Research Topics

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 develops and applies computational image analysis methods to decipher the spatial processes in tumors important to patient outcomes. These include neural network and mechanistic modeling approaches that can be applied to cancer images to distinguish tumor regions, identify cancer subtypes, and classify genetic status (Rubinstein, Domanskyi et al 2025, Noorbakhsh et al 2020, Mukashyaka et al 2024, Farahmand et al 2022). The lab works with advanced imaging data, 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.

For example, aging is the biggest risk factor for cancer, yet we still don’t fully understand why. We are studying how aging changes both tumors and the immune system, and how these changes drive cancer progression, with our first projects focusing on the 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).

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).

Cancer Ecosystems

We study the evolutionary dynamics and mechanistic interactions among cells within tumors that can be deciphered from imaging and omics data using methods such as 3D confocal microscopy (Mukashyaka, Kumar, et al 2023) and subcellular protein imaging. For example, the way cells interact plays a critical role in whether tumors grow or respond to treatment—but these interactions are notoriously hard to detect. Our lab recently developed a method that identifies where cells are interacting by analyzing how proteins accumulate at points of contact (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.

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).