5-marker panel in FFPE tissue from U.S., S. Africa and Vietnam

[00201] To determine if the 5-marker panel validated by in silico analysis of array datasets will show equally high performance when examined by a laboratory test, we performed analytical validation of the markers using the quantitative methylation-specific PCR method, QM-MSP. This assay was performed on clinical FFPE tissue samples (N = 293). The samples from the U.S. (N = 63; FIG 9A) were macrodissected, while whole tissue sections were used for samples from S. Africa (N = 69; Figure 3B), and Vietnam (N = 120; FIG. 9C). In tissue from U.S., cumulative methylation of the 5 markers (CM-5) distinguished between normal and SCC with 100% sensitivity and 91% specificity (ROC AUC = 1 .000, 95% CI 1 .000 to 1 .000, P = 0.0001) and between normal and CIN3 at 100% sensitivity and 91% specificity (ROC AUC = 1.000, 95% CI 82.41 to 100.0; 95% CI 1.000 to 1.000, P < 0.0001) (Figure 3A). Considered individually, the markers achieved significantly higher methylation in both SCC and CIN3 (P < 0.0001; Figure 16; Table 3 below). In tissue from S. Africa, CM-5 distinguished between normal and SCC with 100% sensitivity at 95.65% specificity (ROC AUC = 1.000, 95% CI 1.000 to 1.000, P < 0.0001) and between normal and CIN2/3 with 78.26% sensitivity and 95.65% specificity (ROC AUC = 0.928, 95% CI 0.851 to 1.000, P < 0.0001 ) (Figure 3B). In tissue from Vietnam, CM-5 distinguished between normal and SCC with 100% sensitivity at 93.33% specificity (ROC AUC = 1.000, 95% CI 1.000 to 1.000, P < 0.0001), and between normal and CIN2/3 with 54.84% sensitivity at 93.33% specificity (ROC AUC = 0.793, 95% CI 0.681 to 0.905, P = 0.0001). In contrast, CIN1 methylation was not significantly different from normal (Mann-Whitney P = 0.259) (Figure 3C).

[00202] Left untreated, approximately 10% of CIN2 lesions progress to CIN3+ disease [30], Detection of high levels of methylation in the 5-marker panel in a subset of CIN2 lesions might indicate which CIN2 lesions were at highest risk of progression. To determine baseline and frequency of methylation in CIN2 lesions, a new set of macrodissected lesions (N = 41 CIN2, N = 3 adjacent normal) from the U.S. (N = 20), S. Africa (N = 15), and Vietnam (N = 6) were tested (FIG. 17). Data from macrodissected adjacent normal tissues (N = 11) previously shown in FIG. 9A were also used in this analysis. Results showed that CM-5 discriminated between normal and CIN2 with 59% sensitivity and 86% specificity (ROC AUC = 0.8162, 95% CI 0.702 to 0.930; P = 0.0005). We conclude that a proportion of CIN2 are highly methylated. To determine if they are more likely to progress to cancer will require long term follow up of the patients with unresected CIN2.

[00203] In summary, in cervical tissues obtained from three regions of the world, SCC and CIN3 could be detected with very high sensitivity and specificity. Considered together or separately, in each geographic region, progressively higher methylation occurred as a function of increasing severity of dysplasia.

Assay validation in cervical smears from U.S., S. Africa, and Vietnam [00204] Cervical smears are often performed in the screening setting to collect cells from the cervix and vagina for cytological analysis for early detection of precancerous lesions. The potential clinical utility of the 5-marker panel to detect the presence of CIN3+ disease in cervical smears was evaluated by QM-MSP in a total of 244 cervical samples from the U.S., Vietnam, and S. Africa. As a first pass, data from all three countries was pooled to assess CM-5 of SCC, HSIL, LSIL and normal (FIG. 10). The histogram (FIG. 10A) and box plot (FIG. 10B) showed that CM increased progressively with higher grades of neoplasia. The assay distinguished between normal and SCC with 86.84% sensitivity and 95.35% specificity (ROC AUC = 0.925, 95% CI 0.878 to 0.974, P < 0.0001), and between normal and HSIL with 73.77% sensitivity and 95.35% specificity (ROC AUC = 0.907, 95% CI 0.851 to 0.964, P < 0.0001) (GIG. 10C). Methylation data was then examined separately for the three geographic regions (FIG. 18). In the U.S. samples, the assay distinguished between HSIL and normal with 82.05% sensitivity and 94.74% specificity (ROC AUC = 0.927, 95% CI 0.862 to 0.991, P < 0.0001) (FIG. 18A). In the cervical smears from Vietnam, the assay distinguished SCC from normal with 92.00% sensitivity and 93.75% specificity (ROC AUC = 0.950, 95% CI 0.879 to 1.000, P < 0.0001), and HSIL from normal with 69.57% sensitivity and 93.75% specificity (ROC AUC = 0.885, 95% CI 0.788 to 0.983, P < 0.0001) (FIG. 18B). In cervical smears from S. Africa, in the absence of normal samples, we used the CM-5 cutoff indicated in FIG. 18A (histogram, dotted line), which was based on the 95th percentile of normal in U.S. smears. In the S. Africa sample set, 84% (42/50) of the SCC cervical smears were positive for methylation (FIG. 18C).

