AutoEncoder-Based Computational Framework for Tumor Microenvironment Decomposition and Biomarker Identification in Metastatic Melanoma.
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ABSTRACT:
SUBMITTER: Zhao Y
PROVIDER: S-EPMC8191580 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundMelanoma is a cancer of the skin with potential to spread to other organs and is responsible for most deaths due to skin cancer. It is imperative to identify immune biomarkers for early melanoma diagnosis and treatment.Results63 immune-related genes of the total 1039 unique IRGs retrieved were associated with overall survival of melanoma. A multi-IRGs classifier constructed using eight IRGs showed a powerful predictive ability. The classifier had better predictive power compared with the current clinical data. GSEA analysis showed multiple signaling differences between high and low risk score group. Furthermore, biomarker was associated with multiple immune cells and immune infiltration in tumor microenvironment.ConclusionsThe immune-related genes prognosis biomarker is an effective potential prognostic classifier in the immunotherapies and surveillance of melanoma.MethodsMelanoma samples of genes were retrieved from TCGA and GEO databases while the immune-related genes (IRGs) were retrieved from the ImmPort database. WGCNA, Cox regression analysis and LASSO analysis were used to classify melanoma prognosis. ESTIMATE and CIBERSORT algorithms were used to explore the relationship between risk score and tumor immune microenvironment. GSEA analysis was performed to explore the biological signaling pathway.
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Project description:The presence of immune cells in the tumor microenvironment has been associated with response to immunotherapies across several cancer types, including melanoma. Despite its therapeutic relevance, characterization of the melanoma immune microenvironments remains insufficiently explored. To distinguish the immune microenvironment in a cohort of 180 metastatic melanoma clinical specimens, we developed a method using promoter CpG methylation of immune cell type-specific genes extracted from genome-wide methylation arrays. Unsupervised clustering identified three immune methylation clusters with varying levels of immune CpG methylation that are related to patient survival. Matching protein and gene expression data further corroborated the identified epigenetic characterization. Exploration of the possible immune exclusion mechanisms at play revealed likely dependency on MITF protein level and PTEN loss-of-function events for melanomas unresponsive to immunotherapies (immune-low). To understand whether melanoma tumors resemble other solid tumors in terms of immune methylation characteristics, we explored 15 different solid tumor cohorts from TCGA. Low-dimensional projection based on immune cell type-specific methylation revealed grouping of the solid tumors in line with melanoma immune methylation clusters rather than tumor types. Association of survival outcome with immune cell type-specific methylation differed across tumor and cell types. However, in melanomas immune cell type-specific methylation was associated with inferior patient survival. Exploration of the immune methylation patterns in a pan-cancer context suggested that specific immune microenvironments might occur across the cancer spectrum. Together, our findings underscore the existence of diverse immune microenvironments, which may be informative for future immunotherapeutic applications.
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Project description:Resistance to checkpoint-blockade treatments is a challenge in the clinic. Both primary and acquired resistance have become major obstacles, greatly limiting the long-lasting effects and wide application of blockade therapy. Many patients with metastatic melanoma eventually require further therapy. The absence of T-cell infiltration to the tumor site is a well-accepted contributor limiting immune checkpoint inhibitor efficacy. In this study, we combined intratumoral injection of plasmid IL-12 with electrotransfer and anti-PD-1 in metastatic B16F10 melanoma tumor model to increase tumor-infiltrating lymphocytes and improve therapeutic efficacy. We showed that effective anti-tumor responses required a subset of tumor-infiltrating CD8+ and CD4+ T cells. Additionally, the combination therapy induced higher MHC-I surface expression on tumor cells to hamper tumor cells escaping from immune recognition. Furthermore, we found that activating T cells by exposure to IL-12 resulted in tumors sensitized to anti-PD-1 treatment, suggesting a therapeutic strategy to improve responses to checkpoint blockade.
