Jiguang Wang, PhD

Jiguang Wang, PhD

Hong Kong University of Science and Technology

Associate Professor, Division of Life Science and Department of Chemical and Biological Engineering

Expertise: BioinformaticsBioinformaticsCancer GenomicsCancer GenomicsMachine LearningMachine LearningNetwork biologyNetwork biology





Prof. Wang received his Ph.D. in Applied Mathematics from the Academy of Mathematics and Systems Science, Chinese Academy of Sciences (CAS), and won the Special Prize of President Scholarship and Excellent Ph.D. thesis Award of CAS. Between 2011 and 2015, he was a Postdoctoral Research Scientist at Columbia University. In 2015, he was named as the Precision Medicine Fellow and promoted to Associate Research Scientist. He established the Wang Genomics Laboratory @HKUST in 2016, focusing on the application of data science in biology and medicine. He has made substantial contributions to (1) characterization, modeling, and prediction of cancer evolution from genomics (Nat Genet 2016; Nat Genet 2017; Nat Commun 2021); (2) discovery, elucidation, and clinical application of MGMT fusion (Nat Genet 2016; Nat Commun 2020) and METex14 in adult gliomas (Nat Genet 2018; Cell 2018); (3) discovery of MAP3K3-I441M in CCM (AJHG 2021) and elucidation of EndMT in bAVM (Circ Res 2021); (4) reconstruction of RNA Exosome-regulated non-coding transcriptomes (Nature 2014; Cell 2015). He won the Excellent Young Scientist Award of NSFC (2019), the School of Engineering Young Investigator Research Award (2019), the School of Science Research Award (2021), and the Zhong Nanshan Youth Science and Technology Innovation Award (2021).






 






Research Question






 






Recent advances in next-generation sequencing are revolutionizing numerous areas in life science and medicine. Prof. Wang's research is focused on discovering and elucidating functional genomic alterations in complex human diseases, such as intracranial cancers and vascular malformations, by developing and/or applying computational methods based on multi-omics integration, statistics, and machine learning, aiming to bridge the gaps among data, bench, and bedside. More specifically, Prof. Wang's team has been mainly working on the following two scientific questions.






 






Question 1: How does clonal evolution drive cancer progression that leads to malignant transformation and therapeutic resistance?






 






Clonal evolution of cancer is a major challenge leading to treatment failure, but the molecular mechanisms of how cancer cells evolve and gain the capability of surviving intensive chemo- and/or radio- therapies remain elusive. Therefore, it is critically important to characterize the spatial and temporal dynamics of cancer cells and thereby mathematically modelling this process via big data integration. We have been working on diffuse gliomas, the most common and aggressive forms of primary tumors in adult brain whose treatment outcome is still very poor. Current therapies inevitably lead to tumor recurrence and the recurrent gliomas commonly become treatment resistance and incurable. Analyzing longitudinal and single-cell multi-omics data on this disease, our team aims to address the following questions: a) why cancer cells always display complex patterns of intratumoral heterogeneity; b) what is the temporal order of multiple somatic mutations detected in various cancer clones; c) how to predict the evolutionary path and clinical response of cancer cells under a certain therapy based on the sign seen earlier; and d) what are the key factors in tumor and its microenvironment that shape cancer evolution and determine cancer cell response under clinical intervention. In the process of addressing these questions, we will be able to unravel the mysteries of cancer evolution and it might provide a theoretical foundation for designing new means of treatment or diagnostics for better precision cancer medicine via targeting cancer dynamics.






 






Question 2: What is the role of genetic interaction between germline variants and somatic mutations in initializing and regulating the development of cancer and other genetic disorders?






 






Somatic genomic and epigenomic mutations are regarded as the direct drivers of cancer initialization and evolution, whereas de novo and inherited germline alterations could predispose the cancer risk and regulate population-specific disease incidence and treatment response. However, the underlying genetic interactions between germline variants and somatic mutations remain unclear, and the biological and medical implications of these interactions have not been extensively explored. New technologies of genomic sequencing allow low-cost profiling of somatic and germline mutations in not only case-unaffected-parental trios but also disease lesions at a high resolution, providing a unique opportunity to systematically investigate disease-relevant genomes by uncovering the joint contribution of the germline variants and somatic mutations in the process of disease development. Understanding whether and how the germline risk alleles interact with somatic mutations in terms of pathway activation and/or cellular interaction will help us to better understand disease etiology for the purpose of developing novel methods for genome-guided disease risk evaluation and personalized clinical intervention.






 






Representative Publications






 






    

      

    1. Biaobin Jiang, Quanhua Mu, Fufang Qiu, Xuefeng Li, Weiqi Xu, Jun Yu, Weilun Fu, Yong Cao, Jiguang Wang#. Machine learning of genomic features in organotropic metastases stratifies progression risk of primary tumors. Nature Communications 12, Article number: 6692, 2021.
      2. 

    



 


    

      

    1. Hao Li*, Yoonhee Nam*, Ran Huo*, Weilun Fu*, Biaobin Jiang, Qiuxia Zhou, Dong Song, Yingxi Yang, Yuming Jiao, Jiancong Weng, Zihan Yan, Lin Di, Jie Li, Jie Wang, Hongyuan Xu, Shuo Wang, JiZong Zhao, Zilong Wen, Jiguang Wang#, Yong Cao#. De Novo Germline and Somatic Variants Convergently Promote Endothelial-to-Mesenchymal Transition in Simplex Brain Arteriovenous Malformation. Circulation Research, 129(9), 825–839, 2021.
      2. 

    



 


    

      

    1. Jiancong Weng*, Yingxi Yang*†, Dong Song*†, Ran Huo*, Hao Li, Yoonhee Nam†, Yiyun Chen†, Qiuxia Zhou, Yuming Jiao, Weilun Fu, Zihan Yan, Jie Wang, Hongyuan Xu, Lin Di, Jie Li, Shuo Wang, Jizong Zhao, Jiguang Wang#, Yong Cao#. Somatic MAP3K3 Mutation Defines a Subclass of Cerebral Cavernous Malformation. American Journal of Human Genetics 108(5):942-950, 2021.
      2. 

    



 


    

      

    1. Barbara Oldrini*, Nuria Vaquero-Siguero*, Quanhua Mu*†, Paula Kroon, Ying Zhang, Marcos Galán-Ganga, Zhaoshi Bao‡, Zheng Wang, Hanjie Liu, Jason Sa, Junfei Zhao, Hoon Kim, Sandra Rodriguez-Perales, Do-Hyun Nam, Roel Verhaak, Raul Rabadan§, Tao Jiang#, Jiguang Wang#, and Massimo Squatrito#. MGMT genomic rearrangements contribute to chemotherapy resistance in gliomas. Nature Communications, 11(1):3883, 2020.
      2. 

    



 


    

  1. Hu H*, Mu Q*†, Bao Z*‡, Chen Y*†, Liu Y*, Chen J, Wang K, Wang Z, Nam Y†, Jiang B‡, Sa JK, Cho H-J, Her N-G, Zhang C, Zhao Z, Zhang Y, Zeng F, Wu F, Kang X, Liu Y, Qian Z, Wang Z, Huang R, Wang Q, Zhang W, Qiu X, Li W, Nam D-H, Fan X#, Wang J#, Jiang T#. Mutational landscape of secondary glioblastoma guides MET-targeted trial in brain tumor. Cell; 175 (6), 1665-1678, 2018.
    2. 



 


Full list at https://wang-lab.ust.hk/publications/Publication.html.






 






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