[00205] Considered individually, each of the five markers distinguished HSIL from normal in the U.S. sample set (N = 77) (FIG. 19). As shown in the histograms, cervical smears of HSIL (N = 38) showed higher levels of methylation compared to normal (N = 38). Individually for each gene, ROC AUC ranged from 0.861 (95% CI 0.776 to 0.947, P < 0.0001) to 0.933 (95% CI 0.875 to 0.991, P < 0. 0001) (FIG. 19). Thus, each of the five markers sensitively detected HSIL in cervical smears.

[00206] Analysis of the 5-marker panel in paired tissue and cervical smears from the same individuals

[00207] We performed a pairwise comparison between the tissue and cervical smear from the same individual to evaluate whether the methylation results agreed between the sample types. The QM-MSP results of 92 samples from Vietnam were re-analyzed using data presented in FIG. 9C (tissue) and FIG. 18B (cervical smears). Histogram plots show CM-5 in tissue and cervical smears of patients diagnosed with SCC, HSIL, LSIL and benign lesions (Figure 5). There was a high level of agreement in CM-5 methylation between pairs for SCC (23/25) (FIG. HA), HSIL (17/25) (FIG. 1 IB), LSIL (20/21, FIG. 11C) and benign (23/23, FIG. 1 ID). In SCC, discordance was observed in 2 instances where the tissues were positive while the smear was negative (FIG. 11 A). In HSILs, discordance was observed in 4 pairs where smears were positive while tissues were negative, and in four pairs where tissues were positive while smears were negative (FIG.

1 IB). For these analyses we used the normal threshold defined in Figure 3C and S6B Figure. In LSILs, methylation was consistently low in both tissues and smears (19/21) (FIG. 11C). There were two outliers; in one, methylation was very high in both tissue and smear, while in the other, the tissue was positive while the smear was negative. Strikingly, in all 23 pairs of benign tissues and smears, methylation was below the threshold for normal (FIG. 1 ID). We concluded that, with few exceptions, cervical smears provided a good reflection of the histopathology of the tissue.

[00208] To further clarify the source of samples of tissue, smears, and how many among them were tissue/smear pairs from the same patient, a detailed table of patient samples used in this study is shown in Table 4 below . Available demographic data is presented for patient samples from the U.S (Table 5 below) and Vietnam (Table 6 below).

[00209] The 5-marker panel is methylated in both Human Papilloma Virus (HPV)-positive and HPV-negative cervical cancer

[00210] The majority of cervical carcinomas are HPV-positive. HPV testing is therefore recommended throughout the world to screen for cervical cancer [31, 32], Under these circumstances, the 3-10% of carcinomas that are HPV-negative for all the HPV-subtypes currently tested can be missed [33], To determine whether the 5-marker panel detects cervical carcinoma in both HPV-positive and HPV-negative cases, TCGA-CESC/UCEC and GSE68339 [21] databases were analyzed, correlating [3-methylation levels of the 5-CpG marker panel to HPV status (Figure 6). HPV-negative carcinomas represented 5.5% (17 of 307 cases) in TCGA- CESC (Figure 6A) and 7.4% (20 of 268 samples) in GSE68339 datasets (Figure 6B, S7 Table). As observed in the histogram and box plot of the TCGA datasets, HPV-negative samples had significantly higher cumulative P-methylation (P < 0.0001) in the 5-marker panel compared to normal cervix and uterus (N = 48) In the HPV-negative TCGA-CESC samples, 71% (12/17) of tumors were hypermethylated (FIG. 12A). In the GSE68339 dataset of 268 cancers, where HPV status was determined by a qPCR assay, 95% (19/20) of HPV-negative carcinomas were hypermethylated compared to the normal samples in the TCGA-CESC dataset (FIG. 12B). Interestingly, in both data sets HPV-negative samples were found to have significantly lower methylation than HPV-positive samples (P < 0.0001). Although the numbers were small, the results suggested that the 5-marker panel detects both HPV-positive and HPV-negative samples with similar sensitivity.

[0021 1] Correlation of age to methylation. It is well established that human aging is associated with characteristic changes in DNA methylation throughout the genome [34-36], To estimate the size of this effect in our markers, we fit linear regression models between age and DNA methylation level (FIG. 20). In general, we observed that methylation level of our 5- marker signature increased with patient age, although the association did not reach statistical significance (range r = 0.053 to 0.096 with P = 0.604 to 0.781) in any of our study populations. A negative correlation was observed in the samples from U.S. (r = -0.493, P = 0.123). Importantly, the changes in methylation with age were small in absolute terms, and unlikely to lead to misclassifications. Thus, we concluded that CM-5 methylation was not significantly correlated with age in either CESC and UCEC normal datasets or in QM-MSP data on our study samples.

