Cancers

29 pages, 7518 KiB

Open AccessArticle

Selective Targeting of Protein Kinase C (PKC)-θ Nuclear Translocation Reduces Mesenchymal Gene Signatures and Reinvigorates Dysfunctional CD8⁺ T Cells in Immunotherapy-Resistant and Metastatic Cancers

by Jenny Dunn, Robert D. McCuaig, Abel H. Y. Tan, Wen Juan Tu, Fan Wu, Kylie M. Wagstaff, Anjum Zafar, Sayed Ali, Himanshu Diwakar, Jane E. Dahlstrom, Elaine G. Bean, Jade K. Forwood, Sofiya Tsimbalyuk, Emily M. Cross, Kristine Hardy, Amanda L. Bain, Elizabeth Ahern, Riccardo Dolcetti, Roberta Mazzieri, Desmond Yip, Melissa Eastgate, Laeeq Malik, Peter Milburn, David A. Jans and Sudha Rao Show full author list Hide full author list

Cancers 2022, 14(6), 1596; https://doi.org/10.3390/cancers14061596 - 21 Mar 2022

Cited by 7 | Viewed by 5164

Abstract

Protein kinase C (PKC)-θ is a serine/threonine kinase with both cytoplasmic and nuclear functions. Nuclear chromatin-associated PKC-θ (nPKC-θ) is increasingly recognized to be pathogenic in cancer, whereas its cytoplasmic signaling is restricted to normal T-cell function. Here we show that nPKC-θ is enriched [...] Read more.

Protein kinase C (PKC)-θ is a serine/threonine kinase with both cytoplasmic and nuclear functions. Nuclear chromatin-associated PKC-θ (nPKC-θ) is increasingly recognized to be pathogenic in cancer, whereas its cytoplasmic signaling is restricted to normal T-cell function. Here we show that nPKC-θ is enriched in circulating tumor cells (CTCs) in patients with triple-negative breast cancer (TNBC) brain metastases and immunotherapy-resistant metastatic melanoma and is associated with poor survival in immunotherapy-resistant disease. To target nPKC-θ, we designed a novel PKC-θ peptide inhibitor (nPKC-θi2) that selectively inhibits nPKC-θ nuclear translocation but not PKC-θ signaling in healthy T cells. Targeting nPKC-θ reduced mesenchymal cancer stem cell signatures in immunotherapy-resistant CTCs and TNBC xenografts. PKC-θ was also enriched in the nuclei of CD8⁺ T cells isolated from stage IV immunotherapy-resistant metastatic cancer patients. We show for the first time that nPKC-θ complexes with ZEB1, a key repressive transcription factor in epithelial-to-mesenchymal transition (EMT), in immunotherapy-resistant dysfunctional PD1⁺/CD8⁺ T cells. nPKC-θi2 inhibited the ZEB1/PKC-θ repressive complex to induce cytokine production in CD8⁺ T cells isolated from patients with immunotherapy-resistant disease. These data establish for the first time that nPKC-θ mediates immunotherapy resistance via its activity in CTCs and dysfunctional CD8⁺ T cells. Disrupting nPKC-θ but retaining its cytoplasmic function may offer a means to target metastases in combination with chemotherapy or immunotherapy. Full article

(This article belongs to the Special Issue Epigenetics and Cancer Immunotherapy)

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Figure 1

Figure 1
nPKC-θ signatures are enriched in CTCs and metastatic tissues and are associated with poor patient survival in immunotherapy-resistant disease. (A) Immunohistofluorescence analysis of PKC-θ expression in CD4+ and CD8+ T cells isolated from healthy donor liquid biopsies. Bar/dot plots show the Fn/c (nuclear to cytoplasmic ratio) of PKC-θ phosphorylated at threonine 568 (PKC-θ-Thr568p). A score below 1 indicates cytoplasmic bias. Data are from three separate patients, n ≥ 20 cells per patient. Representative images are shown for each dataset. PKC-θ-Thr568p (red); CD8/CD4 (purple), and DAPI (cyan) were used to visualize expression and nuclei; scale bar represents 10 µM. (B) Immunohistofluorescence analysis of PKC-θ expression in human MCF-7 inducible model (MCF-7-IM) and MDA-MB-231 breast cancer cells. Human MCF-7 epithelial cells (MCF-7epi) were activated with PMA to induce EMT and generate MCF-7 mesenchymal-like (MCF-7mes) breast cancer cells. Bar graphs show the mean nuclear fluorescence intensity (NFI), cytoplasmic fluorescence intensity (CFI), and Fn/c for PKC-θ-Thr568p, n ≥ 20 cells per group. Representative images are shown for each dataset. PKC-θ-Thr568p (green) and DAPI (blue) were used to visualize nuclei. Scale bar represents 10 µM. (C) Contrast-enhanced CT scans of tumors in CR and PD metastatic melanoma patients at baseline and 12 weeks after treatment with immunotherapy (nivolumab). Red circles indicate tumor lesions. Tumor lesions are reduced in CR compared with baseline, while PD shows increased tumor burden. (D) Dot plot quantification of PKC-θ-Thr568p, CSV, and ABCB5 fluorescence intensity in circulating tumor cells (CTCs) isolated from immunotherapy-responsive (CR, partial response (PR)) or resistant (stable disease (SD), PD) melanoma patients defined using RECIST 1.1 criteria. The Fn/c for PKC-θ-Thr568p, mean CFI for CSV, and mean TFI for ABCB5 were quantified using ASI digital pathology. Representative images are shown for each cohort (six patients were profiled per cohort, n ≥ 20 cells per group); scale bar represents 10 µM. (E) Dot plot quantification of PKC-θ-Thr568p, CSV, and ABCB5 fluorescence in FFPE sections of primary melanomas from patients (n = 18 patients) with CR, SD, or PD. The Fn/c for PKC-θ-Thr568p, mean CFI for CSV, and mean TFI for ABCB5 were quantified by ASI digital pathology (n ≥ 40 cells per patient sample, four samples per patient). Representative images for each dataset are shown, scale bar represents 30 µM. (F) Dot plot quantification of PKC-θ-Thr568p, CSV, and ABCB5 fluorescence intensity in FFPE sections from breast cancer brain metastases (n = 30 patients) and primary breast cancer biopsies (n = 15 patients). The Fn/c for PKC-θ-Thr568p, mean CFI for CSV, and mean TFI for ABCB5 were quantified by ASI digital pathology (n > 40 cells per patient sample). Representative images for each dataset are shown (top); scale bar represents 30 µM. (G) Percent change in tumor lesion from baseline for a single patient with metastatic melanoma (Patient D) who was resistant to first-line treatment with pembrolizumab and displayed PD (baseline, 12 weeks) as defined by RECIST 1.1 criteria but subsequently responded to second-line nivolumab and ipilimumab to show a CR (24 weeks, 36 weeks). (H) CT scan showing overall tumor burden in Patient D at baseline, SD, and PR (partial response). (I) Mesenchymal protein expression (PKC-θ-Thr568p, CSV, and ABCB5) was profiled in CTCs isolated from Patient D at 0 (baseline) and 12-, 24-, and 36-weeks post-immunotherapy. The Fn/c for PKC-θ-Thr568p, mean CFI for CSV, and mean TFI for ABCB5 were determined by ASI Digital Pathology. Representative images for each dataset are shown (n ≥ 20 cells per group); scale bar represents 10 µM. (J) Metastatic melanoma patients (n = 18 patients) were scored for the Fn/c of PKC-θ from four liquid biopsies over 12 months, with Fn/c categorized as <3 or ≥3 (Fn/c >1 indicates nuclear bias, whereas <1 indicates cytoplasmic bias). These patients were tracked for an additional two years (total 36 months), and their survival data are plotted as Fn/c <3 or ≥3. Statistical significance is denoted by ns (not significant), * p ≤ 0.05, ** p ≤ 0.005, *** p ≤ 0.0005, and **** p ≤ 0.0001. Full article ">Figure 2
PKC-θ shows high affinity binding to the Impα/β1 heterodimer and dependence on Impα/β for nuclear accumulation in vitro. (A) Affymetrix microarrays previously in MCF-7 cells treated with PMA and FACS sorted into CD44high and CD24low cancer stem cells (CSC) and non-stem cancer cells (NS), respectively were used to profile the mRNA expression of importins [<a href="#B6-cancers-14-01596" class="html-bibr">6</a>]. Graphs plot the mRNA expression of expressed importins in NS and CSC. (B) The strength of binding of recombinant purified His6-PKC-θ to increasing concentrations of the indicated Imps was determined using an AlphaScreen binding assay. (C) Nuclear import of fluorescently labelled PKC-θ (DTAF-PKC-θ) was reconstituted in vitro in mechanically perforated HTC cells in the presence (+) or absence (−) of exogenous cytosol and an ATP regeneration system. CLSM images were acquired periodically for measurement of accumulation of DTAF-PKC-θ (green panels) into intact nuclei. Nuclear integrity was confirmed by the exclusion of Texas red-labelled 70 kDa dextran (TR70; red panels). Antibodies targeting Imps (anti-Impα2, anti-Impβ1, and anti-Impα4 or a combination of anti-Impα2 and anti-Impβ1) were also included, as indicated. Images are shown at 20 min time points. (D) Image analysis was performed on the photomicrographs, such as those shown in C, using ImageJ. The nuclear to cytoplasmic fluorescence ratio (Fn/c) was calculated for the indicated samples at each time point. Curve fits were determined in GraphPad Prism using an exponential one-phase association equation. Results are for the mean Fn/c ± SEM (n > 10). p-values were determined for each time point compared to the + cytosol sample using the t-test with Welch’s correction. *** p < 0.0001. (E) The % maximal Fn/c was determined from graphs such as those shown in D for each sample. Results represent the mean ± SEM (n = 3). ** p < 0.01, *** p < 0.001 compared with the no addition sample (−). (F) Nuclear import of PKC-θ was reconstituted as per (C) in the presence of vehicle or either the PKC-θ inhibitor C27 or the Imp α/β1-dependent nuclear transport inhibitor ivermectin. CLSM images of perforated nuclei at a 20 min time point to examine the nuclear accumulation of DTAF- PKC-θ (green panels) using TR70 (red channel) to monitor nuclear integrity. (G) Maximal % Fn/c for each sample was determined as per (E). *** p < 0.001 compared to no addition (−). Full article ">Figure 3
A novel PKC-θ peptide inhibitor specifically inhibits nuclear translocation of PKC-θ without affecting PKC-θ catalytic activity. (A) Schematic of PKC-θ depicting its domains including the canonical nuclear localization sequence (NLS) and SPT motifs. Red bars indicate location of peptide inhibitor sequences and blue bars indicate the sequence used to create PKC-θ plasmids. Graphical and schematic representation showing that PKC-θ can be targeted therapeutically. The full-length PKC-θ WT gene sequence and its mutants were used to transfect MCF-7 cells, and the localization of expressed PKC-θ was studied by confocal laser scanning microscopy. Fn/c values for each construct are shown, with significant differences between datasets indicated (n > 15 for each dataset). (B) Structure-guided design of the peptide inhibitor targeting the NLS region of PKC-θ. Left, PyMOL-generated PKC-θ based on the previously determined crystal structure (PDB 2ED) showing the region responsible for nuclear localization in red and blue. Right, structure of the nuclear import adapter IMPα (in ribbon format) bound to a cargo (in stick format) at the major binding site [<a href="#B50-cancers-14-01596" class="html-bibr">50</a>], highlighting the strategy for inhibiting nuclear localization of PKC-θ. (C) The nuclear localization of PKC-θ and other PKCs (PKC-β2, PKC-β1, PKC-δ, and PKC-α) were examined in the MCF-7 inducible model (IM). Representative images are displayed and the Fn/c shown. Scale bar represents 5 µm. (D) p65, p53, and Rb protein expression was examined in mesenchymal MCF-7 cells stimulated with PKC-θ activators PMA and TGF-β. MCF-7 cells were pretreated for 24 h with vehicle or 25 µM nPKC-θi2. Representative immunofluorescence images and Fn/c plots for MCF-7 cells treated with nPKC-θi2 are shown: − represents stimulated control; + represents stimulated samples pretreated with nPKC-θi2. Fn/c was assessed for each target protein (n ≥ 20 cells per group). The Mann–Whitney test was used to determine statistical significance. ns (not significant), p > 0.05; * p ≤ 0.05; ** p ≤ 0.01; *** p ≤0.001; **** p ≤ 0.0001. (E) Various concentrations of nPKC-θi2 and C27 inhibit the co-expression of PKC-θ with p65 or H2Bs32 in MDA-MB-231 cells. nPKC-θi2 concentrations: C1 = 12.5 µM, C2 = 25 µM, C3 = 50 µM, C4 = 100 µM. C27 concentrations: C1 = 1.875 µM, C2 = 3.75 µM, C3 = 7.5 µM, C4 = 15 µM. ASI digital pathology system microscopy was performed on MDA-MB-231 metastatic cancer cells probed with antibodies targeting PKC-θ and H2Bs32p with DAPI. >500 cells per group were scanned to profile the % positive population of MDA-MB-231 cells. Graphs show the % PKC-θ+H2Bs32+ population change with increasing concentrations of C27 (purple line) or nPKC-θi2 (green line). (F) Inhibition of recombinant PKC-θ activity (%) by C27 or nPKC-θi2 relative to untreated control using a PKC activity kit (Enzo Life Sciences). Full article ">Figure 4
nPKC-θi2 targets CSC signatures and mesenchymal pathways in metastatic and resistant cancer cell lines. (A) WST proliferation assay in melanoma (RPMI-7951, SK-MEL-3), breast cancer (MDA-MB-231), and immunotherapy-resistant (4T1, 4T1 brain clone, and B16F10) cancer cell lines. Cells were treated with nPKC-θi2 for 72 h before addition of WST reagent. Absorbance was measured at 450 nm. (B) Scratch wound assay in MDA-MB-231 breast cancer cells treated with vehicle, C27 (7.5 µM), or nPKC-θi2 (25 µM). Wound healing images were acquired by real-time imaging using the IncuCyte Zoom live cell analysis system every 6 h for 24 h. Relative wound density (%) was analyzed using IncuCyte Zoom software. One-way ANOVA was used to determine statistical significance. ns (not significant) p > 0.05; * p ≤ 0.05; ** p ≤ 0.01; *** p ≤0.001; **** p ≤ 0.0001. (C) RNA isolated from MDA-MB-231 murine tumors treated with nPKC-θi2 was analyzed using the NanoString pan-cancer panel. * indicates direct PKC-θ binding targets determined by overlaying NanoString data with ChIP sequencing data from PKC-θ enriched MDA-MB-231 samples. (D) Dot plot quantification of PKC-θ-Thr568p, CSV, and ALDH1A fluorescence intensity in the 4T1 TNBC brain cancer clone treated with vehicle, C27 (5 µM), or nPKC-θi2 (25 µM). The Fn/c for PKC-θ-Thr568p, mean CFI for CSV, and mean NFI for ALDH1A were determined by immunohistofluorescence analysis. n ≥ 20 cells per group. (E) Dot plot quantification of PKC-θ-Thr568p in MDA-MB-231 brain cancer clone cells treated with vehicle, C27 (5 µM) or nPKC-θi2 (25 µM). The Fn/c for PKC-θ-Thr568p was determined by immunohistofluorescence analysis. The Mann–Whitney test was used to determine statistical significance. ns (not significant) p > 0.05; * p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001; **** p ≤ 0.0001. (F) FACS plot of % CD44hi/CD24lo CSC inhibition in mesenchymal-like MCF-7 cells activated with PMA and TGF-β and MDA-MB-231 breast cancer cells treated with nPKC-θi2 1 µM (C1), 5 µM (C2), 25 µM (C3), 50 µM (C4), and 100 (C5) µM relative to their respective untreated control cells. Full article ">Figure 5
Impact of the novel PKC-θ inhibitor on tumors in a TNBC xenograft model and CTCs from melanoma patients. (A) Tumor volume in MDA-MB-231 mouse-bearing tumors treated with vehicle control, docetaxel (4 mg/kg), nPKC-θi2 (40 mg/kg), or both (docetaxel given 3 times, 1 week apart and nPKC-θi2 given daily for 5 weeks). Tumor volumes were measured daily for each mouse. Each data point represents a single mouse (n = 4 mice per group). (B) Percent CD44hi/CD24lo CSC cells in total tumor in the MDA-MB-231 mouse model. (C) Immunofluorescence microscopy of tumor cells from MDA-MB-231 TNBC mice treated with nPKC-θi2 in combination with docetaxel showing that nPKC-θi2 inhibits the fluorescence intensity of PKC-θ and key stem cell niche markers CD133, ALDH1A, and ABCB5 and mesenchymal marker CSV. (D) CTCs were isolated from melanoma patient liquid biopsies (CR = complete response, PR = partial response, PD = progressive disease) and were pre-clinically treated with either vehicle control or nPKC-θi2. Samples were fixed and immunofluorescence microscopy performed on these cells with primary antibodies targeting CSV, PKC-θ, and ABCB5. Representative images for each dataset are shown. Graph represents the TCFI values for CSV, NFI for PKC-θ, and TFI for ABCB5 measured using ImageJ to select the nucleus minus background (n ≥ 20 cells/sample). (E) Heatmap of tumor transcriptomes using all significant genes together with a Venn diagram comparison of genes induced by docetaxel/combination therapy or inhibited by docetaxel/combination therapy relative to vehicle control and the overlap between these groups. (F) Heat map of enriched pathways in gene sets induced relative to vehicle control with comparison of geneset pathways induced by docetaxel (DOC) or docetaxel and nPKC-θi2 (COM). Statistical significance is denoted by ns (not significant), * p ≤ 0.05, ** p ≤ 0.005, *** p ≤ 0.0005, and **** p ≤ 0.0001. Full article ">Figure 6
PKC-θ is enriched in the nuclei of dysfunctional CD8+ T cells isolated from stage IV metastatic cancers. (A) Quantification of PKC-θ-Thr568p Fn/c in CD8+ T cells or CSV+ CTCs isolated from immunotherapy-responsive (complete response, CR; partial response PR) or resistant (PD, progressive disease) melanoma patients defined using RECIST 1.1 criteria. The Fn/c for PKC-θ-Thr568p was quantified by ASI digital pathology and ImageJ-Fiji (n ≥ 20 cells per group). (B) Quantification of PKC-θ-Thr568p Fn/c in CD8+ T cells or CSV+ CTCs isolated from healthy donors (HD), stage IV metastatic breast cancer patients (Mets), or stage IV breast cancer patients with brain metastases (Brain Mets). The Fn/c for PKC-θ-Thr568p was quantified by ASI digital pathology and ImageJ-Fiji (n ≥ 40 cells per group). (C) The amino acid sequence of ZEB1 indicating the top eight peptides for peptide phosphorylation by PKC-θ. The top peptide sequences are displayed in a table with the mean signal intensity (2 SD above the mean was considered a positive phosphorylation event). (D) Top peptides positive for phosphorylation and their overlap with the ZEB1 amino acid sequence as well as the structure of ZEB1, adapted from [<a href="#B3-cancers-14-01596" class="html-bibr">3</a>]. (E) Duolink® proximity ligation assay (PLA) for PKC-θ and ZEB1 in CD8+PD1+ T cells isolated from immunotherapy-resistant or responder melanoma patients. Representative images are shown for PKC-θ/ZEB1, scale bar represents 10 µM. Graphs represent the PLA signal intensity of the Duolink® assay; data represent n ≥ 100 cells/sample. Graphs plot the percentage of PLA signal positive cells out of total cells for (A). Data represent n ≥ 100 cells/sample. (F) FFPE sections from primary breast cancers (n = 6 patients, >500 cells counted per patient) or breast cancer brain metastases (n = 20 patients, >500 cells counted per patient) were processed for high-resolution microscopy using the BondRX platform. FFPE sections were fixed and immunofluorescence microscopy performed probing with primary antibodies targeting CD8, PKC-θ (T53p), and ZEB1 with DAPI. Plots represent the % population of CD8+ T cells positive for PKC-θ and ZEB1 out of total CD8+ T cells. Example images are shown with 20 µM scale bar. (G) CD8+ cells were isolated from melanoma patient liquid biopsies (responder = complete response (CR) or resistant, where primary = primary resistance, secondary = secondary resistance, PD = progression of disease) and stimulated with phorbol 12-myristate 13-acetate (PMA) and calcium ionophore (CI) and pre-clinically screened with either vehicle control or nPKC-i2. Samples were then fixed and immunofluorescence microscopy performed with primary antibodies targeting ZEB1, PKC-θ, and CD8. Representative images for each dataset are shown in <a href="#app1-cancers-14-01596" class="html-app">Figure S5E</a>. Graph represents the mean TNFI for PKC-θ and ZEB1 measured using ImageJ to select the nucleus minus background (n > 20 cells/sample). (H) Plot profiles for each cohort for ZEB1 and PKC-θ are also depicted (red = ZEB1, green = PKC-θ) with the Pearson correlation coefficient (PCC) used to quantify colocalization between fluorophore-tagged proteins indicated and plotted. −1 = inverse of colocalization; 0 = no colocalization; +1 = perfect colocalization. Statistical significance is denoted by ns (not significant), ** p ≤ 0.005 and **** p ≤ 0.0001. Full article ">Figure 7
nPKC-θi2 disrupts the nuclear ZEB1/PKC-θ complex and induces cytokine production in CD8+ T cells. (A) Graphs depicting the % inhibition or induction based on protein expression were also plotted for each protein target relative to untreated sample. (B) Percent of PKC-θ+/ZEB1+/CD8+ T cells in samples isolated from melanoma patients responsive (PR/CR) or primary/secondary resistant (PD) to immunotherapy. CD8+ T cells were treated with nPKC-θi2 before activation ex vivo with PMA/ionomycin. (C) Gene expression of key effector cytokines IL2, IFNG, and TNFA in PBMCs isolated from resistant and responder patients either treated with vehicle control or nPKC-θi2 before activation ex vivo with PMA/ionomycin. (D) Protein expression of TNF-α and IFN-γ in CD8+ T cells isolated from primary or secondary resistant or responder patient liquid biopsies and treated with nPKC-θi2. Graphs show the % CD8+ increase in expression of TNF-α or IFN-γ in CD8+ T cells stimulated with PMA/ionomycin in addition to treatment with nPKC-θi2. One-way ANOVA was used to compare groups, where ns (not significant), **** p < 0.0001, *** p < 0.001, ** p < 0.01, and * p < 0.05. Full article ">

