Abstract 1184: Comprehensive analysis with interactive exploration of immune response signatures in 10,000 tumor samples

Abstract

In recent years, analysis of cancer genomics data using methods of immunogenomics has yielded valuable insight into how cancer cells interact with immune cells in the tumor microenvironment. A recent analysis of the multiple molecular platforms by The Cancer Genome Atlas (TCGA) of over 10,000 tumors comprising 33 cancer types identified six immune subtypes, spanning multiple tumor types, that are characterized by differences in: macrophage vs. lymphocyte signatures; Th1:Th2 cell ratio; extent of intratumoral heterogeneity; aneuploidy; extent of neoantigen load; signatures of cell proliferation; expression of immunomodulatory genes; and disease outcome [1]. Particular driver mutations correlate with variation in leukocyte levels across all cancers or with the fraction of individual immune cell types. Intracellular and extracellular networks (involving transcription, microRNAs, copy number and epigenetic processes) are predicted to play a role in establishing the observed tumor-immune cell interactions, both across and within immune subtypes. Additionally, machine learning methods have been applied to HE Immunity 48, p812 - 830.e14 (2018) [2] Saltz, J et al. Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images; Cell Reports 23 pp.181-193.e7 (2018) Citation Format: Vesteinn Thorsson, David L. Gibbs, Mary L. Disis, Elizabeth G. Demicco, Alexander J. Lazar, Jonathan S. Serody, James A. Eddy, Ilya Shmulevich, Justin Guinney, Benjamin G. Vincent. Comprehensive analysis with interactive exploration of immune response signatures in 10,000 tumor samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1184.