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Search Results (824)

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18 pages, 2967 KiB  
Article
Association of Computed Tomography Scan-Assessed Body Composition with Immune and PI3K/AKT Pathway Proteins in Distinct Breast Cancer Tumor Components
by Ting-Yuan David Cheng, Dongtao Ann Fu, Sara M. Falzarano, Runzhi Zhang, Susmita Datta, Weizhou Zhang, Angela R. Omilian, Livingstone Aduse-Poku, Jiang Bian, Jerome Irianto, Jaya Ruth Asirvatham and Martha Campbell-Thompson
Int. J. Mol. Sci. 2024, 25(24), 13428; https://doi.org/10.3390/ijms252413428 - 14 Dec 2024
Viewed by 450
Abstract
This hypothesis-generating study aims to examine the extent to which computed tomography-assessed body composition phenotypes are associated with immune and phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT) signaling pathways in breast tumors. A total of 52 patients with newly diagnosed breast cancer were classified [...] Read more.
This hypothesis-generating study aims to examine the extent to which computed tomography-assessed body composition phenotypes are associated with immune and phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT) signaling pathways in breast tumors. A total of 52 patients with newly diagnosed breast cancer were classified into four body composition types: adequate (lowest two tertiles of total adipose tissue [TAT]) and highest two tertiles of total skeletal muscle [TSM] areas); high adiposity (highest tertile of TAT and highest two tertiles of TSM); low muscle (lowest tertile of TSM and lowest two tertiles of TAT); and high adiposity with low muscle (highest tertile of TAT and lowest tertile of TSM). Immune and PI3K/AKT pathway proteins were profiled in tumor epithelium and the leukocyte-enriched stromal microenvironment using GeoMx (NanoString). Linear mixed models were used to compare log2-transformed protein levels. Compared with the normal type, the low muscle type was associated with higher expression of INPP4B (log2-fold change = 1.14, p = 0.0003, false discovery rate = 0.028). Other significant associations included low muscle type with increased CTLA4 and decreased pan-AKT expression in tumor epithelium, and high adiposity with increased CD3, CD8, CD20, and CD45RO expression in stroma (p < 0.05; false discovery rate > 0.2). With confirmation, body composition can be associated with signaling pathways in distinct components of breast tumors, highlighting the potential utility of body composition in informing tumor biology and therapy efficacies. Full article
(This article belongs to the Special Issue Breast Cancer: From Pathophysiology to Novel Therapies)
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<p>Intra-patient (epithelial and stromal components) and inter-tissue concordance of proteins (<span class="html-italic">n</span> = 52 patients). The Y-axis is Pearson’s correlation coefficient. The box plot on the left shows the correlation of each marker in the tumor compartment within patients. The box plot in the middle shows the correlation of each marker in the stroma compartment within patients. The box plot on the right shows the correlation of each marker between the tumor and stromal components. Each number represents a protein given in the table below.</p>
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<p>Cluster analysis by tissue type (tumor epithelium vs. stroma); <span class="html-italic">n</span> = 52 patients. The “% explained var”. in the X-axis and Y-axis represents the percentage of variance explained.</p>
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<p>Volcano plots for the associations of body composition type with proteins in tumor (<b>A</b>) epithelium and (<b>B</b>) stroma. The horizontal dot lines indicate <span class="html-italic">p</span>&lt;0.05. The vertical dot lines indicate a two-fold increase or decrease. Multivariable models adjusted for analytical batch, race, breast cancer stage, and tumor grade; <span class="html-italic">n</span> = 52 patients.</p>
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<p>Representative images from tissue microarrays. (<b>A</b>) Region of interest in a tissue microarray core (black circle indicates the approximate location). (<b>B</b>) Fluorescence image of the region of interest selected for tumor (guided by panCK, green) and stromal areas enriched for leukocytes (guided by CD45, red) identified using the GeoMx Digital Spatial Profiler. (<b>C</b>) Segmentation by morphology marker (panCK). (<b>D</b>) The epithelial segment. (<b>E</b>) The stromal segment; 008 represents an ROI number. Scale bar: 500 µm for A and 300 µm for (<b>B</b>–<b>D</b>).</p>
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15 pages, 2833 KiB  
Article
Aluminum Concentration Is Associated with Tumor Mutational Burden and the Expression of Immune Response Biomarkers in Colorectal Cancers
by Rita Bonfiglio, Erica Giacobbi, Valeria Palumbo, Stefano Casciardi, Renata Sisto, Francesca Servadei, Maria Paola Scioli, Stefania Schiaroli, Elena Cornella, Giulio Cervelli, Giuseppe Sica, Eleonora Candi, Gerry Melino, Alessandro Mauriello and Manuel Scimeca
Int. J. Mol. Sci. 2024, 25(24), 13388; https://doi.org/10.3390/ijms252413388 - 13 Dec 2024
Viewed by 251
Abstract
Environmental pollution poses a significant risk to public health, as demonstrated by the bioaccumulation of aluminum (Al) in colorectal cancer (CRC). This study aimed to investigate the potential mutagenic effect of Al bioaccumulation in CRC samples, linking it to the alteration of key [...] Read more.
Environmental pollution poses a significant risk to public health, as demonstrated by the bioaccumulation of aluminum (Al) in colorectal cancer (CRC). This study aimed to investigate the potential mutagenic effect of Al bioaccumulation in CRC samples, linking it to the alteration of key mediators of cancer progression, including immune response biomarkers. Aluminum levels in 20 CRC biopsy samples were analyzed using inductively coupled plasma mass spectrometry (ICP-MS). The results indicated that Al bioaccumulation occurred in 100% of the cases. A correlation between Al levels and tumor mutation burden was observed. Furthermore, RNA sequencing revealed a significant association between Al concentration and the expression of the immune checkpoint molecule CTLA-4. Although correlations with PD-1 and PD-L1 were not statistically significant, a trend was observed. Additionally, a correlation between Al levels and both the presence of myeloid cells and IFNγ expression was detected, linking Al exposure to inflammatory responses within the tumor microenvironment. These findings suggested that Al can play a role in CRC progression by promoting both genetic mutations and immune evasion. Given the ubiquitous presence of Al in industrial and consumer products, dietary sources, and environmental pollutants, these results underscored the need for stricter regulatory measures to control Al exposure. Full article
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<p>Histological analysis and aluminum detection. (<b>A</b>) Hematoxylin and eosin staining shows a colorectal adenocarcinoma with high inflammatory infiltrate. (<b>B</b>) High magnification of panel (<b>A</b>) displays numerous inflammatory cells (asterisk) next to cancerous ones. (<b>C</b>) Hematoxylin and eosin staining shows a colorectal adenocarcinoma. (<b>D</b>) High magnification of panel (<b>C</b>). (<b>E</b>) Aluminum concentration detected by ICP-MS analysis. (<b>F</b>) Colon cancer cells with aluminum (green; morin staining) in the cytoplasm. Red line represents the mean value.</p>
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<p>Effect of aluminum concentration on Tumoral mutational burden (TMB) and IFNγ. (<b>A</b>) Graph shows a positive association between aluminum concentration and TMB values. (<b>B</b>) Graph displays a positive association between aluminum concentration and IFNγ expression.</p>
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<p>Effect of aluminum concentration on the expression of immune checkpoints. (<b>A</b>) Positive association between aluminum concentration and CTLA-4 expression (RNASeq). Immunohistochemistry shows CTLA-4 expression in both cancer cells (arrow) and inflammatory cells (asterisk). (<b>B</b>) Graph displays a positive trend between aluminum concentration and PD-L1 expression (RNASeq). Immunohistochemistry shows PD-L1 expression in both cancer cells (arrow) and inflammatory cells (asterisk). (<b>C</b>) Graph displays a positive trend between aluminum concentration and PD-1 expression (RNASeq). Immunohistochemistry shows PD-1 expression in both cancer cells (arrows) and inflammatory cells (asterisk).</p>
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<p>Association between aluminum concentration and immune cell types. (<b>A</b>) The heatmap reports the Pearson correlation values for the association between aluminum and the immune cell types. (<b>B</b>) Graph shows a positive association between aluminum concentration and myeloid cells.</p>
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<p>Association between aluminum concentration and categorical and continuous variables. (<b>A</b>) Graph shows the different aluminum concentration in male and female groups. (<b>B</b>) Graph displays the aluminum concentration in patients without and with lymph node metastasis at the time of surgery. (<b>C</b>) The heatmap reports the Pearson correlation values for the association between aluminum and age.</p>
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27 pages, 1021 KiB  
Systematic Review
Immune Checkpoint Inhibitors in Glioblastoma IDHwt Treatment: A Systematic Review
by Archit Bharathwaj Baskaran, Olivia A. Kozel, Omkar Venkatesh, Derek A. Wainwright, Adam M. Sonabend, Amy B. Heimberger and Rimas Vincas Lukas
Cancers 2024, 16(24), 4148; https://doi.org/10.3390/cancers16244148 - 12 Dec 2024
Viewed by 368
Abstract
Purpose: A glioblastoma (GBM) is a primary brain tumor with significant unmet therapeutic needs. Immune checkpoint inhibitors (ICIs) have marked therapeutic benefits in many different cancers but have yet to show benefit for most GBM patients in phase III trials. Methods: A systematic [...] Read more.
