Wang Laboratory

Wang Computational Genomics, Precision Oncology and Translational Cancer Biology Lab

  • HOME
  • Research
    • The Overview
    • Discover Recurrent Gene Fusions in More Aggressive Breast Cancers
      • Recurrent ESR1-CCDC170 fusions
      • Recurrent BCL2L14-ETV6 Fusions
      • Neoplastic RAD51AP1-DYRK4 fusions
    • Characterization of Breast Cancer Kinase Targets
      • Tousled-Like Kinase 2
      • Nemo-Like Kinase
    • Multi-omics Precision Oncology Modeling
      • Integral Genomic Signature (iGenSig)
      • IndepthPathway
    • Computational Immunogenomics and Tumor Specific Antigenome
      • Intragenic Rearrangement Burden
      • Cataloging tumor-associated antigen targets
  • Publications
  • News & Awards
  • People
  • Ca-Genome Methods
    • iGenSig Initiatives for Big-Data Based Precision Oncology
    • iGenSig-Rx: a white-box machine learning tool for modeling cancer therapeutic responses using multi-omics data
    • uniConSig & CSEA for Cancer Gene and Pathway Discovery
    • IndepthPathway: an integrated tool for in-depth pathway enrichment analysis based on bulk and single cell sequencing data
    • Fusion Zoom: Identification of Driver Gene Fusions in Cancer
    • Intragenic rearrangement burden
    • HEPA: Discovery of Tumor-Specific Antigenome
    • ConSig-Amp: Identifying Amplified Oncogene Targets
  • Join the lab
  • HOME
  • Research
    • The Overview
    • Discover Recurrent Gene Fusions in More Aggressive Breast Cancers
      • Recurrent ESR1-CCDC170 fusions
      • Recurrent BCL2L14-ETV6 Fusions
      • Neoplastic RAD51AP1-DYRK4 fusions
    • Characterization of Breast Cancer Kinase Targets
      • Tousled-Like Kinase 2
      • Nemo-Like Kinase
    • Multi-omics Precision Oncology Modeling
      • Integral Genomic Signature (iGenSig)
      • IndepthPathway
    • Computational Immunogenomics and Tumor Specific Antigenome
      • Intragenic Rearrangement Burden
      • Cataloging tumor-associated antigen targets
  • Publications
  • News & Awards
  • People
  • Ca-Genome Methods
    • iGenSig Initiatives for Big-Data Based Precision Oncology
    • iGenSig-Rx: a white-box machine learning tool for modeling cancer therapeutic responses using multi-omics data
    • uniConSig & CSEA for Cancer Gene and Pathway Discovery
    • IndepthPathway: an integrated tool for in-depth pathway enrichment analysis based on bulk and single cell sequencing data
    • Fusion Zoom: Identification of Driver Gene Fusions in Cancer
    • Intragenic rearrangement burden
    • HEPA: Discovery of Tumor-Specific Antigenome
    • ConSig-Amp: Identifying Amplified Oncogene Targets
  • Join the lab

Latest Updates

  • New Biomarker Identified: TAA Burden Could Predict Immunotherapy Success in Cancer Patients Lacking Traditional Markers
  • Our lab received new break-through award to study predictive genomic biomarker for precision immuno-oncology
  • Welcome Dr. Renu Sharma!
  • Our Lab on the News: Intragenic Rearrangement Could Hold the Key to Improved Immunotherapy
  • Dr. Yolanda received Women’s Cancer Research and Education Pilot Award. Congredulations!
Wang laboratory @ UPMC Hillman Cancer Center
  • HOME
  • