When
Monday, November 4, 2024 - 12:00 p.m.
Jeffrey Chuang
Associate Professor
Jackson Laboratory for Genomic Medicine
"Predicting Cancer Outcome From Spatial Tissue Analysis"
Keating 103
Zoom link | Password: BearDown
Hosts: Alex McGhee and Swarna Ganesh
(Instructor permission required for enrolled students to attend via Zoom)
Persons with a disability may request a reasonable accommodation by contacting the Disability Resource Center at 621-3268 (V/TTY).
Abstract: The spatial organization within the tumor microenvironment is crucial to tumor growth, treatment response and patient outcome. Massively parallel RNA and protein imaging technologies are now revolutionizing the characterization of tumors. Deep learning and interpretable data analysis approaches can be applied to such data to not only decipher tumor biology but also to predict patient outcomes. I will discuss our lab's studies in these areas at the scale of cell-cell interactions, multicellular neighborhoods, and whole-slide images, including examples in breast cancer, melanoma and colorectal cancer.
Biography: Jeffrey Chuang is a professor at the Jackson Laboratory for Genomic Medicine, where he leads a computational lab investigating problems at the intersection of cancer, evolution and image analysis. He obtained his PhD in physics from MIT before switching into computational biology for a postdoc at UCSF. His leadership positions include serving as PI for the NCI Cancer Moonshot Patient-Derived Xenograft Network Data Commons and Coordination Center, co-PI for the NCI Pediatric Cancer In Vivo Testing Consortium Coordination Center, co-Research Program Leader and interim Deputy Director of the JAX Cancer Center and faculty advisor for the JAX Computational Sciences core.