Purpose: A glioblastoma (GBM) is a primary brain tumor with significant unmet therapeutic needs. Immune checkpoint inhibitors (ICIs) have marked therapeutic benefits in many different cancers but have yet to show benefit for most GBM patients in phase III trials. Methods: A systematic review querying ClinicalTrials.gov for prospective clinical trials investigating ICI in GBM between 1950 and July 2024 was performed. Search terms comprised 11 distinct ICIs. Data abstracted include clinical trial NCT numbers with study titles and status, enrollment information, interventions, and more. Clinical trial identifying information, interventions, and outcomes were extracted. Results: One hundred and seventeen clinical trials were identified; four were phase 3. Most involved PD-1 or CTLA-4 blockade as monotherapy or in combination with standard-of-care. The large, randomized trials included CHECKMATE 143, CHECKMATE 498, CHECKMATE 548, and NRG BN007. These showed no improvement in median overall survival or progression-free survival in unselected patients. Biomarker-directed analyses suggest that a subset of GBM patients may benefit. Conclusions: ICI for the treatment of GBM has not demonstrated clear evidence of efficacy thus far. This review serves as a quick reference of ICI trial results in GBM. Biomarker-driven patient selection and/or novel approaches to overcome resistance mechanisms remain areas of viable inquiry. Full article
(This article belongs to the Special Issue Novel Insights into Glioblastoma and Brain Metastases)
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<p>Immune checkpoint inhibitor molecular targets and FDA approval timeline.</p>
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<p>PRISMA flow diagram for systematic reviews, which included searches of databases and registers only.</p>
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14 pages, 276 KiB  
Review
Immune Checkpoint Inhibitor-Associated Celiac Disease: A Retrospective Analysis and Literature Review
by Malvika Gupta, Christopher Graham and Supriya Gupta
Diseases 2024, 12(12), 315; https://doi.org/10.3390/diseases12120315 - 3 Dec 2024
Viewed by 498
Abstract
Introduction: Immune checkpoint inhibitors (ICI) are used to treat various malignancies. They block the inhibitory signals of tumor cells and enhance the inflammatory cascade, which results in tumor killing. However, this can lead to unchecked inflammation throughout the body, leading to various adverse [...] Read more.
Introduction: Immune checkpoint inhibitors (ICI) are used to treat various malignancies. They block the inhibitory signals of tumor cells and enhance the inflammatory cascade, which results in tumor killing. However, this can lead to unchecked inflammation throughout the body, leading to various adverse effects. A rare gastrointestinal adverse effect of ICI therapy is the development of immune-mediated celiac disease. This entity has a similar clinical presentation to the more common ICI-induced enterocolitis. Our study aims to determine the clinical characteristics and optimal treatment strategies for this rare ICI toxicity and differentiate it from ICI-induced enterocolitis. Methods and Material: We conducted a retrospective analysis of eight cases of ICI-induced celiac disease and 24 cases of ICI-induced enterocolitis from the literature. Data on patient demographics, clinical history, therapeutic interventions and outcomes were collected. A comparative analysis was performed to identify the key differences between the two groups. Results: Patients with ICI-induced celiac disease were more likely to have a pre-existing autoimmune condition and HLA-DQ2 positivity. Significant differences in clinical manifestations, histological findings, and treatment outcomes were observed. Notably, weight loss, nutritional deficiencies and electrolyte abnormalities were more commonly associated with ICI-induced celiac disease. Regarding pathology, duodenal villous blunting was noted more commonly with ICI-induced celiac disease. Initiating a gluten-free diet led to a rapid improvement in patients with ICI-induced celiac disease, while immunosuppressive therapy did not have an impact. Conclusion: ICI-induced celiac disease is a rare and underrecognized gastrointestinal adverse effect of ICI therapy, often misdiagnosed as ICI-induced enterocolitis. Early recognition and treatment with a gluten-free diet can lead to rapid symptom resolution, sparing patients from unnecessary systemic immunosuppression and the discontinuation of antineoplastic immunotherapy. Full article
23 pages, 1241 KiB  
Review
Exploring the Immunoresponse in Bladder Cancer Immunotherapy
by Inmaculada Ruiz-Lorente, Lourdes Gimeno, Alicia López-Abad, Pedro López Cubillana, Tomás Fernández Aparicio, Lucas Jesús Asensio Egea, Juan Moreno Avilés, Gloria Doñate Iñiguez, Pablo Luis Guzmán Martínez-Valls, Gerardo Server, José Félix Escudero-Bregante, Belén Ferri, José Antonio Campillo, Eduardo Pons-Fuster, María Dolores Martínez Hernández, María Victoria Martínez-Sánchez, Diana Ceballos and Alfredo Minguela
Cells 2024, 13(23), 1937; https://doi.org/10.3390/cells13231937 - 22 Nov 2024
Viewed by 561
Abstract
Bladder cancer (BC) represents a wide spectrum of diseases, ranging from recurrent non-invasive tumors to advanced stages that require intensive treatments. BC accounts for an estimated 500,000 new cases and 200,000 deaths worldwide every year. Understanding the biology of BC has changed how [...] Read more.
Bladder cancer (BC) represents a wide spectrum of diseases, ranging from recurrent non-invasive tumors to advanced stages that require intensive treatments. BC accounts for an estimated 500,000 new cases and 200,000 deaths worldwide every year. Understanding the biology of BC has changed how this disease is diagnosed and treated. Bladder cancer is highly immunogenic, involving innate and adaptive components of the immune system. Although little is still known of how immune cells respond to BC, immunotherapy with bacillus Calmette–Guérin (BCG) remains the gold standard in high-risk non-muscle invasive BC. For muscle-invasive BC and metastatic stages, immune checkpoint inhibitors targeting CTLA-4, PD-1, and PD-L1 have emerged as potent therapies, enhancing immune surveillance and tumor cell elimination. This review aims to unravel the immune responses involving innate and adaptive immune cells in BC that will contribute to establishing new and promising therapeutic options, while reviewing the immunotherapies currently in use in bladder cancer. Full article
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<p>Immunological mechanisms of intravesical BCG treatment in bladder cancer: (<b>A</b>) Classification of BC according to the TNM staging system. CIS (carcinoma in situ) represents a non-muscle invasive bladder cancer (NMIBC) confined to the bladder lining; Ta, papillary NMIBC limited to the inner lining; T1, NMIBC that invades the subepithelial connective tissue without penetrating the muscle layer; T2, muscle-invasive bladder (MIBC) cancer; T3, MIBC that invades the perivesical tissue surrounding the bladder; and T4, advanced MIBC that invades surrounding structures such as the prostate, uterus, or pelvic wall. (<b>B</b>) NMIBC is treated with BCG. Upon instillation, BCG is taken up by bladder urothelial cells, antigen-presenting cells (APC), macrophages, and dendritic cells (DCs), leading to the release of pro-inflammatory cytokines and the activation of the immune response, including T and natural killer (NK) cells, which recognize and attack tumor cells. DCs express toll-like receptors (TLRs) that recognize pathogen-associated molecular patterns (PAMPs), promoting the secretion of cytokines and the presentation of tumor antigens via the major histocompatibility complex (MHC) to CD4+ and CD8+ T lymphocytes, thus contributing to tumor eradication. BCG induces NK cell functional maturation, increasing the expression of CD56 and the release of proinflamatory cytokines, granzyme, and perforin, which contribute to the destruction of tumor cells. Understanding these mechanisms is vital for optimizing BCG therapy and improving outcomes for patients with bladder cancer.</p>
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<p>Mechanisms of immune checkpoint blockade in cancer therapy. The adequate activation of T lymphocytes requires a primary specific signal delivered by the TCR/MHC interaction together with co-stimulatory signals mainly delivered by the CD28/CD80-CD86 interaction. In contrast, the interactions of CTLA-4/CD80-CD86, PD-1/PD-L1, NKG2A/HLA-E, or TIGIT/CD155 inhibit and regulate T cell activation and function. These inhibitory interactions can be blocked using immunotherapeutic monoclonal antibodies: anti-PD-1 (Nivolumab, Pembrolizumab), anti-PD-L1 (Atezolizumab, Avelumab, and Durvalumab), anti-CTLA-4 (Ipilimumab, Tremelimumab), anti-NKG2A (Monalizumab), or anti-TIGIT (Tiragolumab, Sacituzumab).</p>
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12 pages, 1640 KiB  
Article
Enhancing Tumor Immunity with IL-12 and PD-1 Blockade: A Strategy for Inducing Robust Central Memory T Cell Responses in Resistant Cancer Model
by Fentian Chen, Kexin Wu, Shiqi Lin, Jinlong Cui, Xiaoqing Chen, Zhiren Zeng, Na Yuan, Mujin Fang, Xue Liu, Yuanzhi Chen and Wenxin Luo
Antibodies 2024, 13(4), 94; https://doi.org/10.3390/antib13040094 - 20 Nov 2024
Viewed by 619
Abstract
Background: Although immune checkpoint inhibitors (ICIs) have demonstrated efficacy in treating advanced cancers, their therapeutic success remains limited for many patients, with initial responders often experiencing resistance and relapse. Interleukin-12 (IL-12) is a powerful cytokine for antitumor immunotherapy, enhancing both lymphocyte recruitment into [...] Read more.
