Research

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. We collaborate closely with experimental and computational colleagues at JAX Genomic Medicine, JAX Mammalian Genetics, and other scientists around the world.

For the most up-to-date information on our work, see the Publications page.

Tissue AI

A major theme of the lab is the study of Tissue AI, in which we use artificial intelligence approaches to understand the spatial processes within biological tissues, including approaches combining Data-Driven Spatial AI, Constraint-Based AI, and Mechanistic Spatial Modeling. Our studies focus on analyzing cancer image data to understand treatment response, including how it is mediated by tumor subtype status, intratumoral heterogeneity, immune cells, and other microenvironmental features (Rubinstein, Domanskyi et al 2025, Noorbakhsh et al 2020, Mukashyaka et al 2024, Farahmand et al 2022). The lab works with state-of-the-art imaging datatypes including spatial transcriptomics (Visium, Xenium, VisiumHD, merFISH), spatial proteomics (CODEX, imaging mass cytometry), spatial long-read sequencing, and H&E/IHC . 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.

As an example, aging is the biggest risk factor for cancer, yet it is not fully understood why. We are studying how aging changes both tumors and the immune system, as well as how these changes drive cancer progression, especially in breast and lung. By studying tumors from aged mice, we are uncovering spatial patterns of cells that also appear in human cancers. Linking these patterns to patient outcomes could reveal why cancer becomes more dangerous with age and point to new ways to improve treatment for older patients (Angarola et al 2024). Our lab also studies spatial biology for a variety of normal tissue types. 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).

Cancer Ecosystems

We study how intratumoral ecosystems and cellular interactions impact tumor growth and treatment response. For example, our lab recently developed a method to identify cell interactions based on how proteins accumulate at points of contact between cells (Wang et al 2024). Using this approach, we discovered that interactions between two types of immune cells—T cells and B cells—within tumors are linked to better patient survival. This finding opens a promising new strategy for identifying patients who may respond well to treatment and potentially improving outcomes by targeting these immune cell interactions. We have also identified and study cellular behaviors using techniques such as 3D confocal microscopy (Mukashyaka, Kumar, et al 2023), subcellular protein imaging, and single cell spatial profiling.

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.