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

MORE PULICATIONS
An Empirical Approach Leveraging Tumorgrafts to Dissect the Tumor Microenvironment in Renal Cell Carcinoma Identifies Missing Link to Prognostic Inflammatory Factor

Cancer discovery. 2018; 8(9):1142-1155.
Tao Wang, Rong Lu, Payal Kapur, Bijay S. Jaiswal, Raquibul Hannan, Ze Zhang, Ivan Pedrosa, Jason J. Luke, He Zhang, Leonard D. Goldstein, Qurratulain Yousuf, Yi-Feng Gu, Tiffani McKenzie, Allison Joyce, Min S. Kim, Xinlei Wang, Danni Luo, Oreoluwa Onabolu, Christina Stevens, Zhiqun Xie, Mingyi Chen, Alexander Filatenkov, Jose Torrealba, Xin Luo, Wenbin Guo, Jingxuan He, Eric Stawiski, Zora Modrusan, Steffen Durinck, Somasekar Seshagiri, James Brugarolas

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UProbability of phenotypically detectable protein damage by predicted deleterious mutations: analysis of ENU-induced mutations in the Mutagenetix database

Nature Communications. 2017
Tao Wang, Chun Hui Bu, Sara Hildebrand, Gaoxiang Jia, Owen Siggs, Stephen Lyon, David Pratt, Lindsay Scott, Jamie Russell, Sara Ludwig, Anne Murray, Eva Marie Moresco.

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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

Prostate Cancer Challenge DREAM Community. Lancet Oncology . 2016
Guinney J, Wang T, Laajala TD, Winner KK, Bare JC, Neto EC, Khan SA, Peddinti G, Airola A, Pahikkala T, Mirtti T, Yu T, Bot BM, Shen L, Abdallah K, Norman T, Friend S, Stolovitzky G, Soule H, Sweeney CJ, Ryan CJ, Scher HI, Sartor O, Xie Y, Aittokallio T, Zhou FL, Costello JC

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