Spatial omic imaging and machine learning are rapidly transforming the nature of biology research, providing new avenues for discovery at the nexus of AI and mechanistic interpretation. The Chuang lab uses computational, mathematical, and high-throughput data generation approaches to study how cancer ecosystems function, evolve, and respond to therapeutic treatment. In particular, we specialize in the analysis of cancer multi-omic images to discover treatment-targetable processes in the tumor microenvironment. These projects span multiple cancer types including breast cancer, colorectal cancer, melanoma, and lung cancer. They involve collaborations with experimental and computational colleagues at JAX Genomic Medicine, JAX Mammalian Genetics, and external partners.
For the most up-to-date information on our work, see the Publications page.
Tissue AI
Cancer treatment response is affected by tumor cell heterogeneity, immune cells, and other microenvironmental features. Our lab creates and applies computational image analysis methods to decipher the spatial processes in tumors important to patient outcomes. We have developed neural network and mechanistic modeling approaches that can be applied to cancer images to distinguish tumor regions, identify cancer subtypes, and classify genetic status (such as HER2 amplification in breast cancers, Farahmand et al 2022). The lab works with advanced imaging data, including spatial transcriptomics (Visium, Xenium, VisiumHD, merFISH), spatial proteomics (CODEX, imaging mass cytometry), 3D confocal microscopy, spatial long-read sequencing, and H&E/IHC. We develop approaches to evaluate images from tumors (Noorbakhsh et al 2020, Mukashyaka et al 2024) and individual cells (Mukashyaka, Kumar, et al 2023), and we also study cell-cell interactions associated with patient outcomes (Wang et al 2024). Our research team is made up of computational biologists, cancer biologists, immunologists, cancer surgeons, physicists, and pathologists, providing expertise to interrogate these problems in new ways.
In addition, our lab studies spatial biology for normal tissues. We co-lead the Data Analysis Core for the KAPP-Sen U54 in the NIH Cellular Senescence Network. In this project we analyze H&E, spatial transcriptomics, and spatial proteomics datasets for kidney, adipose, pancreas, and placental tissues (NIH SenNet Consortium, 2023). Another major initiative is to study the effects of aging on normal and cancerous tissues in humans and mice (Angarola et al 2024) to discern why aging is a critical risk factor for cancer.
Patient-Derived Xenografts
The Chuang lab is a leader in the field of patient-derived xenografts (PDXs), a model system in which human tumors are engrafted and studied in NSG mice. Xenografts play a critical role in cancer research, as they are used in therapeutic testing to verify drug activity before embarking on a clinical trial. Since 2017, Dr. Chuang has led the Data Coordination Center for the PDX Network, a multi-institute consortium supported by the US National Cancer Institute. We are partnering with institutes including the Huntsman Cancer Institute, Baylor College of Medicine, MD Anderson, Washington University, the Wistar Institute, the University of Pennsylvania, Dana Farber Cancer Institute, Virginia Commonwealth University, the University of California-Davis, and Frederick National Laboratory, to study cancer using xenografts. Our work has produced pioneering studies on the genetic (Woo, Giordano et al 2021) and therapeutic (Evard et al 2020) robustness of xenografts for preclinical testing. Our lab also contributes to the PIVOT Consortium, an NCI project to test the efficacy of drugs for pediatric cancers using xenografts. Within these xenograft projects, our lab is especially focused on the dynamic response of cancers to treatment (Rubinstein, Domanskyii et al, 2025), as xenografts make it possible to reproducibly interrogate this process at critical moments including pre-treatment, early treatment, minimal residual disease, and the onset of resistance.
Prior Interests
The lab has studied a range of prior topics, including mammalian gene regulation (Ritter et al 2010), selection pressures on RNA (Dotu et al 2018), translational regulation (Ishimura et al 2014), molecular evolution (Chuang and Li 2004), and statistical physics (Chuang et al, Phys Rev E 2001).