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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SBME Seminar: Statistical genetics elucidates disease biology, drug discovery, and personalized medicine – Dr. Yukinori Okada
May 6, 2024 @ 11:00 am - 12:00 pm PDT
SBME Seminar: Statistical genetics elucidates disease biology, drug discovery, and personalized medicine – Dr. Yukinori Okada
Seminar Abstract:
Statistical genetics is a research field that evaluates causality of human genetic variations on disease. Human omics technologies project biological mechanisms and disease pathophysiology into multi-layered matrix information with diverse resolutions. Anchored by genotype-phenotype associations highlighted by genome-wide association studies (GWAS), statistical genetics integrates omics matrix as molecular quantitative trait locus (mQTL) catalogues. Multi-layered mQTLs synergistically answered variant functional annotations, connecting disease genetics with clinical phenotypes. We have developed methodology to integrate large-scale human genome data with such molecular resources. Our results empirically show the value of statistical genetics to dissect disease biology, novel drug discovery, and personalized medicine. Finally, we would like to introduce our activity on young researcher developments (“Summer school of statistical genetics”).
Dr. Yukinori Okada’s Biography
Yukinori Okada received the M.D. and PhD from the University of Tokyo. His research theme is the elucidation of mechanism where genetic variants affect biological and clinical phenotype. He has multiple professional backgrounds as a physician, statistician, and bioinformatician. Prof. Okada has conducted large-scale genomics studies of a variety of human complex traits. His interests are now moving towards statistical genetics and bioinformatics analysis generated by the latest omics technologies, such as single cell sequencing and microbiome metagenomics, and its application to novel drug discovery and personalized medicine.
Location:
LSC 1003 (LT3)