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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Propels: Navigating the Biomedical Job Market: Understanding industry demands and how to position oneself for success
November 21, 2023 @ 12:00 pm - 1:00 pm PST
Marketing yourself effectively as a biomedical engineer; creating a strong application: CV and cover letter basics; interview prep and best practices.
Registration is required.
Speakers:
Jacqui Brinkman, UBC Faculty of Graduate + Postdoctoral Studies
Christine Genge, STEMCELL Technologies
Location:
This session is virtual.
Join through the Zoom Link.