Our research has a strong focus on applying a multiple disciplinary approach inclusive of computational genomics, cancer genetics, molecular and cell biology, and translational studies to detect driving genetic aberrations, qualify appropriate cancer targets, and develop predictive biomarkers and models to achieve precision therapeutics. The research goals of our lab include: 1) Discover driver genetic alternations, viable therapeutic targets, and predictive biomarkers in cancer for the development of precision therapeutics; 2) develop novel computational approaches to predict therapeutic responses of cancer targeted therapies and immunotherapies based on multi-omics data.
Our wet laboratory research focus is to explore the “dark-side” of cancer genetics for identification of novel therapeutic targets and predictive genetic biomarkers. By interrogating multi-omics datasets, we have identified five recurrent gene fusions in cancer (Nature Biotech 2009, Cancer Discovery 2012, Nature Commun. 2014, PNAS 2020, Clinical Cancer Res. 2020). In particular, our lab identified two cryptic recurrent gene fusions called ESR1-CCDC170 and BCL2L14-ETV6 in more aggressive breast cancer forms including luminal B and triple-negative breast cancers respectively. To date, these are the only two canonical gene fusions identified in major breast cancer entities. Our lab also characterized two amplified kinase targets in breast cancer, TLK2 and NLK, and performed mechanistic studies and preclinical studies of kinase inhibitors in vitro and in vivo (Nature Commun. 2016, Clinical Cancer Res. 2021). These new discoveries will yield novel insights into driving genetic abnormalities leading to more aggressive and therapy-resistant breast cancer and establish robust targets for effective and personalized therapies.
Our dry lab projects focus on developing computational technologies to discover driver cancer genes and therapeutic targets as well as modeling therapeutic responses based on multi-omics datasets. We have developed a computational approach called HEPA for high-throughput identification of tumor associated antigen targets which has been validated through large scale analysis of patient blood using a PARSE assay he developed (Cancer Research 2012). We also pioneered a new class of multi-omics modeling methods for precision oncology called “integral genomic signature analysis” (Nature Commun. 2022). Through integrating biological insights with robust algorithms, we are developing multiple clinical-grade computational models for predicting immunotherapy, targeted therapy, and chemotherapy responses.
- Lee S, Deng L, Wang Y, Wang K, Sartor MA, Wang XS#
IndepthPathway: an integrated tool for in-depth pathway enrichment analysis based on single-cell sequencing data.
Bioinformatics. 2023 Jun 1;39(6): btad325 - Wang XS#, Lee S, Zhang H, Tang G, Wang Y.
An integral genomic signature approach for tailored cancer therapy using genome-wide sequencing data.
Nature Communications. 2022 13(1):2936. - Wang X, Veeraraghavan J, Liu C, Cao X, Qin L, Kim J, Tan Y, Loo S, Hu Y, Lin L, Lee S, Shea M, Mitchell T, Li S, Ellis M, Hilsenbeck SG, Schiff R, Wang XS#.
Therapeutic targeting of nemo-like kinase in primary and acquired endocrine-resistant breast cancer.
Clinical Cancer Research. 2021 May 1;27(9):2648-2662. Doi: 10.1158/1078-0432.CCR-20-2961. - Liu CC*, Veeraraghavan J*, Tan Y, Kim JA, Wang X, Loo SK, Lee S, Hu Y, and Wang XS#. A novel neoplastic fusion transcript, RAD51AP1-DYRK4, confers sensitivity to the MEK inhibitor trametinib in aggressive breast cancers. Clinical Cancer Research. 2021 Feb 1;27(3):785-798. Doi: 10.1158/1078-0432.CCR-20-2769.
- Li L, Lin L, Veeraraghavan J, Hu Y, Wang X, Lee S, Tan Y, Schiff R, Wang XS#.
Therapeutic role of recurrent ESR1-CCDC170 gene fusions in breast cancer endocrine resistance.
Breast Cancer Research. 2020 22:84. https://doi.org/10.1186/s13058-020-01325-3 - Lee S*, Hu Y*, Loo SK, Tan Y, Bhargava R, Lewis MT, Wang XS#.
Landscape analysis of adjacent gene rearrangements reveals BCL2L14-ETV6 gene fusions in more aggressive triple-negative breast cancer.
Proc Natl Acad Sci U S A. 2020 Apr 22:201921333. doi: 10.1073/pnas.1921333117. - Chi X, Sartor MA, Lee S, Anurag M, Patil S, Hall P, Wexler M, Wang XS#.
Universal Concept Signature Analysis: Genome-Wide Quantification of New Biological and Pathological Functions of Genes and Pathways.
Briefings in Bioinformatics, 2020 Sep 25;21(5):1717-1732 - Kim JA, Tan Y, Wang X, Cao X, Veeraraghavan J, Liang Y, Edwards DP, Huang S, Pan X, Li K, Schiff R. and Wang XS#.
Comprehensive functional analysis of the tousled-like kinase 2 frequently amplified in aggressive luminal breast cancers.
Nature Communications. 2016 7:12991. - Veeraraghavan J, Tan Y, Cao XX, Kim JA, Wang X, Chamness GC, Maiti SN, Cooper LJN, Edwards DP, Contreras A, Hilsenbeck SG, Chang EC, Schiff R, Wang XS#.
Recurrent ESR1-CCDC170 rearrangements in an aggressive subset of estrogen-receptor positive breast cancers.
Nature Communications. 2014 5:4577. - Xu QW, Zhao W, Wang Y, Sartor MA, Han DM, Deng JX, Ponnala R, Yang JY, Zhang QY, Liao GQ, Qu YM, Li L, Liu FF, Zhao HM, Yin YH, Chen WF, Zhang Y#, Wang XS#.
An integrated genome-wide approach to discover tumor specific antigens as potential immunological and clinical targets in cancer.
Cancer Research. 2012 72:6351-61. - Wang XS*, Shankar S*, Dhanasekaran SM*, Ateeq B, Prensner JR, Yocum AK, Pflueger D, Jing X, Fries DF, Han B, Li Yong, Cao Q, Cao X, Maher CA, Kumar SC, Demichelis F, Tewari AK, Kuefer R, Omenn GS, Palanisamy S, Rubin MA, Varambally S, Chinnaiyan AM.
Characterization of KRAS Rearrangements in Metastatic Prostate Cancer.
Cancer Discovery. 2011 1:35-43. - Wang XS, Prensner JR, Chen G, Cao Q, Han B, Dhanasekaran SM, Ponnala R, Cao X, Varambally S, Thomas DG, Giordano TJ, Beer DG, Palanisamy N, Sartor MA, Omenn GS, Chinnaiyan AM.
An integrative approach to reveal driver gene fusions from paired-end sequencing data in cancer.
Nature Biotechnology. 2009 27:1005-1011.
View Dr. Wang's complete bibliography on PubMed.