SBME Seminar: Simulation of Kidney Cystogenesis – Dr. James Glazier
Dr. Glazier will illustrate their use in a variety of contexts new and old focusing on epithelial organization, from the simulation of somite formation during development to epithelial homeostasis in the skin and the eye, kidney cystogenesis and developmental toxicology. Dr. Glazier will also discuss the kinds of questions we can answer with Virtual Tissue models to gain scientific insight and for biomedical engineering applications.
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SBME Research Seminar: Using interpretable machine learning to study the genetic determinants of immunotherapy response – Dr. Hannah Carter
February 6, 2025 @ 11:00 am - 12:00 pm PST
SBME Research Seminar: Using interpretable machine learning to study the genetic determinants of immunotherapy response – Dr. Hannah Carter
Seminar Abstract:
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.
Dr. Hannah Carter Biography:
Dr. Carter is a Professor in the Department of Medicine in the Division of Genomics and Precision Medicine at UCSD. The Carter Lab develops and applies computational approaches to aid the interpretation of genetic variation and to advance precision cancer medicine. Dr. Carter is developing methods to model genetic variants as perturbations to biological systems and networks, and to study the interplay between the inherited variants and somatic mutations in shaping cancer risk, somatic tumor evolution and therapeutic response. Dr. Carter is a member of the UCSD Institute for Genomic Medicine, the Bioinformatics and Systems Biology Program and the Moores Cancer Center. She is an NIH Director’s Early Independence Awardee, a Mark Foundation Emerging Leader and Jaime Wyatt Miller Fellow, a CIFAR Azrieli Global Scholar and a Siebel Scholar.