Tiangen Chang-上海市免疫学研究所

Name:Tiangen Chang
Office Location:West #5 Building, 280 South Chongqing Road, Shanghai, China 200025
Telephone:
Email:tiangen.chang@sjtu.edu.cn

Education & Career

· 2026–Present, Shanghai Institute of Immunology (SII), School of Basic Medical Sciences, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Principal Investigator

· 2022–2026, U.S. National Cancer Institute (NCI), Postdoctoral Fellow

· 2018–2022, Center for Excellence in Molecular Plant Sciences, Chinese Academy of Sciences, Research Scientist

· 2012–2018, University of Chinese Academy of Sciences, PhD in Computational Biology

· 2008–2012, University of Science and Technology of China, Bachelor’s in Mathematics

About the PI

Dr. Chang received his PhD from the Chinese Academy of Sciences and completed his postdoctoral training at the U.S. National Cancer Institute (NCI), National Institutes of Health (NIH). His research program lies at the intersection of clinically driven artificial intelligence (AI) and mechanistic systems biology, with a focus on decoding the complex, multi-scale interplay between the tumor microenvironment and the systemic immune system, and translating these mechanistic insights into predictable, interpretable, and programmable precision immunotherapy strategies.

Over the past five years, Dr. Chang has published 13 papers as first and/or corresponding author in leading journals including Nature Cancer, Science Immunology, Annals of Oncology, Nature Machine Intelligence, and Cancer Discovery. His work has been highlighted in multiple leading journals and mainstream media outlets.

The laboratory maintains close collaborative relationships with leading international research institutions and medical centers, including Memorial Sloan Kettering Cancer Center, Huntsman Cancer Institute, Cedars-Sinai Medical Center, the U.S. National Cancer Institute, the Weizmann Institute of Science, Tel Aviv University, Sheba Medical Center, and Attikon University Hospital (National and Kapodistrian University of Athens). Collaborative work has published in top journals such as Cell, Cancer Cell, and Cancer Discovery. Dr. Chang also serves as an invited reviewer for more than 20 international journals, including Nature Medicine, Genome Biology, and the Journal of the National Cancer Institute.

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Research Vision

Cancer immunotherapy has fundamentally transformed oncology, yet its benefits remain highly uneven. Many patients do not respond to current treatments, while some experience severe immune-related adverse events. These challenges arise because antitumor immune responses are not governed by single pathways, but instead emerge from complex, multi-scale interactions among tumors, immune cells, and the host microenvironment.

The AISI Lab addresses these challenges through the integration of clinically driven artificial intelligence and mechanistic systems modeling. Our goal is to advance precision immunotherapy from empirical discovery toward a more predictive, quantitative, and engineering-guided discipline.

We integrate diverse data sources — including electronic health records, routine laboratory measurements, medical imaging, flow cytometry, proteomics, single-cell sequencing, and spatial omics — to build unified computational frameworks capable of simultaneously predicting treatment response, identifying mechanisms of resistance, and informing therapeutic strategies. While our primary focus is computational immuno-oncology, the conceptual and methodological frameworks developed in the lab are also applicable to autoimmune and other immune-mediated diseases.

Research Directions

1. Predictive AI for Precision Immunotherapy

From Data Integration to Clinical Decision Support

We develop predictive models designed for real-world clinical settings that estimate treatment response probabilities and toxicity risks prior to therapy initiation. A key principle of our approach is the prioritization of routinely available clinical data — such as hematological indices, electronic health records, and standard pathology — over expensive specialized assays. This strategy aims to make precision oncology more broadly accessible across diverse healthcare systems.

2. Systems Immunology of Resistance Mechanisms

From Correlation to Causal Inference

By integrating single-cell omics, spatial transcriptomics, and multi-scale systems modeling, we investigate cellular heterogeneity, state plasticity, and spatial organization within the tumor microenvironment. Our work explores how metabolic stress (such as tumor hypoxia) and intercellular communication shape immune suppression and treatment resistance. Through this approach, we aim to identify mechanistic drivers and actionable vulnerabilities that can inform next-generation immunotherapy strategies.

3. The eOnco-Immune Digital Twin Platform

From Mechanistic Understanding to Rational Design

A long-term goal of the AISI Lab is the development of a digital twin platform that integrates patient-specific data, immunological knowledge, and multi-scale computational models. Such a framework will enable simulation of tumor–immune co-evolution in silico, supporting virtual clinical trials and rational immunotherapy design. Ultimately, this platform aims to move cancer immunotherapy toward a future where therapeutic strategies can be quantitatively modeled and optimized before clinical implementation.