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: Molecularly Programmable Materials for Biological Interfacing
March 6, 2024 @ 11:00 am - 12:00 pm PST
Special Seminar: Molecularly Programmable Materials for Biological Interfacing
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Seminar Abstract:
The success of medical nanotechnology in response to the recent COVID-19 pandemic has boosted development of nanomaterials for broad diagnostic and therapeutic use. Their size-dependent properties allow us to spy on cellular machinery without introducing much interference. To interact with complex biological systems, engineered nanomaterials require information-rich and autonomous attributes which rely on precise molecular design and engineering of both organization in three-dimensional space, and physicochemical properties imparted by assembled structure. This talk presents engineering efforts of nanomaterials comprise sequence-defined biomolecules to generate high-performing structures at multi-scales and how they can be applied to understand biological processing and nanomedicine. First, I will focus on engineering DNA constructs to organize proteins and nanoparticles into precise and programmable architectures. This part revolves around designs at the interface of molecular and nanoscale objects that impact assembly and reconfiguration behaviors. Next, I will discuss the critical role of peptide molecular designs for an understanding of sequence-structure relationships of disease-associated, plaque-forming amyloid proteins. Further, by integrating with in vivo nanosensor designs, I will show that engineered peptides can act as multiplexed synthetic biomarkers to reveal aberrant activities in the disease microenvironments and for non-invasive diagnostics of lung cancer.
Dr. Shih-Ting (Christine) Wang’s Bio:
Dr. Shih-Ting (Christine) Wang is postdoctoral associate in the lab of Dr. Sangeeta Bhatia in the Koch Institute and fellow of the Ludwig Cancer Center at the Massachusetts Institute of Technology. Her current project focuses on the development of in vivo activity-based nanosensors for non-invasive detection of lung cancer and infection. Prior to this, she was a research associate in the lab of Dr. Oleg Gang at Brookhaven National Laboratory, where she worked on DNA nanotechnology for protein lattice engineering and nanomedicine applications. Wang completed her Ph.D. research in the lab of Dr. Molly Stevens on type II diabetes-related biosensing and amyloid fibrillation. From 2016 to 2020, she has been a facility user and collaborated on multiple research projects with the Molecular Foundry at Lawrence Berkeley National Laboratory.
Location:
DMCBH 101 LT