Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade

Abstract

The Cancer Genome Atlas revealed the genomic landscapes of human cancers. In parallel, immunotherapy is transforming the treatment of advanced cancers. Unfortunately, the majority of patients do not respond to immunotherapy, making the identification of predictive markers and the mechanisms of resistance an area of intense research. To increase our understanding of tumor-immune cell interactions, we characterized the intratumoral immune landscapes and the cancer antigenomes from 20 solid cancers and created The Cancer Immunome Atlas (https://tcia.at/). Cellular characterization of the immune infiltrates showed that tumor genotypes determine immunophenotypes and tumor escape mechanisms. Using machine learning, we identified determinants of tumor immunogenicity and developed a scoring scheme for the quantification termed immunophenoscore. The immunophenoscore was a superior predictor of response to anti-cytotoxic T lymphocyte antigen-4 (CTLA-4) and anti-programmed cell death protein 1 (anti-PD-1) antibodies in two independent validation cohorts. Our findings and this resource may help inform cancer immunotherapy and facilitate the development of precision immuno-oncology.

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

“…Another immune signature seen in C1, C3, C5, and C6 includes transcription factors NFKB1, STAT3, EGR1, and JUN/FOS, as well as TNF and chemokines CXCL1–3 mRNAs implicated in recruiting such cellular immune responses (Figures 4A and S4) (Davis et al, 2016). We explored if expression of PD-L1 overlaps signatures that were recently developed and validated in other cancers for MDSCs, CD8+ CTL, Tregs, and other immune cells (Charoentong et al, 2017; Gentles et al, 2015). Consensus clustering using an MDSC-related signature sorted 4 clusters with very high to low expression of 49 MDSC-related genes, including PD-L1 (Figure S5A).…”

“…mRNA clustering viewed using interactive Next-Generation Clustered Heat-maps (NG-CHMs) (Broom et al, 2017), and an updated Pathway Recognition Algorithm using Data Integration on Genomic Models (PARADIGM) tool (Vaske et al, 2010), helped to integrate omics data with pathways related to squamous cell stemness, differentiation, growth, immortalization, proliferation, survival, and inflammation. Clustered mRNA alterations for immune checkpoint PD-L1, cytokines, and cell determinants were deconvoluted using validated gene signatures for immune cell types and CIBERSORT, revealing overlap between effector T cell and immune checkpoint signatures with those of T-regulatory and Myeloid suppressor cells, which are linked to reduced efficacy of immune therapy (Charoentong et al, 2017; Gentles et al, 2015). These analyses and findings have the potential to influence how we classify SCCs into molecular subtypes, with possible implications for diagnosis, prognosis, and therapy.…”

Quantifying tumor-infiltrating immune cells from transcriptomics data

“…Using this approach, Angelova et al defined 31 custom gene sets representing genes up-regulated in specific immune cell sub-populations and used GSEAPreranked to characterize tumor-infiltrating immune cells in colorectal cancer (CRC) patients [10]. This approach was later extended through the definition of 28 pan-cancer immune gene sets and used to analyze more than 8000 samples across 19 different TCGA solid cancers (results available at https://tcia.at/) [11]. …”

“…Specifically, we considered tumor samples from three TCGA cancers for which both microarrays and RNA-seq data were available and estimated a gene-specific model by fitting a smoothing spline with four degrees of freedom to transform RNA-seq data, as log-transformed transcripts per millions (TPM), into “microarrays-like” data [11]. We then used the model to transform RNA-seq data from more than 8000 TCGA tumors across 19 different cancer types and inferred the fractions of tumor-infiltrating immune cells with CIBERSORT (results available at https://tcia.at) [11]. Similarly, Ali et al [61] analyzed with CIBERSORT more than 11,000 breast tumor RNA-seq data sets normalized with limma voom, a method that transforms RNA-seq log counts to enable downstream application of microarray-specific methodologies [62].…”

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

“…Molecular subtypes of ovarian, melanoma, and pancreatic cancer have been defined based on measures of immune infiltration (Cancer Genome Atlas Research Network, 2011; Cancer Genome Atlas Network, 2015; Bailey et al, 2016), and a number of other tumors show variation in immune gene expression by molecular subtype (Iglesia et al, 2014, 2016; Kardos et al, 2016). Recent publications (Charoentong et al, 2017; Li et al, 2016; Rooney et al, 2015) have presented comprehensive analyses of TCGA data on the basis of immune content response. A recent study (Thorsson et al, 2018) reports on a series of immunogenomic characterizations that include assessments such as total lymphocytic infiltrate, immune cell type fractions, immune gene expression signatures, HLA type and expression, neoantigen prediction, T cell and B cell repertoire, and viral RNA expression.…”