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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BMEG 598 Seminar: November 16 Session
November 16, 2023 @ 11:00 am - 12:00 pm PST
Through the BMEG 598: Biomedical Engineering Seminar, Biomedical Engineering graduate students will be exposed to innovative and cutting-edge research happening both on the UBC campus with their colleagues and around the world. This is the fifth in a series of sessions during which students will present their research. The students presenting and the title of their presentation can be found below.
Students Presenting:
• Yujie Hu “Melanin-specific contrast retinal imaging of small animals with polarization-diversity optical coherence tomography“
• Matt Hickey “TBD“
Location:
Life Sciences Institute
Lecture Theatre 101 (LSC 1001)