14 pages, 407 KiB

Open AccessReview

The Role of Surgery in Spinal Intradural Metastases from Renal Cell Carcinoma: A Literature Review

by Sergio Corvino, Giuseppe Mariniello, Domenico Solari, Jacopo Berardinelli and Francesco Maiuri

Cancers 2022, 14(6), 1595; https://doi.org/10.3390/cancers14061595 - 21 Mar 2022

Cited by 8 | Viewed by 2595

Abstract

Background: Due to the few reported cases of spinal intradural metastases from renal cell carcinoma (RCC), there is no unanimous consensus on the best treatment strategy, including the role of surgery. Methods: A wide and accurate literature review up to January 2022 has [...] Read more.

Background: Due to the few reported cases of spinal intradural metastases from renal cell carcinoma (RCC), there is no unanimous consensus on the best treatment strategy, including the role of surgery. Methods: A wide and accurate literature review up to January 2022 has disclosed only 51 cases of spinal intradural metastases from RCC. Patients with extramedullary (19) and those with intramedullary (32) localization have been separately considered and compared. Demographics, clinical, pathological, management, and outcome features have been analyzed. Results: Extramedullary lesions more frequently showed the involvement of the lumbar spine, low back pain, and solitary metastasis at diagnosis. Conversely, the intramedullary lesions were most often detected in association with multiple localizations of disease, mainly in the brain. Surgery resulted in improvement of clinical symptoms in both groups. Conclusion: Several factors affect the prognosis of metastatic RCC. The surgical removal of spinal metastases resulted in pain relief and the arresting of neurological deficit progression, improving the quality of life and overall survival of the patient. Considering the relative radioresistant nature of the RCC, the surgical treatment of the metastasis is a valid option even if it is subtotal, with a consequent increased risk of recurrence, and/or a nerve root should be sacrificed. Full article

(This article belongs to the Special Issue Clear Cell Renal Cell Carcinoma: From Biology to Treatment)

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13 pages, 679 KiB

Open AccessReview

Genomics of Plasma Cell Leukemia

by Elizabeta A. Rojas and Norma C. Gutiérrez

Cancers 2022, 14(6), 1594; https://doi.org/10.3390/cancers14061594 - 21 Mar 2022

Cited by 6 | Viewed by 3757

Abstract

Plasma cell leukemia (PCL) is a rare and highly aggressive plasma cell dyscrasia characterized by the presence of clonal circulating plasma cells in peripheral blood. PCL accounts for approximately 2–4% of all multiple myeloma (MM) cases. PCL can be classified in primary PCL [...] Read more.

Plasma cell leukemia (PCL) is a rare and highly aggressive plasma cell dyscrasia characterized by the presence of clonal circulating plasma cells in peripheral blood. PCL accounts for approximately 2–4% of all multiple myeloma (MM) cases. PCL can be classified in primary PCL (pPCL) when it appears de novo and in secondary PCL (sPCL) when it arises from a pre-existing relapsed/refractory MM. Despite the improvement in treatment modalities, the prognosis remains very poor. There is growing evidence that pPCL is a different clinicopathological entity as compared to MM, although the mechanisms underlying its pathogenesis are not fully elucidated. The development of new high-throughput technologies, such as microarrays and new generation sequencing (NGS), has contributed to a better understanding of the peculiar biological and clinical features of this disease. Relevant information is now available on cytogenetic alterations, genetic variants, transcriptome, methylation patterns, and non-coding RNA profiles. Additionally, attempts have been made to integrate genomic alterations with gene expression data. However, given the low frequency of PCL, most of the genetic information comes from retrospective studies with a small number of patients, sometimes leading to inconsistent results. Full article

(This article belongs to the Special Issue Genomics of Rare Hematologic Cancers)

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14 pages, 1761 KiB

Open AccessArticle

The Multi-Kinase Inhibitor EC-70124 Is a Promising Candidate for the Treatment of FLT3-ITD-Positive Acute Myeloid Leukemia

by Belen Lopez-Millan, Paula Costales, Francisco Gutiérrez-Agüera, Rafael Díaz de la Guardia, Heleia Roca-Ho, Meritxell Vinyoles, Alba Rubio-Gayarre, Rémi Safi, Julio Castaño, Paola Alejandra Romecín, Manuel Ramírez-Orellana, Eduardo Anguita, Irmela Jeremias, Lurdes Zamora, Juan Carlos Rodríguez-Manzaneque, Clara Bueno, Francisco Morís and Pablo Menendez

Cancers 2022, 14(6), 1593; https://doi.org/10.3390/cancers14061593 - 21 Mar 2022

Cited by 3 | Viewed by 3215

Abstract

Acute myeloid leukemia (AML) is the most common acute leukemia in adults. Patients with AML harboring a constitutively active internal tandem duplication mutation (ITD^MUT) in the FMS-like kinase tyrosine kinase (FLT3) receptor generally have a poor prognosis. Several tyrosine kinase/FLT3 inhibitors [...] Read more.

Acute myeloid leukemia (AML) is the most common acute leukemia in adults. Patients with AML harboring a constitutively active internal tandem duplication mutation (ITD^MUT) in the FMS-like kinase tyrosine kinase (FLT3) receptor generally have a poor prognosis. Several tyrosine kinase/FLT3 inhibitors have been developed and tested clinically, but very few (midostaurin and gilteritinib) have thus far been FDA/EMA-approved for patients with newly diagnosed or relapse/refractory FLT3-ITD^MUT AML. Disappointingly, clinical responses are commonly partial or not durable, highlighting the need for new molecules targeting FLT3-ITD^MUT AML. Here, we tested EC-70124, a hybrid indolocarbazole analog from the same chemical space as midostaurin with a potent and selective inhibitory effect on FLT3. In vitro, EC-70124 exerted a robust and specific antileukemia activity against FLT3-ITD^MUT AML primary cells and cell lines with respect to cytotoxicity, CFU capacity, apoptosis and cell cycle while sparing healthy hematopoietic (stem/progenitor) cells. We also analyzed its efficacy in vivo as monotherapy using two different xenograft models: an aggressive and systemic model based on MOLM-13 cells and a patient-derived xenograft model. Orally disposable EC-70124 exerted a potent inhibitory effect on the growth of FLT3-ITD^MUT AML cells, delaying disease progression and debulking the leukemia. Collectively, our findings show that EC-70124 is a promising and safe agent for the treatment of AML with FLT3-ITD^MUT. Full article

(This article belongs to the Special Issue New Therapeutic Strategies for Acute Myeloid Leukemia)

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Figure 1
EC-70124 exerts profound cytotoxic effects in FLT3-ITDMUT AML cells. (a) Cytotoxicity dose–response curves of midostaurin (right panel) and EC-70124 (left panel) on FLT3-ITDMUT AML cell lines. (b) Effect of EC-70124 (EC) and midostaurin (MID) on cell viability (left panel) and CFU formation (right panel) in primary FLT3-ITDMUT AML patient cells. Viability data are relative to the untreated group (Ctrl). Patient samples are color-codified, and black lines represent the mean. (c–e) Effect of EC-70124 on CFU formation (c), apoptosis (d) and cell cycle (e) in FLT3-ITDMUT AML cell lines. Representative CFUs are shown as inset in C. Representative cell cycle FACS analysis is shown E (right panel). (f) Representative Western blots showing the effect of EC-70124 and midostaurin on the main targets related to FLT3 signaling (<a href="#app1-cancers-14-01593" class="html-app">Figure S1</a>). Lysates from cells treated with cytarabine (AraC) and idarubicin (Ida) were included as controls. Red asterisks depict the specific effect of EC-70124 or midostaurin on the indicated phosphorylation. Blue numbers depict the ratio phosphorylated/total protein for each band. (g) Cytotoxicity dose–response curves of EC-70124 on FLT3-ITDWT AML cell lines. (h) Effect of EC-70124 on cell viability cell (left panel) and CFU formation (right panel) in primary FLT3-ITDWT AML patient samples. Viability data are relative to Ctrl group. Patient samples are color-codified, and black lines represent the mean. (i–k) Effect of EC-70124 on CFU formation (i), apoptosis (j) and cell cycle (k) in FLT3-ITDWT AML cell lines (HL60 and THP1). Representative CFUs are shown as inset in (i). Representative cell cycle FACS analysis is shown (k) (right panel). (l) Cytotoxicity dose–response curves of EC-70124 on Baf3-WT and BaF-ITD cells (n = 3). Data represent mean ± SEM. * p < 0.05; ** p < 0.01; *** p < 0.001; ns, not significant (Student’s t test). Abbreviations: EC, EC-70124; MID, midostaurin. Full article ">Figure 2
EC-70124 has no cytotoxic effects on healthy hematopoietic (stem/progenitor) cells. (a) Cytotoxic effect of EC-70124 on HD-PBMCs (B, T and myeloid cells). (b) Number of CFUs from CB-CD34+ cells after treatment with EC-70124. Right panel, scoring of the colony types. * p < 0.05 (Student’s t test). Full article ">Figure 3
Oral administration of EC-70124 delays the growth of FLT3-ITDMUT AML cells in NSG mice. (a) Experimental design for the MOLM-13 leukemic model. (b) Upper panel, IVIS imaging of tumor burden monitored by bioluminescence at the indicated time points in the MOLM-13 leukemic model. Bottom panel, percentage of MOLM-13 cells at the endpoint in the BM and PB of vehicle- and EC-70124-treated mice. (c) Upper panel, experimental design for the FLT3-ITDMUTLuc+ AML-640 PDX model. Bottom panel, IVIS imaging of tumor burden by bioluminescence at the indicated time points in the Luc-FLT3-ITDMUT PDX model. (d) Percentage of FLT3-ITDMUT AML blasts in BM at the beginning (D0) and the end (D17) of the treatment in the FLT3-ITDMUT AML PDX model. Each point depicts a mouse. (e) Total radiance quantification at the indicated time points. Ec-70124 was stopped on day 17. * p < 0.05; ** p < 0.01 (Multiple t test). Full article ">

23 pages, 4109 KiB

Open AccessArticle

Targeting an MDM2/MYC Axis to Overcome Drug Resistance in Multiple Myeloma

by Omar Faruq, Davidson Zhao, Mariusz Shrestha, Andrea Vecchione, Eldad Zacksenhaus and Hong Chang

Cancers 2022, 14(6), 1592; https://doi.org/10.3390/cancers14061592 - 21 Mar 2022

Cited by 11 | Viewed by 4012

Abstract

Background: MDM2 is elevated in multiple myeloma (MM). Although traditionally, MDM2 negatively regulates p53, a growing body of research suggests that MDM2 plays several p53-independent roles in cancer pathogenesis as a regulator of oncogene mRNA stability and translation. Yet, the molecular mechanisms underlying [...] Read more.