Background: Although immune checkpoint inhibitors (ICIs) have demonstrated efficacy in treating advanced cancers, their therapeutic success remains limited for many patients, with initial responders often experiencing resistance and relapse. Interleukin-12 (IL-12) is a powerful cytokine for antitumor immunotherapy, enhancing both lymphocyte recruitment into tumors and immune cell activation. Methods: In this study, we successfully produced mouse interleukin-12 (mIL12) through eukaryotic recombinant expression. In vivo, mIL12 exhibited significant control of tumor immunity in ICI-resistant and aggressive tumor models. Further mechanistic analysis indicated that treatment with mIL12 led to a substantial increase in tumor-infiltrating CD4+ T, CD8+ T, cDC1, and CD103+ cDC1 cells. Results: Our data underscore the potential of a combined therapeutic strategy involving IL-12 with PD-1 and CTLA-4 blockade to elicit a potent antitumor immune response. Notably, the co-administration of mIL12 and PD-1 blockade significantly enhanced the presence of central memory T cells (TCM) within tumors. Conclusions: This study is the first to provide evidence that the combination of mIL12 and PD-1 blockers promotes the generation of TCM, potentially contributing to a robust and durable antitumor effect. Full article
(This article belongs to the Section Antibody-Based Therapeutics)
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<p>Expression and characterization of mIL12. (<b>A</b>) Genetic map illustrating the coding gene for mIL12. (<b>B</b>) SDS-PAGE analysis of mIL12 under reducing (R) and non-reducing (NR) conditions, with molecular weight (MW) markers shown. (<b>C</b>) mIFN-γ levels in the culture medium of splenocytes from C57BL/6 mice, stimulated ex vivo with PBS, anti-mouse CD3+ anti-mouse CD28 (amCD3+amCD28), or amCD3+amCD28+mIL12. mIFN-γ levels were quantified using an ELISA assay, and the data are representative of three independent experiments. (<b>D</b>) Treatment regimen for MC38 tumor−bearing C57BL/6J mice, which received PBS, mIL12 (1 mg/kg), or anti-mIL12 (10 mg/kg) therapy (n = 5 per group). (<b>E</b>) Growth curves of tumors and (<b>F</b>) body weight changes in C57BL/6J mice. Data are presented as means ± SEM. Statistical analysis was conducted using one−way ANOVA for panel (<b>C</b>) and two-way ANOVA for panel (<b>E</b>). Significance levels are indicated as * <span class="html-italic">p</span> &lt; 0.05; *** <span class="html-italic">p</span> &lt; 0.001; **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Dose-dependent antitumor efficacy of mIL12 in mouse tumor models. Treatment regimens for tumor-bearing mice: CT26 (n = 8), MC38 (n = 8), B16-F10 (n = 5), and EMT6 (n = 7) (Panels (<b>A</b>,<b>D</b>,<b>G</b>,<b>J</b>). Mice were treated intraperitoneally with PBS or mIL12 at doses of 0.01 mg/kg, 0.1 mg/kg, or 1 mg/kg every three days. Tumor growth curves (Panels (<b>B</b>,<b>E</b>,<b>H</b>,<b>K</b>) and body weight changes (Panels (<b>C</b>,<b>F</b>,<b>I</b>,<b>L</b>) of the mice are shown. Data are presented as means ± SEM. Statistical analyses for tumor growth (Panels (<b>B</b>,<b>E</b>,<b>H</b>,<b>K</b>) were conducted using two-way ANOVA. Significance levels are denoted as ns (no significant difference), ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, and **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Reshaping of the tumor immune microenvironment and upregulation of immune checkpoints by mIL12. (<b>A</b>) Schematic of the treatment strategy for the MC38 tumor model. Tumors from mice treated with mIL12 were dissociated and analyzed by flow cytometry. (<b>B</b>–<b>F</b>) Proportions of tumor-infiltrating T cells and dendritic cells (DCs) among live CD45<sup>+</sup> cells. (<b>G</b>) Percentage of CTLA-4<sup>+</sup>CD4<sup>+</sup> T cells and (<b>H</b>) PD-1<sup>+</sup>CD8<sup>+</sup> T cells within the live CD45<sup>+</sup> cell population. Data are presented as means ± SEM. Statistical analyses for panels (<b>B</b>–<b>H</b>) were performed using unpaired two-tailed Student’s <span class="html-italic">t</span>-test. Significance levels are indicated as * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Enhanced antitumor efficacy of mIL12 combined with PD-1 and CTLA-4 immune checkpoint inhibitors. (<b>A</b>) Treatment scheme comparing the therapeutic efficacy of mIL12 combined with anti-mouse PD-1 (amPD-1) antibody versus monotherapy. Mice bearing MC38 tumors received treatments with PBS, mIL12, amPD-1, or a combination of amPD-1 with mIL12. (<b>B</b>) Tumor growth curves and (<b>C</b>) body weight changes for monotherapy and combination therapy groups. (<b>D</b>) Individual tumor growth curves for each treatment group (n = 7 per group). (<b>E</b>) Treatment scheme comparing the therapeutic efficacy of mIL12 combined with anti-mouse CTLA-4 (amCTLA-4) antibody versus monotherapy. Mice bearing MC38 tumors received PBS, mIL12, amCTLA-4, or a combination of amCTLA-4 and mIL12. (<b>F</b>) Tumor growth curves and (<b>G</b>) body weight changes for monotherapy and combination therapy groups. (<b>H</b>) Individual tumor growth curves for each treatment group (n = 6 per group). (<b>I</b>) Schematic of the treatment strategy for the MC38 tumor model. Tumors from mice treated with PBS, mIL12, mIL12+amPD-1 and mIL12+amCTLA-4 were dissociated and analyzed by flow cytometry. (<b>J</b>) The proportion of tumor-infiltrating central memory T cells. Data are presented as means ± SEM. Statistical analyses for (<b>B</b>,<b>F</b>) were performed using two-way ANOVA. Statistical analyses for (<b>J</b>) were performed using one-way ANOVA. Significance levels are indicated as * <span class="html-italic">p</span> &lt; 0.05, *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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12 pages, 592 KiB  
Review
Exploration of the Dual Role of Dectin-1 in Tumor Development and Its Therapeutic Potential
by Yuxuan Cai and Ke Wu
Curr. Oncol. 2024, 31(11), 7275-7286; https://doi.org/10.3390/curroncol31110536 - 17 Nov 2024
Viewed by 789
Abstract
Immunotherapy, particularly immune checkpoint inhibitors like PD-1, PD-L1, and CTLA-4, has revolutionized cancer treatment. However, the role of the innate immune system, especially pattern recognition receptors, in cancer development and immunity is gaining more and more attention. Dectin-1, a C-type lectin receptor primarily [...] Read more.
