SBME Research Seminar - Dr. Hannah Carter
Immune Checkpoint Blockade (ICB) has revolutionized cancer treatment, however mechanisms determining patient response remain poorly understood. We used machine learning to predict ICB response from germline and somatic biomarkers and studied feature usage by the learned model to uncover putative mechanisms driving superior outcomes. Patients with higher T follicular helper infiltrates were robust to defects in the class-I Major Histocompatibility Complex (MHC-I). Further investigation uncovered different ICB responses in MHC-I versus MHC-II neoantigen reliant tumors across patients. Despite similar response rates, MHC-II reliant responses were associated with significantly longer durable clinical benefit (Discovery: Median OS=63.6 vs. 34.5 months P=0.0074; Validation: Median OS=37.5 vs. 33.1 months, P=0.040). Characteristics of the tumor immune microenvironment reflected MHC neoantigen reliance, and analysis of immune checkpoints revealed LAG3 as a potential target in MHC-II but not MHC-I reliant responses. This study highlights the value of interpretable machine learning models in elucidating the biological basis of therapy responses.
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Research Seminar: Mechanobiology & Topology of Collective Cell Migration – Dr. Ian Wong
November 9, 2023 @ 11:00 am - 12:00 pm PST
Event Description:
Epithelial cells transition between collective and individual migration during development and disease, analogous to interacting building blocks (dis)assembling as an active material. In this seminar, I will present recent results on my group to investigate so-called epithelial-mesenchymal transitions in the context of soft matter physics, mechanobiology, and machine learning. First, we investigate how mammary epithelial cells transition from a fluid-like “unjammed” phase to a solid-like “jammed” phase. We show that these collective behaviors exhibit striking analogies with a gelation-like mechanism during the diffusion limited aggregation of non living colloidal particles. Second, we analyze the disorganization and dissemination of multicellular clusters cultured in 3D matrix, which exhibit both collective and individual invasion phenotypes with spatially non-uniform traction signatures. Third, we describe the use of topological barcodes for automated classification of tissue architecture based on spatial connectivity (i.e. persistent homology). These emergent phenomena in living and non-living systems exhibit striking similarities, which may enable new fundamental insights into the morphogenesis of tissues and tumors.
Ian Wong Biography
Ian Wong is Associate Professor of Engineering, and of Pathology / Laboratory Medicine at Brown University. He engineers new miniaturized technologies based on biomaterials and microfluidics to investigate cancer cell invasion, drug resistance, and heterogeneity. He is also interested in the unconventional fabrication of bio and nano materials using self-assembly and 3D printing. He did his graduate work on the directed self-assembly of biomolecular materials with Nick Melosh, receiving a Ph.D. in Materials Science and Engineering from Stanford University. His postdoctoral training was with Mehmet Toner and Daniel Irimia at the Center for Engineering in Medicine at Massachusetts General Hospital. He has been recognized with an NSF Graduate Research Fellowship, a Damon Runyon Cancer Research Fellowship, and the Brown University Pierrepont Award for Outstanding Advising.
Location:
Life Sciences Institute
Lecture Theatre 1003 (LSC 1003)