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Project description:Skin cutaneous melanoma (SKCM) is one of the most destructive skin malignancies and has attracted worldwide attention. However, there is a lack of prognostic biomarkers, especially tumour microenvironment (TME)-based prognostic biomarkers. Therefore, there is an urgent need to investigate the TME in SKCM, as well as to identify efficient biomarkers for the diagnosis and treatment of SKCM patients. A comprehensive analysis was performed using SKCM samples from The Cancer Genome Atlas and normal samples from Genotype-Tissue Expression. TME scores were calculated using the ESTIMATE algorithm, and differential TME scores and differentially expressed prognostic genes were successively identified. We further identified more reliable prognostic genes via least absolute shrinkage and selection operator regression analysis and constructed a prognostic prediction model to predict overall survival. Receiver operating characteristic analysis was used to evaluate the diagnostic efficacy, and Cox regression analysis was applied to explore the relationship with clinicopathological characteristics. Finally, we identified a novel prognostic biomarker and conducted a functional enrichment analysis. After considering ESTIMATEScore and tumour purity as differential TME scores, we identified 34 differentially expressed prognostic genes. Using least absolute shrinkage and selection operator regression, we identified seven potential prognostic biomarkers (SLC13A5, RBM24, IGHV3OR16-15, PRSS35, SLC7A10, IGHV1-69D and IGHV2-26). Combined with receiver operating characteristic and regression analyses, we determined PRSS35 as a novel TME-based prognostic biomarker in SKCM, and functional analysis enriched immune-related cells, functions and signalling pathways. Our study indicated that PRSS35 could act as a potential prognostic biomarker in SKCM by investigating the TME, so as to provide new ideas and insights for the clinical diagnosis and treatment of SKCM.
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Project description:BackgroundPyroptosis, mediated by gasdermins with the release of multiple inflammatory cytokines, has emerged as playing an important role in targeted therapy and immunotherapy due to its effectiveness at inhibiting tumor growth. Melanoma is one of the most commonly used models for immunotherapy development, though an inadequate immune response can occur. Moreover, the development of pyroptosis-related therapy and combinations with other therapeutic strategies is limited due to insufficient understanding of the role of pyroptosis in the context of different tumor immune microenvironments (TMEs).MethodsHere, we present a computational model (pyroptosis-related gene score, PScore) to assess the pyroptosis status. We applied PScore to 1388 melanoma samples in our in-house cohort and eight other publicly available independent cohorts and then calculated its prognostic power of and potential as a predictive marker of immunotherapy efficacy. Furthermore, we performed association analysis for PScore and the characteristics of the TME by using bulk, single-cell, and spatial transcriptomics and assessed the association of PScore with mutation status, which contributes to targeted therapy.ResultsPyroptosis-related genes (PRGs) showed distinct expression patterns and prognostic predictive ability in melanoma. Most PRGs were associated with better survival in metastatic melanoma. Our PScore model based on genes associated with prognosis exhibits robust performance in survival prediction in multiple metastatic melanoma cohorts. We also found PScore to be associated with BRAF mutation and correlate positively with multiple molecular signatures, such as KRAS signaling and the IFN gamma response pathway. Based on our data, melanoma with an immune-enriched TME had a higher PScore than melanoma with an immune-depleted or fibrotic TME. Additionally, monocytes had the highest PScore and malignant cells and fibroblasts the lowest PScore based on single-cell and spatial transcriptome analyses. Finally, a higher PScore was associated with better therapeutic efficacy of immune checkpoint blockade, suggesting the potential of pyroptosis to serve as a marker of immunotherapy response.ConclusionsCollectively, our findings indicate that pyroptosis is a prognostic factor and is associated with the immune response in metastatic melanoma, as based on multiomics data. Our results provide a theoretical basis for drug combination and reveal potential immunotherapy response markers.
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Project description:BackgroundInteraction between malignant cells and immune cells that reside within the tumor microenvironment (TME) modulate different aspects of tumor development and progression. Recent works showed the importance of miRNA-containing extracellular vesicles in this crosstalk.MethodsInterested in understanding the interplay between melanoma and immune-related TME cells, we characterized the TCGA's metastatic melanoma samples according to their tumor microenvironment profiles, HLA-I neoepitopes, transcriptome profile and classified them into three groups. Moreover, we combined our results with melanoma single-cell gene expression and public miRNA data to better characterize the regulatory network of circulating miRNAs and their targets related to immune evasion and microenvironment response.ResultsThe group associated with a worse prognosis showed phenotypic characteristics that favor immune evasion, including a strong signature of suppressor cells and less stable neoantigen:HLA-I complexes. Conversely, the group with better prognosis was marked by enrichment in lymphocyte and MHC signatures. By analyzing publicly available melanoma single-cell RNA and microvesicle microRNAs sequencing data we identified circulating microRNAs potentially involved in the crosstalk between tumor and TME cells. Candidate miRNA/target gene pairs with previously reported roles in tumor progression and immune escape mechanisms were further investigated and demonstrated to impact patient's overall survival not only in melanoma but across different tumor types.ConclusionOur results underscore the impact of tumor-microenvironment interactions on disease outcomes and reveal potential non-invasive biomarkers of prognosis and treatment response.
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