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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Research Seminar: Many-to-many protein networks: modules of multicellularity – Dr. Michael Elowitz
January 25, 2024 @ 11:00 am - 12:00 pm PST
Research Seminar: Many-to-many protein networks: modules of multicellularity – Dr. Michael Elowitz
Talk Summary:
In multicellular organisms, many biological pathways exhibit a curious structure, involving sets of protein variants that bind or interact with one another in a many-to-many fashion. What functions do these seemingly complicated architectures provide? And can similar architectures be useful in synthetic biology? Here, I will discuss recent work in our lab that shows how many-to-many circuits can function as versatile computational devices, explore the roles these computations play in natural biological contexts, and show how many-to-many architectures can be used to design synthetic multicellular behaviors.
Dr. Michael Elowitz Biography:
Michael Elowitz is an HHMI Investigator and Professor of Biology and Biological Engineering at Caltech. His laboratory takes a “build to understand” approach to understand principles of biological circuit design. With Stanislas Leibler, Elowitz developed the Repressilator, an artificial genetic clock that generates gene expression oscillations in bacteria. He showed that gene expression is intrinsically stochastic, or ‘noisy,’ and revealed how noise functions to enable probabilistic differentiation, time-based regulation, and other functions. His group has discovered, designed, and analyzed a variety of synthetic and natural circuit designs for cell-cell communication, epigenetic memory, and cell fate control. His lab currently develops synthetic biological circuits that enable multicellular behaviors and provide foundations for future cell-based therapeutics. Honors include the MacArthur Fellowship, HFSP Nakasone Award, Presidential Early Career Award, Allen Distinguished Investigator Award, and election to the American Academy of Arts and Sciences and National Academy of Sciences.
Location:
Life Sciences Centre
LSC 1003 (LT3)
Join Virtually:
Zoom Meeting ID: 91885 379846
Password: 379846