[00212] DISCUSSION

[00213] This Example 3 describes our systematic effort to identify specific CpG dinucleotides that are highly and differentially methylated in cancer but not in normal tissues, and compile and validate a new 5-marker panel for cervical cancer. We also describe the technical validation of this panel of methylated gene markers. Through QM-MSP analysis of the 5-marker panel in more than 500 histologically confirmed tissues and in cervical smears obtained from three countries we show that the test performs with a high level of sensitivity and specificity to detect CIN3+ disease. Although preliminary, to our knowledge the study describes markers that perform with a high level of accuracy. [00214] An inability to achieve optimal sensitivity and specificity using various versions of commercial HPV tests in detecting early precancerous cervical lesions has led to a strategy of combining HPV DNA testing with cytology triage, and other molecular markers [37-40], Others have tested methylated gene markers alone, or combined with HPV-testing and cytology to determine if the three-pronged approach would improve detection and risk stratification of low- grade lesions [9], A number of commercial methylation tests have used single host gene methylation of POU4F3 (sensitivity 74% and specificity 89%) [41], PAX1 (sensitivity 78%, and specificity 92%) [42], or panels of two, PAX1/ZNF582 (sensitivity 78,85% and specificity 73.55%) [43] to six markers [34], A number of studies have used the S5-classifier [12], that consists of markers of HPV gene methylation combined with a host gene marker, EPB41L3 (sensitivity 93.2% and specificity 41.8%). Among these, the FAM19A4 and hsa-miR124-2 markers, marketed as the QIAsure methylation test, showed highest sensitivity (77%, N = 228; 95% CI 71 to 82) and specificity (78.3%, N = 2012; 95% CI 76 to 80) of detection of CIN3 [10], while the others display a high sensitivity or specificity, but not both. In pooled data analysis of cervical smears, comparing HSIL to benign, our QM-MSP based determination of CM-5 achieved a ROC AUC of 0.907 [95% CI 0.851 to 0.964], with 74% sensitivity (95% CI 62 to 83) at 95% specificity (95% CI 89 to 98). Analyzed separately by region, the data in HSILs was stronger in samples from the U.S. where the assay achieved a ROC AUC of 0.927 [95% CI 0.862 to 0.991] with 82% sensitivity at 95% specificity. In HSIL samples from Vietnam, the assay achieved a ROC AUC of 0.885 [95% CI 0.788 to 0.983] with 70% sensitivity at 94% specificity. Although we have not yet validated our markers in uniformly collected samples from a well- planned prospective study, our 5-marker panel for cervical cancer shows strong potential for future testing and development.

[00215] Our preferred methylated marker panel of this Example 3 consists of five genes, FMN2, EDNRB, ZNF671, TBXT, and MOS. The genes are potential growth suppressors with varied functions (Table 2 below). The products of two of these genes are DNA-binding transcription factors. TBXT has a potential role in promoting cellular transformation and progression through epithelial mesenchymal transition [44], The zinc finger-containing ZNF671 protein has a metastasis suppressor role through regulating the Notch and Wnt/p-catenin pathways [44, 45], EDNRB was identified as a G-protein coupled receptor that activates the phosphatidylinositol calcium signaling cascade, and is also reported to be aberrantly expressed and differentially methylated in cancer [46, 47] . Another molecule involved in cell signaling is MOS, a serine threonine kinase that activates MAPK signaling [48], MOS has also been implicated in inducing aneuploidy/polyploidy in cancer cells by regulating actin filaments during cell division [49, 50], FMN2, a member of the Formin family implicated in multiple neurodevelopmental disorders, is an actin-binding protein that regulates actin networks and cell polarity and is essential for meiotic metaphase [51], While EDNRB and FMN2 as well as TBXT and ZNF671 could have potential tumor suppressor functions, MOS is a well-known oncogene. One approach to test whether the differential methylation at the selected CpG sites is biologically relevant is to query whether methylation of the gene in that CpG-rich region is associated with reduced expression. Examining methylation and expression in the same samples in TCGA-CESC revealed that hypermethylation of three of the genes, ZNF671, EDNRB and FMN2 was associated with loss of gene expression, while TBXT and MOS, although hypermethylated in tumor, showed essentially no expression in tumors or normal tissues (FIG. 15).

[00216] Other factors that might affect our data are that both the tissue and cervical smears were samples of convenience obtained from three different countries, and suffers from an uneven distribution of grades in the different cohorts. Other limiting factors were that HPV status and age information was not available for all cases and controls. Diagnoses was based on cytology for the cervical smears that was confirmed by histopathology at the collaborating center by the study pathologist, but no central pathology review was performed. Preliminary data analysis of the 5-marker panel in the GSE68339 dataset did not show correlation of methylation in SCC with progression-free survival (data not shown).

[00217] This is the first report of the 5-marker panel and the results are encouraging. The robust performance of the markers presents a strong rationale for further investigation in a large prospective clinical validation study with an independent sample set, accompanied by accurate HPV-testing, detailed patient characteristics, and centralized cytology and pathology diagnosis. The QM-MSP assay could be a valuable triaging tool for further clinical intervention, for risk stratification, and along with HPV testing, could provide a higher sensitivity and specificity for detection of cervical neoplasia. The test could be automated and modified for high throughput as demonstrated in our studies using GeneXpert cartridges for early detection of breast cancer in fine needle aspirates of the breast lesion, and validated in cell-free DNA in blood for monitoring disease in patients undergoing chemotherapy [9, 52-54], A liquid biopsy assay for colon cancer detection is also under development [55],

[00218] In summary, in this study we have demonstrated the value of a systematic stepwise search for methylated markers focused on the detection of CIN3+ cervical cancer. The markers underwent rigorous technical validation on tissues and cervical smears representing each stage of disease progression. Moreover, the marker panel was equally sensitive for the detection of HPV- positive or HPV-negative cancer. If reproduced in large studies, it will result in change of practice, streamline the pathway to biopsy, and result in tremendous savings in healthcare.

