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Magen, A.

Publications and source records attributed to Magen, A..

2 recordsLinked to original sources

Single-cell profiling of tumor-reactive CD4+ T-cells reveals unexpected transcriptomic diversity

Most current tumor immunotherapy strategies leverage cytotoxic CD8+ T cells. Despite evidence for clinical potential of CD4+ tumor-infiltrating lymphocytes (TILs), their functional diversity has limited our ability to harness their activity. To address this issue, we have used single-cell mRNA sequencing 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). Designing new computational approaches to characterize subpopulations, we identify 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 a type I 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 specific CD4+ T cell effector programs. Targeting these programs should help improve immunotherapy strategies. One Sentence SummarySingle-cell RNA sequencing reveals novel and highly diverse transcriptomic patterns characteristic of CD4+ T cell responses to tumors.

immunology

Beyond synthetic lethality: multiple gene interaction types play a key functional role in cancer

The phenotypic effect of perturbing a genes 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. Here, we describe a novel data-driven approach called EnGIne, that applied to TCGA 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. The GI network is accessible online via a web portal [https://amagen.shinyapps.io/cancerapp/].

cancer biology