Dr Xin Wang (王鑫博士)

PhD (Cambridge), Postdoc (Harvard)

Assistant Professor

Dr Xin Wang

Contact Information

Office: 1B-102, 1/F, Block 1,
To Yuen Building
Phone: +852 3442-2367
Fax: +852 3442-0549
Email: xin.wang@cityu.edu.hk
Web: Personal Homepage

Research Interests

  • Cancer genomics
  • Prognostic and predictive biomarkers
  • Regulatory networks
  • Therapeutic targets
  • Bioinformatic tools

Dr Wang is a computational biologist. He was trained as a computer scientist initially in China. Impressed by the beauty of Life Sciences and importance of cancer research, he decided to dedicate to the interdisciplinary field of computational biology. Dr Wang pursued his PhD at the University of Cambridge Department of Oncology and Cancer Research UK Cambridge Institute, where his research was concerned with how to infer intracellular signalling pathways from phenotyping screens. In collaboration with experimental oncologists, he also identified three molecularly distinct colon cancer subtypes using an unsupervised classification approach. From 2013 to 2015, Dr Wang did his postdoc training at Harvard Medical School Department of Biomedical Informatics, where he learned advanced techniques in next-generation sequencing data analysis.

Dr Wang's major research interest is to better understand the biology underlying cancer using quantitative approaches. Especially, he has more passion in projects that bridge basic biological studies and clinical research. He has rich experience in integrative analysis of multilevel (epi)genomic data and clinical outcomes for dissecting cancer heterogeneity. Currently, his research consists of four parts:

  1. identifying and characterizing molecularly distinct cancer subtypes that are in relation to clinical responses;
  2. identifying prognostic and predictive cancer biomarkers that are more accessible in the clinic;
  3. identifying potential therapeutic targets using network-based approaches;
  4. developing bioconductor packages and web tools for analyzing next-generation sequencing data (SPP2), high-throughput screens (HTSanalyzeR), etc.

Selected Publications

(† co-first author; * co-corresponding author)

  1. Yu VWC, Yusuf RZ, Oki T, Wu J, Saez B, Wang X, Cook C, Baryawno N, Ziller MJ, Lee E, Gu H, Meissner A, Lin CP, Kharchenko PV, Scadden DT, Epigenetic Memory Underlies Cell-Autonomous Heterogeneous Behavior of Hematopoietic Stem Cells, Cell, 167(5):1310 - 1322
  2. Trinh A, Trumpi K, De Sousa E Melo F, Wang X, de Jong JH, Fessler E, Kuppen PJK, Reimers MS, Swets M, Koopman M, Nagtegaal I, Jansen M, Hooijer GKJ, Offerhaus GJ, Kranenburg O, Punt CJ, Medema JP, Markowetz F, Vermeulen L, Practical and Robust Identification of Molecular Subtypes in Colorectal Cancer by Immunohistochemistry, Clinical Cancer Research 2016, doi: 10.1158/1078-0432.CCR-16-0680
  3. Fessler E, Drost J, van Hooff SR, Linnekamp JF, Wang X, Jansen M, De Sousa E Melo F, Prasetyanti PR, IJspeert JEG, Franitza M, Nürnberg P, van Noesel CJM, Dekker E, Vermeulen L, Clevers H, and Medem JP, TGFβ signaling directs serrated adenomas to the mesenchymal colorectal cancer subtype, EMBO Molecular Medicine 2016, 8(7): 745–760, doi: 10.15252/emmm.201606184
  4. Fessler E, Jansen M, De Sousa E Melo F, Zhao L, Prasetyanti PR, Rodermond H, Kandimalla R, Linnekamp JF, Franitza M, van Hooff SR, de Jong JH, Oppeneer SC, van Noesel CJM, Dekker E, Stassi G, Wang X*, Medema JP*, and Vermeulen L*, A multidimensional network approach reveals microRNAs as determinants of the mesenchymal colorectal cancer subtype, Oncogene 2016, doi: 10.1038/onc.2016.134 (co-corresponding author)
  5. Masuda T, Wang X, Maeda M, Canver MC, Sher F, Funnell APW, Fisher C, Suciu M, Martyn GE, Norton LJ, Zhu C, Kurita R, Nakamura Y, Xu J, Higgs DR, Crossley M, Bauer DE, Orkin SH, Kharchenko PV, Maeda T, Transcription factors LRF and BCL11A independently repress expression of fetal hemoglobin, Science 2016, 351(6270):285-289
  6. Guinney J, Dienstmann R, Wang X, de Reyniès A, Schlicker A, Soneson C, Marisa L, Roepman P, Nyamundanda G, Angelino P, Bot BM, Morris JS, Simon IM, Gerster S, Fessler E, De Sousa E Melo F, Missiaglia E, Ramay H, Barras D, Homicsko K, Maru D, Manyam GC, Broom B, Boige V, Perez-Villamil B, Laderas T, Salazar R, Gray JW, Hanahan D, Tabernero J, Bernards R, Friend SH, Laurent-Puig P, Medema JP, Sadanandam A, Wessels L, Delorenzi M, Kopetz S, Vermeulen L & Sabine Tejpar, The consensus molecular subtypes of colorectal cancer, Nature Medicine 2015, 10.1038/nm.3967
  7. Alekseyenko AA, Walsh EM, Wang X, Grayson AR, Hsi PT, Kharchenko PV, Kuroda MI, and French CA, The oncogenic BRD4-NUT chromatin regulator drives aberrant transcription within large topological domains, Genes Dev 2015, 29: 1507-1523
  8. Wang X and Markowetz F, "Joining the dots–network analysis of gene perturbation data", book chapter, In Systems Genetics: linking phenotype and genotype, Markowetz F and Boutros M, Cambridge University Press, 2015
  9. Sadanandam A, Wang X, Felipe de Sousa E, Gray JW, Vermeulen L, Hanahan D, & Medema JP, Reconciliation of classification systems defining molecular subtypes of colorectal cancer, Cell Cycle 2014, 13(3): 353-357
  10. De Sousa E Mello, F, Wang X, Jansen M, Fessler E, Trinh A, et al., Poor prognosis colon cancer is defined by a distinct molecular subtype and develops from serrated precursor lesions, Nature Medicine 2013, 19(5): 614-618
  11. Wang X, Yuan K, Hellmayr C, Liu W and Markowetz F, Reconstructing evolving signaling networks by hidden Markov nested effects models, Annals of Applied Statistics 2014, 8(1): 448-480
  12. Mulder KW, Wang X, Escriu C, Ito Y, Schwarz RF, Gillis J, et al., Diverse epigenetic strategies interact to control epidermal differentiation. Nature Cell Biology 2012, 14(7):753-763
  13. Wang X, Castro MA, Mulder KW and Markowetz F, Posterior association networks and functional modules inferred from rich phenotypes of gene perturbations. PLoS Computational Biology 2012, 8(6):e1002566
  14. Castro MA, Wang X, Fletcher M, Ponder BAJ, Meyer KB, Markowetz F, RedeR: R/Bioconductor package for representing modular structures, nested networks and multiple levels of hierarchical associations, Genome Biology 2012, 13:R29
  15. Wang X, Terfve C, Rose JC, Markowetz F, HTSanalyzeR: an R/Bioconductor package for integrated network analysis of high-throughput screens. Bioinformatics 2011, 27(6):879