Jin Zhang, PhD, MPHS

Assistant Professor
Radiation Oncology
Institute for Informatics
Siteman Cancer Center

Biomedical Informatics and Data Science Program
Cancer Biology Program
Computational and Systems Biology Program

  • 4511 Forest Park Ave Rm 200

  • jin.zhang@wustl.edu

  • https://sites.wustl.edu/jinzhang/

  • RNA-seq, WGS, genomics, sequencing, deep learning, machine learning, bioinformatics, computational biology, algorithm, omics, cancer, HPV, radiation, PET, MRI, image

  • Deep learning; Multi-omics; Translational cancer research

Research Abstract:

The Zhang Translational Genomics Laboratory develops and applies multi-omics and deep learning approaches in cancer biology and personalized radiation oncology. We collaborate with researchers and physician scientists from Department of Radiation Oncology, McDonnell Genome Institute, Institute for Informatics, Siteman Cancer Center, and other Washington University, national and international institutes.

My previous research interests included computational biology and genomics algorithms as they apply to translational cancer research. My doctoral work focused on developing structural variation discovery tools using next-generation sequencing data, including SVseq 1 & 2. I also worked on algorithmic problems in haplotype inference, recombination, rare variants, etc. My postdoctoral work focused on developing algorithms analyzing whole transcriptome sequencing data to discover RNA specific aberrations and their applications in cancers. We designed and implemented the state-of-the-art gene fusion discovery tool, INTEGRATE, leading to the discoveries of novel biomarkers in breast cancer, liver cancer, leukemia, etc. We implemented the first tool in cancer immunology, INTEGRATE-Neo, to predict neo-antigens from tumor specific gene fusion peptides. We discovered a single-gene biomarker, lncRNA PCAT-14, in prostate cancer metastasis, and novel mid-sized small RNAs in acute myeloid leukemia and prostate cancer.

We are currently working on creating and integrating deep learning and radiogenomics approaches using high-throughput sequencing data and imaging data into the development of novel diagnostic, prognostic, and therapeutic strategies in cancers. My long-term goal is to leverage my unique training and expertise in computer science, data science, cancer genomics, population health sciences, and radiation oncology to create novel computational approaches using muti-omics and longitudinal data (i.e., genomics, proteomics, metabolomics, imaging, and clinical) to interpret how molecular alterations in cancers affect patient responses to therapies. These translational deep learning models, and radio-genomic and multi-omics analyses, will ultimately facilitate the prevention, diagnosis, and treatment of cancers and improve patient outcomes.

Current funded projects include:
a. NCI ITCR R21 Developmental Research Grant Award – Title: Deep learning in cervical cancer radiogenomics.
b. ICTS Clinical and Translational Research Funding Program Award – Title: Examining cervical cancer HPV genotypic radiation response using augmented structural gene expression differences.
c. NCI K22 Transition Career Development Award – Title: HPV alternative splicing in cervical cancer radiation response.
d. SIP Pre-R01 multi-PI Award – Title: HPV genomic structural subtypes in oropharyngeal squamous cell carcinoma.

Selected Publications:

Full list of publications: https://sites.wustl.edu/jinzhang/publications/

I. Bioinformatics algorithms and next-generation sequencing:
a. Zhang J, Wu Y. SVseq: an approach for detecting exact breakpoints of deletions with low-coverage sequence data. Bioinformatics. 2011 Dec 1;27(23):3228-34. PubMed PMID: 21994222.
b. Zhang J, Wang J, Wu Y. An improved approach for accurate and efficient calling of structural variations with low-coverage sequence data. BMC Bioinformatics. 2012 Apr 19;13 Suppl 6:S6. PubMed Central PMCID: PMC3358659.
c. Dang HX, Krasnick BA, White BS, Grossman JG, Strand MS, Zhang J, Cabanski CR, Miller CA, Fulton RS, Goedegebuure SP, Fronick CC, Griffith M, Larson DE, Goetz BD, Walker JR, Hawkins WG, Strasberg SM, Linehan DC, Lim KH, Lockhart AC, Mardis ER, Wilson RK, Ley TJ, Maher CA, Fields RC. The clonal evolution of metastatic colorectal cancer. Science Advances. 2020 Jun;6(24):eaay9691. PubMed Central PMCID: PMC7286679.

II. Gene fusion discovery and applications:
a. Zhang J, White NM, Schmidt HK, Fulton RS, Tomlinson C, Warren WC, Wilson RK, Maher CA. INTEGRATE: gene fusion discovery using whole genome and transcriptome data. Genome Research. 2016 Jan;26(1):108-18. PubMed Central PMCID: PMC4691743.
b. Zhang J, Mardis ER, Maher CA. INTEGRATE-neo: a pipeline for personalized gene fusion neoantigen discovery. Bioinformatics. 2017 Feb 15;33(4):555-557. PubMed Central PMCID: PMC5408800.
c. Lei JT*, Shao J*, Zhang J*, Iglesia M, Chan DW, Cao J, Anurag M, Singh P, He X, Kosaka Y, Matsunuma R, Crowder R, Hoog J, Phommaly C, Goncalves R, Ramalho S, Peres RMR, Punturi N, Schmidt C, Bartram A, Jou E, Devarakonda V, Holloway KR, Lai WV, Hampton O, Rogers A, Tobias E, Parikh PA, Davies SR, Li S, Ma CX, Suman VJ, Hunt KK, Watson MA, Hoadley KA, Thompson EA, Chen X, Kavuri SM, Creighton CJ, Maher CA, Perou CM, Haricharan S, Ellis MJ. Functional Annotation of ESR1 Gene Fusions in Estrogen Receptor-Positive Breast Cancer. Cell Reports. 2018 Aug 7;24(6):1434-1444.e7. PubMed Central PMCID: PMC6171747.
d. Nickless A*, Zhang J*, Othoum G, Webster J, Inkman MJ, Coonrod E, Fontes S, Rozycki EB, Maher CA, White NM. Pan-Cancer Analysis Reveals Recurrent BCAR4 Gene Fusions across Solid Tumors. Molecular Cancer Research. 2022 Jul 19; PubMed PMID: 35852383.