Immunotherapy, particularly immune checkpoint inhibitors like PD-1, PD-L1, and CTLA-4, has revolutionized cancer treatment. However, the role of the innate immune system, especially pattern recognition receptors, in cancer development and immunity is gaining more and more attention. Dectin-1, a C-type lectin receptor primarily involved in antifungal immunity, has emerged as a significant player in cancer biology, exhibiting both pro-tumor and anti-tumor roles. This dual function largely depends on the tumor type and microenvironment. Dectin-1 can promote immune responses against tumors like melanoma and breast cancer by enhancing both innate and adaptive immunity. However, in tumors like pancreatic ductal adenocarcinoma and colorectal cancer, Dectin-1 activation suppresses T cell immunity, facilitating tumor progression. This review explores the complex mechanisms by which Dectin-1 modulates the tumor microenvironment and discusses its potential as a therapeutic target for cancer treatment. Full article
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<p>The Dual Role of Dectin-1 in Tumor Biology.</p>
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19 pages, 2635 KiB  
Article
Association of Cytotoxic T-Lymphocyte Antigen-4 (CTLA-4) Genetic Variants with Risk and Outcome of Cutaneous Melanoma
by Ana Maria Castro Ferreira, Juliana Carron, Gabriela Vilas Bôas Gomez, Vinicius de Lima Vazquez, Sergio Vicente Serrano, Gustavo Jacob Lourenço and Carmen Silvia Passos Lima
Int. J. Mol. Sci. 2024, 25(22), 12327; https://doi.org/10.3390/ijms252212327 - 17 Nov 2024
Viewed by 707
Abstract
This study aimed to verify whether germline single nucleotide variants (SNV) in CTLA-4 gene, c.-1765C>T, c.-1661A>G, c.-1577G>A, and c.-1478G>A, influence the risk, clinicopathological aspects, and survival of patients with CM, as well as its functional consequences. A total of 432 patients with CM [...] Read more.
This study aimed to verify whether germline single nucleotide variants (SNV) in CTLA-4 gene, c.-1765C>T, c.-1661A>G, c.-1577G>A, and c.-1478G>A, influence the risk, clinicopathological aspects, and survival of patients with CM, as well as its functional consequences. A total of 432 patients with CM and 504 controls were evaluated. CTLA-4 genotypes were identified by real-time polymerase chain reaction (RT-PCR) and expression of CTLA-4 by quantitative PCR (qPCR) and luciferase assay. Cell cycle, proliferation, apoptosis/necrosis, and migration analyses were performed in SK-MEL-28 and A-375 cell lines modified to present homozygous ancestral or variant genotypes by CRISPR technique. Individuals with the CTLA-4 c.-1577 AA genotype and the combined CTLA-4 c.-1577 and c.-1478 AA + AA genotypes were at 1.60- and 3.12-fold higher risk of developing CM, respectively. The CTLA-4 c.-1577 AA genotype was seen as an independent predictor of worse event-free survival and was also associated with higher gene expression, higher cell proliferation, lower cell apoptosis, and higher cell migration. Our data present, for the first time, evidence that CTLA-4 c.-1577G>A alters the risk and clinical aspects of CM treated with conventional procedures and may be used for selecting individuals for tumor prevention and patients for distinct treatment. Full article
(This article belongs to the Special Issue Genetic and Molecular Susceptibility in Human Diseases: 2nd Edition)
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<p>Signaling pathway of CTLA-4. The dendritic cell identifies antigens in the microenvironment and, with the help of the major histocompatibility complex (MHC), presents these antigens to inactive T lymphocytes, initiating the activation process (<b>A</b>). The first step occurs when the MHC binds to the T-cell receptor (TCR). After this binding, surface proteins of the CD80/86 family on dendritic cells bind to the CD28 receptor on T lymphocytes, promoting increased cell proliferation, enhancing cytokine production, and combating tumor melanocytes. During activation, CTLA-4, initially stored in vesicles within the cytoplasm, is released, becomes a receptor, and binds with higher affinity than CD28 to the CD80/86 family proteins (<b>B</b>). This binding leads to the inactivation and apoptosis of T lymphocytes, allowing tumor melanocytes to survive, as lymphocytes do not target them, blocking T-lymphocyte activation from the binding of melanocytes to antigen-presenting cells (<b>C</b>). By binding to tumor melanocytes, the dendritic cell prevents the antigen from being presented to T lymphocytes, thus inhibiting T-lymphocyte activation. As a result, the tumor melanocyte evades the immune system’s response.</p>
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<p>Functional analyses of the ancestral and variant genotypes of the <span class="html-italic">CTLA-4</span> c.-1577G&gt;A. Analysis of <span class="html-italic">CTLA-4</span> gene expression in peripheral blood of patients with cutaneous melanoma (<b>A</b>). Gene expression was higher in patients with AA genotype than in those with GG genotype. Relative luciferase activity in SK-MEL-28 and A-375 melanoma cell lines transfected with the ancestral plasmid (GG genotype) or with the variant plasmid (AA genotype) (<b>B</b>). Luciferase activity was higher in cells with AA genotype than in cells with GG genotype. Assessment of the cell cycle in strains modified to present ancestral and variant genotypes (<b>C</b>). Cells were identified in the G1, S, and G2 phases using flow cytometry. A higher percentage of SK-MEL-28 cells with the GG genotype was found in the G1 phase compared to those with the AA genotype *, and a higher percentage of SK-MEL-28 cells with the AA genotype was found in S phase compared to those with GG genotype **; a similar percentage of A-375 cells were seen in the G1, S, and G2 phases. Cell proliferation in SK-MEL-28 and A-375 melanoma cell lines (<b>D</b>). A higher percentage of SK-MEL-28 and A-375 cells with AA genotype was found in proliferation when compared to those with the GG genotype. Analysis of the assessment of apoptosis and necrosis by flow cytometry with stimulation of the immunotherapy drug ipilimumab (<b>E</b>). A higher percentage of SK-MEL-28 cells with GG genotype was found in necrosis compared to those with the AA genotype *; a higher percentage of cells with the AA genotype were alive compared to those with the GG genotype **; the A-375 cells with the GG genotype were in initial apoptosis when compared to cells with the AA genotype.</p>
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<p>Transcription factor binding sites for the <span class="html-italic">CTLA-4</span> 1577G&gt;A (rs11571316) single-nucleotide polymorphism (SNP). Binding of the transcription factor POUPF2 in the 3′-5′ direction of the <span class="html-italic">CTLA-4</span> gene (<b>A</b>). Binding of the transcription factor HMGA1 in the 3′-5′ direction of the <span class="html-italic">CTLA-4</span> gene (<b>B</b>). The gray square represents the SNP alleles.</p>
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<p>Cell migration by wound healing assay in melanoma cell lines SK-MEL-28 (<b>A</b>) and A-375 (<b>B</b>). Cells with the AA genotype of the <span class="html-italic">CTLA-4</span> c.-1577G&gt;A single nucleotide variant showed a higher percentage of wound closure after 16 h.</p>
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11 pages, 1927 KiB  
Article
Serum CYFRA 21-1 as a Prognostic Marker in Non-Small-Cell Lung Cancer Patients Treated with Immune Checkpoint Inhibitors
by Keiki Miyadera, Sho Kakuto, Mayu Sugai, Ryosuke Tsugitomi, Yoshiaki Amino, Ken Uchibori, Noriko Yanagitani, Hisatoshi Sugiura, Masahiro Seike, Makoto Nishio and Ryo Ariyasu
Cancers 2024, 16(21), 3712; https://doi.org/10.3390/cancers16213712 - 4 Nov 2024
Viewed by 930
Abstract
Background: A prognostic marker in patients with non-small-cell lung cancer (NSCLC) treated with anti-PD-1/PD-L1 antibodies must be established. This study explored serum cytokeratin fraction 21–1 (CYFRA 21-1), which represents a squamous cell histology, as a prognostic factor in anti-PD-1/PD-L1 antibody treatment, stratifying by [...] Read more.