Background: MDM2 is elevated in multiple myeloma (MM). Although traditionally, MDM2 negatively regulates p53, a growing body of research suggests that MDM2 plays several p53-independent roles in cancer pathogenesis as a regulator of oncogene mRNA stability and translation. Yet, the molecular mechanisms underlying MDM2 overexpression and its role in drug resistance in MM remain undefined. Methods: Both myeloma cell lines and primary MM samples were employed. Cell viability, cell cycle and apoptosis assays, siRNA transfection, quantitative real-time PCR, immunoblotting, co-immunoprecipitation (Co-IP), chromatin immunoprecipitation (ChIP), soft agar colony formation and migration assay, pulse-chase assay, UV cross-linking, gel-shift assay, RNA-protein binding assays, MEME-analysis for discovering c-Myc DNA binding motifs studies, reporter gene constructs procedure, gene transfection and reporter assay, MM xenograft mouse model studies, and statistical analysis were applied in this study. Results: We show that MDM2 is associated with poor prognosis. Importantly, its upregulation in primary MM samples and human myeloma cell lines (HMCLs) drives drug resistance. Inhibition of MDM2 by RNAi, or by the MDM2/XIAP dual inhibitor MX69, significantly enhanced the sensitivity of resistant HMCLs and primary MM samples to bortezomib and other anti-myeloma drugs, demonstrating that MDM2 can modulate drug response. MDM2 inhibition resulted in a remarkable suppression of relapsed MM cell growth, colony formation, migration and induction of apoptosis through p53-dependent and -independent pathways. Mechanistically, MDM2 was found to reciprocally regulate c-Myc in MM; MDM2 binds to AREs on c-Myc 3′UTR to increase c-Myc mRNA stability and translation, while MDM2 is a direct transcriptional target of c-Myc. MDM2 inhibition rendered c-Myc mRNA unstable, and reduced c-Myc protein expression in MM cells. Importantly, in vivo delivery of MX69 in combination with bortezomib led to significant regression of tumors and prolonged survival in an MM xenograft model. Conclusion: Our findings provide a rationale for the therapeutic targeting of MDM2/c-Myc axis to improve clinical outcome of patients with refractory/relapsed MM. Full article

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16 pages, 2986 KiB

Open AccessArticle

Pterygium and Ocular Surface Squamous Neoplasia: Optical Biopsy Using a Novel Autofluorescence Multispectral Imaging Technique

by Abbas Habibalahi, Alexandra Allende, Jesse Michael, Ayad G. Anwer, Jared Campbell, Saabah B. Mahbub, Chandra Bala, Minas T. Coroneo and Ewa M. Goldys

Cancers 2022, 14(6), 1591; https://doi.org/10.3390/cancers14061591 - 21 Mar 2022

Cited by 8 | Viewed by 7289

Abstract

In this study, differentiation of pterygium vs. ocular surface squamous neoplasia based on multispectral autofluorescence imaging technique was investigated. Fifty (N = 50) patients with histopathological diagnosis of pterygium (PTG) and/or ocular surface squamous neoplasia (OSSN) were recruited. Fixed unstained biopsy specimens were [...] Read more.

In this study, differentiation of pterygium vs. ocular surface squamous neoplasia based on multispectral autofluorescence imaging technique was investigated. Fifty (N = 50) patients with histopathological diagnosis of pterygium (PTG) and/or ocular surface squamous neoplasia (OSSN) were recruited. Fixed unstained biopsy specimens were imaged by multispectral microscopy. Tissue autofluorescence images were obtained with a custom-built fluorescent microscope with 59 spectral channels, each with specific excitation and emission wavelength ranges, suitable for the most abundant tissue fluorophores such as elastin, flavins, porphyrin, and lipofuscin. Images were analyzed using a new classification framework called fused-classification, designed to minimize interpatient variability, as an established support vector machine learning method. Normal, PTG, and OSSN regions were automatically detected and delineated, with accuracy evaluated against expert assessment by a specialist in OSSN pathology. Signals from spectral channels yielding signals from elastin, flavins, porphyrin, and lipofuscin were significantly different between regions classified as normal, PTG, and OSSN (p < 0.01). Differential diagnosis of PTG/OSSN and normal tissue had accuracy, sensitivity, and specificity of 88 ± 6%, 84 ± 10% and 91 ± 6%, respectively. Our automated diagnostic method generated maps of the reasonably well circumscribed normal/PTG and OSSN interface. PTG and OSSN margins identified by our automated analysis were in close agreement with the margins found in the H&E sections. Such a map can be rapidly generated on a real time basis and potentially used for intraoperative assessment. Full article

(This article belongs to the Topic Biomedical Photonics)

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(a) Right nasal pterygium with atypical changes at the superior margin. (b) Gelatinous lesion (arrows) contiguous with and arising in the super-limbal aspect of the pterygium—confirmed as OSSN by biopsy. Full article ">Figure 2
(a–e) Sample preparation and histological assessment. (a) Ocular surface biopsy collected from patients. (b) Histology sample processed following formalin fixation into paraffin embedded sections. (c) Two adjacent sections were cut using a microtome and then dewaxed. (d) Example cut tissue section, which was H&E stained and coverslipped for histology assessment and used as reference. (e) The unstained tissue section adjacent to that shown in (d). Such sections were placed on a slide, coverslipped, and used for multispectral imaging analysis. (e–j) Example tissue images in selected channels (channels number 3, 16, 22, 31, and 45, respectively). (k) H&E stained section of example tissue shown in (e–j). Full article ">Figure 3
Patient classification performance of the SVM classifier. (a) ROC curve obtained from PTG and OSSN classification. (b) ROC curve obtained from normalized normal and OSSN classification. (c) ROC curve obtained from normal and PTG classification. (d) ROC curve obtained from normal, PTG, and OSSN classification. Full article ">Figure 4
Spectral differences between normal, PTG, and OSSN. (a–c) H&E image for normal, PTG, and OSSN sections, respectively. (d–f/g–i) Channel 1/Channel 20 for normal, PTG, and OSSN sections, respectively. (j–l) PCA false color image for normal, PTG, and OSSN sections respectively. OSSN is green, while normal and PTG are violet. Full article ">Figure 5
Analysis of fluorophore signals in the tissue. (a) Intensity analysis for Channel 3 containing a contribution from elastin. (b) Intensity analysis for Channel 12 containing a contribution from lipopigment. (c) Intensity analysis for Channel 30 containing a contribution from flavins. (d) Intensity analysis for Channel 52 tentatively attributed to PPIX (*** represents p−value < 0.01). Corresponding sample channels are shown in <a href="#app1-cancers-14-01591" class="html-app">Supplementary Figure S5</a>. Full article ">Figure 6
False color map generated to locate the normal, PTG, and OSSN boundary on the testing section based on intra-patient classification framework compared to the associated histology images. The block data position on a single spectral channel (Chno = 10) image is colored in red/orange/green if they are predicted to be OSSN/PTG/normal. First/third column is the multispectral false-color map. Second/forth column is corresponding H&E section with red, orange, and green dash lines highlighting OSSN, PTG, and normal section. Full article ">

22 pages, 72889 KiB

Open AccessArticle

Immune Landscape and an RBM38-Associated Immune Prognostic Model with Laboratory Verification in Malignant Melanoma

by Jinfang Liu, Jun Xu, Binlin Luo, Jian Tang, Zuoqiong Hou, Zhechen Zhu, Lingjun Zhu, Gang Yao and Chujun Li

Cancers 2022, 14(6), 1590; https://doi.org/10.3390/cancers14061590 - 21 Mar 2022

Cited by 3 | Viewed by 2630

Abstract

Background: Current studies have revealed that RNA-binding protein RBM38 is closely related to tumor development, while its role in malignant melanoma remains unclear. Therefore, this research aimed to investigate the function of RBM38 in melanoma and the prognosis of the disease. Methods: Functional [...] Read more.

Background: Current studies have revealed that RNA-binding protein RBM38 is closely related to tumor development, while its role in malignant melanoma remains unclear. Therefore, this research aimed to investigate the function of RBM38 in melanoma and the prognosis of the disease. Methods: Functional experiments (CCK-8 assay, cell colony formation, transwell cell migration/invasion experiment, wound healing assay, nude mouse tumor formation, and immunohistochemical analysis) were applied to evaluate the role of RBM38 in malignant melanoma. Immune-associated differentially expressed genes (DEGs) on RBM38 related immune pathways were comprehensively analyzed based on RNA sequencing results. Results: We found that high expression of RBM38 promoted melanoma cell proliferation, invasion, and migration, and RBM38 was associated with immune infiltration. Then, a five-gene (A2M, NAMPT, LIF, EBI3, and ERAP1) model of RBM38-associated immune DEGs was constructed and validated. Our signature showed superior prognosis capacity compared with other melanoma prognostic signatures. Moreover, the risk score of our signature was connected with the infiltration of immune cells, immune-regulatory proteins, and immunophenoscore in melanoma. Conclusions: We constructed an immune prognosis model using RBM38-related immune DEGs that may help evaluate melanoma patient prognosis and immunotherapy modalities. Full article

(This article belongs to the Special Issue Novel Targets and Approaches in Cancer Therapy)

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13 pages, 1695 KiB

Open AccessArticle

The Barcelona Predictive Model of Clinically Significant Prostate Cancer

by Juan Morote, Angel Borque-Fernando, Marina Triquell, Anna Celma, Lucas Regis, Manel Escobar, Richard Mast, Inés M. de Torres, María E. Semidey, José M. Abascal, Carles Sola, Pol Servian, Daniel Salvador, Anna Santamaría, Jacques Planas, Luis M. Esteban and Enrique Trilla

Cancers 2022, 14(6), 1589; https://doi.org/10.3390/cancers14061589 - 21 Mar 2022

Cited by 23 | Viewed by 2700

Abstract

A new and externally validated MRI-PM for csPCa was developed in the metropolitan area of Barcelona, and a web-RC designed with the new option of selecting the csPCa probability threshold. The development cohort comprised 1486 men scheduled to undergo a 3-tesla multiparametric MRI [...] Read more.

A new and externally validated MRI-PM for csPCa was developed in the metropolitan area of Barcelona, and a web-RC designed with the new option of selecting the csPCa probability threshold. The development cohort comprised 1486 men scheduled to undergo a 3-tesla multiparametric MRI (mpMRI) and guided and/or systematic biopsies in one academic institution of Barcelona. The external validation cohort comprised 946 men in whom the same diagnostic approach was carried out as in the development cohort, in two other academic institutions of the same metropolitan area. CsPCa was detected in 36.9% of men in the development cohort and 40.8% in the external validation cohort (p = 0.054). The area under the curve of mpMRI increased from 0.842 to 0.897 in the developed MRI-PM (p < 0.001), and from 0.743 to 0.858 in the external validation cohort (p < 0.001). A selected 15% threshold avoided 40.1% of prostate biopsies and missed 5.4% of the 36.9% csPCa detected in the development cohort. In men with PI-RADS <3, 4.3% would be biopsied and 32.3% of all existing 4.2% of csPCa would be detected. In men with PI-RADS 3, 62% of prostate biopsies would be avoided and 28% of all existing 12.4% of csPCa would be undetected. In men with PI-RADS 4, 4% of prostate biopsies would be avoided and 0.6% of all existing 43.1% of csPCa would be undetected. In men with PI-RADS 5, 0.6% of prostate biopsies would be avoided and none of the existing 42.0% of csPCa would be undetected. The Barcelona MRI-PM presented good performance on the overall population; however, its clinical usefulness varied regarding the PI-RADS category. The selection of csPCa probability thresholds in the designed RC may facilitate external validation and outperformance of MRI-PMs in specific PI-RADS categories. Full article

(This article belongs to the Topic Prostate Cancer: Symptoms, Diagnosis & Treatment)

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24 pages, 1677 KiB

Open AccessEditor’s ChoiceReview

Clinical Applications of Short Non-Coding RNA-Based Therapies in the Era of Precision Medicine

by Ellen S. Smith, Eric Whitty, Byunghee Yoo, Anna Moore, Lorenzo F. Sempere and Zdravka Medarova

Cancers 2022, 14(6), 1588; https://doi.org/10.3390/cancers14061588 - 21 Mar 2022

Cited by 40 | Viewed by 5621

Abstract

Traditional targeted therapeutic agents have relied on small synthetic molecules or large proteins, such as monoclonal antibodies. These agents leave a lot of therapeutic targets undruggable because of the lack or inaccessibility of active sites and/or pockets in their three-dimensional structure that can [...] Read more.

Traditional targeted therapeutic agents have relied on small synthetic molecules or large proteins, such as monoclonal antibodies. These agents leave a lot of therapeutic targets undruggable because of the lack or inaccessibility of active sites and/or pockets in their three-dimensional structure that can be chemically engaged. RNA presents an attractive, transformative opportunity to reach any genetic target with therapeutic intent. RNA therapeutic design is amenable to modularity and tunability and is based on a computational blueprint presented by the genetic code. Here, we will focus on short non-coding RNAs (sncRNAs) as a promising therapeutic modality because of their potency and versatility. We review recent progress towards clinical application of small interfering RNAs (siRNAs) for single-target therapy and microRNA (miRNA) activity modulators for multi-target therapy. siRNAs derive their potency from the fact that the underlying RNA interference (RNAi) mechanism is catalytic and reliant on post-transcriptional mRNA degradation. Therapeutic siRNAs can be designed against virtually any mRNA sequence in the transcriptome and specifically target a disease-causing mRNA variant. Two main classes of microRNA activity modulators exist to increase (miRNA mimics) or decrease (anti-miRNA inhibitors) the function of a specific microRNA. Since a single microRNA regulates the expression of multiple target genes, a miRNA activity modulator can have a more profound effect on global gene expression and protein output than siRNAs do. Both types of sncRNA-based drugs have been investigated in clinical trials and some siRNAs have already been granted FDA approval for the treatment of genetic, cardiometabolic, and infectious diseases. Here, we detail clinical results using siRNA and miRNA therapeutics and present an outlook for the potential of these sncRNAs in medicine. Full article

(This article belongs to the Collection Molecular Function and Clinical Value of Non-coding RNAs (ncRNAs) in Cancer Regulation)

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Processing, delivery strategies, and target engagement of RNA therapeutics. (A) miRNA precursor stem-loop hairpin and longer siRNA precursors (e.g., dicer substrate [DsiRNA] by IDT, or small hairpin RNAs such as the Dicerna nicked dsRNA stemloop) are processed by the DICER-containing complex. Transitory double-stranded product is similarly unwound and loaded into the ARGONAUTE-containing RNA-induced silencing complex (RISC). (B) While siRNAs are designed to specifically and perfectly match the complementary sequence of the cognate target mRNA, miRNAs bind to partially complementary sequences of multiple target mRNAs. (C) We provide representative examples of local and systemic delivery strategies to enhance the accumulation of the RNA therapeutics in the intended site of treatment. For systemic delivery, chemical modifications (shown in <a href="#cancers-14-01588-f002" class="html-fig">Figure 2</a>B), encapsulation, and/or targeting moieties can facilitate retention by a specific organ or cell type. Abbreviations: AGO = argonaute RISC component; CCR4-NOT = carbon catabolite repression-negative on TATA-less complex; DICER = ribonuclease III Dicer1; Dicerna = Dicerna Pharmaceuticals; eiF4 = eukaryotic translation initiation factor 4; GalNAc = N-acetylgalactosamine; IDT = Integrated DNA Technologies; LDHA = hepatic lactate dehydrogenase A; LPN = lipid nanoparticle; PCSK9 = proprotein convertase subtilisin/kexin type 9; PLGA = poly(lactic-co-glycolic acid); TARBP = TAR (HIV-1) RNA-binding protein 1; TRPV1 = transient receptor potential cation channel subfamily V member 1; TP53 = tumor protein p53; TTR = transthyretin; VEGF = vascular endothelial growth factor. Full article ">Figure 2
Chemical modifications and specific sequence patterns of these modifications facilitate the clinical application of RNA therapeutics. (A) Stage of clinical development of representative RNA therapies and chemical formulations behind their therapeutic effect. Pattern and location of chemical modifications are approximations (in some cases, the exact sequence is not disclosed). For siRNAs, the top strand (5′-end on the left) is the sense strand (SS), and the bottom strand (5′-end on the right) is the active antisense (AS) strand. For miRNA mimics, the top strand is the active mature miRNA guide (GS), and the bottom strand is the passenger strand (P*S). Nucleotides and other molecules are not drawn to scale. (B) Chemical structure of common sugar and backbone modifications of RNA therapeutics in clinical trials are depicted (inset). Abbreviations: 2′-F = 2′-deoxy-2′- fluoro; 2′-O-Me = 2′-O-methyl; 2′-O-MOE = 2′-O-methoxyethyl; ALAS1 = delta-aminolaevulinic acid-synthase; ASO = antisense oligonucleotide; EGFR = epidermal growth factor receptor; GalNAc = N-acetylgalactosamine; HAO1 = hydroxyacid oxidase 1; IND = investigational new drug; LDHA = hepatic lactate dehydrogenase A; LPN = lipid nanoparticle; NP = nanoparticle; PCSK9 = proprotein convertase subtilisin/kexin type 9; PEG = polyethylene glycol; PS = phosphorothioate; PO = phosphodiester; TP53 = tumor protein p53; TTR = transthyretin. Full article ">

10 pages, 257 KiB

Open AccessReview

Considerations and Challenges in the Management of the Older Patients with Gastric Cancer

by Sotiris Loizides and Demetris Papamichael

Cancers 2022, 14(6), 1587; https://doi.org/10.3390/cancers14061587 - 21 Mar 2022

Cited by 10 | Viewed by 2445

Abstract

Gastric cancer is one of the commonest malignancies with high rates of mortality worldwide. Older patients represent a substantial proportion of cases with this diagnosis. However, there are very few ‘elderly-specific’ trials in this setting. In addition, the inclusion rate of such patients [...] Read more.

