Comprehensive analyses of tumor immunity: implications for cancer immunotherapy

By Bo Li, E. Severson, Jean-Christophe Pignon, Haoquan Zhao, Taiwen Li, Jesse S. Novak, Peng Jiang, Hui Shen, J. Aster, S. Rodig and 3 more. Genome Biology, 2016

2,015 citations recorded by the source corpus.

Understanding 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. We 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. We 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.

Subfields: Immunology, Molecular Biology, Oncology

View Paper: https://www.semanticscholar.org/paper/f9245407d1306e5743cc5cd7329bce629c9a7276

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Citations in the corpus

TIMER: A web server for comprehensive analysis of tumor-infiltrating immune cells (2017, methodology): “TIMER applies a deconvolution method we previously published (9) to infer the abundanceofTIICs fromgene expressionprofiles.”
Comprehensive evaluation of transcriptome-based cell-type quantification methods for immuno-oncology (2019, background): “(2017) and Racle et al. (2017), regulatory and non-regulatory CD4þ T cells are hard to distinguish.”
xCell: digitally portraying the tissue cellular heterogeneity landscape (2017, background): “an increasing number of closely related cell types [10].”
TIMER2.0 for analysis of tumor-infiltrating immune cells (2020, methodology): “0 utilizes the immunedeconv (16), an R package which integrates six state-of-the-art algorithms, including TIMER (9), xCell (7), MCP-counter (8), CIBERSORT (10), EPIC (11) and quanTIseq (12).”
Allele-Specific HLA Loss and Immune Escape in Lung Cancer Evolution (2017, background): “Previous work has identified immune signatures indicative of immune activity and/or immune cell infiltrates (Davoli et al., 2017; Li et al., 2016; Rooney et al., 2015).”
The Immune Landscape of Cancer (2018, background): “Characterization of the immune microenvironment using gene expression signatures, T cell receptor (TCR), and B cell receptor (BCR) repertoire, and analyses to identify neo-antigenic immune targets provide a wealth of information in many cancer types and have prognostic value (Bindea et al., 2013; Br”
Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade (2016, background): “To estimate TILs we first built a compendium of genes (1625) related to specific immune cells using gene expression profiles from sorted immune cell subpopulations from 37 studies comprising 366 microarrays (Figure 1b).”