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Cancer is the second leading cause of death globally. Tumors form intricate ecosystems in which malignant and immune cells interact to shape disease progression. Yet, the molecular underpinnings of tumorigenesis and immunological responses to tumors are poorly understood, limiting their manipulation to elicit favorable clinical outcomes. This thesis lays conceptual frameworks for investigating the molecular interactions taking place in tumors as well as the diversity of the immune response to cancer.

In the molecular level of individual cancer cells, the phenotypic effect of perturbing a gene’s activity depends on the activity level of other genes, reflecting the notion that phenotypes are emergent properties of a network of functionally interacting genes. In the context of cancer, contemporary investigations have primarily focused on just one type of functional genetic interaction (GI) – synthetic lethality (SL). However, there may be additional types of GIs whose systematic identification would enrich the molecular and functional characterization of cancer. This thesis describes a novel data-driven approach called EnGIne, that applied to large-scale cancer data identifies 71,946 GIs spanning 12 distinct types, only a small minority of which are SLs. The detected GIs explain cancer driver genes’ tissue- specificity and differences in patients’ response to drugs, and stratify breast cancer tumors into refined subtypes. These results expand the scope of cancer GIs and lay a conceptual and computational basis for future studies of additional types of GIs and their translational applications.

Furthermore, tumor growth is continuously shaped by the immune response. However, T cells typically adopt a dysfunctional phenotype may be reversed using immunotherapy strategies. Most current tumor immunotherapies leverage cytotoxic CD8+ T cells to elicit an effective anti-tumor response. Despite evidence for clinical potential of CD4+ tumor-infiltrating lymphocytes (TILs), their functional diversity has limited our ability to harness their anti-tumor activity. To address this issue, we have used single-cell mRNA sequencing (scRNAseq) to analyze the response of CD4+ T cells specific for a defined recombinant tumor antigen, both in the tumor microenvironment and draining lymph nodes (dLN). New computational approaches to characterize subpopulations identified TIL transcriptomic patterns strikingly distinct from those elicited by responses to infection, and dominated by diversity among T-bet-expressing T helper type 1 (Th1)-like cells. In contrast, the dLN response includes Follicular helper (Tfh)-like cells but lacks Th1 cells. We identify an interferon-driven signature in Th1-like TILs, and show that it is found in human liver cancer and melanoma, in which it is negatively associated with response to checkpoint therapy. Our study unveils unsuspected differences between tumor and virus CD4+ T cell responses, and provides a proof-of-concept methodology to characterize tumor- control CD4+ T cell effector programs. Targeting these programs should help improve immunotherapy strategies.