Gastric cancer is one of the commonest malignancies with high rates of mortality worldwide. Older patients represent a substantial proportion of cases with this diagnosis. However, there are very few ‘elderly-specific’ trials in this setting. In addition, the inclusion rate of such patients in randomised clinical trials is poor, presumably due to concerns about increased toxicity, co-existing comorbidities and impaired performance status. Therapeutic strategies for this patient group are therefore mostly based on retrospective subgroup analysis of randomised clinical trials. Review of currently available evidence suggests that older gastric cancer patients who are fit for trial inclusion may benefit from surgical intervention and peri-operative systemic chemotherapy strategies. For patients with metastatic disease, management has been revolutionized by the use of anti-HER2 directed therapies as well as immune checkpoint inhibitors with or without chemotherapy. Early data suggest that fit older patients may also benefit from these therapeutic interventions. However, once again there may be limitations in extrapolating these data to everyday clinical practice with older patients being less likely to have a good performance status and an intact immune system. Therefore, determining the functional age and not just the chronological age of a patient prior to initiating therapy becomes very important. The functional decline including reduced organ function that may occur in older patients makes the integration of some form of geriatric assessment in routine clinical practice very relevant. Full article

(This article belongs to the Special Issue Contemporary Perspectives and Emerging Trends in the Management of Gastric Cancer)

18 pages, 3503 KiB

Open AccessArticle

Prometastatic Effect of ATX Derived from Alveolar Type II Pneumocytes and B16-F10 Melanoma Cells

by Mélanie A. Dacheux, Sue Chin Lee, Yoojin Shin, Derek D. Norman, Kuan-Hung Lin, Shuyu E, Junming Yue, Zoltán Benyó and Gábor J. Tigyi

Cancers 2022, 14(6), 1586; https://doi.org/10.3390/cancers14061586 - 21 Mar 2022

Cited by 8 | Viewed by 3195

Abstract

Although metastases are the principal cause of cancer-related deaths, the molecular aspects of the role of stromal cells in the establishment of the metastatic niche remain poorly understood. One of the most prevalent sites for cancer metastasis is the lungs. According to recent [...] Read more.

Although metastases are the principal cause of cancer-related deaths, the molecular aspects of the role of stromal cells in the establishment of the metastatic niche remain poorly understood. One of the most prevalent sites for cancer metastasis is the lungs. According to recent research, lung stromal cells such as bronchial epithelial cells and resident macrophages secrete autotaxin (ATX), an enzyme with lysophospholipase D activity that promotes cancer progression. In fact, several studies have shown that many cell types in the lung stroma could provide a rich source of ATX in diseases. In the present study, we sought to determine whether ATX derived from alveolar type II epithelial (ATII) pneumocytes could modulate the progression of lung metastasis, which has not been evaluated previously. To accomplish this, we used the B16-F10 syngeneic melanoma model, which readily metastasizes to the lungs when injected intravenously. Because B16-F10 cells express high levels of ATX, we used the CRISPR-Cas9 technology to knock out the ATX gene in B16-F10 cells, eliminating the contribution of tumor-derived ATX in lung metastasis. Next, we used the inducible Cre/loxP system (Sftpc-CreER^T2/Enpp2^fl/fl) to generate conditional knockout (KO) mice in which ATX is specifically deleted in ATII cells (i.e., Sftpc-KO). Injection of ATX-KO B16-F10 cells into Sftpc-KO or Sftpc-WT control littermates allowed us to investigate the specific contribution of ATII-derived ATX in lung metastasis. We found that targeted KO of ATX in ATII cells significantly reduced the metastatic burden of ATX-KO B16-F10 cells by 30% (unpaired t-test, p = 0.028) compared to Sftpc-WT control mice, suggesting that ATX derived from ATII cells could affect the metastatic progression. We detected upregulated levels of cytokines such as IFNγ (unpaired t-test, p < 0.0001) and TNFα (unpaired t-test, p = 0.0003), which could favor the increase in infiltrating CD8⁺ T cells observed in the tumor regions of Sftpc-KO mice. Taken together, our results highlight the contribution of host ATII cells as a stromal source of ATX in the progression of melanoma lung metastasis. Full article

(This article belongs to the Special Issue Metastatic Progression of Human Melanoma)

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In vitro characterization of Sftpc-WT and Sftpc-KO mice. (A) Agarose electrophoresis of PCR-amplified Enpp2 allele of ATII cells isolated from Sftpc-WT (lanes 1 and 2) and Sftpc-KO (lanes 3 and 4) mice, treated in vivo with TAM (100 mg/kg/day for 5 days). The size of the Enpp2 WT allele is 441 bp, Enpp2 floxed allele is 540 bp, and Enpp2 deleted allele is 370 bp. The PCR product from ATII cells isolated from Sftpc-WT mouse showed a band corresponding to Enpp2 WT allele (lane 1) and no Enpp2 deleted allele was detected (lane 2). In contrast, ATII cells isolated from Sftpc-KO mouse showed a floxed allele (lane 3) and a deleted Enpp2 allele (lane 4). (B) WB analysis of cell lysates from ATII cells from Sftpc-WT mice (lane 2) and Sftpc-KO mice (lane 3) treated with TAM. Recombinant ATX (rATX, lane 1) was used as a positive control. Two weeks post-TAM treatment, ATII cells were isolated from Sftpc-WT and Sftpc-KO mice and put in culture for 5 days. Eighteen hours prior to lysate being harvested, cells were cultured in serum-free medium + 10 ng/mL of TNFα, in order to stimulate ATX production. One hundred fifty micrograms of protein was loaded into an 8% SDS-PAGE. A ~100 kDa band corresponding to ATX can be observed. Graph of the densitograms represents the percent of ATX band intensity normalized to the WT. ATII cells isolated from Sftpc-KO mice show a 90% decrease in band intensity (mean ± SD of 3 independent experiments). (C) Representative images of H&E stained 5 μm lung sections from TAM-treated naïve Sftpc-WT (left) and Sftpc-KO (right) and corn-oil-treated control (lower panel) mice. There was no sign of major histopathological lesion observed between the three different cohorts of lungs. Lungs were harvested two weeks post-TAM treatment, inflated with 10% formalin, fixed, and sectioned. Scale bars represent 100 μm (10× magnification). Full article ">Figure 2
ATX derived from B16-F10 partially controls the progression of lung metastasis. (A) Western blot analysis of cell lysates performed in two technical repeats of WT B16-F10 cells (lanes 1 and 2, respectively) and ATX-KO B16-F10 cells (lanes 3 and 4, respectively). Recombinant ATX (rATX, lane 6) was used as a positive control. Densitometric quantification of the ATX band showed an average 91% decrease in ATX expression in ATX-KO B16-F10 cell lysate compared to WT B16-F10 cells (mean ± SD of 4 independent experiments). Cell lines were cultured for 18 h in serum-free medium before lysates were harvested. One hundred micrograms of protein was loaded into an 8% SDS-PAGE. (B) ATX immunofluorescence staining in WT and ATX-KO B16-F10 cells. Cells were stained for ATX (green) using the 4F1 antibody at 1:100 dilution, with DAPI nuclear counterstain (1:5000). Upper panels show the staining of WT B16-F10 cells, whereas lower panels show the staining of ATX-KO B16-F10 cells. Scale bars represent 100 μm (20× magnification). (C) Quantification of ATX activity in concentrated conditioned medium (CCM) from WT and ATX-KO B16-F10. Crosshatched bar represents 10 nM recombinant ATX (rATX) positive control, white bar corresponds to the ATX activity in the CCM of WT B16-F10 cells (n = 5), and gray bar is the activity measured in CCM of ATX-KO B16-F10 cells (n = 5). ATX-KO B16-F10 cells present a 74.3% decrease in ATX activity compared to WT B16-F10 cells (Mann–Whitney, ** p = 0.0079). (D) Comparison of the growth rate between WT (black) and ATX-KO (red) B16-F10 performed in six replicates; representative of three independent experiments. (E) Metastatic foci in the lungs of C57BL/6 mice inoculated with 1 × 105 WT B16-F10 cells (white bar, n = 18) or ATX-KO B16-F10 cells (gray bar, n = 20), and representative lung pictures from this experiment (below). Mice inoculated with ATX-KO B16-F10 cells showed a 34% decrease in lung metastases (unpaired t-test, * p = 0.04, data from 2 independent experiments). Full article ">Figure 3
Only combined KO of ATX in B16-F10 cells and ATII cells decreases lung metastasis burden compared to KO in ATII cells alone. (A) Metastatic nodules in the lungs of Sftpc-WT (white bar, n = 16) and Sftpc-KO (gray bar, n = 23) mice inoculated with 1.5 × 105 ATX-KO B16-F10 cells. Sftpc-KO mice showed a 30% decrease in metastatic nodules (unpaired t-test, * p = 0.028 from 2 independent experiments). (B) ATX activity in the BALF from Sftpc-WT (white bar, n = 6) and Sftpc-KO (gray bar, n = 14) mice inoculated with 1.5 × 105 ATX-KO B16-F10 cells. There was no statistical difference between the two genotypes (unpaired t-test, p = 0.1551). (C) Total LPA species in the plasma of Sftpc-WT (white bar, n = 16) and Sftpc-KO (gray bar, n = 23) analyzed by mass spectrometry. Values are mean ± SD. Unpaired t-test, * p = 0.0426. (D) LPA species in the plasma of Sftpc-WT (white bar, n = 16) and Sftpc-KO (gray bar, n = 23) mice analyzed by mass spectrometry. Values are mean ± SD. * p = 0.0305, unpaired t-test. (E) Metastatic nodules in the lungs of Sftpc-WT (white bar, n = 13) and Sftpc-KO (gray bar, n = 10) mice inoculated with 1.5 × 105 WT B16-F10 cells. There was no statistical difference between the two genotypes (unpaired t-test, p = 0.1063). (F) ATX activity in the BALF from Sftpc-WT (white bar, n = 13) and Sftpc-KO (gray bar, n = 10) mice. There was no statistical difference between the two genotypes (unpaired t-test, p = 0.052). Full article ">Figure 4
ATX derived from ATII cells does not impact the transmigration ability of B16-F10 cells. (A) Transmigration of WT (white bar) and ATX-KO (gray bar) B16-F10 cells after incubation in complete medium for 6 h. The experiment was performed in quadruplicate wells. No statistical difference was observed (Mann–Whitney, p = 0.083). (B) Transmigration of WT (white bar) and ATX-KO (gray bar) B16-F10 cells in the presence of ATII cells isolated from Sftpc-WT mice plated in the lower chamber. Membranes were analyzed after 6 h of incubation and performed in quadruplicate. No statistical difference was observed (Mann–Whitney, p = 0.404). (C) Transmigration of WT (white bar) and ATX-KO (gray bar) B16-F10 cells in the presence of ATII cells isolated from Sftpc-KO mice plated in the lower chamber. Membranes were analyzed after 6 h of incubation and performed in quadruplicate. No statistical difference was found (Mann–Whitney, p = 0.083). Full article ">Figure 5
Plasma cytokine measurements in naïve Sftpc-WT and Sftpc-KO mice treated with TAM. Flow cytometry was performed to compare the basal concentration levels of 13 cytokines in the plasma of 7 Sftpc-WT (white bars) and 6 Sftpc-KO (gray bars) mice. Note that only IL-27 was different between the groups with a 3-fold higher concentration in the plasma of Sftpc-KO mice (unpaired t-test, * p = 0.032). Full article ">Figure 6
Cytokine measurements in the plasma of Sftpc-WT and Sftpc-KO mice on post-inoculation day 21. Flow cytometry was performed to compare the concentration of 13 cytokines in the plasma of Sftpc-WT (white bars) and Sftpc-KO (gray bars) mice, on day 21 post-inoculation, inoculated with 1.5 × 105 ATX-KO B16-F10 cells. Sftpc-KO mice presented an increase in 2 out of the 13 cytokines (unpaired t-test, IFNγ, **** p < 0.0001; TNFα, *** p = 0.0003). Full article ">Figure 7
Immunostaining performed on Sftpc-WT and Sftpc-KO lung sections. Five-micrometer lung sections were stained for (A) CD8a, Sftpc-KO mice presented a higher CD8+ T cell infiltration (black arrows); (B) CD4, both groups presented nodules with sparse CD4+ infiltration; and (C) CD68, no infiltration of CD68+ cells was observed. Scale bars represent 200 μm (10× magnification). Full article ">

20 pages, 2563 KiB

Open AccessReview

The Role of ROS as a Double-Edged Sword in (In)Fertility: The Impact of Cancer Treatment

by Sara Mendes, Rosália Sá, Manuel Magalhães, Franklim Marques, Mário Sousa and Elisabete Silva

Cancers 2022, 14(6), 1585; https://doi.org/10.3390/cancers14061585 - 21 Mar 2022

Cited by 19 | Viewed by 3688

Abstract

Tumor cells are highly resistant to oxidative stress resulting from the imbalance between high reactive oxygen species (ROS) production and insufficient antioxidant defenses. However, when intracellular levels of ROS rise beyond a certain threshold, largely above cancer cells’ capacity to reduce it, they [...] Read more.

