The Chuang Laboratory
The Jackson Laboratory for Genomic Medicine
Advances in tissue imaging and artificial intelligence are fundamentally transforming cancer research, providing new opportunities to visualize, understand, and then treat tumors. The Chuang Lab develops computational approaches to analyze tumor images and uncover how cancer cells interact with the immune system—insights that could lead to more effective, personalized treatments for patients. Our team specializes in combining deep learning with biophysical modeling to study solid tumors, including breast cancer, melanoma, colorectal cancer, and lung cancer. Our projects lie at the nexus of multiple fields, and our lab members have backgrounds that span computational biology, genetics, immunology, surgery, pathology, computer science, and theoretical physics.
Goal: We seek to decipher the spatial patterns within tissues, especially cancers, into simple features that can be: 1) analyzed to predict patient outcomes, and 2) modulated to develop new treatments. We are grateful for the support we have received toward this goal from the National Institutes of Health, the National Science Foundation, the Department of Defense, and other philanthropic groups.
We work at the intersection of tissue AI, evolutionary analysis, and biophysical modeling. Our recent studies focus on the development of AI approaches for multiplex cancer image analysis from histology, protein, and transcriptomic imaging data. At the same time, we study how AI-based findings can be connected to biophysical processes within tissues and to the cellular behaviors underlying them. Recent papers from our lab include:
Spatiotemporal profiling defines persistence and resistance dynamics during targeted treatment of melanoma. Jill C. Rubinstein, Sergii Domanskyi, Todd B. Sheridan, Brian Sanderson, SungHee Park, Jessica Kaster, Haiyin Li, Olga Anczukow, Meenhard Herlyn, Jeffrey H. Chuang. Cancer Research (2025) 85 (5): 987–1002.
Comprehensive single cell aging atlas of healthy mammary tissues reveals shared epigenomic and transcriptomic signatures of aging and cancer. Brittany Angarola , Siddhartha Sharma , Neerja Katiyar , Hyeon Gu Kang , Djamel Nehar-Belaid , SungHee Park , Rachel Gott , Giray Eryilmaz , Mark LaBarge , Karolina Palucka , Jeffrey Chuang , Ron Korstanje , Duygu Ucar, Olga Anczukow. Nature Aging volume 5, pages 122–143 (2025).
SAMPLER: unsupervised representations for rapid analysis of whole slide tissue images. Patience Mukashyaka, Todd B. Sheridan, Ali Foroughi pour, Jeffrey H. Chuang. eBioMedicine (2024). 99:104908
High-throughput deconvolution of 3D organoid dynamics at cellular resolution for cancer pharmacology with Cellos. Patience Mukashyaka, Pooja Kumar, David J. Mellert, Shadae Nicholas, Javad Noorbakhsh, Mattia Brugiolo, Olga Anczukow, Edison T. Liu, Jeffrey H. Chuang. Nature Communications 14, Article number: 8406 (2023)