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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2023 Synergy Research Day
August 17, 2023 @ 8:30 am - 12:30 pm PDT
SBME Synergy Research Day is an opportunity for students in the Synergy Summer Undergraduate Studentship Program to showcase their summer research projects. We invite faculty, staff, students, researchers, and other BME enthusiasts to join us for a half day of posters and research talks!
Event Details:
- 8:30 am | Welcome
- 8:40 am | Keynote: Dr. Kobi Benenson
- 9:40 am | Trainee Talks
- 10:40 am | Poster Session
- 12:15 pm | Awards
Item Locations:
- Keynote & Trainee Talks | CBH Room 101
- Poster Session | West Atrium of the Life Sciences Centre
- Awards | LSC 1003
Keynote:
Dr. Kobi Benenson
Founder and CEO, Pattern Biosciences
Professor, ETH Zurich
kobi.benenson@patternbio.com
www.patternbio.com
Harnessing DNA computing to save lives
Most therapies act via engaging a target – a disease-associated molecular entity in human cells. However, no disease can be reduced to a single molecular feature, resulting in adverse effects and modest efficacies of many drugs. To overcome this fundamental limitation, we must create therapeutics that adequately capture disease complexity by intercepting disease phenotype at multiple points instead of just one.
DNA emerged as a substrate that can be programmed to sense and process multiple inputs in living human cells. After two decades of fundamental research, DNA molecules programmed to home in on complex molecular disease patterns are ready to start their journey to the clinic. We founded Pattern Biosciences with the vision of bringing the power of DNA computing to patients. I will talk about the scientific underpinnings of the technologies we use at Pattern, and about our vision to use DNA computing to treat cancer and other diseases.
To learn more about the underlying science please visit the following links:
Publication in Science Translational Medicine
Publication in Cell Systems
https://www.statnews.com/2021/12/22/biological-computer-target-cancer-spare-healthy-cells
https://www.fiercebiotech.com/research/precisely-targeting-liver-cancer-logic-gated-gene-therapy