Tumor cells are highly resistant to oxidative stress resulting from the imbalance between high reactive oxygen species (ROS) production and insufficient antioxidant defenses. However, when intracellular levels of ROS rise beyond a certain threshold, largely above cancer cells’ capacity to reduce it, they may ultimately lead to apoptosis or necrosis. This is, in fact, one of the molecular mechanisms of anticancer drugs, as most chemotherapeutic treatments alter redox homeostasis by further elevation of intracellular ROS levels or inhibition of antioxidant pathways. In traditional chemotherapy, it is widely accepted that most therapeutic effects are due to ROS-mediated cell damage, but in targeted therapies, ROS-mediated effects are mostly unknown and data are still emerging. The increasing effectiveness of anticancer treatments has raised new challenges, especially in the field of reproduction. With cancer patients’ life expectancy increasing, many aiming to become parents will be confronted with the adverse effects of treatments. Consequently, concerns about the impact of anticancer therapies on reproductive capacity are of particular interest. In this review, we begin with a short introduction on anticancer therapies, then address ROS physiological/pathophysiological roles in both male and female reproductive systems, and finish with ROS-mediated adverse effects of anticancer treatments in reproduction. Full article

(This article belongs to the Section Cancer Therapy)

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Graphical abstract

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Full article ">Figure 1
ROS-mediated activation of cell signaling pathways. Major sites of reactive oxygen species (ROS) production in cells, enzymes responsible for ROS production at each of the cellular compartments, and principal signaling pathways activated. Full article ">Figure 2
Spermatogenesis and spermiogenesis. The diagram describes the different stages of spermatogenesis and spermiogenesis. Full article ">Figure 3
Oogenesis and folliculogenesis. The diagram describes the different stages of oogenesis and folliculogenesis. Full article ">Figure 4
Spermatogenesis dysfunction after anticancer treatment. ROS overproduction due to treatments depletes the antioxidant systems, leading to OS. Both the normal and abnormal spermatozoa can be damaged by ROS; however, in the treatment case (right side), damage is more prevalent since ROS are present/produced in higher quantity due to anticancer treatments. OS impinges on spermatozoa (represented by the red stars) and damages to cell/sperm and mitochondria membranes, DNA damage, and defects in the sperm mid-piece and axonemal region can be observed. The establishment of this compromised process leads to abnormal semen characteristics and is responsible for the fertility decline present in men submitted to anticancer treatments. Reactive oxygen species (ROS). Full article ">Figure 5
Ovarian tissue dysfunction after anticancer treatments. Increase in OS-derived from anticancer treatment, due to increased ROS production and impaired antioxidant response leads to the establishment of an oxidative microenvironment. In a post-treatment ovarian stroma, a depletion in the number of primordial and primary follicles and the presence of collagen deposition can be observed (fibrosis). The establishment of this compromised microenvironment impairs ovarian function and is responsible for the fertility decline present in women submitted to anticancer treatments. Reactive oxygen species (ROS). Cisplatin and doxorubicin are two widely used chemotherapeutic drugs to treat several types of cancer, including those of the reproductive tract. Their ROS-mediated effects on fertility will now be revised. Full article ">

15 pages, 1499 KiB

Open AccessArticle

APOBEC SBS13 Mutational Signature—A Novel Predictor of Radioactive Iodine Refractory Papillary Thyroid Carcinoma

by Sarah Siraj, Tariq Masoodi, Abdul K. Siraj, Saud Azam, Zeeshan Qadri, Sandeep K. Parvathareddy, Rong Bu, Khawar S. Siddiqui, Saif S. Al-Sobhi, Mohammed AlDawish and Khawla S. Al-Kuraya

Cancers 2022, 14(6), 1584; https://doi.org/10.3390/cancers14061584 - 21 Mar 2022

Cited by 6 | Viewed by 3027

Abstract

Standard surgery followed by radioactive iodine (¹³¹I, RAI) therapy are not curative for 5–20% of papillary thyroid carcinoma (PTC) patients with RAI refractory disease. Early predictors indicating therapeutic response to RAI therapy in PTC are yet to be elucidated. Whole-exome sequencing [...] Read more.

Standard surgery followed by radioactive iodine (¹³¹I, RAI) therapy are not curative for 5–20% of papillary thyroid carcinoma (PTC) patients with RAI refractory disease. Early predictors indicating therapeutic response to RAI therapy in PTC are yet to be elucidated. Whole-exome sequencing was performed (at median depth 198x) on 66 RAI-refractory and 92 RAI-avid PTCs with patient-matched germline. RAI-refractory tumors were significantly associated with distinct aggressive clinicopathological features, including positive surgical margins (p = 0.016) and the presence of lymph node metastases at primary diagnosis (p = 0.012); higher nonsilent tumor mutation burden (p = 0.011); TERT promoter (TERTp) mutation (p < 0.0001); and the enrichment of the APOBEC-related single-base substitution (SBS) COSMIC mutational signatures 2 (p = 0.030) and 13 (p < 0.001). Notably, SBS13 (odds ratio [OR] 30.4, 95% confidence intervals [CI] 1.43–647.22) and TERTp mutation (OR 41.3, 95% CI 4.35–391.60) were revealed to be independent predictors of RAI refractoriness in PTC (p = 0.029 and 0.001, respectively). Although SBS13 and TERTp mutations alone highly predicted RAI refractoriness, when combined, they significantly increased the likelihood of predicting RAI refractoriness in PTC. This study highlights the APOBEC SBS13 mutational signature as a novel independent predictor of RAI refractoriness in a distinct subgroup of PTC. Full article

(This article belongs to the Special Issue Advances in Thyroid Carcinoma)

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Figure 1

Figure 1
Box plots showing (A) the absolute number of alterations, including mutations (tumor mutation burden; TMB) and copy number variations (CNV) in RAI-refractory and avid PTC tumors, and (B) the number of mutations contributing to selected single-base substitution (S; SBS) mutational signatures, with Benjamini–Hochberg-adjusted p-values (Mann–Whitney Test; alpha level 0.05). For each SBS mutational signature, only tumors with ≥1 mutation contributing to the SBS mutational signature were included for box plot representation. S1 represents age-related mutations; S2 and S13 represent APOBEC-related mutagenesis; S6, S15, S20, S21 and S26 represent defective DNA mismatch-repair (MMR) mutations; S3 represents defective homologous recombination-related DNA repair (HR); S30 represents defective base-nucleotide excision repair (BER); S4 represents tobacco smoking; S7 represents ultraviolet (UV) exposure; S24 represents aflatoxin exposure; S29 represents chewing tobacco; and S5, S8, S12, S16, S19 and S23 are of unknown etiology. Full article ">Figure 2
(A) Overview of absolute number of mutations contributing to each mutational signature per sample. (B) Distribution of each mutational signature per sample. Patients are ordered by hierarchical clustering. An alpha level of 0.05 was used for statistical significance. S1 represents age-related mutations; S2 and S13 represent APOBEC-related mutagenesis; S6, S15, S20, S21 and S26 represent defective DNA mismatch-repair (MMR) mutations; S3 represents defective homologous recombination-related DNA repair (HR); S30 represents defective base-nucleotide excision repair (BER); S4 represents tobacco smoking; S7 represents ultraviolet (UV) exposure; S24 represents aflatoxin exposure; S29 represents chewing tobacco; and S5, S8, S12, S16, S19 and S23 are of unknown etiology. Full article ">Figure 3
Multivariable logistic regression model (when adjusting for age, tumor size and tumor purity as continuous variables), using mutational signatures, BRAFV600E mutation, TERT promoter mutation and TMB to predict RAI refractoriness in PTC. An alpha level of 0.05 was used for statistical significance. S1 represents age-related mutations; S2 and S13 represent APOBEC-related mutagenesis; S6, S15, S20, S21 and S26 represent defective DNA mismatch-repair (MMR) mutations; S3 represents defective homologous recombination-related DNA repair (HR); S30 represents defective base-nucleotide excision repair (BER); S4 represents tobacco smoking; S7 represents ultraviolet (UV) exposure; S24 represents aflatoxin exposure; S29 represents chewing tobacco; and S5, S8, S12, S16, S19 and S23 are of unknown etiology. Full article ">

16 pages, 2022 KiB

Open AccessArticle

Immunophenotypic Analysis of Acute Megakaryoblastic Leukemia: A EuroFlow Study

by Nienke Brouwer, Sergio Matarraz, Stefan Nierkens, Mattias Hofmans, Michaela Nováková, Elaine Sobral da Costa, Paula Fernandez, Anne E. Bras, Fabiana Vieira de Mello, Ester Mejstrikova, Jan Philippé, Georgiana Emilia Grigore, Carlos E. Pedreira, Jacques J. M. van Dongen, Alberto Orfao, Vincent H. J. van der Velden and on behalf of the EuroFlow Consortium

Cancers 2022, 14(6), 1583; https://doi.org/10.3390/cancers14061583 - 21 Mar 2022

Cited by 16 | Viewed by 4443

Abstract

Acute megakaryoblastic leukemia (AMKL) is a rare and heterogeneous subtype of acute myeloid leukemia (AML). We evaluated the immunophenotypic profile of 72 AMKL and 114 non-AMKL AML patients using the EuroFlow AML panel. Univariate and multivariate/multidimensional analyses were performed to identify most relevant [...] Read more.

Acute megakaryoblastic leukemia (AMKL) is a rare and heterogeneous subtype of acute myeloid leukemia (AML). We evaluated the immunophenotypic profile of 72 AMKL and 114 non-AMKL AML patients using the EuroFlow AML panel. Univariate and multivariate/multidimensional analyses were performed to identify most relevant markers contributing to the diagnosis of AMKL. AMKL patients were subdivided into transient abnormal myelopoiesis (TAM), myeloid leukemia associated with Down syndrome (ML-DS), AML—not otherwise specified with megakaryocytic differentiation (NOS-AMKL), and AMKL—other patients (AML patients with other WHO classification but with flowcytometric features of megakaryocytic differentiation). Flowcytometric analysis showed good discrimination between AMKL and non-AMKL patients based on differential expression of, in particular, CD42a.CD61, CD41, CD42b, HLADR, CD15 and CD13. Combining CD42a.CD61 (positive) and CD13 (negative) resulted in a sensitivity of 71% and a specificity of 99%. Within AMKL patients, TAM and ML-DS patients showed higher frequencies of immature CD34+/CD117+ leukemic cells as compared to NOS-AMKL and AMKL-Other patients. In addition, ML-DS patients showed a significantly higher expression of CD33, CD11b, CD38 and CD7 as compared to the other three subgroups, allowing for good distinction of these patients. Overall, our data show that the EuroFlow AML panel allows for straightforward diagnosis of AMKL and that ML-DS is associated with a unique immunophenotypic profile. Full article

(This article belongs to the Special Issue Leukemia and Lymphoma Immunophenotyping)

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Figure 1
Multivariate analysis of non-AMKL (green dots) and AMKL patients (red dots) using the MFI values of all markers present in EuroFlow AML tubes 1–7 (A) or tubes 1–6 (B). Pattern classification was performed using NAPS, and the markers contributing to the pattern classification are shown in the bottom part of the figure. The three AMKL patients not expressing CD42a.CD61, CD41 or CD42b (MFI < 1000) are indicated by arrows. Full article ">Figure 2
Differentiation of CD34+/CD117+ AML cells in AMKL (red bars) and non-AMKL patients (green bars). Percentage of positive cells, defined as cells with an MFI > 1000 (mean ± SEM). Differentiation towards B-cell (CD19+), megakaryocytic/erythroid (CD36+/CD64−), erythroid (CD105+), monocytic (CD64+), granulocytic (CD15+) and megakaryocytic (CD42a.CD61+) lineage is shown. Full article ">Figure 3
Immunophenotypic profile of CD34+/CD117+, CD34−/CD117+, CD34+/CD117− or CD34−/CD117− subsets of AMKL cells. Expression of markers is depicted as log10 transformed MFI data. Statistical analysis was performed by the Kruskal–Wallis test, followed by the Mann–Whitney test if p < 0.05. The horizontal lines between populations represent statistically significant differences (p < 0.05). The grey zone indicates MFI levels < 1000; markers with such MFI values were considered to be negative. Full article ">Figure 4
Univariate analysis of marker expression between the four AMKL subgroups. Data represent the MFI values after log10 transformation. The lines the on top of the figures represent statistically significant differences (p < 0.05) between the two groups. The grey zone indicates MFI levels < 1000; markers with such MFI values were considered to be negative. Full article ">Figure 5
Distribution of AMKL cells over the various maturation stages, as defined by CD34 and CD117 expression. The percentage of CD34+/CD117+ leukemic cells was significantly higher in TAM and ML-DS patients as compared to NOS-AMKL and AMKL-Other patients (p < 0.05 by Mann–Whitney test); in contrast, the percentage of CD34−/CD117− leukemic cells was higher in the NOS-AMKL patients (p < 0.05) and AMKL-Other patients (not significant). Full article ">Figure 6
Multivariate analysis of marker expression between the four AMKL subgroups. (A) TAM versus ML-DS (contributing markers: CD7 (47%), CD11b (31%) and CD13 (21%)); (B) TAM versus NOS-AMKL (contributing markers: CD117 (28%), CD4 (21%) and CD42b (19%)); (C) TAM versus AMKL-Other (contributing markers: CD117 (61%), CD13 (36%) and CD19 (3%)); (D) ML-DS versus NOS-AMKL (contributing markers: CD7 (68%), CD117 (27%) and CD11b (3%)); (E) ML-DS versus AMKL-Other (contributing markers: CD33 (51%), CD203c (28%) and CD38 (21%)); (F) NOS-AMKL versus AMKL-Other (contributing markers: CD7 (97%), CD38 (2%) nad CD117 (0.4%)). The arrows indicate NOS-AMKL patients with t(1;22). Full article ">

13 pages, 414 KiB

Open AccessSystematic Review

The Impact of Cognitive Impairment on Treatment Toxicity, Treatment Completion, and Survival among Older Adults Receiving Chemotherapy: A Systematic Review

by Schroder Sattar, Kristen Haase, Isabel Tejero, Cara Bradley, Caroline Mariano, Heather Kilgour, Ridhi Verma, Eitan Amir and Shabbir Alibhai

Cancers 2022, 14(6), 1582; https://doi.org/10.3390/cancers14061582 - 21 Mar 2022

Cited by 7 | Viewed by 2617

Abstract

Cognitive impairment (CI) is common among older adults with cancer, but its effect on cancer outcomes is not known. This systematic review sought to identify research investigating clinical endpoints (toxicity risk, treatment completion, and survival) of chemotherapy treatment in those with baseline CI. [...] Read more.

Cognitive impairment (CI) is common among older adults with cancer, but its effect on cancer outcomes is not known. This systematic review sought to identify research investigating clinical endpoints (toxicity risk, treatment completion, and survival) of chemotherapy treatment in those with baseline CI. A systematic search of five databases (inception to March 2021) was conducted. Eligible studies included randomized trials, prospective studies, and retrospective studies in which the sample or a subgroup were older adults (aged ≥ 65) screened positive for CI prior to receiving chemotherapy. Risk of bias assessment was performed using the Quality in Prognosis Studies (QUIPS) tool. Twenty-three articles were included. Sample sizes ranged from n = 31 to 703. There was heterogeneity of cancer sites, screening tools and cut-offs used to ascertain CI, and proportion of patients with CI within study samples. Severity of CI and corresponding proportion of each level within study samples were unclear in all but one study. Among studies investigating CI in a qualified multivariable model, statistically significant findings were found in 4/6 studies on survival and in 1/1 study on nonhematological toxicity. The lack of robust evidence indicates a need for further research on the role of CI in predicting survival, treatment completion, and toxicity among older adults receiving chemotherapy, and the potential implications that could shape treatment decisions. Full article

(This article belongs to the Special Issue Advances in Geriatric Oncology: Exploring Practical Ways to Optimize Treatment in Older Patients with Cancer)

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3 pages, 177 KiB

Open AccessEditorial

Vulvar Cancer: Facing a Rare Disease

by Mario Preti and Denis Querleu

Cancers 2022, 14(6), 1581; https://doi.org/10.3390/cancers14061581 - 20 Mar 2022

Cited by 6 | Viewed by 2279

Abstract

“We must never be afraid to go too far, for truth lies beyond [...] Full article

(This article belongs to the Special Issue Recent Advances in Vulvar Cancer)

21 pages, 1395 KiB

Open AccessReview

Addressing the Elephant in the Immunotherapy Room: Effector T-Cell Priming versus Depletion of Regulatory T-Cells by Anti-CTLA-4 Therapy

by Megan M Y Hong and Saman Maleki Vareki

Cancers 2022, 14(6), 1580; https://doi.org/10.3390/cancers14061580 - 20 Mar 2022

Cited by 24 | Viewed by 8801

Abstract

Cytotoxic T-lymphocyte Associated Protein 4 (CTLA-4) is an immune checkpoint molecule highly expressed on regulatory T-cells (Tregs) that can inhibit the activation of effector T-cells. Anti-CTLA-4 therapy can confer long-lasting clinical benefits in cancer patients as a single agent or in combination with [...] Read more.

