Tumors form an intricate ecosystem of malignant, stromal and immune cells.
At the cellular level, immune cells may both eliminate cancer cells or support tumor progression. And at the molecular level within individual cells, gene-gene interactions may contribute to changes in cancer cells' viability, leading to overall difference in tumor fitness. Various interactions between and within cells may exert a significant effect on cancer progression and patients' survival and response to therapy. The available large-scale cancer molecular and clinical data as well as the state-of-the-art single-cell sequencing methods allow us to acquire a more detailed snapshot of the various components within the tumor's ecosystem and thus obtain a better understanding of these multi-level interactions' contribution to the tumor fitness, the response to therapy, and in turn, to patients' health. Toward this broad long-term goal, I propose to develop computational methods and tools to address two specific problems: (1) discovery of context specific effects of genes' effect on a phenotype by characterizing the landscape of multi-type gene-gene interactions, (2) discovery of tumor-infiltrating lymphocyte population composition and its association with clinical outcome.
Chair: Dr. Sridhar Hannenhalli
Dept rep: Dr. Ramani Duraiswami
Members: Dr. Eytan Ruppin
Dr. Remy Bosselut