In Methods in enzymology
The remarkable success of cancer immunotherapies, especially the checkpoint blocking antibodies, in a subset of patients has reinvigorated the study of tumor-immune crosstalk and its role in heterogeneity of response. High-throughput sequencing and imaging technologies can help recapitulate various aspects of the tumor ecosystem. Computational approaches provide an arsenal of tools to efficiently analyze, quantify and integrate multiple parameters of tumor immunity mined from these diverse but complementary high-throughput datasets. This chapter describes numerous such computational approaches in tumor immunology that leverage high-throughput data from diverse sources (genomic, transcriptomics, epigenomics and digitized histopathology images) to systematically interrogate tumor immunity in context of its microenvironment, and to identify mechanisms that confer resistance or sensitivity to cancer therapies, in particular immunotherapy.
Bhinder Bhavneet, Elemento Olivier
Checkpoint blocking, Deconvolution, Deep learning, Immune clusters, Immune escape, Immune scores, Immunotherapy, Neoantigen prioritization, Neoantigens, Resistance to therapy, Survival, Tumor heterogeneity, Tumor immunity, Tumor microenvironment, Tumor mutation burden