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.
Events
Calendar

SBME Propels: Workshop 4: Securing Funding – Mastering Dilutive & Non-Dilutive Funding For Your Startup Idea
February 13 @ 12:00 pm - 1:00 pm PST
SBME Propels: Workshop 4: Securing Funding – Mastering Dilutive & Non-Dilutive Funding For Your Startup Idea
This workshop provides an in-depth look at non-dilutive funding opportunities, such as NSERC’s Idea to Innovation (I2I) grants and GenomeBC GeneSolve, that support the translation of research into market-ready solutions. Participants will learn how to align their research and solutions with the goals of these funding programs, craft compelling narratives, and leverage funding to advance their projects towards commercialization. The session will include insights from funding experts and practical advice on navigating the application process.
Registration is required.
Location:
Zoom/Virtual