Background: A prognostic marker in patients with non-small-cell lung cancer (NSCLC) treated with anti-PD-1/PD-L1 antibodies must be established. This study explored serum cytokeratin fraction 21–1 (CYFRA 21-1), which represents a squamous cell histology, as a prognostic factor in anti-PD-1/PD-L1 antibody treatment, stratifying by histology and treatment regimen. Methods: This study retrospectively evaluated patients with advanced NSCLC without driver mutations receiving anti-PD-1/PD-L1 antibodies between November 2015 and March 2023. Cutoff values for CYFRA 21-1 and carcinoembryonic antigen (CEA) were 3.5 and 5.0 ng/mL, respectively. The Kaplan–Meier method and a log-rank test were conducted. The Cox proportional hazards model was utilized for univariate and multivariate analyses. Results: This study included 258 patients. The squamous NSCLC group demonstrated a shorter overall survival (OS) than the non-squamous NSCLC group (median, 17.8 vs. 23.7 months, p = 0.141). Patients with high serum CYFRA 21-1 and CEA levels exhibited a significantly shorter OS than those with normal levels (median, 11.7 vs. 32.7 months, p < 0.005; 15.8 vs. 29.7 months, p < 0.005). The multivariate analysis identified a performance status (PS) of ≥2, a PD-L1 expression of ≥50%, and a serum CYFRA 21-1 of >3.5 ng/mL as independent prognostic factors. Patients with high serum CYFRA 21-1 levels exhibited a significantly shorter OS even focusing on non-squamous NSCLC, anti-PD-1/PD-L1 antibody and chemotherapy combination therapy, or anti-CTLA-4 antibody combination therapy. Conclusion: Serum CYFRA 21-1 is a poor prognostic marker for patients with NSCLC receiving anti-PD-1/PD-L1 antibody treatment even when stratifying by histology or treatment regimen. Full article
(This article belongs to the Section Cancer Immunology and Immunotherapy)
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<p>Patient flow.</p>
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<p>Overall survival of all patients in (<b>A</b>) histology, (<b>B</b>) serum CYFRA 21-1 level, and (<b>C</b>) serum CEA level. Time is expressed in months.</p>
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<p>Overall survival of patients with non-squamous NSCLC based on the serum CYFRA 21-1 level. Time is expressed in months.</p>
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<p>Overall survival of patients receiving the combination therapy of ICIs and (<b>A</b>) chemotherapy and (<b>B</b>) anti-CTLA-4 antibodies as first-line therapy in serum CYFRA 21-1 level. Time is expressed in months.</p>
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26 pages, 1239 KiB  
Review
Amelanotic Melanoma—Biochemical and Molecular Induction Pathways
by Piotr Misiąg, Klaudia Molik, Monika Kisielewska, Paulina Typek, Izabela Skowron, Anna Karwowska, Jacek Kuźnicki, Aleksandra Wojno, Marcin Ekiert and Anna Choromańska
Int. J. Mol. Sci. 2024, 25(21), 11502; https://doi.org/10.3390/ijms252111502 - 26 Oct 2024
Viewed by 1267
Abstract
Amelanotic melanoma (AM) is a subtype of hypomelanotic or completely amelanotic melanoma. AM is a rare subtype of melanoma that exhibits a higher recurrence rate and aggressiveness as well as worse surveillance than typical melanoma. AM shows a dysregulation of melanin production, cell [...] Read more.
Amelanotic melanoma (AM) is a subtype of hypomelanotic or completely amelanotic melanoma. AM is a rare subtype of melanoma that exhibits a higher recurrence rate and aggressiveness as well as worse surveillance than typical melanoma. AM shows a dysregulation of melanin production, cell cycle control, and apoptosis pathways. Knowing these pathways has an application in medicine due to targeted therapies based on the inhibiting elements of the abovementioned pathways. Therefore, we summarized and discussed AM biochemical and molecular induction pathways and personalized medicine approaches, clinical management, and future directions due to the fact that AM is relatively rare. AM is commonly misdiagnosed. Hence, the role of biomarkers is becoming significant. Nonetheless, there is a shortage of biomarkers specific to AM. BRAF, NRAS, and c-KIT genes are the main targets of therapy. However, the role of BRAF and KIT in AM varied among studies. BRAF inhibitors combined with MAK inhibitors demonstrate better results. Immune checkpoint inhibitors targeting CTLA-4 combined with a programmed death receptor 1 (PD-1) show better outcomes than separately. Fecal microbiota transplantation may overcome resistance to immune checkpoint therapy of AM. Immune-modulatory vaccines against indoleamine 2,3-dioxygenase (IDO) and PD ligand (PD-L1) combined with nivolumab may be efficient in melanoma treatment. Full article
(This article belongs to the Special Issue Melanoma: Molecular Mechanisms and Therapy)
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<p>The schema of the melanogenesis pathway. Tyrosinase is an enzyme that catalyzes the conversion of L-tyrosine into L-DOPA. Tyrosinase-related protein 1 (TRP-1) and tyrosinase-related protein 2 (TRP-2) stabilize and increase the activity of TYR. Moreover, TRP-1 upturns the eumelanin vs. pheomelanin ratio. TRP-2 takes part in the tautomerization of DOPA chrome into DHI-2-carboxylic acid (DHICA). TRP-1 oxidases DHICA. The figure is based on the data of the studies [<a href="#B27-ijms-25-11502" class="html-bibr">27</a>,<a href="#B28-ijms-25-11502" class="html-bibr">28</a>,<a href="#B29-ijms-25-11502" class="html-bibr">29</a>].</p>
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<p>The role of the PI3K/AKT pathway in AM. RTK and RAS induce PI3K, which triggers PIP2 to PIP3 transition. PIP3 activates through AKT mTOR, Bad, and MdM2. These factors cause cell growth and survival.</p>
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<p>The role of Tregs in immune system suppression in AM.</p>
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17 pages, 2439 KiB  
Article
How Co-Stimulatory/Inhibitory Molecules Vary Across Immune Cell Subtypes in the Severity of Systemic Lupus Erythematosus Compared to Controls
by Kuang-Hui Yu, Wei-Tzu Lin and Ding-Ping Chen
Biomedicines 2024, 12(11), 2444; https://doi.org/10.3390/biomedicines12112444 - 24 Oct 2024
Viewed by 704
Abstract
Background: Co-stimulatory and co-inhibitory molecules are critical to T cell responses and involved in the pathogenesis of systemic lupus erythematosus (SLE). This study aimed to comprehensively analyze the surface expression of these molecules in various phenotypic immune cells, comparing the differences between various [...] Read more.
Background: Co-stimulatory and co-inhibitory molecules are critical to T cell responses and involved in the pathogenesis of systemic lupus erythematosus (SLE). This study aimed to comprehensively analyze the surface expression of these molecules in various phenotypic immune cells, comparing the differences between various levels of the severity in SLE and control groups. Methods: Peripheral blood mononuclear cells (PBMCs) were isolated using Ficoll-Paque from blood samples of severe SLE patients (treatment with immunosuppressants), mild SLE patients (excluding those with persistent proteinuria or thrombocytopenia), and healthy controls (n = 10 each). PBMCs were stimulated for 48 h. The cells were stained with anti-CD3, CD4, CD28, PD-1, and CTLA-4 antibodies and analyzed by flow cytometry. Differences between groups were assessed using the Kruskal–Wallis test and Mann–Whitney U-test, with median values and statistical significance (p < 0.05) reported. Results: The results showed that CD28 expression was significantly higher in SLE patients compared to controls, with the highest levels in mild SLE. However, CD3+ CD28+ and CD4+ CD28+ cells were more prevalent in controls (p = 0.032 and 0.017, respectively). Mild SLE patients exhibited the highest CTLA-4 expression, with significant differences from severe SLE and controls (p = 0.030 and 0.037, respectively). PD-1 expression was lowest in severe SLE but highest in mild SLE within CD3+ CD4+ cells (p = 0.001). After 48 h of activation, CD4+ CTLA4+ and CD3+ CTLA4+ expression levels were significantly higher in controls compared to SLE groups. Conclusions: Our study highlighted that the expression of CD28, CTLA-4, and PD-1 in lymphocytes and specific T cell subsets was various according the severity of SLE in patients, underscoring their roles in disease pathogenesis. Full article