Table 1. Descriptive statistics for 5-marker panel comparing beta methylation in cervical tumor to normal/benign tissues in TCGA database.

Table 2. The Cancer Genome Atlas (TCGA) cervical marker probe identification (ID), gene name, location and function. Provided is the CpG probe location in the TCGA databases which was used for discovery of the 5-marker panel. Also provided is the genomic location used for developing Quantitative Multiplex Methylation Specific PCR (QM-MSP) primers and probes; the known gene functions are described.

References for Table 2.

1. Ma X, Liu J. Wang H. Jiang Y, Wan Y. Xia Y, et al. Identification of crucial aberrantly methylated and differentially expressed genes related to cervical cancer using an integrated bioinformatics analysis. Biosci Rep 2020:40

2. Chen M, Wu Y, Zhang H, Li S, Zhou J, Shen J. The Roles of Embryonic Transcription Factor BRACHYURY in Tumorigenesis and Progression. Front Oncol 2020;10:961

3. Vitale I, Senovilla L, Jemaa M, Michaud M, Galluzzi L, Kepp 0, et al. Multipolar mitosis of tetrapioid cells: inhibition by p53 and dependency on Mos. EMBO J 2010;29: 1272-84

4. Erenpreisa J, Cragg MS. MOS, aneuploidy and the ploidy cycle of cancer cells. Oncogene 2010;29:5447-51

5. Acevedo N, Smith GD. Oocytc-spccific gene signaling and its regulation of mammalian reproductive potential. Front Biosci 2005;10:2335-45

6. Wang Y. Chen FR, Wei CC. Sun LL, Liu CY. Yang LB. et al. Zinc finger protein 671 has a cancer- inhibiting function in colorectal carcinoma via the deactivation of Notch signaling. Toxicol Appl Pharmacol 2023:458: 116326

7. Zhan W, Li Y, Liu X, Zheng C, Fu Y. ZNF671 Inhibits the Proliferation and Metastasis of NSCLC via the Wnt/beta-Catenin Pathway. Cancer Manag Res 2020;12:599-610

8. Kundu T, Siva Das S, Sewatkar LK, Kumar DS, Nagar D, Ghose A. Antagonistic Activities of Fmn2 and ADF Regulate Axonal F-Actin Patch Dynamics and the Initiation of Collateral Branching. J Neurosci 2022;42:7355-69

9. Yamada K, Ono M, Perkins ND, Rocha S, Lamond AL Identification and functional characterization of FMN2, a regulator of the cyclin-dependent kinase inhibitor p21. Mol Cell 2013:49:922-33

10. Tsuiko O, Noukas M, Zilina O, Hensen K, Tapanainen JS, Magi R et al. Copy number variation analysis detects novel candidate genes involved in follicular growth and oocyte maturation in a cohort of premature ovarian failure cases. Hum Reprod 2016;31: 1913-25

Table 3. Descriptive statistics for QM-MSP methylation of individual markers in the 5-marker panel. Tissue sections from the United States (N = 63) were macrodissected and methylation was quantified using Quantitative Multiplex Methylation Specific PCR (QM-MSP) for EDNRB, ZNF671, FMN2, MOS and TBXT. Data was analyzed using Mann Whitney statistic for the indicated comparisons. Figure 16 shows accompanying data. %M, percent methylation.

[0002]

Table 4. Detailed description of samples used in this study. Quantitative Multiplex- Methylation Specific PCR (QM-MSP) was performed on archival formalin fixed paraffin embedded (FFPE)-tissue and cervical smear samples. Paired smear/tissue samples were available from Vietnam, which is a subset of the Vietnam samples shown in Set 1 and Set 2. Set 3 CIN2 was an independent sample set. This information supports Figure 1. SCC- Squamous cell carcinoma; CIN2/3- Cervical intraepithelial neoplasia 2/3; CIN1- Cervical intraepithelial neoplasia 1; HSIL- High grade intraepithelial lesion, LSIL-low grade intraepithelial lesion; CIN2- Cervical intraepithelial neoplasia 2.

[0003 ] References of Example 3

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25. Fackler MJ, Sukumar S. Quantitation of DNA Methylation by Quantitative Multiplex Methylation-Specific PCR (QM-MSP) Assay. Methods Mol Biol. 2018;1708:473-96.

26. Swift-Scanlan T, Blackford A, Argani P, Sukumar S, Fackler MJ. Two-color quantitative multiplex methylation-specific PCR. Biotechniques. 2006;40(2):210-9.

27. Gagnon RC, Peterson JJ. Estimation of confidence intervals for area under the curve from destructively obtained pharmacokinetic data. J Pharmacokinet Biopharm. 1998;26(1 ): 87- 102.

28. Westfall P. Simultaneous small-sample multivariate Bernoulli confidence intervals. Biometrics. 1985;41(4): 1001-13.

29. Thorsson V, Gibbs DL, Brown SD, Wolf D, Bortone DS, Ou Yang TH, et al. The Immune Landscape of Cancer. Immunity. 2019;51 (2):411 -2.