III. Non-coding RNAs:
a. Zhang J, Griffith M, Miller CA, Griffith OL, Spencer DH, Walker JR, Magrini V, McGrath SD, Ly A, Helton NM, Trissal M, Link DC, Dang HX, Larson DE, Kulkarni S, Cordes MG, Fronick CC, Fulton RS, Klco JM, Mardis ER, Ley TJ, Wilson RK, Maher CA. Comprehensive discovery of noncoding RNAs in acute myeloid leukemia cell transcriptomes. Experimental Hematology. 2017 Nov;55:19-33. PubMed Central PMCID: PMC5772960.
b. Quigley DA, Dang HX, Zhao SG, Lloyd P, Aggarwal R, Alumkal JJ, Foye A, Kothari V, Perry MD, Bailey AM, Playdle D, Barnard TJ, Zhang L, Zhang J, Youngren JF, Cieslik MP, Parolia A, Beer TM, Thomas G, Chi KN, Gleave M, Lack NA, Zoubeidi A, Reiter RE, Rettig MB, Witte O, Ryan CJ, Fong L, Kim W, Friedlander T, Chou J, Li H, Das R, Li H, Moussavi-Baygi R, Goodarzi H, Gilbert LA, Lara PN Jr, Evans CP, Goldstein TC, Stuart JM, Tomlins SA, Spratt DE, Cheetham RK, Cheng DT, Farh K, Gehring JS, Hakenberg J, Liao A, Febbo PG, Shon J, Sickler B, Batzoglou S, Knudsen KE, He HH, Huang J, Wyatt AW, Dehm SM, Ashworth A, Chinnaiyan AM, Maher CA, Small EJ, Feng FY. Genomic Hallmarks and Structural Variation in Metastatic Prostate Cancer. Cell. 2018 Jul 26;174(3):758-769.e9. PubMed Central PMCID: PMC6425931.
c. Zhang J, Eteleeb AM, Rozycki EB, Inkman MJ, Ly A, Scharf RE, Jayachandran K, Krasnick BA, Mazur T, White NM, Fields RC, Maher CA. DANSR: A Tool for the Detection of Annotated and Novel Small RNAs. Noncoding RNA. 2022. Jan 13;8(1):9. PMID: 35076605.

IV. HPV genomics and cancer biology:
a. Inkman MJ, Jayachandran K, Ellis TM, Ruiz F, McLellan MD, Miller CA, Wu Y, Ojesina AI, Schwarz JK, and Zhang J. HPV-EM: an accurate HPV detection and genotyping EM algorithm. Scientific Reports. 2020 Aug 31;10(1):14340. PubMed Central PMCID: PMC7459114.
b. Ruiz F., Ramachandran R., Muhammad N., Inkman M., Markovina S., Grigsby P., Zhang J., and Schwarz JK, HPV viral transcript expression affects cervical cancer response to chemoradiation treatment, JCI Insight, PubMed Central PMCID: PMC8409981.
c. Wisdom AJ, Hong CS, Lin AJ, Xiang Y, Cooper DE, Zhang J, Xu ES, Kuo HC, Mowery YM, Carpenter DJ, Kadakia KT, Himes JE, Luo L, Ma Y, Williams N, Cardona DM, Haldar M, Diao Y, Markovina S, Schwarz JK, Kirsch DG. Neutrophils promote tumor resistance to radiation therapy. Proceedings of the National Academy of Sciences of the United States of America (PNAS). 2019 Sep 10;116(37):18584-18589. PubMed Central PMCID: PMC6744874.

V. Radio-genomics and deep learning:
a. Zhang J, Rashmi R, Inkman M, Jayachandran K, Ruiz F, Waters MR, Grigsby PW, Markovina S, Schwarz JK. Integrating imaging and RNA-seq improves outcome prediction in cervical cancer. Journal of Clinical Investigation. 2021 Mar 1;131(5) PubMed Central PMCID: PMC7919714.
b. Floberg JM, Zhang J, Muhammad N, DeWees TA, Inkman M, Chen K, Lin AJ, Rashmi R, Jayachandran K, Edelson BT, Siegel BA, Dehdashti F, Grigsby PW, Markovina S, Schwarz JK. Standardized Uptake Value for 18F-Fluorodeoxyglucose Is a Marker of Inflammatory State and Immune Infiltrate in Cervical Cancer. Clinical Cancer Research. 2021 Apr 5; PubMed Central PMCID: PMC8338789.
c. Waters M, Inkman M, Andruska N, Brenneman R, Markovina S, Schwarz JK, Zhang J. Generative adversarial neural networks augment marker and pathway analysis of treatment resistant HPV+ head and neck squamous cell carcinoma. Journal of Clinical Oncology. 2022 40(16)S.
d. Waters M, Inkman M, Grigsby P, Markovina S, Schwarz JK, Zhang J. An 18-gene expression model predicts resistance to standard of care therapy on 3-month follow up 18FDG-PET in locally advanced cervical cancer. Cancer Research. 2022 82(12)S.

Last Updated: 8/4/2022 12:25:24 PM

Back To Top

Follow us: