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: Synthetic protein circuits for programmable smart therapeutics
March 12, 2024 @ 11:00 am - 12:00 pm PDT
Special Seminar: Synthetic protein circuits for programmable smart therapeutics
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Seminar Abstract:
Tissue engineering has shown promising therapeutic potential; however, the immune system remains a key barrier that must be overcome for long-term cellular engraftment. Current strategies focused on local immune modulation often generate acute responses, largely due to their inability to dynamically respond, or finite reservoir of immune-modulating biologics. In comparison, the field of synthetic biology is uniquely suited to provide the means for probing and controlling such interactions using engineered cells with synthetic circuits that can respond to combinatorial environmental inputs, interrogate natural systems, and produce controlled therapeutic responses. An effective approach for programming cellular behavior involves the use of synthetic protein circuits, however, these circuits are limited to cytosolic proteins and achieving immune modulation requires the ability to control intercellular signalling.
To overcome these challenges, I developed a generalizable platform called RELEASE to enable synthetic protein circuits to control intercellular signaling. I then expanded the programming capabilities of RELEASE, while minimizing the overall genetic payload for engineering mammalian cells. In a separate project, I developed a high-throughput assay to determine key design principles of human transmembrane domains to guide the future engineering of synthetic protein tools and receptors. Through leveraging synthetic protein circuits, my vision is to engineer cells that interface with complex multicellular systems and identify essential signals to bias therapeutic responses, particularly in enhancing long-term cellular engraftment.
Dr. Alexander Vlahos’s Bio:
Alexander Vlahos is currently a Human Frontier Science Program Fellow in the laboratory of Dr. Xiaojing Gao at Stanford University. He applies principles in synthetic biology and protein engineering to develop tools for programming intercellular signalling in mammalian cells. Previously, he completed his PhD in Biomedical Engineering with Dr. Michael Sefton at the University of Toronto, where he developed biomaterial- and tissue engineering-based platforms to improve vascularization of the subcutaneous space for islet transplantation. His goal is to converge his background in synthetic biology, protein engineering, and tissue engineering to mechanistically study multicellular systems, such as the immune system with the goal of improving long-term cell engraftment.
Location:
DMCBH 101 LT