(This article belongs to the Section Immunology and Immunotherapy)
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<p>Flow cytometry analysis of lymphocytes and CD3<sup>+</sup> CD4<sup>+</sup> cells. Red dots: Unstained or lower-expressing cells. Blue dots: Higher-expressing cells for the relevant markers.</p>
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<p>A schematic diagram of the distribution of CD3<sup>+</sup> and CD4<sup>+</sup> T cells in lymphocyte subsets.</p>
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<p>Expression levels of CD28 in different lymphocyte populations and analysis of differences among severe SLE, mild SLE, and control. Only statistically significant areas are indicated, and the <span class="html-italic">p</span>-values are based on the results of the Mann–Whitney U-test analysis. The ‘X’ in the box represents the mean, and the circle symbol indicate outliers. (<b>a</b>) The expression level of CD28 in lymphocytes. (<b>b</b>) The co-expression level of CD3<sup>+</sup> CD28<sup>+</sup> in lymphocytes. (<b>c</b>) The co-expression level of CD4<sup>+</sup> CD28<sup>+</sup> in lymphocytes. (<b>d</b>) The co-expression level of CD3<sup>+</sup> CD28<sup>+</sup> in CD3<sup>+</sup> CD4<sup>+</sup> cells.</p>
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<p>Expression levels of CTLA-4 in different lymphocyte populations and analysis of differences among severe SLE, mild SLE, and control. Only statistically significant areas are indicated, and the <span class="html-italic">p</span>-values are based on the results of the Mann–Whitney U-test analysis. The ‘X’ in the box represents the mean, and the circles indicate outliers. (<b>a</b>) The expression level of CTLD-4 in CD3<sup>+</sup> CD4<sup>+</sup> cells. (<b>b</b>) The co-expression level of CD3<sup>+</sup> CTLA4<sup>+</sup> in CD3<sup>+</sup> CD4<sup>+</sup> cells.</p>
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<p>Expression levels of PD-1 in different lymphocyte populations and analysis of differences among severe SLE, mild SLE, and control. Only statistically significant areas are indicated, and the <span class="html-italic">p</span>-values are based on the results of the Mann–Whitney U-test analysis. The ‘X’ in the box represents the mean, and the circles indicate outliers. (<b>a</b>) The expression level of PD-1 in lymphocytes. (<b>b</b>) The co-expression level of CD4<sup>+</sup> PD1<sup>+</sup> in lymphocytes. (<b>c</b>) The co-expression level of CD3<sup>+</sup> PD1<sup>+</sup> in lymphocytes. (<b>d</b>) The expression level of PD-1 in CD3<sup>+</sup> CD4<sup>+</sup> cells. (<b>e</b>) The co-expression level of CD3<sup>+</sup> PD1<sup>+</sup> in CD3<sup>+</sup> CD4<sup>+</sup> cells.</p>
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<p>Expression levels and differential analysis of CTLA-4 and PD-1 in various lymphocyte populations after 48 h of activation. Only statistically significant differences are indicated. The <span class="html-italic">p</span>-values are based on the results of the Mann–Whitney U-test. The ‘X’ in the box represents the mean, and the circles indicate outliers. (<b>a</b>) The co-expression level after 48 h of CD4+ CTLA4+ in lymphocytes. (<b>b</b>) The co-expression level after 48 h of CD3+ CTLA4+ in CD3<sup>+</sup> CD4<sup>+</sup> cells. (<b>c</b>) The co-expression level after 48 h of CD4<sup>+</sup> PD1<sup>+</sup> in lymphocytes. (<b>d</b>) The co-expression level after 48 h of CD3<sup>+</sup> PD1<sup>+</sup> in CD3<sup>+</sup> CD4<sup>+</sup> cells. (<b>e</b>) The increased expression (48-0 h) of CD4<sup>+</sup> CTLA4<sup>+</sup> in lymphocytes.</p>
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21 pages, 7364 KiB  
Article
Double-Negative T-Cells during Acute Human Immunodeficiency Virus and Simian Immunodeficiency Virus Infections and Following Early Antiretroviral Therapy Initiation
by Alexis Yero, Tao Shi, Julien A. Clain, Ouafa Zghidi-Abouzid, Gina Racine, Cecilia T. Costiniuk, Jean-Pierre Routy, Jérôme Estaquier and Mohammad-Ali Jenabian
Viruses 2024, 16(10), 1609; https://doi.org/10.3390/v16101609 - 14 Oct 2024
Viewed by 1197
Abstract
HIV infection significantly affects the frequencies and functions of immunoregulatory CD3+CD4CD8 double-negative (DN) T-cells, while the effect of early antiretroviral therapy (ART) initiation on these cells remains understudied. DN T-cell subsets were analyzed prospectively in 10 HIV+ individuals [...] Read more.
HIV infection significantly affects the frequencies and functions of immunoregulatory CD3+CD4CD8 double-negative (DN) T-cells, while the effect of early antiretroviral therapy (ART) initiation on these cells remains understudied. DN T-cell subsets were analyzed prospectively in 10 HIV+ individuals during acute infection and following early ART initiation compared to 20 HIV-uninfected controls. In this study, 21 Rhesus macaques (RMs) were SIV-infected, of which 13 were assessed during acute infection and 8 following ART initiation four days post-infection. DN T-cells and FoxP3+ DN Treg frequencies increased during acute HIV infection, which was not restored by ART. The expression of activation (HLA-DR/CD38), immune checkpoints (PD-1/CTLA-4), and senescence (CD28CD57+) markers by DN T-cells and DN Tregs increased during acute infection and was not normalized by ART. In SIV-infected RMs, DN T-cells remained unchanged despite infection or ART, whereas DN Treg frequencies increased during acute SIV infection and were not restored by ART. Finally, frequencies of CD39+ DN Tregs increased during acute HIV and SIV infections and remained elevated despite ART. Altogether, acute HIV/SIV infections significantly changed DN T-cell and DN Treg frequencies and altered their immune phenotype, while these changes were not fully normalized by early ART, suggesting persistent HIV/SIV-induced immune dysregulation despite early ART initiation. Full article
(This article belongs to the Special Issue Acute HIV Infections)
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<p>Study protocol. A total of 21 female Rhesus macaques (RMs) were infected intravenously with 20 50% animal infectious doses (AIDs) of SIVmac251 virus, and the specimens were collected in the acute phase of infection in 13 animals in the absence of ART. Eight monkeys were treated four days after the infection in a daily manner with an ART cocktail. Blood specimens were obtained from 10 SIV-uninfected animals that were used as controls. Black arrows represent the time when samples from whole blood were taken. Of note, each “D” followed by a number indicates one animal; therefore, in some cases, blood was collected from more than one animal on the same day. <span class="html-italic">Nota bene</span>: Blood samples from 3 animals were collected before and after SIV infection.</p>
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<p>(<b>A</b>) Gating strategy used in flow cytometry to determine total CD3<sup>+</sup>CD4<sup>−</sup>CD8<sup>−</sup> (double-negative, DN) T-cell (left) and DN T-cell memory subsets based on CD45RA/CD28 and CD127 expression (right) in the human study. Percentages determined in flow cytometry of total DN T-cells (<b>B</b>), central memory (CM, CD45RA<sup>−</sup>CD28<sup>+</sup>) (<b>C</b>), terminally differentiated (TD, CD45RA<sup>+</sup>CD28<sup>−</sup>) (<b>D</b>), effector memory (EM, CD45RA<sup>−</sup>CD28<sup>−</sup>) (<b>E</b>), and naïve (CD45RA<sup>+</sup>CD28<sup>+</sup>) (<b>F</b>) subsets within DN T-cells in the human study. (<b>G</b>) Percentages determined in flow cytometry of CD127<sup>+</sup> DN T-cells in the human study. After the Kruskal–Wallis analysis, the differences among the three study groups were determined by a nonparametric Mann–Whitney rank test for unpaired variables (non-infected vs. acute, non-infected vs. ART-treated) and a Wilcoxon signed-rank test for paired variables (acute vs. ART-treated). Sample sizes in flow cytometry analysis: non-infected (<span class="html-italic">n</span> = 20), acute, and ART-treated (<span class="html-italic">n</span> = 10). Horizontal lines in graphs represent the median. Only statistical significances are presented (*, <span class="html-italic">p</span> &lt; 0.05; **, <span class="html-italic">p</span> &lt; 0.01).</p>