30. Zhang J, Lu CX. Spontaneous Regression of Cervical Intraepithelial Neoplasia 2: A Meta-analysis. Gynecol Obstet Invest. 2019;84(6):562-7.

31. Simms KT, Steinberg J, Caruana M, Smith MA, Lew JB, Soerjomataram I, et al. Impact of scaled up human papillomavirus vaccination and cervical screening and the potential for global elimination of cervical cancer in 181 countries, 2020-99: a modelling study. Lancet Oncol. 2019;20(3):394-407.

32. Leinonen M, Nieminen P, Kotaniemi-Talonen L, Malila N, Tarkkanen J, Laurila P, et al. Age-specific evaluation of primary human papillomavirus screening vs conventional cytology in a randomized setting. J Natl Cancer Inst. 2009;101(23): 1612-23.

33. Fernandes A, Viveros-Carreno D, Hoegl J, Avila M, Pareja R. Human papillomavirus- independent cervical cancer. Int J Gynecol Cancer. 2022;32(l):l-7.

34. Chen JQ, Salas LA, Wiencke JK, Koestler DC, Molinaro AM, Andrew AS, et al. Genome-Scale Methylation Analysis Identifies Immune Profiles and Age Acceleration Associations with Bladder Cancer Outcomes. Cancer Epidemiol Biomarkers Prev.

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35. Allegra A, Caserta S, Mirabile G, Gangemi S. Aging and Age-Related Epigenetic Drift in the Pathogenesis of Leukemia and Lymphomas: New Therapeutic Targets. Cells. 2023; 12(19).

36. Varshavsky M, Harari G, Glaser B, Dor Y, Shemer R, Kaplan T. Accurate age prediction from blood using a small set of DNA methylation sites and a cohort-based machine learning algorithm. Cell Rep Methods. 2023;3(9): 100567. 37. Arbyn M, Simon M, de Sanjose S, Clarke MA, Poljak M, Rezhake R, et al. Accuracy and effectiveness of HPV mRNA testing in cervical cancer screening: a systematic review and meta- analysis. Lancet Oncol. 2022;23(7):950-60.

38. Williams J, Kostiuk M, Biron VL. Molecular Detection Methods in HPV-Related Cancers. Front Oncol. 2022; 12:864820.

39. Gradissimo A, Burk RD. Molecular tests potentially improving HPV screening and genotyping for cervical cancer prevention. Expert Rev Mol Diagn. 2017;17(4):379-91.

40. Hesselink AT, Heideman DA, Steenbergen RD, Coupe VM, Overmeer RM, Rijkaart D, et al. Combined promoter methylation analysis of CADM1 and MAL: an objective triage tool for high-risk human papillomavirus DNA-positive women. Clin Cancer Res. 2011;17(8):2459-65.

41. Kocsis A, Takacs T, Jeney C, Schaff Z, Koiss R, Jaray B, et al. Performance of a new HPV and biomarker assay in the management of hrHPV positive women: Subanalysis of the ongoing multicenter TRACE clinical trial (n > 6,000) to evaluate POU4F3 methylation as a potential biomarker of cervical precancer and cancer. Int J Cancer. 2017; 140(5): 1119-33.

42. Chang CL, Ho SC, Su YF, Juan YC, Huang CY, Chao AS, et al. DNA methylation marker for the triage of hrHPV positive women in cervical cancer screening: Real-world evidence in Taiwan. Gynecol Oncol. 2021;161(2):429-35.

43. Tian Y, Yuan Wu NY, Liou YL, Yeh CT, Cao L, Kang YN, et al. Utility of gene methylation analysis, cytological examination, and HPV- 16/18 genotyping in triage of high-risk human papilloma virus-positive women. Oncotarget. 2017;8(37):62274-85.

44. Chen M, Wu Y, Zhang H, Li S, Zhou J, Shen J. The Roles of Embryonic Transcription Factor BRACHYURY in Tumorigenesis and Progression. Front Oncol. 2020; 10:961.

45. Wang Y, Chen FR, Wei CC, Sun LL, Liu CY, Yang LB, et al. Zinc finger protein 671 has a cancer-inhibiting function in colorectal carcinoma via the deactivation of Notch signaling. Toxicol Appl Pharmacol. 2023 ;458: 116326.

46. Lin H, Ma Y, Wei Y, Shang H. Genome-wide analysis of aberrant gene expression and methylation profiles reveals susceptibility genes and underlying mechanism of cervical cancer. Eur J Obstet Gynecol Reprod Biol. 2016;207: 147-52.

47. Ma X, Liu J, Wang H, Jiang Y, Wan Y, Xia Y, et al. Identification of crucial aberrantly methylated and differentially expressed genes related to cervical cancer using an integrated bioinformatics analysis. Biosci Rep. 2020;40(5).

48. Fukasawa K, Vande Woude GF. Synergy between the Mos/mitogen-activated protein kinase pathway and loss of p53 function in transformation and chromosome instability. Mol Cell Biol. 1997; 17(l):506-18.

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50. Erenpreisa J, Cragg MS. MOS, aneuploidy and the ploidy cycle of cancer cells. Oncogene. 2010;29(40): 5447-51.