Cytotoxic T-lymphocyte Associated Protein 4 (CTLA-4) is an immune checkpoint molecule highly expressed on regulatory T-cells (Tregs) that can inhibit the activation of effector T-cells. Anti-CTLA-4 therapy can confer long-lasting clinical benefits in cancer patients as a single agent or in combination with other immunotherapy agents. However, patient response rates to anti-CTLA-4 are relatively low, and a high percentage of patients experience severe immune-related adverse events. Clinical use of anti-CTLA-4 has regained interest in recent years; however, the mechanism(s) of anti-CTLA-4 is not well understood. Although activating T-cells is regarded as the primary anti-tumor mechanism of anti-CTLA-4 therapies, mounting evidence in the literature suggests targeting intra-tumoral Tregs as the primary mechanism of action of these agents. Tregs in the tumor microenvironment can suppress the host anti-tumor immune responses through several cell contact-dependent and -independent mechanisms. Anti-CTLA-4 therapy can enhance the priming of T-cells by blockading CD80/86-CTLA-4 interactions or depleting Tregs through antibody-dependent cellular cytotoxicity and phagocytosis. This review will discuss proposed fundamental mechanisms of anti-CTLA-4 therapy, novel uses of anti-CTLA-4 in cancer treatment and approaches to improve the therapeutic efficacy of anti-CTLA-4. Full article

(This article belongs to the Section Cancer Immunology and Immunotherapy)

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Figure 1
Enhancing the priming of effector T-cells by blockading CD80/86-CTLA-4 interactions. High expression of CTLA-4 on Tregs contributes to their immunosuppressive phenotype. Effector T-cells can express CTLA-4 transiently after T-cell activation. CTLA-4 engagement with CD80/86 on antigen-presenting cells inhibits CD28 costimulation that is required for T-cell activation and the upregulation of ICOS. Anti-CTLA-4 binds to CTLA-4 and inhibits CD80/86-CTLA-4 interactions to increase the activation of anti-tumor effector T-cells. T-cell activation results in clonal expansion and the employment of effector mechanisms that facilitate anti-tumor immune responses. Full article ">Figure 2
Antibody-mediated Treg depletion. Anti-CTLA-4 bound to Tregs can engage with FcγRs expressed on innate cells to deplete Tregs. Macrophages and natural killer cells can deplete Tregs through antibody-dependent cellular phagocytosis (ADCP) and antibody-dependent cellular cytotoxicity (ADCC). Depleting intra-tumoral Tregs promotes anti-tumor immune responses by transforming the immunosuppressive nature of the tumor microenvironment into a pro-inflammatory microenvironment. This is enabled by indirectly increasing anti-tumor effector T-cells’ activation, infiltration, and effector functions. Full article ">Figure 3
Alteration of Treg metabolism and plasticity. Anti-CTLA-4 allows CD28 on Tregs to engage with CD80/86 on antigen-presenting cells. Costimulatory signaling shifts the metabolism of Tregs from oxidative phosphorylation (OXPHOS) to glycolysis. Increasing glycolysis can functionally and phenotypically destabilize the immunosuppressive nature of Tregs. Tregs can adopt the pro-inflammatory characteristics of Th1 and Th17 cells to contribute to anti-tumor immune responses. Full article ">

15 pages, 1337 KiB

Open AccessArticle

MRI Response Assessment in Glioblastoma Patients Treated with Dendritic-Cell-Based Immunotherapy

by Johanna Heugenhauser, Malik Galijasevic, Stephanie Mangesius, Georg Goebel, Johanna Buchroithner, Friedrich Erhart, Josef Pichler, Georg Widhalm, Günther Stockhammer, Sarah Iglseder, Christian F. Freyschlag, Stefan Oberndorfer, Karin Bordihn, Gord von Campe, Thomas Czech, Birgit Surböck, Tadeja Urbanic Purkart, Christine Marosi, Thomas Felzmann and Martha Nowosielski

Cancers 2022, 14(6), 1579; https://doi.org/10.3390/cancers14061579 - 20 Mar 2022

Cited by 8 | Viewed by 3579

Abstract

Introduction: In this post hoc analysis we compared various response-assessment criteria in newly diagnosed glioblastoma (GB) patients treated with tumor lysate-charged autologous dendritic cells (Audencel) and determined the differences in prediction of progression-free survival (PFS) and overall survival (OS). Methods: 76 patients enrolled [...] Read more.

Introduction: In this post hoc analysis we compared various response-assessment criteria in newly diagnosed glioblastoma (GB) patients treated with tumor lysate-charged autologous dendritic cells (Audencel) and determined the differences in prediction of progression-free survival (PFS) and overall survival (OS). Methods: 76 patients enrolled in a multicenter phase II trial receiving standard of care (SOC, n = 40) or SOC + Audencel vaccine (n = 36) were included. MRI scans were evaluated using MacDonald, RANO, Vol-RANO, mRANO, Vol-mRANO and iRANO criteria. Tumor volumes (T1 contrast-enhancing as well as T2/FLAIR volumes) were calculated by semiautomatic segmentation. The Kruskal-Wallis-test was used to detect differences in PFS among the assessment criteria; for correlation analysis the Spearman test was used. Results: There was a significant difference in median PFS between mRANO (8.6 months) and Vol-mRANO (8.6 months) compared to MacDonald (4.0 months), RANO (4.2 months) and Vol-RANO (5.4 months). For the vaccination arm, median PFS by iRANO was 6.2 months. There was no difference in PFS between SOC and SOC + Audencel. The best correlation between PFS/OS was detected for mRANO (r = 0.65) and Vol-mRANO (r = 0.69, each p < 0.001). A total of 16/76 patients developed a pure T2/FLAIR progressing disease, and 4/36 patients treated with Audencel developed pseudoprogression. Conclusion: When comparing different response-assessment criteria in GB patients treated with dendritic cell-based immunotherapy, the best correlation between PFS and OS was observed for mRANO and Vol-mRANO. Interestingly, iRANO was not superior for predicting OS in patients treated with Audencel. Full article

(This article belongs to the Special Issue Biomarker in Glioblastoma)

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Post-OP and follow-up MRI scans of patient VAX_0083 (Audencel-arm): T2- (b,d,f,h,j,l) and postgadolinium T1-weighted MRI sequences (a,c,e,g,i,k) are displayed. This figure illustrates the different time points of progression by different response assessments. At the first follow-up scan (c,d), a new contrast-enhancing lesion is seen on postgadolinium T1-weighted MRI sequences (c) and a significant increase or ≥100% increase in volume of non-enhancing abnormalities (d) compared to the post-OP scan (baseline, (b)) is seen. Progressive disease (PD) by MacDonald, RANO, Vol-RANO, while preliminary progressive disease (pPD) by mRANO, Vol-mRANO is diagnosed. Because the first follow-up MRI (c,d) is within the first six weeks of immunotherapy-treatment start, pPD by iRANO is defined. In the second follow-up MRI (e,f) this patient is diagnosed with pseudoprogression (PsP) because no further ≥25% increase in the cross-section area or ≥40% increase in total volume of the contrast-enhancing tumor mass is seen (e) compared to the first follow-up MRI (c). In the third follow-up MRI (g,h) the contrast-enhancing tumor mass does not increase in size (g) compared to the second follow-up MRI (e), hence stable disease (SD) by mRANO and Vol-mRANO is defined. In the fourth follow-up MRI (i,j), confirmed progressive disease (cPD) by iRANO is defined, as a significant increase in non-enhancing abnormalities (j) compared to the post-OP scan (baseline, (b)) is seen and this scan (i,j) is ≥3 months after the first follow-up MRI (c,d), where pPD by iRANO was diagnosed. In the fifth follow-up MRI (k,l) a ≥25% increase in the cross-section area or ≥40% increase in total volume of the contrast-enhancing tumor mass (k) compared to the second follow-up MRI (e) is seen and PD by mRANO and Vol-mRANO is diagnosed. Full article ">Figure 2
Post-OP and follow-up MRI scans of patient VAX_0066 (Audencel-arm): T2- (b,d,f,h,j,l) and postgadolinium T1-weighted MRI sequences (a,c,e,g,i,k) are displayed. This figure illustrates the progression of non-enhancing abnormalities. At the first follow-up MRI (c,d) non-enhancing abnormalities are decreased and no contrast-enhancing tumor mass is seen compared to post-OP (a,b) where no measurable disease is seen. Hence, the patient is defined as stable disease (SD) by all assessment criteria.Therefore, the first follow-up MRI (c,d) is used as baseline MRI, as it shows the best response. The second follow-up MRI (e,f) still shows SD compared to baseline (c,d). At the third follow-up MRI (g,h) an increase in non-enhancing abnormalities (corpus callosum, (h)) compared to T2-weighted sequence of the first follow-up (d) is seen. On the fourth- (i,j) and fifth follow-up scans (k,l), T2-changes are further increased (j,l). On T1-weighted MRI scans from first to fifth follow-up (c,e,g,i,k), no measurable contrast-enhancing tumor mass is seen, including the last T1-weighted follow-up MRI scan (k). Full article ">

22 pages, 2366 KiB

Open AccessReview

Circadian and Immunity Cycle Talk in Cancer Destination: From Biological Aspects to In Silico Analysis

by Mina Mirian, Amirali Hariri, Mahtasadat Yadollahi and Mohammad Kohandel

Cancers 2022, 14(6), 1578; https://doi.org/10.3390/cancers14061578 - 20 Mar 2022

Cited by 10 | Viewed by 5461

Abstract

Cancer is the leading cause of death and a major problem to increasing life expectancy worldwide. In recent years, various approaches such as surgery, chemotherapy, radiation, targeted therapies, and the newest pillar, immunotherapy, have been developed to treat cancer. Among key factors impacting [...] Read more.

Cancer is the leading cause of death and a major problem to increasing life expectancy worldwide. In recent years, various approaches such as surgery, chemotherapy, radiation, targeted therapies, and the newest pillar, immunotherapy, have been developed to treat cancer. Among key factors impacting the effectiveness of treatment, the administration of drugs based on the circadian rhythm in a person and within individuals can significantly elevate drug efficacy, reduce adverse effects, and prevent drug resistance. Circadian clocks also affect various physiological processes such as the sleep cycle, body temperature cycle, digestive and cardiovascular processes, and endocrine and immune systems. In recent years, to achieve precision patterns for drug administration using computational methods, the interaction of the effects of drugs and their cellular pathways has been considered more seriously. Integrated data-derived pathological images and genomics, transcriptomics, and proteomics analyses have provided an understanding of the molecular basis of cancer and dramatically revealed interactions between circadian and immunity cycles. Here, we describe crosstalk between the circadian cycle signaling pathway and immunity cycle in cancer and discuss how tumor microenvironment affects the influence on treatment process based on individuals’ genetic differences. Moreover, we highlight recent advances in computational modeling that pave the way for personalized immune chronotherapy. Full article

(This article belongs to the Special Issue Quantitative Approaches to Cancer Immunotherapy)

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Figure 1
Intracellular circadian cycle pathway. The accumulating PER and CRY proteins bind to CLOCK/BMAL1 and switch them from an activated state to an inhibited state, blocking the transcriptional activity of downstream genes. ROR/REV-ERB regulates the main feedback loop by acting on RORE. Full article ">Figure 2
Effect of the circadian cycle on the main process of cancer such as cell cycle and immune behavior. Full article ">Figure 3
Cross talk between immunity cycle and circadian cycle genes; Recognizing, destroying, and releasing antigens from cancer cells are all part of this process. When NK cells are activated in tumor locations and come into direct contact with malignant cells, they kill them without the need for any kind of pre-exposure. In effector T cells, the circadian clock (ROR, PER1, CRY2, and BMAL1) adversely regulates PD-1 expression. CTLA-4 and PD-L1 are similarly negatively regulated by BMAL1 in effector T cells. IFN-γ, granzyme B, and perforin production by NK cells can be enhanced by PER-1 and BMAL1. Full article ">Figure 4
Our connection between the immune system and circadian rhythms could lead to a new area in cancer treatment. Using cellular and molecular data such as Omix data and pathology images, analyzing them, and establishing a network between these two cycles, determines the appropriate treatment for each person based on the administration time and the type of drug. Full article ">Figure 5
Systems pharmacology in cancer is a new field that has a significant role in the design, discovery, and repositioning of drugs due to the growth of basic cancer studies and the existence of accurate genetic and protein data and reliable data on drugs. These data, along with accurate bioinformatics models, can predict drug effects very well. Full article ">

27 pages, 1155 KiB

Open AccessReview

At the Intersection of Cardiology and Oncology: TGFβ as a Clinically Translatable Therapy for TNBC Treatment and as a Major Regulator of Post-Chemotherapy Cardiomyopathy

by Andrew Sulaiman, Jason Chambers, Sai Charan Chilumula, Vishak Vinod, Rohith Kandunuri, Sarah McGarry and Sung Kim

Cancers 2022, 14(6), 1577; https://doi.org/10.3390/cancers14061577 - 19 Mar 2022

Cited by 5 | Viewed by 3492

Abstract

Triple-negative breast cancer (TNBC) is a subtype of breast cancer that accounts for the majority of breast cancer-related deaths due to the lack of specific targets for effective treatments. While there is immense focus on the development of novel therapies for TNBC treatment, [...] Read more.

Triple-negative breast cancer (TNBC) is a subtype of breast cancer that accounts for the majority of breast cancer-related deaths due to the lack of specific targets for effective treatments. While there is immense focus on the development of novel therapies for TNBC treatment, a persistent and critical issue is the rate of heart failure and cardiomyopathy, which is a leading cause of mortality and morbidity amongst cancer survivors. In this review, we highlight mechanisms of post-chemotherapeutic cardiotoxicity exposure, evaluate how this is assessed clinically and highlight the transforming growth factor-beta family (TGF-β) pathway and its significance as a mediator of cardiomyopathy. We also highlight recent findings demonstrating TGF-β inhibition as a potent method to prevent cardiac remodeling, fibrosis and cardiomyopathy. We describe how dysregulation of the TGF-β pathway is associated with negative patient outcomes across 32 types of cancer, including TNBC. We then highlight how TGF-β modulation may be a potent method to target mesenchymal (CD44⁺/CD24⁻) and epithelial (ALDH^high) cancer stem cell (CSC) populations in TNBC models. CSCs are associated with tumorigenesis, metastasis, relapse, resistance and diminished patient prognosis; however, due to plasticity and differential regulation, these populations remain difficult to target and continue to present a major barrier to successful therapy. TGF-β inhibition represents an intersection of two fields: cardiology and oncology. Through the inhibition of cardiomyopathy, cardiac damage and heart failure may be prevented, and through CSC targeting, patient prognoses may be improved. Together, both approaches, if successfully implemented, would target the two greatest causes of cancer-related morbidity in patients and potentially lead to a breakthrough therapy. Full article

(This article belongs to the Special Issue Signalling Pathways of Cancer Stem Cells)

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11 pages, 1391 KiB

Open AccessArticle

Expression of Membranous CD155 Is Associated with Aggressive Phenotypes and a Poor Prognosis in Patients with Bladder Cancer

by Kohei Mori, Kazumasa Matsumoto, Noriyuki Amano, Dai Koguchi, Soichiro Shimura, Masahiro Hagiwara, Yuriko Shimizu, Masaomi Ikeda, Yuichi Sato and Masatsugu Iwamura

Cancers 2022, 14(6), 1576; https://doi.org/10.3390/cancers14061576 - 19 Mar 2022

Cited by 6 | Viewed by 2657

Abstract

Objective: To investigate the relationship between clinicopathological findings and membranous CD155 (mCD155) or cytoplasmic CD155 (cCD155) expression in bladder cancer (BC). Methods: We retrospectively analyzed 103 patients with BC who underwent radical cystectomy between 1990 to 2015 at Kitasato University Hospital. Immunohistochemical staining [...] Read more.

Objective: To investigate the relationship between clinicopathological findings and membranous CD155 (mCD155) or cytoplasmic CD155 (cCD155) expression in bladder cancer (BC). Methods: We retrospectively analyzed 103 patients with BC who underwent radical cystectomy between 1990 to 2015 at Kitasato University Hospital. Immunohistochemical staining was performed to evaluate CD155 expression in tumor cells. Cases with > 10% expression on the membrane or cytoplasm of tumor cells were positive. The Fisher′s exact test was used for categorical variables and the Kaplan–Meier method was used for survival outcomes. Univariate and multivariate Cox regression hazard models were used to evaluate the survival risk factors. Results: Cases that were mCD155-positive were associated with high-grade tumors (p = 0.02), nodal status (p < 0.01), and pT stage (p = 0.04). No association with any clinicopathological factor was observed in the cCD155 cases. Kaplan–Meier analysis showed that mCD155-positive cases had shorter periods of recurrence-free survival (p = 0.015) and cancer-specific survival (p = 0.005). Only nodal status was an independent predictor for both cancer-specific survival and recurrence-free survival in multivariate analysis (p = 0.02 and p < 0.01, respectively). Conclusion: mCD155 expression may be a marker of an aggressive phenotype and a poor prognosis in patients with BC. Full article

(This article belongs to the Special Issue Insights into Urologic Cancer)

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16 pages, 2695 KiB

Open AccessArticle

Identification of New Vulnerabilities in Conjunctival Melanoma Using Image-Based High Content Drug Screening

by Katya Nardou, Michael Nicolas, Fabien Kuttler, Katarina Cisarova, Elifnaz Celik, Mathieu Quinodoz, Nicolo Riggi, Olivier Michielin, Carlo Rivolta, Gerardo Turcatti and Alexandre Pierre Moulin

Cancers 2022, 14(6), 1575; https://doi.org/10.3390/cancers14061575 - 19 Mar 2022

Viewed by 3404

Abstract

Recent evidence suggests that numerous similarities exist between the genomic landscapes of both conjunctival and cutaneous melanoma. Since alterations of several components of the MAP kinases, PI3K/mTOR, and cell cycle pathways have been reported in conjunctival melanoma, we decided to assess the sensitivity [...] Read more.

