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The cervical cancer tumor microenvironment is a diverse and complex ecosystem. Tumor-immune cell infiltration (ICI) may influence immune escape and immunotherapeutic responses. However, the relationship between immune cell infiltrations, immune escape, and immunotherapy in cervical cancer has not been fully clarified. Here, Principal component analysis (PCA) and Tumor immune dysfunction and exclusion (TIDE) were applied to calculate individual ICI scores and probabilities of immune escape, respectively. Through the IMvigor210 and the Cancer Immunome Atlas (TCIA) datasets, we validated the different responses to immunotherapy in two subgroups of patients. Furthermore, therapeutic benefits of different patients were predicted by the pRRophetic package. We found that patients with high ICI scores were prone to immune escape due to the activated JAK-STAT signaling pathway, along with lower CD8+ T cells. High ICI scores patients could benefit more from anti-PD-L1 immunotherapy, and individuals with low scores may be better candidates for the anti-CTLA-4 treatment. Combinations of immunotherapies with targeted inhibitors may improve clinical efficacy and reduce the risk of tumor recurrence. The ICI model not only helps us enhance the cognition of immune escape, but also guides the application of immunotherapy in cervical cancer patients.
Pubmed ID: 36917087
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A database which provides results of comprehensive immunogenomic analyses of next generation sequencing data for 19 solid cancers from The Cancer Genome Atlas and other datasources. The database can be queried for the gene expression of specific immune-related gene sets, cellular composition of immune infiltrates (characterized using gene set enrichment analyses and deconvolution), neoantigens and cancer-germline antigens, HLA types, and tumor heterogeneity (estimated from cancer cell fractions). It also provides survival analyses for different types immunological parameters.
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