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<p>(<b>A</b>) Gating strategy used in flow cytometry to determine CD73/CD39, CD38/HLA-DR, CTLA-4/PD-1, and CD57/CD28 expression within DN T-cells in the human study. Percentages determined in flow cytometry of CD73<sup>+</sup> (<b>B</b>), CD39<sup>+</sup> (<b>C</b>), CD39<sup>+</sup>CD73<sup>+</sup> (<b>D</b>), CD38<sup>+</sup> (<b>E</b>), HLA-DR<sup>+</sup> (<b>F</b>), HLA-DR<sup>+</sup>CD38<sup>+</sup> (<b>G</b>), PD-1<sup>+</sup> (<b>H</b>), CTLA-4<sup>+</sup> (<b>I</b>), CTLA-4<sup>+</sup>PD-1<sup>+</sup> (<b>J</b>), and CD28<sup>−</sup>CD57<sup>+</sup> (<b>K</b>) within DN T-cells in the human study. After the Kruskal–Wallis analysis, the differences among the three study groups were determined by a nonparametric Mann–Whitney rank test for unpaired variables (non-infected vs. acute, non-infected vs. ART-treated) and Wilcoxon signed-rank test for paired variables (acute vs. ART-treated). Sample sizes in flow cytometry analysis: non-infected (<span class="html-italic">n</span> = 20), acute, and ART-treated (<span class="html-italic">n</span> = 10). Horizontal line s in graphs represent the median. Only statistical significances are presented (*, <span class="html-italic">p</span> &lt; 0.05; **, <span class="html-italic">p</span> &lt; 0.01; ***, <span class="html-italic">p</span> &lt; 0.001; ****, <span class="html-italic">p</span> &lt; 0.0001).</p>
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<p>(<b>A</b>) Gating strategy used in flow cytometry to determine total FoxP3<sup>+</sup> DN T-cells (DN Tregs) (left) in the human study. (<b>B</b>) Percentages determined in flow cytometry of total DN Tregs within DN T-cells in the human study. (<b>C</b>) Gating strategy used in flow cytometry to determine DN Treg memory subsets based on CD45RA/CD28, CD73/CD39, CD38/HLA-DR, CTLA-4/PD-1, and CD57/CD28 expression within DN Tregs in the human study. Percentages determined in flow cytometry of naïve (CD45RA<sup>+</sup>CD28<sup>+</sup>) (<b>D</b>), terminally differentiated (TD, CD45RA<sup>+</sup>CD28<sup>−</sup>) (<b>E</b>), effector memory (EM, CD45RA<sup>−</sup>CD28<sup>−</sup>) (<b>F</b>), central memory (CM, CD45RA<sup>−</sup>CD28<sup>+</sup>) (<b>G</b>), CD73<sup>+</sup>FoxP3<sup>+</sup> (<b>H</b>), CD39<sup>+</sup>FoxP3<sup>+</sup> (<b>I</b>), CD39<sup>+</sup>CD73<sup>+</sup>FoxP3<sup>+</sup> (<b>J</b>), CD38<sup>+</sup>FoxP3<sup>+</sup> (<b>K</b>), HLA-DR<sup>+</sup>FoxP3<sup>+</sup> (<b>L</b>), HLA-DR<sup>+</sup>CD38<sup>+</sup>FoxP3<sup>+</sup> (<b>M</b>), PD-1<sup>+</sup>FoxP3<sup>+</sup> (<b>N</b>), CTLA-4<sup>+</sup>FoxP3<sup>+</sup> (<b>O</b>), CTLA-4<sup>+</sup>PD-1<sup>+</sup>FoxP3<sup>+</sup> (<b>P</b>), and CD28<sup>−</sup>CD57<sup>+</sup>FoxP3<sup>+</sup> (<b>Q</b>) DN T-cells in the human study. After the Kruskal–Wallis analysis, the differences among the three study groups were determined by a nonparametric Mann–Whitney rank test for unpaired variables (non-infected vs. acute, non-infected vs. ART-treated) and Wilcoxon signed-rank test for paired variables (acute vs. ART-treated). Sample sizes in flow cytometry analysis: non-infected (<span class="html-italic">n</span> = 20), acute, and ART-treated (<span class="html-italic">n</span> = 10). Horizontal line in graphs represent the median. Only statistical significances are presented (*, <span class="html-italic">p</span> &lt; 0.05; **, <span class="html-italic">p</span> &lt; 0.01; ***, <span class="html-italic">p</span> &lt; 0.001; ****, <span class="html-italic">p</span> &lt; 0.0001).</p>
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<p>(<b>A</b>) Gating strategy used in flow cytometry to determine CCR6, CCR9, and CXCR3 expression within DN T-cells in the human study. Percentages determined in flow cytometry of CCR6<sup>+</sup> (<b>B</b>), CCR9<sup>+</sup> (<b>C</b>), and CXCR3<sup>+</sup> (<b>D</b>) within DN T-cells in the human study. (<b>E</b>) Gating strategy used in flow cytometry to determine CCR6, CCR9, and CXCR3 expression within DN Tregs in the human study. Percentages determined in flow cytometry of CCR6<sup>+</sup>FoxP3<sup>+</sup> (<b>F</b>), CCR9<sup>+</sup>FoxP3<sup>+</sup> (<b>G</b>), and CXCR3<sup>+</sup>FoxP3<sup>+</sup> (<b>H</b>) within DN T-cells in the human study. After the Kruskal–Wallis analysis, the differences among the three study groups were determined by a nonparametric Mann–Whitney rank test for unpaired variables (non-infected vs. acute, non-infected vs. ART-treated) and Wilcoxon signed-rank test for paired variables (acute vs. ART-treated). Sample sizes in flow cytometry analysis: non-infected (<span class="html-italic">n</span> = 20), acute, and ART-treated (<span class="html-italic">n</span> = 10). Horizontal lines in graphs represent the median. Only statistical significances are presented (*, <span class="html-italic">p</span> &lt; 0.05; **, <span class="html-italic">p</span> &lt; 0.01).</p>
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<p>(<b>A</b>) Gating strategy used in flow cytometry to determine total CD3<sup>+</sup>CD4<sup>−</sup>CD8<sup>−</sup> (double-negative, DN) T-cells, total FoxP3<sup>+</sup> DN T-cells (DN Tregs), CD127<sup>+</sup>, CD37/CD73, and HLA-DR<sup>+</sup> DN T-cells in Rhesus macaques. Percentages determined in flow cytometry of total DN T-cells (<b>B</b>), total DN Tregs (<b>C</b>), CD127<sup>+</sup> (<b>D</b>), CD73<sup>+</sup> (<b>E</b>), CD39<sup>+</sup> (<b>F</b>), and HLA-DR<sup>+</sup> (<b>G</b>) within DN T-cells in Rhesus macaques. (<b>H</b>) Gating strategy used in flow cytometry to determine CD39/CD73 within DN Tregs in Rhesus macaques. Percentages determined in flow cytometry of CD39<sup>+</sup>FoxP3<sup>+</sup> (<b>I</b>) and CD73<sup>+</sup>FoxP3<sup>+</sup> (<b>J</b>) within DN T-cells in Rhesus macaques. After the Kruskal–Wallis analysis, the differences among the three study groups were determined by a nonparametric Mann–Whitney rank test for unpaired variables (non-infected vs. acute, non-infected vs. early ART-treated, and acute vs. early ART-treated) in Rhesus macaques. Sample sizes in flow cytometry analysis in Rhesus macaques: non-infected (<span class="html-italic">n</span> = 10), acute (<span class="html-italic">n</span> = 13), and early ART-treated (<span class="html-italic">n</span> = 8). Horizontal lines in graphs represent the median. Only statistical significances are presented (*, <span class="html-italic">p</span> &lt; 0.05; **, <span class="html-italic">p</span> &lt; 0.01; ***, <span class="html-italic">p</span> &lt; 0.001; ****, <span class="html-italic">p</span> &lt; 0.0001).</p>
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17 pages, 3339 KiB  
Article
The Goat Cytotoxic T Lymphocyte-Associated Antigen-4 Gene: mRNA Expression and Association Analysis of Insertion/Deletion Variants with the Risk of Brucellosis
by Congliang Wang, Xiaoyu Liu, Zhaofei Ren, Xiaomin Du, Na Li, Xiaoyue Song, Weiwei Wu, Lei Qu, Haijing Zhu and Jinlian Hua
Int. J. Mol. Sci. 2024, 25(20), 10948; https://doi.org/10.3390/ijms252010948 - 11 Oct 2024
Viewed by 846
Abstract
The cytotoxic T lymphocyte-associated antigen-4 (CTLA4) gene, a member of the immunoglobulin superfamily, is crucial for maintaining immune homeostasis and preventing autoimmune diseases. Studies have shown that polymorphisms in the CTLA4 gene are linked to an increased risk of brucellosis in [...] Read more.