51 . Kundu T, Siva Das S, Sewatkar LK, Kumar DS, Nagar D, Ghose A. Antagonistic Activities of Fmn2 and ADF Regulate Axonal F-Actin Patch Dynamics and the Initiation of Collateral Branching. J Neurosci. 2022;42(39):7355-69.

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OTHER EMBODIMENTS

[00222] From the foregoing description, it will be apparent that variations and modifications may be made to the disclosure described herein to adopt it to various usages and conditions. Such embodiments are also within the scope of the following claims.

[00223] All citations to sequences, patents and publications in this specification are herein incorporated by reference to the same extent as if each independent patent and publication was specifically and individually indicated to be incorporated by reference.

Claims

What is claimed:

1. A method of treating a subject suspected of having cancer, comprising: diagnosing the subject as having cancer, wherein diagnosis comprises assaying a methylation profile of biomarkers obtained from a sample from the subject; and, administering to the subject a therapy to treat the cancer.

2. The method of claim 1, wherein the biomarkers comprise two or more of ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2, HIST1H3C and combinations thereof.

3. The method of claim 1 wherein the biomarkers comprise five or more of ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2, HIST1H3C and combinations thereof.

4. The method of any one of claims 1 through 3 wherein the biomarkers are methylated.

5. The method of any one of claims 1 through 4 wherein the biomarkers comprise or consist of ZNF671, EDNRB, TMEFF2, FMN2, MOS and TBXT.

6. The method of any one of claims 1 through 4 wherein the biomarkers comprise FMN2, EDNRB, ZNF671, TBXT and MOS.

7. The method of any one of claims 1 through 4 wherein the biomarkers consist of FMN2, EDNRB, ZNF671 , TBXT and MOS

8. The method of any one of claims 1 through 7, wherein the magnitude of methylation and/or frequency of methylation of one or more of the biomarkers ZNF671 EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2, HIST1H3C and combinations thereof, from the subject having cancer is above a threshold value as compared to each of the biomarkers from healthy subjects.

9. The method of claim 8 wherein the magnitude of methylation and/or frequency of methylation of each of the biomarkers ZNF671, EDNRB, TMEFF2, FMN2, MOS and TBXT is above the threshold value.

10. The method of claim 8 wherein the magnitude of methylation and/or frequency of methylation of each of the biomarkers ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2, HIST1H3C is above the threshold value.

11. The method of any one of claims 1 through 8 wherein the cumulative methylation of one or more biomarkers ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2, HIST1H3C, or combinations thereof, from the subject having cancer is above a threshold value as compared to the cumulative methylation of the biomarkers from healthy subjects.

12. The method of any one of claims 1 through 8 wherein the cumulative methylation of biomarkers ZNF671, EDNRB, TMEFF2, FMN2, MOS and TBXT, from the subject having cancer is above a threshold value as compared to the cumulative methylation of the biomarkers from healthy subjects.

13. The method of any one of claims 1 through 8 wherein the cumulative methylation of biomarkers FMN2, EDNRB, ZNF671 , TBXT and MOS, from the subject having cancer is above a threshold value as compared to the cumulative methylation of the biomarkers from healthy subjects.

14. The method of any one of claims 1 1 through 13 wherein the cumulative magnitude of methylation and/or frequency of methylation of the biomarkers is biomarkers is at a level that is at least a 10 percent increase of the threshold value.

15. The method of any one of claims 1 through 14 wherein the cancer comprises cervical cancer, uterine cancer, ovarian cancer.

16. The method of any one of claims 1 through 14 wherein the cancer is cervical cancer.

17. The method of any one of claims 1 through 13 wherein the sample comprises: whole blood, serum, plasma, saliva, cervical pap smears, stool, urine, bladder washing, uterine washing, sputum, lymphatic fluid, cerebrospinal fluid, ascites fluid, cystic fluid, fine needle aspiration, a tissue sample from one or both ovaries, uterus, cervix, or lymph node, frozen tissues, formalin- fixed, paraffin-embedded (FFPE) tissues or metastatic tumor tissue, at any site, of the subject.

18. The method of any one of claims 1 through 17 wherein the assay for measuring methylation comprises QM-MSP assays, cMethDNA assays, DNA methylation arrays, whole genome bisulfite sequencing (WGBS), bisulfite sequencing, reduced representation bisulfite sequencing (RRBS), Array or Bead Hybridization, CpG island arrays, serial analysis of gene expression (SAGE), Pyrosequencing, Methylation-sensitive single-nucleotide primer extension (Ms-SNuPE), Methylation-sensitive single stranded conformation analysis (MS-SSCA), and High resolution melting analysis (HRM), liquid biopsy assay or combinations thereof.

19. The method of any one of claims 1 through 18 wherein the therapy for treating cancer comprises: a surgical therapy, a locally applied chemical, chemotherapy, radiation therapy, cryotherapy, hyperthermia treatment, phototherapy, radioablation therapy, hormonal therapy, immunotherapy, small molecule therapy, receptor kinase inhibitor therapy, anti -angiogenic therapy, cytokine therapy, gene therapy, a biological therapy or combinations thereof.

20. A method of treating cervical cancer, comprising: obtaining a sample from a subject; assaying for a methylation profile of a panel of biomarkers obtained from a subject’s sample, wherein the biomarkers comprise ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2, HIST1H3C and combinations thereof, wherein if the methylation profile of the biomarkers is diagnostic of cervical cancer, the subject is administered a therapy; thereby treating the cervical cancer.

21. The method of claim 20 wherein the biomarkers comprise two or more of ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2, HIST1H3C and combinations thereof.

22. The method of claim 20 wherein the biomarkers comprise six or more of ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2, HIST1H3C and combinations thereof.

23. The method of claim 20 wherein the biomarkers comprise five or more of ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2, HIST1H3C and combinations thereof.

24. The method of any one of claims 20 through 23 wherein the biomarkers are methylated.

25. The method of any one of claims 20 through 24 wherein the biomarkers comprise or consist of ZNF671, EDNRB, TMEFF2, FMN2, MOS and TBXT.

26. The method of any one of claims 20 through 24 wherein the biomarkers comprise or consist of FMN2, EDNRB, ZNF671, TBXT and MOS.

27. The method of any one of claims 20 through 26 wherein the methylation profde is a measure of the magnitude of methylation and/or frequency of methylation of each of the biomarkers individually or in combination.

28. The method of any one of claims 20 through 24 wherein the magnitude of methylation and/or frequency of methylation of the biomarkers comprising two or more of ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2, HIST1H3C and combinations thereof, from the subject having cancer is above a threshold value as compared to the biomarkers from healthy subjects.

29. The method of claim 28 wherein the magnitude of methylation and/or frequency of methylation of each of the biomarkers ZNF671, EDNRB, TMEFF2, FMN2, MOS and TBXT is above the threshold value.

30. The method of claim 28 wherein the magnitude of methylation and/or frequency of methylation of each of the biomarkers FMN2, EDNRB, ZNF671, I B-XT and MOS is above the threshold value.

31. The method of any one of claims 20 through 30 wherein the methylation profile comprises a measure of the cumulative methylation of the biomarkers.

32. The method of any one of claim 20 through 27 wherein the cumulative methylation of biomarkers two or more ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2, HIST1H3C, or combinations thereof, from the subject having cancer is above a threshold value as compared to the cumulative methylation of the biomarkers from healthy subjects.

33. The method of any one of claims 20 through 27 wherein the cumulative methylation biomarkers ZNF671, EDNRB, TMEFF2, FMN2, MOS and TBXT, from the subject having cancer is above a threshold value as compared to the cumulative methylation of the biomarkers from healthy subjects.

34. The method of any one of claims 20 through 27 wherein the cumulative methylation biomarkers FMN2. EDNRB, ZNF671, TBXT and MOS from the subject having cancer is above a threshold value as compared to the cumulative methylation of the biomarkers from healthy subjects.

35. The method of any one of claims 20 through 34 wherein the cumulative magnitude of methylation and/or frequency of methylation of the biomarkers is at a level that is at least a 10 percent increase of the threshold value.

36. The method of any one of claims 20 through 35 wherein the cancer comprises cervical cancer, uterine cancer, ovarian cancer.

37. The method of any one of claims 20 through 35 wherein the cancer is cervical cancer.

38. The method of any one of claim 20 through 37 wherein the sample comprises: whole blood, serum, plasma, saliva, , cervical pap smears, urine, bladder washing, uterine washing, sputum, lymphatic fluid, cerebrospinal fluid, ascites fluid, cystic fluid, fine needle aspiration, a tissue sample from one or both ovaries, uterus, cervix, or lymph node, frozen tissues, formalin- fixed, paraffin-embedded (FFPE) tissues or metastatic tumor tissue, at any site, of the subject.

39. The method of any one of claims 20 through 38 wherein the assay for measuring methylation comprises QM-MSP assays, cMethDNA assays, DNA methylation arrays, whole genome bisulfite sequencing (WGBS), bisulfite sequencing, reduced representation bisulfite sequencing (RRBS), Array or Bead Hybridization, CpG island arrays, serial analysis of gene expression (SAGE), Pyrosequencing, Methylation-sensitive single-nucleotide primer extension (Ms-SNuPE), Methylation-sensitive single stranded conformation analysis (MS-SSCA), and High resolution melting analysis (HRM), liquid biopsy assay or combinations thereof.

40. The method of any one of claims 20 through 39 wherein the therapy for treating cancer comprises: a surgical therapy, a locally applied chemical, chemotherapy, radiation therapy, cryotherapy, hyperthermia treatment, phototherapy, radioablation therapy, hormonal therapy, immunotherapy, small molecule therapy, receptor kinase inhibitor therapy, anti-angiogenic therapy, cytokine therapy, gene therapy, a biological therapy or combinations thereof.

41. A method of distinguishing between and treating of invasive squamous cell carcinomas (ISCC), high grade squamous intraepithelial lesions (HSIL) and low- grade squamous intraepithelial lesions (LSIL) in a subject, comprising: determining a methylation profde of biomarkers obtained from a sample from the subject; and, administering to the subject a therapy to treat the ISCC, HSIL or LSIL.

42. The method of claim 41 wherein the biomarkers comprise two or more of ZNF671,

EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2, HIST1H3C and combinations thereof.

43. The method of claim 41 wherein the biomarkers comprise six or more of ZNF671 , EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2,

HIST1H3C and combinations thereof.

44. The method of claim 41 wherein the biomarkers comprise five or more of ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2, HIST1H3C and combinations thereof.

