Possible Rotation Projects:
Develop novel mathematical, statistical, and artificial intelligence models to predict therapeutic outcome of targeted therapies and immunotherapies based on multi-omics datasets.
Identify pathological genomic rearrangements in cancer through integrative analysis of transcriptomic and genomic datasets, and elucidate their clinical significance through translational cancer biology studies.
Identify and qualify predictive biomarkers for cancer targeted therapies through integrative analysis of deep-sequencing and drug sensitivity datasets as well as molecular cancer biology studies.
Training Technologies Used:
Computational Genomics
Translational Cancer Biology
- B.S.,M.D. (equivalent)-China Medical University, Shenyang, China, 1994-2001
- Ph.D. Peking University, Health Science Center, Beijing, China, 2003-2006
Lawal B, Gupta A, Sharma R, Ren H, Bhargava R, Wang Y, Wang XS#. Tumors Hijack Immune-Privileging Regulons via Distinct Cell Types to Confer T Cell Desertion and Immunotherapy Resistance Across Various Cancers. Nature Communications. 2026 May 8. doi: 10.1038/s41467-026-72538-x.
Wang Y, Hu MH, Finn OJ, Wang XS#. Tumor-Associated Antigen Burden Correlates with Immune Checkpoint Blockade Benefit in Tumors with Low Levels of T-cell Exhaustion. Cancer Immunology Research. 2024 12(11):1589-1602
Zhang H., Lee S., Muthakana R., Lu B., Boone D.N., Lee D., Wang XS#. Associations of intragenic rearrangement burden with immune cell infiltration and response to immune checkpoint blockade in cancer. Cancer Immunology Research. 2024 Mar 4;12(3):287-295. Featured Article.
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.
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.
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.
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.
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.
The convergence of the “$100 genome” and the AI revolution makes it possible to reimagine omics-driven precision care. Today’s NGS panels still leave 85–90% of cancer patients without an actionable finding. Our research program is conceived to close this gap—unlocking new discoveries from the same genomic data through AI-driven precision oncology. Our research program is distinguished by its unusual breadth and translational depth, spanning from AI-Genomics, Uncharted Cancer Genetics, Precision genetic markers, Immuno-Oncology Markers, to Omics-based Precision Oncology, which bridges the gaps between computational discovery, experimental validation, and clinical translation.
Our research program advances five synergistic pillars of innovation:
Genomics to Precision care (G2P) AI: We pioneered an integral genomic signature framework (iGenSig, iGenSig-Rx; Nature Commun. 2022; BMC Bioinformatics 2024) for explainable multi-omics modeling, with iGenSig-AI extending this to mechanism-driven in silico drug screening for individualized therapy. We are also developing a G2P Agentic-AI system that autonomously assembles evidence-graded therapeutic options, clinical context, and biomarker-driven predictions to guide individualized care.
Genomics to knowledge (G2K) agents. We built EnSEMBLE, an agent for enhancer-anchored pathway analysis that locks in enhancer-RNA–corroborated pathways from transcriptome sequencing data for biological validation, and BRACE-AI, an autonomous multi-agent framework that accepts omics data and a research goal, designs and executes iterative analyses, reasons as a cancer biologist would, self-validates code and statistics, grounds claims in PubMed, and delivers publication-grade reports.
Precision Immuno-Oncology Biomarker Panels. We discovered intragenic rearrangement (IGR) burden as a predictor of ICB response in TMB-low tumors such as breast and ovarian cancers (Cancer Immunology Research 2024), the tumor-associated antigen (TAA) burden algorithm as a predictor of ICB response for PD-L1-negative tumors (Cancer Research 2012; Cancer Immunology Research 2024), and the IMPREG (immuno-privileging regulon) signature for predicting immunotherapy resistance, validated across 40 transcriptomic trial datasets (Nature Commun., 2026). These are being consolidated into our precision immuno-oncology panel to deliver actionable insights for the majority of patients overlooked by PD-L1 or TMB criteria.
Uncharted Cancer Genetics for Precision Therapy. My lab identified the only canonical recurrent gene fusions described in common breast cancers—ESR1-CCDC170, BCL2L14-ETV6, and RAD51AP1-DYRK4—each matched to an effective genotype-directed targeted therapy, with DOD-funded translational work now advancing toward investigator-initiated clinical trials (Nature Commun. 2014; PNAS, 2020; Clin Cancer Res. 2021). In parallel, we are mapping intragenic rearrangements (IGRs) as a previously overlooked, second-most-prevalent class of protein-altering cancer aberration and defining their roles in breast cancer progression and immunotherapy resistance.
Actionable Kinase Targets for refractory breast and ovarian cancers. We have characterized TLK2 as a key kinase target in aggressive luminal breast cancer (Nature Commun. 2016) and developed a dual p38/NLK inhibitor to target NLK-driven endocrine-resistant breast cancer (Clinical Cancer Res. 2021). Funded by a NCI R21 award, our ongoing work explores novel structural mutations in EPHA3 and other actionable kinases for refractory breast and ovarian disease.
