Issue level insights from cellular measurements - discovery of multi-cellular hubs by co-variation analysis of single-cell expression
Speaker: Matan Hofree (Weizmann Institute)
Room 420 | Checkpoint Building
Therapy response varies considerably among cancer patients and depends on tumor intrinsic factors and interactions with the tumor microenvironment and the host immune system. One example is the response of colorectal cancer (CRC) patients to immune therapy, which varies considerably between patients with mismatch repair-deficient (MMRd) and mismatch-repair proficient tumors (MMRp). MMRd tumors show an elevated presence and activity of immune cells and a favorable response to immune therapy. To understand the difference between these cancer subtypes, we transcriptionally profiled ~400 thousand tumor and adjacent normal cells from 28 MMRp and 34 MMRd patients. Unsupervised analysis identified 88 cell subsets from 7 distinct cell lineages and an associated compendium of 204 gene expression programs. Examination of these programs revealed extensive transcriptional and spatial reprogramming, characteristic of MMRd and MMRp tumors. To discover multi-cellular interactions between malignant and immune cells, we identified expression programs with elevated co-variation across patient tumors. Using signed graph clustering uncovered MMRd/MMRp specific hubs of significant co-variation between cell types. Using spatial profiling, we localized two hubs of particular interest involving the co-activation of malignant and immune-specific programs. This work demonstrates the potential of co-variation analysis to reveal logic underlying multi-cellular interactions in cancer and disease. Throughout the talk, I will highlight computational approaches and data analysis ideas applicable to single-cell and other high-dimensional measurement types.
Matan Hofree received his B.Sc. in Computer Science and Computational Biology from the Hebrew University of Jerusalem. He earned his Ph.D. in Computer Science from UC San Diego, working under Prof. Trey Ideker, where he developed approaches for improved inference, classification, and clustering, using prior biological knowledge encoded in gene interaction networks. He was a Postdoctoral Associate at the Broad Institute of MIT and Harvard, a member of the Regev lab and the Klarman Cell Observatory. Dr. Hofree is currently a visiting scientist at the Department of Biological Regulation of the Weizmann Institute. He develops computational analysis techniques for studying cancer and complex genetic disease, using algorithms and ideas from machine learning and graph theory. Matan focuses on advancing our understanding of the interplay between genetic mutations, transcriptional changes, and cell-cell interactions and how these drive the emergence and evolution of tumors.