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.
Events
Calendar
- This event has passed.
SBME Research Seminar: Towards quantitative and universal single molecule biophysics with mass photometry – Dr. Philipp Kukura
May 1, 2024 @ 11:00 am - 12:00 pm PDT
SBME Research Seminar: Towards quantitative and universal single molecule biophysics with mass photometry – Dr. Philipp Kukura
Seminar Abstract:
Biomolecular mechanisms and interactions provide the basis for the function and regulation of cellular processes. Elucidating the underlying processes traditionally relies on a combination of structural characterization and bulk studies aimed at revealing the associated energetics and kinetics. Both approaches, however, come with some intrinsic bias, such as towards the most stable species or the averaging of individual to ensemble behaviour. I will describe recent efforts to overcome these fundamental limitations using mass photometry, the mass measurement of single biomolecules in solution. Following a brief introduction to the technology and its capabilities, I will present recent results that focus on using the unique capabilities of mas photometry to decipher biomolecular mechanisms by quantifying all critical biomolecular interactions. I will focus on protein-DNA machines, such as cohesin and Ku70/80 involved in DNA storage and repair.
Dr. Philipp Kukura’s Biography:
Philipp Kukura is Professor of Chemistry and Fellow of Exeter College at the University of Oxford. He received an MChem from the University of Oxford (2002) and a Ph.D. from the University of California, Berkeley (2006). After postdoctoral work at ETH Zurich, he joined the Chemistry Department at the University of Oxford as Research Fellow (2010) before becoming a Lecturer (2011), and promotion to full Professor (2016). Honours and awards include the Klung- Wilhelmy Science award in Chemistry (2018), the Blavatnik Award for Young Scientists UK in Chemistry (2019), the Emil Thomas Kaiser Ward by the Protein Society (2022) and the Sackler International Prize in Biophysics (2023). He is the founder of Refeyn Ltd, which has commercialised and thereby enabled broad access to Mass Photometry. His current research focusses on the application of light microscopy combined with mass measurement at the single molecule level to study biomolecular structure and interactions.
Location:
LSC 1003 LT3