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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Special Seminar: Engineering Bacteria as Living Drug Delivery Systems
February 7, 2024 @ 11:00 am - 12:00 pm PST
Speaker: Tetsuhiro Harimoto, PhD (he/him)
Wednesday, Feb 7, 2024
Time: 11:00am-12:00pm Pacific time
Location: LSC 1003 LT3 + online
Zoom: https://ubc.zoom.us/j/62334561670?pwd=SVJISFJISWhIWHU1cmtWVTFmeUVLQT09
Meeting ID: 623 3456 1670
Passcode: 502196
Abstract: Engineered living cells as therapeutic agents are transforming modern medicine. An emerging focus is tumour-colonizing bacteria, where systemically delivered bacteria have been demonstrated to selectively grow within solid tumours. This natural tropism to tumours presents a unique opportunity to engineer bacteria as programmable drug delivery vehicles to regions inaccessible with existing chemo- and immuno-therapeutics. In this talk, I will describe our recent efforts to enhance bacterial cancer therapies through synthetic biology. I will focus on strategies to address several key challenges for clinical translation, including bacterial delivery, therapeutic identification, and off-target effects. Our multidisciplinary approach, spanning from gene circuit design to in vitro and in vivo models, advances bacteria as next-generation drug carriers capable of sensing and responding to diseases within the body.
Bio: Tetsuhiro received his Ph.D. in Biomedical Engineering from Columbia University in 2022. His graduate work in Dr. Tal Danino’s lab focused on the engineering of living microbes as advanced drug delivery vehicles, with a specific focus on tumour-homing bacteria as cancer therapeutics. Currently, Tetsuhiro is an NCI F99/K00 postdoctoral fellow in the Wyss Institute for Biologically Inspired Engineering at Harvard University. Working with Dr. David Mooney, Tetsuhiro is developing engineered living materials as next-generation drug delivery systems. Tetsuhiro was named as one of STAT’s Wunderkinds and recognized in MIT Technology Review’s Innovators Under 35 in 2023.