Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade

By Pornpimol Charoentong, Francesca Finotello, Mihaela Angelova, Clemens Mayer, Mirjana Efremova, Dietmar Rieder, Hubert Hackl, Zlatko Trajanoski. bioRxiv, 2016

4,070 citations recorded by the source corpus.

The Cancer Genome Atlas revealed the genomic landscapes of common human cancers. In parallel, immunotherapy with checkpoint blockers is transforming the treatment of advanced cancers. As only a minority of the patients is responsive to checkpoint blockers, the identification of predictive markers and the mechanisms of resistance is a subject of intense research. To facilitate understanding of the tumor-immune cell interactions, we characterized the intratumoral immune landscapes and the cancer antigenomes from 20 solid cancers, and created The Cancer Immunome Atlas (http://tcia.at). Cellular characterization of the immune infiltrates revealed a role of cancer-germline antigens in spontaneous immunity and 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 superior predictor of response to anti-CTLA-4 and anti-PD-1 antibodies in two independent validation cohorts. Our findings and the developed resource may help informing cancer immunotherapy and facilitate the development of precision immune-oncology.

Subfields: Immunology, Oncology, Pulmonary and Respiratory Medicine

View Paper: https://www.semanticscholar.org/paper/a28f074c4b914f9bdc63c5db0a4ddf6c9b8d1c24

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Citations in the corpus

IOBR: Multi-Omics Immuno-Oncology Biological Research to Decode Tumor Microenvironment and Signatures (2020, methodology): “IOBR integrates eight open-source deconvolution methodologies, namely, CIBERSORT (12), ESTIMATE (13), quanTIseq (14), TIMER (15), IPS (16), MCPCounter (17), xCell (18), and EPIC (19).”
TIMER: A web server for comprehensive analysis of tumor-infiltrating immune cells (2017, methodology): “Its results have been presented as online resources, such as PRECOG (6) and TCIA (7), to analyze the impact of TIICs and individual gene expression levels on clinical outcome.”
xCell: digitally portraying the tissue cellular heterogeneity landscape (2017, methodology): “Published signatures were collected from their corresponding papers [6, 12, 27, 28] (Additional file 5).”
Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images (2018, positive): “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.”
Human Tumor-Associated Macrophage and Monocyte Transcriptional Landscapes Reveal Cancer-Specific Reprogramming, Biomarkers, and Therapeutic Targets (2019, background): “This threshold was selected in order to satisfy two main aims: a) genes with relatively high correlation would not be considered a chance event; b) selection of a relatively small number of genes in order to be suitable for gene set enrichment and survival analysis (Charoentong et al., 2017).”
Cooperation between Constitutive and Inducible Chemokines Enables T-cell Engraftment and Immune Attack in Solid Tumors (2019, methodology): “By the CIBERSORT algorithm, CCL5hiCXCL9hi tumors exhibited higher percentage of activated CD8+ and CD4+ T cells and M1 macrophages, and higher immunophenoscores (Charoentong et al., 2017) than CCL5loCXCL9lo tumors in ovarian and breast cancer as well as melanoma (Figure S4A–S4C).”
A guide to deep learning in healthcare (2019, background): “They can be trained to quantify the amount of PD-L1 present in a histopathology image 20 —a task important in determining which type of immuno-oncology drug a patient would be receptive to.”
Molecular and pharmacological modulators of the tumor immune contexture revealed by deconvolution of RNA-seq data (2017, background): “However, it appears that some cancer subtypes exhibit similar immune contextures associated with specific genotypes as recently shown by us [13] and others [51].”