Recent evidence suggests that numerous similarities exist between the genomic landscapes of both conjunctival and cutaneous melanoma. Since alterations of several components of the MAP kinases, PI3K/mTOR, and cell cycle pathways have been reported in conjunctival melanoma, we decided to assess the sensitivity of conjunctival melanoma to targeted inhibition mostly of kinase inhibitors. A high content drug screening assay based on automated fluorescence microscopy was performed in three conjunctival melanoma cell lines with different genomic backgrounds with 489 kinase inhibitors and 53 other inhibitors. IC50 and apoptosis induction were respectively assessed for 53 and 48 compounds. The genomic background influenced the response to MAK and PI3K/mTOR inhibition, more specifically cell lines with BRAF ^V600E mutations were more sensitive to BRAF/MEK inhibition, while CRMM2 bearing the NRAS^Q61L mutation was more sensitive to PI3k/mTOR inhibition. All cell lines demonstrated sensitivity to cell cycle inhibition, being more pronounced in CRMM2, especially with polo-like inhibitors. Our data also revealed new vulnerabilities to Hsp90 and Src inhibition. This study demonstrates that the genomic background partially influences the response to targeted therapy and uncovers a large panel of potential vulnerabilities in conjunctival melanoma that may expand available options for the management of this tumor. Full article

(This article belongs to the Special Issue Systemic Therapies in Melanoma)

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Live/Dead viability/cytotoxicity assay screen. (A) Example images of the control conditions for all 3 conjunctival melanoma cell lines. Upper row: CRMM1; middle row: CRMM2; lower row: CM2005.1. Left: DMSO; Right: Gambogic Acid. All nuclei are stained blue with Hoechst 33342, live cells, green with Calcein AM, and dead nuclei red with Ethidium homodimer-1. Objective 4×/0.2. The full well area is contained in a single image per well. Scale bar 500 μm. (B) Full plate snapshot of the Z’ plate validating the assay for CRMM1 and CRMM2 cell lines. All images of the plate, acquired in the same conditions as in (A), are montaged together. (C) Image segmentation performed with CellProfiler. DMSO control image of CRMM1 cells stained as in (A) (top row), and with the overlays of segmented regions (bottom row). Cyan: all nuclei. Yellow: live Calcein AM-positive cells. Pink: dead Ethidium homodimer-1-positive nuclei. Right: zoomed crop of the area highlighted by a dashed rectangle. Scale bars 500 µm (left) and 100 µm (right). (D) Full plate snapshot example of one of the screen plates with CRMM1 cells, composed of 32 replicates of each of the controls and 320 screen compounds, montaged together after imaging as in (A). Full article ">Figure 2
Annexin V apoptosis assay screen. (A) Full plate snapshot example of the Annexin V screen with CM2005.1 cells, composed of 8 replicates of each of the controls and 80 screen compounds, montaged together. All nuclei are stained blue with Hoechst, apoptotic cells green with Annexin V, and dead nuclei red with Ethidium homodimer-1. Objective 10×/0.45. The snapshot is composed of a single field of view out of the 9 fields acquired per well. (B) Example images of the control conditions for CM2005.1 cells. Top: DMSO. Bottom: Staurosporine. Left panel: brightfield images. Middle panel: fluorescent images acquired as in A. Right panel: zoomed crop of the area highlighted by a dashed rectangle, with an overlay of image segmentation performed by CellProfiler. Brown: all nuclei. Yellow: Annexin V-positive apoptotic cells. Red: dead Ethidium homodimer-1 positive nuclei. Scale bar 200 µm. Full article ">

17 pages, 1829 KiB

Open AccessArticle

Effect of Osimertinib on CTCs and ctDNA in EGFR Mutant Non-Small Cell Lung Cancer Patients: The Prognostic Relevance of Liquid Biopsy

by Galatea Kallergi, Emmanouil Kontopodis, Aliki Ntzifa, Núria Jordana-Ariza, Niki Karachaliou, Evangelia Pantazaka, Haris A. Charalambous, Amanda Psyrri, Emily Tsaroucha, Ioannis Boukovinas, Anna Koumarianou, Dora Hatzidaki, Evi Lianidou, Vassilis Georgoulias, Rafael Rosell and Athanasios Kotsakis

Cancers 2022, 14(6), 1574; https://doi.org/10.3390/cancers14061574 - 19 Mar 2022

Cited by 14 | Viewed by 3434

Abstract

Introduction: Liquid biopsy is a useful tool for monitoring treatment outcome in solid tumors, including lung cancer. The relevance of monitoring CTCs and plasma ctDNA as predictors of clinical outcome was assessed in EGFR-mutant NSCLC patients treated with osimertinib. Methods: Forty-seven EGFR-mutant NSCLC [...] Read more.

Introduction: Liquid biopsy is a useful tool for monitoring treatment outcome in solid tumors, including lung cancer. The relevance of monitoring CTCs and plasma ctDNA as predictors of clinical outcome was assessed in EGFR-mutant NSCLC patients treated with osimertinib. Methods: Forty-seven EGFR-mutant NSCLC patients who had progressed on prior first- or second-generation EGFR inhibitors were enrolled in the study and treated with osimertinib, irrespective of the presence of the T790M mutation in the primary tumor or the plasma. Peripheral blood was collected at baseline (n = 47), post-Cycle 1 (n = 47), and at the end of treatment (EOT; n = 39). CTCs were evaluated in 32 patients at the same time points (n = 32, n = 27, and n = 21, respectively) and phenotypic characterization was performed using triple immunofluorescence staining (CK/VIM/CD45). Results: Osimertinib resulted in an ORR of 34% (2 CR) and a DCR of 76.6%. The median PFS and OS values were 7.5 (range, 0.8–52.8) and 15.1 (range, 2.1–52.8) months, respectively. ctDNA was detected in 61.7%, 27.7%, and 61.5% of patients at baseline, post-Cycle 1, and EOT, respectively. CTCs (CK+/CD45-) were detected in 68.8%, 48.1%, and 61.9% of patients at the three time points, respectively. CTCs expressing both epithelial and mesenchymal markers (CK+/VIM+/CD45-) were detected in 56.3% and 29.6% of patients at baseline and post-Cycle 1, respectively. The detection of ctDNA at baseline and post-Cycle 1 was associated with shorter PFS and OS, whereas the ctDNA clearance post-Cycle 1 resulted in a significantly longer PFS and OS. Multivariate analysis revealed that male sex and the detection of ctDNA at baseline were independent predictors of shorter PFS (HR: 2.6, 95% C.I.: 1.2–5.5, p = 0.015 and HR: 3.0, 95% C.I.: 1.3–6.9; p = 0.009, respectively). Conclusions: The decrease in both CTCs and ctDNA occurring early during osimertinib treatment is predictive of better outcome, implying that liquid biopsy monitoring may be a valuable tool for the assessment of treatment efficacy. Full article

(This article belongs to the Special Issue Liquid Biopsy in Cancer)

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20 pages, 12021 KiB

Open AccessArticle

A Gene Co-Expression Network-Based Drug Repositioning Approach Identifies Candidates for Treatment of Hepatocellular Carcinoma

by Meng Yuan, Koeun Shong, Xiangyu Li, Sajda Ashraf, Mengnan Shi, Woonghee Kim, Jens Nielsen, Hasan Turkez, Saeed Shoaie, Mathias Uhlen, Cheng Zhang and Adil Mardinoglu

Cancers 2022, 14(6), 1573; https://doi.org/10.3390/cancers14061573 - 19 Mar 2022

Cited by 14 | Viewed by 4242

Abstract

Hepatocellular carcinoma (HCC) is a malignant liver cancer that continues to increase deaths worldwide owing to limited therapies and treatments. Computational drug repurposing is a promising strategy to discover potential indications of existing drugs. In this study, we present a systematic drug repositioning [...] Read more.

Hepatocellular carcinoma (HCC) is a malignant liver cancer that continues to increase deaths worldwide owing to limited therapies and treatments. Computational drug repurposing is a promising strategy to discover potential indications of existing drugs. In this study, we present a systematic drug repositioning method based on comprehensive integration of molecular signatures in liver cancer tissue and cell lines. First, we identify robust prognostic genes and two gene co-expression modules enriched in unfavorable prognostic genes based on two independent HCC cohorts, which showed great consistency in functional and network topology. Then, we screen 10 genes as potential target genes for HCC on the bias of network topology analysis in these two modules. Further, we perform a drug repositioning method by integrating the shRNA and drug perturbation of liver cancer cell lines and identifying potential drugs for every target gene. Finally, we evaluate the effects of the candidate drugs through an in vitro model and observe that two identified drugs inhibited the protein levels of their corresponding target genes and cell migration, also showing great binding affinity in protein docking analysis. Our study demonstrates the usefulness and efficiency of network-based drug repositioning approach to discover potential drugs for cancer treatment and precision medicine approach. Full article

(This article belongs to the Special Issue Drug Repurposing for Cancer Therapy)

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Flow chart of systematic drug repositioning approach for HCC. Full article ">Figure 2
Identification and functional analysis of HCC SPGs. (A) The correlation plot shows great consistency in gene expression level between LIHC and LIRI-JP cohort. (B) Identification of signature prognostic genes in LIHC (marked with blue color) and LIRI-JP cohorts (marked with orange color). The table shows the number of prognostic genes in Cox survival analysis and KM analysis in two cohorts. We further identified the 1036 SPGs shared by both prognostic gene sets (Venn diagram). (C) Functional analysis showed the top 20 most significantly GO terms in favorable and unfavorable SPGs, presented with pink and green dots, respectively. Full article ">Figure 3
High-centrality functional modules in HCC cohorts. The networks were limited to modules with a minimum number of 20 nodes and a connectivity coefficient larger than 0.5. (A) and (B) showed the modules identified in LIHC and LIRI-JP cohort, respectively. The prognostic attributes of modules were marked by different color, as shown in legend. Modules with similar biological functions were circled with same background color (red—DNA replication, green—Immune response, purple—Metabolic process, yellow—Mitochondrial process, blue—RNA-related process, pink—Virus infection and gray—Other functions). Top biological processes were listed beside the functional circle for detailed information. Full article ">Figure 4
Identification of HCC target genes. (A) Venn diagram showed the relative overlapping outcomes of M80 (LIHC module), M7 (LIRI-JP module) and SPGs. (B) Essential scores for potential target genes in 16 primary HCC cell lines. (C) Protein-staining IHC images for potential target genes among normal and liver tumor cells. (D) The average gene expression level of target genes in normal and tumor tissues among 50 HCC patients. Full article ">Figure 5
Drug prediction for HCC target genes. (A) Workflow of drug identification for target genes. The MdnCorr here stands for the median correlation coefficient. (B) The box plot showed top three effective drugs for each target gene. Each point in the box plot represents a shRNA for knockdown of corresponding target genes. Full article ">Figure 6
Validation of top effective drugs. (A) Protein expression changes with drugs treatment in TOP2A, PLK1 and MCM2. (B) The proliferation assay showed MTX and WFA significantly suppressed TOP2A formation in HepG2 cell line (*** means p < 0.001 in t-test). (C) The scratch wound-healing assay showed MTX and WFA strongly inhibit HepG2 cells migration. (D) The docked conformation of the MTX inside the binding site of TOP2A. H-bond interactions were represented as black dotted lines. (E) The docked conformation of the WFA inside the binding site of TOP2A. H-bond interactions were represented as black dotted lines. Full article ">

13 pages, 1598 KiB

Open AccessArticle

Identification of a Steroid Hormone-Associated Gene Signature Predicting the Prognosis of Prostate Cancer through an Integrative Bioinformatics Analysis

by Yo-Liang Lai, Chia-Hsin Liu, Shu-Chi Wang, Shu-Pin Huang, Yi-Chun Cho, Bo-Ying Bao, Chia-Cheng Su, Hsin-Chih Yeh, Cheng-Hsueh Lee, Pai-Chi Teng, Chih-Pin Chuu, Deng-Neng Chen, Chia-Yang Li and Wei-Chung Cheng

Cancers 2022, 14(6), 1565; https://doi.org/10.3390/cancers14061565 - 19 Mar 2022

Cited by 4 | Viewed by 3206

Abstract

The importance of anti-androgen therapy for prostate cancer (PC) has been well recognized. However, the mechanisms underlying prostate cancer resistance to anti-androgens are not completely understood. Therefore, identifying pharmacological targets in driving the development of castration-resistant PC is necessary. In the present study, [...] Read more.

The importance of anti-androgen therapy for prostate cancer (PC) has been well recognized. However, the mechanisms underlying prostate cancer resistance to anti-androgens are not completely understood. Therefore, identifying pharmacological targets in driving the development of castration-resistant PC is necessary. In the present study, we sought to identify core genes in regulating steroid hormone pathways and associating them with the disease progression of PC. The selection of steroid hormone-associated genes was identified from functional databases, including gene ontology, KEGG, and Reactome. The gene expression profiles and relevant clinical information of patients with PC were obtained from TCGA and used to examine the genes associated with steroid hormone. The machine-learning algorithm was performed for key feature selection and signature construction. With the integrative bioinformatics analysis, an eight-gene signature, including CA2, CYP2E1, HSD17B, SSTR3, SULT1E1, TUBB3, UCN, and UGT2B7 was established. Patients with higher expression of this gene signature had worse progression-free interval in both univariate and multivariate cox models adjusted for clinical variables. The expression of the gene signatures also showed the aggressiveness consistently in two external cohorts, PCS and PAM50. Our findings demonstrated a validated eight-gene signature could successfully predict PC prognosis and regulate the steroid hormone pathway. Full article

(This article belongs to the Special Issue Cytokine and Steroid Hormone Signaling in Prostate Cancer)

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Figure 1
The signature exploration workflow. The workflow of generating the 8-gene signature associated with steroid hormone and prostate cancer progression. Full article ">Figure 2
Progression-free interval (PFI) and overall survival (OS). Kaplan–Meier curves for (A) 5-year progression-free interval and (B) 5-year overall survival of the 8-gene signature. Patients were dichotomized into the “Low risk” group and the “High risk” group according to the 8-gene signature scores. The number of patients of the two risk groups in different following time in month were shown in the bottom tables of KM plots, respectively. Full article ">Figure 3
Multivariate analysis for progression-free interval. Multivariate Cox regression of the 8-gene signature with clinical variables. Significance levels are annotated. Clinical factors such as gleason score, psa level, tumor TNM stage, and age at diagnosis were considered as confounding variables in the analysis. Both hazard ratios and 95% confidence intervals were shown in the forest plot and factors reached a significant level were plotted in red. *** p-value < 0.001. Full article ">Figure 4
External validation. The expression of the 8-gene signature based on (A) PCS subtypes and (B) PAM50. The distributions of z-score transformed expression values in each group are shown in lollipop plot (top) and box plot (bottom). Higher expression of 8-gene signature in both aggressive subtypes (PCS1 and LumB) of two independent cohorts (PCS and PAM50) demonstrated the consistent results in external validation. Full article ">Figure 5
The regulatory pathways. Functional annotation of 8 genes based on three databases in aspects of (A) steroid hormone-specific and (B) all functions containing more than 3 of 8 signature genes. The gene is illustrated as a filled grey circle. Databases are drawn as an empty triangle, rectangle, and diamond. The grey edge represents linkage between annotated gene and the corresponding function. Full article ">

10 pages, 4619 KiB

Open AccessArticle

Intraoperative Near-Infrared Fluorescence Imaging with Indocyanine Green for Identification of Gastrointestinal Stromal Tumors (GISTs), a Feasibility Study

by Gijsbert M. Kalisvaart, Ruben P. J. Meijer, Okker D. Bijlstra, Hidde A. Galema, Wobbe O. de Steur, Henk H. Hartgrink, Cornelis Verhoef, Lioe-Fee de Geus-Oei, Dirk J. Grünhagen, Yvonne M. Schrage, Alexander L. Vahrmeijer and Jos A. van der Hage

Cancers 2022, 14(6), 1572; https://doi.org/10.3390/cancers14061572 - 18 Mar 2022

Cited by 5 | Viewed by 2302

Abstract

Background: Optimal intraoperative tumor identification of gastrointestinal stromal tumors (GISTs) is important for the quality of surgical resections. This study aims to assess the potential of near-infrared fluorescence (NIRF) imaging with indocyanine green (ICG) to improve intraoperative tumor identification. Methods: Ten GIST patients, [...] Read more.

