Comprehensive analyses of tumor immunity: implications for cancer immunotherapy

Abstract

BackgroundUnderstanding the interactions between tumor and the host immune system is critical to finding prognostic biomarkers, reducing drug resistance, and developing new therapies. Novel computational methods are needed to estimate tumor-infiltrating immune cells and understand tumor–immune interactions in cancers.ResultsWe analyze tumor-infiltrating immune cells in over 10,000 RNA-seq samples across 23 cancer types from The Cancer Genome Atlas (TCGA). Our computationally inferred immune infiltrates associate much more strongly with patient clinical features, viral infection status, and cancer genetic alterations than other computational approaches. Analysis of cancer/testis antigen expression and CD8 T-cell abundance suggests that MAGEA3 is a potential immune target in melanoma, but not in non-small cell lung cancer, and implicates SPAG5 as an alternative cancer vaccine target in multiple cancers. We find that melanomas expressing high levels of CTLA4 separate into two distinct groups with respect to CD8 T-cell infiltration, which might influence clinical responses to anti-CTLA4 agents. We observe similar dichotomy of TIM3 expression with respect to CD8 T cells in kidney cancer and validate it experimentally. The abundance of immune infiltration, together with our downstream analyses and findings, are accessible through TIMER, a public resource at http://cistrome.org/TIMER.ConclusionsWe develop a computational approach to study tumor-infiltrating immune cells and their interactions with cancer cells. Our resource of immune-infiltrate levels, clinical associations, as well as predicted therapeutic markers may inform effective cancer vaccine and checkpoint blockade therapies.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-016-1028-7) contains supplementary material, which is available to authorized users.

Tissue-Specific Immunoregulation: A Call for Better Understanding of the “Immunostat” in the Context of Cancer

“…Nevertheless, the data together with the genomic data as described above illustrate that different cancer types may differ not only by genetics but also by their organ-specific immunologic microenvironments. Consistent with these data, a recent paper integrated the molecular profiles of over 10,000 tumor samples across 23 cancer types and found that the intertumor heterogeneity of immune infiltration may be caused by both cancer genetic variations as well as the disease-specific expression pattern of the chemokine/receptor network (41).…”

Smoker and non-smoker lung adenocarcinoma is characterized by distinct tumor immune microenvironments

“…20–22 A recent study using in silico analysis of tumor immunity also underpinned this conclusion, when taking patient age and stage into consideration. 23 …”

A major chromatin regulator determines resistance of tumor cells to T cell–mediated killing

“…We used TCGA RNA sequencing (RNA-seq) data sets and TIMER (32) to examine the relevance of the CRIPSR screen (fig. S8) and PBAF complex in human cancers.…”

“…Volcano plot showing the Spearman’s correlation and estimated significance of ARID2 (left) or PBRM1 (right) with GZMB mRNA levels from RNA-seq data across TCGA cancer types calculated by TIMER (Tumor Immune Estimation Resource) and adjusted for tumor purity (32). Each dot represents a cancer type in TCGA; red dots indicate significant correlations ( P < 0.01).…”