45. The method of any one of claims 41 through 44 wherein the biomarkers are methylated.

46. The method of any one of claims 41 through 45 wherein the biomarkers comprise or consist of ZNF671, EDNRB, TMEFF2, FMN2, MOS and TBXT.

47. The method of any one of claims 41 through 45 wherein the biomarkers comprise or consist of 1X1X2. EDNRB, ZNF671, TBXT and MOS.

48. The method of any one of claims 41 through 47 wherein the methylation profile is a measure of the magnitude of methylation and/or frequency of methylation of each of the biomarkers individually or in combination.

49. The method of any one of claims 41 through 48 wherein the magnitude of methylation and/or frequency of methylation of the biomarkers comprising two or more of ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2, HIST1H3C and combinations thereof, from the subject having cancer is above a threshold value as compared to the biomarkers from healthy subjects.

50. The method of claim 48 or 49 wherein the magnitude of methylation and/or frequency of methylation of each of the biomarkers ZNF671, EDNRB, TMEFF2, FMN2, MOS and TBXT is above the threshold value.

51. The method of claim 48 or 49 wherein the magnitude of methylation and/or frequency of methylation of each of the biomarkers ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2, HIST1H3C is above the threshold value.

52. The method of any one of claims 41 through 51 wherein the methylation profile comprises a measure of the cumulative methylation of the biomarkers.

53. The method of any one of claim 41 through 52 wherein the cumulative methylation of biomarkers two or more ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2, HIST1H3C, or combinations thereof, from the subject having cancer is above a threshold value as compared to the cumulative methylation of the biomarkers from healthy subjects.

54. The method of any one of claims 41 through 53 wherein the cumulative methylation biomarkers ZNF671, EDNRB, TMEFF2, FMN2, MOS and TBXT, from the subject having cancer is above a threshold value as compared to the cumulative methylation of the biomarkers from healthy subjects.

55. The method of any one of claims 41 through 53 wherein the cumulative methylation biomarkers of FMN2, EDNRB, ZNF67I, TBXT and MOS, from the subject having cancer is above a threshold value as compared to the cumulative methylation of the biomarkers from healthy subjects.

56. The method of any one of claims 41 through 55 wherein the cancer comprises cervical cancer, uterine cancer, ovarian cancer.

57. The method of any one of claims 41 through 55 wherein the cancer is cervical cancer.

58. The method of any one of claim 41 through 57 wherein the sample comprises: whole blood, serum, plasma, saliva, buccal swab, cervical pap smears, stool, urine, bladder washing, uterine washing, sputum, lymphatic fluid, cerebrospinal fluid, ascites fluid, cystic fluid, fine needle aspiration, a tissue sample from one or both ovaries, uterus, cervix, or lymph node, frozen tissues, formalin-fixed, paraffin-embedded (FFPE) tissues or metastatic tumor tissue, at any site, of the subject.

59. The method of any one of claims 41 through 58 wherein the assay for measuring methylation comprises QM-MSP assays, cMethDNA assays, DNA methylation arrays, whole genome bisulfite sequencing (WGBS), bisulfite sequencing, reduced representation bisulfite sequencing (RRBS), Array or Bead Hybridization, CpG island arrays, serial analysis of gene expression (SAGE), Pyrosequencing, Methylation-sensitive single-nucleotide primer extension (Ms-SNuPE), Methylation-sensitive single stranded conformation analysis (MS-SSCA), and High resolution melting analysis (HRM), liquid biopsy assay or combinations thereof.

60. The method of any one of claims 41 through 59 wherein the therapy for treating cancer comprises: a surgical therapy, a locally applied chemical, chemotherapy, radiation therapy, cryotherapy, hyperthermia treatment, phototherapy, radioablation therapy, hormonal therapy, immunotherapy, small molecule therapy, receptor kinase inhibitor therapy, anti -angiogenic therapy, cytokine therapy, gene therapy, a biological therapy or combinations thereof.

61. The method of any one of claims 1 through 60 wherein the subject has a transient HPV infection or is HPV-negative or HPV-undetectable.

61. A kit for diagnosing and treating cervical cancer, comprising: one or more assay components to determine methylation profiles of a group of biomarkers consisting of ZNF671, EDNRB, TMEFF2, FMN2, MOS, TBXT, GAS7C, MAL, COL6A2, TM6SF1, RASGRF2 and HIST1H3C.

61. A kit for diagnosing and treating cervical cancer, comprising: one or more assay components to determine methylation profiles of a group of biomarkers consisting of FMN2, EDNRB, ZNF67I, TBXT and MOS.

62. The kit of claim 60 or 61 wherein the assay comprises QM-MSP assays, cMethDNA assays, DNA methylation arrays, whole genome bisulfite sequencing (WGBS), bisulfite sequencing, reduced representation bisulfite sequencing (RRBS), Array or Bead Hybridization, CpG island arrays, serial analysis of gene expression (SAGE), Pyrosequencing, Methylation- sensitive single-nucleotide primer extension (Ms-SNuPE), Methylation-sensitive single stranded conformation analysis (MS-SSCA), High resolution melting analysis (HRM) or liquid biopsy assay.

63. The kit of any one of claims 60 through 62 further comprising instructions for diagnosis of cervical cancer.