Background: Optimal intraoperative tumor identification of gastrointestinal stromal tumors (GISTs) is important for the quality of surgical resections. This study aims to assess the potential of near-infrared fluorescence (NIRF) imaging with indocyanine green (ICG) to improve intraoperative tumor identification. Methods: Ten GIST patients, planned to undergo resection, were included. During surgery, 10 mg of ICG was intravenously administered, and NIRF imaging was performed at 5, 10, and 15 min after the injection. The tumor fluorescence intensity was visually assessed, and tumor-to-background ratios (TBRs) were calculated for exophytic lesions. Results: Eleven GIST lesions were imaged. The fluorescence intensity of the tumor was visually synchronous and similar to the background in five lesions. In one lesion, the tumor fluorescence was more intense than in the surrounding tissue. Almost no fluorescence was observed in both the tumor and healthy peritoneal tissue in two patients with GIST lesions adjacent to the liver. In three GISTs without exophytic growth, no fluorescence of the tumor was observed. The median TBRs at 5, 10, and 15 min were 1.0 (0.4–1.2), 1.0 (0.5–1.9), and 0.9 (0.7–1.2), respectively. Conclusion: GISTs typically show similar fluorescence intensity to the surrounding tissue in NIRF imaging after intraoperative ICG administration. Therefore, intraoperatively administered ICG is currently not applicable for adequate tumor identification, and further research should focus on the development of tumor-specific fluorescent tracers for GISTs. Full article

(This article belongs to the Special Issue Gastrointestinal Stromal Tumors (GIST): Opportunity and Challenges in Diagnosis and Treatment)

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Imaging of 4 cases representing examples of all 4 scenarios described in the qualitative analysis results section, at 10 min after ICG administration. (A,D,G,J) Intraoperative white-light images. (B,E,H,K) Corresponding NIRF images. (C,F,I,L) Corresponding fused images. (A–C) Patient 6. An exophytic GIST arising from the duodenum, after imatinib treatment. The fluorescence in the lesion is similar to that in the surrounding tissue. * A hyperfluorescent region within the diffusely fluorescent tumor, endorsing the biological tumor heterogeneity, possibly caused by the preoperative systemic treatment. The resected tumor was found to have 50% of tumor cells remaining. (D–F) Patient 1. An exophytic GIST arising from the stomach during laparoscopy, showing relatively high and specific fluorescence, and the highest TBR in the current study. † In particular, the central tumor bulk shows intense fluorescence. (G–I) Patient 8. An exophytic GIST arising from the stomach, showing close to no fluorescence, while intense fluorescence is observed in the adjacent liver tissue. The lowest TBRs were calculated in this GIST for every timepoint. (J–L) Patient 2. An intraluminal GIST of the stomach. The stomach shows diffuse fluorescence. ‡ A region of the stomach wall, at the tumor location, shows more intense fluorescence than the surrounding stomach tissue. Nevertheless, directly after resection, no specific tumor fluorescence could be observed in the resected material. Full article ">Figure 2
TBRs measured at 3 timepoints. Boxplots represent the median, quartiles, and range of the population. Full article ">

25 pages, 790 KiB

Open AccessReview

PI3K Inhibitors for the Treatment of Chronic Lymphocytic Leukemia: Current Status and Future Perspectives

by Iwona Hus, Bartosz Puła and Tadeusz Robak

Cancers 2022, 14(6), 1571; https://doi.org/10.3390/cancers14061571 - 18 Mar 2022

Cited by 29 | Viewed by 5028

Abstract

Phosphoinositide 3-kinases (PI3Ks) signaling regulates key cellular processes, such as growth, survival and apoptosis. Among the three classes of PI3K, class I is the most important for the development, differentiation and activation of B and T cells. Four isoforms are distinguished within class [...] Read more.

Phosphoinositide 3-kinases (PI3Ks) signaling regulates key cellular processes, such as growth, survival and apoptosis. Among the three classes of PI3K, class I is the most important for the development, differentiation and activation of B and T cells. Four isoforms are distinguished within class I (PI3Kα, PI3Kβ, PI3Kδ and PI3Kγ). PI3Kδ expression is limited mainly to the B cells and their precursors, and blocking PI3K has been found to promote apoptosis of chronic lymphocytic leukemia (CLL) cells. Idelalisib, a selective PI3Kδ inhibitor, was the first-in-class PI3Ki introduced into CLL treatment. It showed efficacy in patients with del(17p)/TP53 mutation, unmutated IGHV status and refractory/relapsed disease. However, its side effects, such as autoimmune-mediated pneumonitis and colitis, infections and skin changes, limited its widespread use. The dual PI3Kδ/γ inhibitor duvelisib is approved for use in CLL patients but with similar toxicities to idelalisib. Umbralisib, a highly selective inhibitor of PI3Kδ and casein kinase-1ε (CK1ε), was found to be efficient and safe in monotherapy and in combination regimens in phase 3 trials in patients with CLL. Novel PI3Kis are under evaluation in early phase clinical trials. In this paper we present the mechanism of action, efficacy and toxicities of PI3Ki approved in the treatment of CLL and developed in clinical trials. Full article

(This article belongs to the Special Issue Therapeutic Targets in Chronic Lymphocytic Leukemia)

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14 pages, 3568 KiB

Open AccessArticle

Aptamer Targets Triple-Negative Breast Cancer through Specific Binding to Surface CD49c

by Quanyuan Wan, Zihua Zeng, Jianjun Qi, Yingxin Zhao, Xiaohui Liu, Zhenghu Chen, Haijun Zhou and Youli Zu

Cancers 2022, 14(6), 1570; https://doi.org/10.3390/cancers14061570 - 18 Mar 2022

Cited by 6 | Viewed by 3683

Abstract

Although targeted cancer therapy can induce higher therapeutic efficacy and cause fewer side effects in patients, the lack of targetable biomarkers on triple-negative breast cancer (TNBC) cells limits the development of targeted therapies by antibody technology. Therefore, we investigated an alternative approach to [...] Read more.

Although targeted cancer therapy can induce higher therapeutic efficacy and cause fewer side effects in patients, the lack of targetable biomarkers on triple-negative breast cancer (TNBC) cells limits the development of targeted therapies by antibody technology. Therefore, we investigated an alternative approach to target TNBC by using the PDGC21T aptamer, which selectively binds to poorly differentiated carcinoma cells and tumor tissues, although the cellular target is still unknown. We found that synthetic aptamer probes specifically bound cultured TNBC cells in vitro and selectively targeted TNBC xenografts in vivo. Subsequently, to identify the target molecule on TNBC cells, we performed aptamer-mediated immunoprecipitation in lysed cell membranes followed by liquid chromatography tandem mass spectrometry (LC-MS/MS). Sequencing analysis revealed a highly conserved peptide sequence consistent with the cell surface protein CD49c (integrin α3). For target validation, we stained cultured TNBC and non-TNBC cells with an aptamer probe or a CD49c antibody and found similar cell staining patterns. Finally, competition cell-binding assays using both aptamer and anti-CD49c antibody revealed that CD49c is the biomarker targeted by the PDGC21T aptamer on TNBC cells. Our findings provide a molecular foundation for the development of targeted TNBC therapy using the PDGC21T aptamer as a targeting ligand. Full article

(This article belongs to the Special Issue Breast Cancer: Novel Histological and Molecular Markers for Diagnosis, Prognosis, Therapeutic Prediction, and Drug Resistance)

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Graphical abstract

Graphical abstract
Full article ">Figure 1
Specific binding of the PDGC21T aptamer to cultured triple-negative breast cancer (TNBC) cells. (A) Flow cytometry analysis demonstrates that PDGC21T aptamer binds to suspended TNBC cells but does not react with non-TNBC cells. (B) Fluorescent microscopy confirmed that PDGC21T binds to adherent TNBC cells but does not react with non-TNBC cells. Red font indicates non-TNBC cell line. BF; bright field, FITC; fluorescein isothiocyanate. Scale bars = 100 μm. The final incubation concentration of aptamers or random ssDNA was 200 nM. Full article ">Figure 2
Preparation and validation of IRD800CWPDGC21TPEG5000 aptamers. (A) Schematic showing IRD800CWPDGC21TPEG5000 aptamer production. (B) Flow cytometry analysis of IRD800CWPDGC21TPEG5000 aptamer binding to TNBC cells. The final incubation concentration of aptamer or random ssDNA was 200 nM. Full article ">Figure 3
The IRD800CWPDGC21TPEG5000 aptamer specifically targets TNBC xenograft tumors and has an extended in vivo half-life. (A) Flow diagram of the in vivo tumor targeting study using aptamer probes. The red sphere indicates the xenograft tumor site. s.c., subcutaneous; i.v., intravenous. (B) Relative to IRD800CWPDGC21T aptamers, the signal enhancement from IRD800CWPDGC21TPEG5000 aptamers was stronger and persisted longer in MDA-MB-231 tumors (up to 24 h post-aptamer administration). (C) In contrast, weak signals were observed peripherally in MCF7 tumor sites at 30 min post IRD800CWPDGC21TPEG5000 aptamer administration but faded rapidly and became undetectable under the same imaging conditions. (D,E) Ex vivo imaging of resected MDA-MB-231 xenograft tumors, hearts, lungs, kidneys, livers, and spleens from mice treated with IRD800CWPDGC21T aptamers (D), or IRD800CWPDGC21TPEG5000 aptamers (E) post-whole-body imaging. (F) Ex vivo imaging of resected MCF7 xenograft tumors and major organs from mice treated with the IRD800CWPDGC21TPEG5000 aptamers. Full article ">Figure 4
Identification of the PDGC21T aptamer target molecule. (A) Biotinylated PDGC21T aptamer targeted MDA-MB-231 cells with a similar binding capacity to that of Cy3-labeled PGC21T aptamers. In contrast, random ssDNA controls did not bind MDA-MB-231 cells. Biotin-PDGC21T→Cy3-PDGC21T indicates MDA-MB-231 cells that were first incubated with biotinylated PDGC21T and then incubated with Cy3-labeled PDGC21T. The final incubation concentration of aptamers or random ssDNA was 200 nM. (B) Aptamer-mediated immunoprecipitation assays. Membrane proteins derived from TNBC cells (MDA-MB-231, HCC38, and Hs578T mixtures) and non-TNBC cells (MCF7, T47D, and Jurkat mixture) were prepared and used for co-precipitation with biotinylated PDGC21T aptamer, biotinylated random ssDNA, or vehicle alone as a blank control. Resultant aptamer and target complexes were pulled down by streptavidin-immobilized agarose beads and separated on sodium dodecyl sulphate-polyacrylamide gel electrophoresis (SDS-PAGE), followed by silver staining. The red-dotted box shows the protein band of interest, which was collected for liquid chromatography tandem mass spectrometry (LC-MS/MS) identity analysis. The uncropped figures are shown in <a href="#app1-cancers-14-01570" class="html-app">Figure S4</a>. (C,D) LC-MS/MS identification of the target of PDGC21T. The proteins detected in PDGC21T aptamer and random sequence pull-down experiments were identified and quantified with label-free LC-MS. (C) Volcano plot of identified proteins. Each dot represents one protein. Proteins above the cutoff curves are statistically significant (Student’s t-test with 1% permutation-based FDR below 0.01). (D) Annotated MS/MS spectra of CD49c (ITGA3) peptide, STEVLTCATGR. Full article ">Figure 5
PDGC21T aptamer selectively targets CD49c-expressing TNBC cells. (A) Both PDGC21T aptamer and anti-CD49c targeted TNBC cells with high binding capacity but had (B) little or no binding to non-TNBC cells. Resultant mean fluorescent intensities of cell binding by aptamer (C) and antibody (D) were quantified by flow cytometry and graphed for comparison. The final incubation concentration of aptamer was 200 nM. Full article ">Figure 6
PDGC21T aptamer and anti-CD49c share an overlapping binding site on CD49c. Pre-treatment with anti-CD49c did not affect FAM-labeled PDGC21T aptamer binding to MDA-MB-231 (A) or HCC38 cells (B). IgG isotype and a random ssDNA were used as controls. Pre-treatment with PDGC21T aptamer inhibited PE-labeled anti-CD49c binding to MDA-MB-231 (C) and HCC38 cells (D). Arrows indicate incubation sequence of antibodies and aptamers. Full article ">

26 pages, 1958 KiB

Open AccessReview

The New Treatment Methods for Non-Hodgkin Lymphoma in Pediatric Patients

by Justyna Derebas, Kinga Panuciak, Mikołaj Margas, Joanna Zawitkowska and Monika Lejman

Cancers 2022, 14(6), 1569; https://doi.org/10.3390/cancers14061569 - 18 Mar 2022

Cited by 8 | Viewed by 5628

Abstract

One of the most common cancer malignancies is non-Hodgkin lymphoma, whose incidence is nearly 3% of all 36 cancers combined. It is the fourth highest cancer occurrence in children and accounts for 7% of cancers in patients under 20 years of age. Today, [...] Read more.

One of the most common cancer malignancies is non-Hodgkin lymphoma, whose incidence is nearly 3% of all 36 cancers combined. It is the fourth highest cancer occurrence in children and accounts for 7% of cancers in patients under 20 years of age. Today, the survivability of individuals diagnosed with non-Hodgkin lymphoma varies by about 70%. Chemotherapy, radiation, stem cell transplantation, and immunotherapy have been the main methods of treatment, which have improved outcomes for many oncological patients. However, there is still the need for creation of novel medications for those who are treatment resistant. Additionally, more effective drugs are necessary. This review gathers the latest findings on non-Hodgkin lymphoma treatment options for pediatric patients. Attention will be focused on the most prominent therapies such as monoclonal antibodies, antibody–drug conjugates, chimeric antigen receptor T cell therapy and others. Full article

(This article belongs to the Special Issue Non-Hodgkin Lymphoma in Children)

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15 pages, 1356 KiB

Open AccessArticle

Tumor Treating Fields Concomitant with Sorafenib in Advanced Hepatocellular Cancer: Results of the HEPANOVA Phase II Study

by Eleni Gkika, Anca-Ligia Grosu, Teresa Macarulla Mercade, Antonio Cubillo Gracián, Thomas B. Brunner, Michael Schultheiß, Monika Pazgan-Simon, Thomas Seufferlein and Yann Touchefeu

Cancers 2022, 14(6), 1568; https://doi.org/10.3390/cancers14061568 - 18 Mar 2022

Cited by 17 | Viewed by 4055

Abstract

Advanced hepatocellular carcinoma (HCC) is an aggressive disease associated with poor prognosis. Tumor Treating Fields (TTFields) therapy is a non-invasive, loco-regional treatment approved for glioblastoma and malignant pleural mesothelioma. HCC preclinical and abdominal simulation data, together with clinical results in other solid tumors, [...] Read more.

Advanced hepatocellular carcinoma (HCC) is an aggressive disease associated with poor prognosis. Tumor Treating Fields (TTFields) therapy is a non-invasive, loco-regional treatment approved for glioblastoma and malignant pleural mesothelioma. HCC preclinical and abdominal simulation data, together with clinical results in other solid tumors, provide a rationale for investigating TTFields with sorafenib in this patient population. HEPANOVA was a phase II, single arm, historical control study in adults with advanced HCC (NCT03606590). Patients received TTFields (150 kHz) for ≥18 h/day concomitant with sorafenib (400 mg BID). Imaging assessments occurred every 12 weeks until disease progression. The primary endpoint was the overall response rate (ORR). Safety was also evaluated. Patients (n = 27 enrolled; n = 21 evaluable) had a poor prognosis; >50% were Child–Turcotte–Pugh class B and >20% had a baseline Eastern Clinical Oncology Group performance status (ECOG PS) of 2. The ORR was higher, but not statistically significant, for TTFields/sorafenib vs. historical controls: 9.5% vs. 4.5% (p = 0.24), respectively; all responses were partial. Among patients (n = 11) with ≥12 weeks of TTFields/sorafenib, ORR was 18%. Common adverse events (AEs) were diarrhea (n = 15/27, 56%) and asthenia (n = 11/27, 40%). Overall, 19/27 (70%) patients had TTFields-related skin AEs; none were serious. TTFields/sorafenib improved response rates vs. historical controls in patients with advanced HCC, with no new safety concerns or related systemic toxicity. Full article

(This article belongs to the Collection Novel Therapies for Hepatocellular Carcinoma)

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Cancers, Volume 14, Issue 6 (March-2 2022) – 236 articles

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