The cytotoxic T lymphocyte-associated antigen-4 (CTLA4) gene, a member of the immunoglobulin superfamily, is crucial for maintaining immune homeostasis and preventing autoimmune diseases. Studies have shown that polymorphisms in the CTLA4 gene are linked to an increased risk of brucellosis in humans, but its association with brucellosis in goats remains unexplored. In this study, the tissue expression profile of CTLA4 in goats was investigated, and the correlation between InDel polymorphisms in the CTLA4 gene and susceptibility to brucellosis in goats was examined. The findings reveal the widespread expression of CTLA4 in goat tissues, particularly in the spleen and testes. The tested goat populations presented genotypes insertion/insertion (II), insertion/deletion (ID), and deletion/deletion (DD) at both the P1 and P2 loci, and an association analysis revealed significant differences in the distribution of genotypes and allele frequencies at the P1 and P2 loci of the CTLA4 gene between the Brucella goat case and the control groups (p < 0.05). Specifically, compared with the II genotype, the P1 and P2 loci were significantly associated with an elevated risk of brucellosis development in goats under both the codominant (ID/II) and dominant (ID + DD/II) models (P1, p = 0.042, p = 0.016; P2, p = 0.011, p = 0.014). Additionally, haplotype analysis indicated that haplotypes IP1DP2, DP1IP2, and DP1DP2 were significantly associated with an increased risk of brucellosis in goats compared to the reference haplotype IP1IP2 (p = 0.029, p = 0.012, p = 0.034). Importantly, the Lipopolysaccharide (LPS) stimulation of peripheral blood monocytes and/or macrophages from goats with the II, ID, and DD genotypes resulted in increased CTLA4 expression levels in the II genotype, leading to a robust LPS-induced inflammatory response. Through bioinformatic analysis, the observed effect of the InDel locus on Brucella pathogenesis risk in goats could be attributed to the differential binding of the transcription factors nuclear factor kappaB (NF-κB) and CCAAT/enhancer-binding protein α (C/EBPα). These findings offer potential insights for breeding strategies against brucellosis. Full article
(This article belongs to the Section Molecular Genetics and Genomics)
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<p><span class="html-italic">CTLA4</span> gene bioinformatic analysis in goats: (<b>A</b>) nucleotide sequence homology analysis of <span class="html-italic">CTLA4</span> gene; and (<b>B</b>) phylogenetic tree of <span class="html-italic">CTLA4</span> gene in different animal species.</p>
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<p>Tissue expression profile of <span class="html-italic">CTLA4</span> gene. <span class="html-italic">n</span> = 3 samples of each tissues. Columns with different letters (a–e) mean <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Mode pattern of identified indel positions of goat <span class="html-italic">CTLA4</span> gene. The black box represents the exons of the goat <span class="html-italic">CTLA4</span> gene.</p>
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<p>InDel electrophoresis and sequencing of <span class="html-italic">CTLA4</span> gene in goats: P1 locus electrophoresis (<b>A</b>) and sequencing map (<b>B</b>); and P2 locus electrophoresis (<b>C</b>) and sequencing map (<b>D</b>). M: 600 bp marker; II: insertion/insertion; ID: insertion/deletion; and DD: deletion/deletion.</p>
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<p>Linkage disequilibrium (LD) between P1 and P2 mutation locus of <span class="html-italic">CTLA4</span> gene in goats. (<b>A</b>) D’ value and (<b>B</b>) r<sup>2</sup> value.</p>
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<p>Changes in <span class="html-italic">CTLA4</span> and cytokines in peripheral blood monocytes and/or macrophages of goats of different genotypes after LPS stimulation. (<b>A</b>) Changes in <span class="html-italic">CTLA4</span> expression at different time points in LPS-stimulated peripheral blood monocytes and/or macrophages; and (<b>B</b>–<b>E</b>) changes in cytokine expression of <span class="html-italic">IL</span>-6, <span class="html-italic">IFN-γ</span>, <span class="html-italic">TNF-α</span>, <span class="html-italic">IL</span>-10, <span class="html-italic">IL</span>-12, and <span class="html-italic">TGF-β</span>, respectively, at different time points after LPS stimulation. * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Transcription factor-binding site prediction for the goat <span class="html-italic">CTLA4</span> gene variant locus. (<b>A</b>,<b>B</b>) represent the P1 and P2 loci, and the black triangles represent potential transcription factor-binding sites.</p>
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23 pages, 2417 KiB  
Review
Balancing Tumor Immunotherapy and Immune-Related Adverse Events: Unveiling the Key Regulators
by Jianshang Huang, Lei Xiong, Sainan Tang, Junhao Zhao and Li Zuo
Int. J. Mol. Sci. 2024, 25(20), 10919; https://doi.org/10.3390/ijms252010919 - 10 Oct 2024
Viewed by 1160
Abstract
Tumor immunotherapy has emerged as a promising approach in cancer treatment in recent years, offering vast potential. This method primarily involves targeting and inhibiting the suppressive checkpoints present in different immune cells to enhance their activation, ultimately leading to tumor regression. However, tumor [...] Read more.
Tumor immunotherapy has emerged as a promising approach in cancer treatment in recent years, offering vast potential. This method primarily involves targeting and inhibiting the suppressive checkpoints present in different immune cells to enhance their activation, ultimately leading to tumor regression. However, tumor cells exploit the surrounding immune cells and tissues to establish a tumor microenvironment (TME) that supports their survival and growth. Within the TME, the efficacy of effector immune cells is compromised, as tumor cells exploit inhibitory immune cells to suppress their function. Furthermore, certain immune cells can be co-opted by tumor cells to facilitate tumor growth. While significantly enhancing the body’s tumor immunity can lead to tumor regression, it can also result in severe toxic side effects and an inflammatory factor storm. As a consequence, patients often discontinue treatment due to immune-related adverse events (irAEs) or, in extreme cases, succumb to toxic side effects before experiencing tumor regression. In this analysis, we examined several remission regimens for irAEs, each with its own drawbacks, including toxic side effects or suppression of tumor immunotherapy, which is undesirable. A recent research study, specifically aimed at downregulating intestinal epithelial barrier permeability, has shown promising results in reducing the severity of inflammatory bowel disease (IBD) while preserving immune function. This approach effectively reduces the severity of IBD without compromising the levels of TNF-α and IFN-γ, which are crucial for maintaining the efficacy of tumor immunotherapy. Based on the substantial similarities between IBD and ICI colitis (combo immune checkpoint inhibitors-induced colitis), this review proposes that targeting epithelial cells represents a crucial research direction for mitigating irAEs in the future. Full article
(This article belongs to the Section Molecular Biology)
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<p>Immunocytes in Tumor Immunotherapy: (<b>A</b>) PD-1 and CTLA-4, functioning as immune checkpoints, exhibit potent inhibitory effects on anti-tumor immune cells. The blockade of immune checkpoint inhibitors (ICI) has shown the potential to enhance the capability of tumor immune cells. (<b>B</b>) Interactions among various effector immune cells during the process of tumor immunotherapy have been shown to enhance the efficacy of tumor immune responses.</p>
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<p>Tumor Microenvironment in Tumor Immunotherapy: The tumor microenvironment possesses distinctive physicochemical characteristics, capable of converting infiltrating immune cells into suppressive immune cells, thereby counteracting tumor immunotherapy and facilitating tumor growth and evasion.</p>
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<p>The Relationship between the Occurrence of ICI Colitis and Intestinal Epithelial Mucosal Barrier: (<b>A</b>) Simplified mechanisms underlying the occurrence of ICI colitis in tumor patients treated with combination anti-PD-1/PD-L1 and anti-CTLA-4 therapy. (<b>B</b>) Significant downregulation of GZMB levels in CD8+ cells upon MLCK knockout in a murine model of graft-versus-host disease [<a href="#B114-ijms-25-10919" class="html-bibr">114</a>]. (<b>C</b>) MLCK knockout significantly reduces phosphorylation levels of myosin light chain in intestinal epithelial mucosa of murine GVHD model [<a href="#B114-ijms-25-10919" class="html-bibr">114</a>]. (<b>D</b>) Remarkable upregulation of MLCK1 expression levels in patients with Crohn’s disease [<a href="#B115-ijms-25-10919" class="html-bibr">115</a>]. (<b>E</b>) Significant increase in the interaction level of MLCK1 with FKBP8, and downregulation of ZO1 expression in intestinal epithelial mucosa of Crohn’s disease patients: an immunofluorescence study [<a href="#B115-ijms-25-10919" class="html-bibr">115</a>].</p>
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22 pages, 1136 KiB  
Review
Personalized Treatment Strategies via Integration of Gene Expression Biomarkers in Molecular Profiling of Laryngeal Cancer
by Antonino Maniaci, Giovanni Giurdanella, Carlos Chiesa Estomba, Simone Mauramati, Andy Bertolin, Marco Lionello, Miguel Mayo-Yanez, Paolo Boscolo Rizzo, Jerome R. Lechien and Mario Lentini
J. Pers. Med. 2024, 14(10), 1048; https://doi.org/10.3390/jpm14101048 - 10 Oct 2024
Viewed by 1890
Abstract
Laryngeal cancer poses a substantial challenge in head and neck oncology, and there is a growing focus on customized medicine techniques. The present state of gene expression indicators in laryngeal cancer and their potential to inform tailored therapy choices are thoroughly examined in [...] Read more.
Laryngeal cancer poses a substantial challenge in head and neck oncology, and there is a growing focus on customized medicine techniques. The present state of gene expression indicators in laryngeal cancer and their potential to inform tailored therapy choices are thoroughly examined in this review. We examine significant molecular changes, such as TP53, CDKN2A, PIK3CA, and NOTCH1 mutations, which have been identified as important participants in the development of laryngeal cancer. The study investigates the predictive and prognostic significance of these genetic markers in addition to the function of epigenetic changes such as the methylation of the MGMT promoter. We also go over the importance of cancer stem cell-related gene expression patterns, specifically CD44 and ALDH1A1 expression, in therapy resistance and disease progression. The review focuses on indicators, including PD-L1, CTLA-4, and tumor mutational burden (TMB) in predicting immunotherapy responses, highlighting recent developments in our understanding of the intricate interactions between tumor genetics and the immune milieu. We also investigate the potential for improving prognosis accuracy and treatment selection by the integration of multi-gene expression panels with clinicopathological variables. The necessity for uniform testing and interpretation techniques is one of the difficulties, in implementing these molecular insights into clinical practice, that are discussed. This review seeks to provide a comprehensive framework for promoting personalized cancer therapy by combining the most recent data on gene expression profiling in laryngeal cancer. Molecularly guided treatment options may enhance patient outcomes. Full article
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<p>New emerging biomarkers and the need for standardization of molecular testing in laryngeal cancer.</p>
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<p>Personalized and targeted therapies available.</p>
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