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Discovering the causes of cancer and the means of prevention

Tongwu Zhang, Ph.D.

Staff Scientist
Division of Cancer Epidemiology & Genetics

NCI Shady Grove | Room 7E218



Dr. Tongwu Zhang received his Ph.D. in bioinformatics from Zhejiang University, China, in June 2012. He conducted part of his doctoral research at the Beijing Institute of Genomics, Chinese Academy of Science, where he worked on whole-genome assembly and RNA-sequencing analysis. Before joining NCI, he was a visiting scholar at the King Abdulaziz City for Science and Technology, Kingdom of Saudi Arabia. Dr. Zhang joined the Laboratory of Translational Genomics (LTG) as a visiting postdoctoral fellow in July 2012, under the mentorship of Kevin M. Brown, Ph.D. He became a staff scientist in the Integrative Tumor Epidemiology Branch (ITEB) in October 2017.

Dr. Zhang has received numerous awards for his work, including an NCI Director’s Innovation Award in 2013, a Fellowship Achievement Award for Excellence in 2014, a DCEG Intramural Research Award in 2015, a DCEG Fellow Award for Research Excellence (DFARE) in 2016, a DCEG Outstanding Research Paper by a Staff Scientist distinction in 2019, and a winning entry in the 2016, 2019, and 2020 DCEG Informatics Tool Challenge.

Research Interests

Dr. Zhang's research focuses on analyzing, integrating, and interpreting multi-omics data for both cancer genomic and genetic studies. He leads a study developing a cell-type specific QTL resource of primary melanocytes to characterize melanoma susceptibility loci. He also leads the genomic analyses and develops new bioinformatics tools and pipelines for Sherlock-Lung, a study that aims to elucidate the etiology of lung cancer in never smokers (LCINS).

Cell-Type Specific QTL Resource of Primary Melanocytes

Genome-wide association studies (GWAS) for melanoma risk, nevus count, and multiple pigmentation traits have identified numerous associated genetic loci. To identify functional variants and affected genes from these loci simultaneously, Dr. Zhang is building a cell-type specific QTL resource including expression quantitative loci (eQTL), DNA methylation QTL (meQTL), alternative splice QTL (sQTL), and other QTL datasets (e.g. microRNA expression). These multi-QTL datasets are powerful tools when used to identify and characterize susceptibility genes for nevi, pigmentation, and melanoma. Dr. Zhang has additionally led the analysis of data derived from massively-parallel reporter assays (MPRA), a high-throughput functional genomics approach used to identify functional risk variants from melanoma GWAS data.

Genomic Analyses for Sherlock-Lung 

The Sherlock-Lung study aims to trace lung cancer etiology in never smokers. Whole genome sequencing, whole transcriptome, and genome-wide methylation data will be used for multi-omic analysis of tumors and surrounding lung tissue. Dr. Zhang leads bioinformatic analyses and develops different pipelines for cancer genomic analyses, including somatic calling for mutations, copy numbers, and structural variations. In the pilot study of Sherlock-Lung, he investigated the landscape of genomic alterations and the evolutionary history of LCINS. Dr. Zhang will continue to analyze and integrate large-scale multi-omics data for Sherlock-Lung to identify the exogenous and endogenous processes involved in lung tumorigenesis in LCINS.

Bioinformatics Tools Development for Cancer Genomics and Genetic Study

Dr. Zhang is the creator of a web-based tool, ezQTL, used for interactive visualization and colocalization of quantitative trait loci (QTL) and GWAS. For his work in cancer genomics, he also created mSigPortal, an integrative mutational signature portal designed to visualize, analyze, and explore the mutational signature data from public genomic studies and/or the user's input. mSigPortal will greatly benefit studies investigating mutagenetic processes involved in tumorigenesis. Dr. Zhang is additionally developing the PurityNGS, a software that can be used to visualize and estimate tumor purity, ploidy, and clonal architecture by integrating somatic copy number alteration, single nucleotide variants, and cancer cell fraction.