UT Southwestern Medical Center

PROF. Tao Wang

Assistant Professor of the Quantitative Biomedical Research Center in the Department of Population and Data Sciences at UT Southwestern Medical Center, Dr. Wang's research revolves around using state-of-the-art bioinformatics and biostatistics approaches to study the implications of tumor immunology for tumorigenesis, metastasis, prognosis, and treatment response in a variety of cancers. Dr. Wang is affiliated with the Harold C. Simmons Comprehensive Cancer Center. He is also the Bioinformatics co-Leader of the Kidney Cancer Program of UT Southwestern and serves as the Data Analytics Core Co-Director within the UT Southwestern SPORE for kidney cancer research. Dr. Wang graduated from Peking University in China in 2011 and studied at UT Southwestern under the mentoring of Drs. Yang Xie and Guanghua Xiao. He became an Assistant Professor at UTSW after obtaining his Ph.D. in 2015. Aside from his research and investigative work, Dr. Wang also enjoys movies, cooking, and sports with family and friends.

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Research Interests

Bioinformatics, biostatistics, biology, and medicine are the four integral pillars of my interdisciplinary research program. I am currently the Data Analytics Core co-Director of UT Southwestern’s SPORE in kidney cancer (Renal Cell Carcinoma, RCC) and the Bioinformatics co-Leader of the Kidney Cancer Program (KCP) of UTSW. I have been working on mining public and in-house cancer patient data, including the data collected through the kidney SPORE program/KCP at UT Southwestern, to achieve a deeper understanding of the biology of cancer and its implications for diagnosis, prognosis, and treatment. I am especially interested in a comprehensive characterization and modeling of tumor immunity in multiple cancer types through bioinformatics and biostatistics approaches. These include modeling of neoantigens and T cell receptor sequences (molecular level), profiling immune cell infiltrates in bulk tumors and analyses of single cell RNA-seq data (cellular level), and integrative analyses of genomics data with electronic medical record data (patient level). The ultimate goal of my research is to impact the prognosis and treatment of cancer patients through mining of big genomics data and other forms of high-dimensional data. Although I previously focused more on kidney cancer, my current research is actively expanding to other cancer types, such as lung cancer, liver cancer, etc.

DisHet: A Bayesian Hierarchical model for dissecting the cellular heterogeneity of bulk tumor RNA-seq data reveals kidney cancer-specific immune signatures

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SCINA: A Semi-supervised category identification and assignment model for single cell sequencing and proteomics data analysis

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My other research interests

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Selected Publications

Somatic Mutations Increase Hepatic Clonal Fitness and Regeneration in Chronic Liver Disease. Cell. 2019. publisher's website
An Empirical Approach Leveraging Tumorgrafts to Dissect the Tumor Microenvironment in Renal Cell Carcinoma Identifies Missing Link to Prognostic Inflammatory Factor. Cancer discovery. 2018. publisher's website
Probability of phenotypically detectable protein damage by predicted deleterious mutations: analysis of ENU-induced mutations in the Mutagenetix database. Nature Communications. 2017. publisher's website
Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open clinical trial data. Lancet Oncology. 2016. publisher's website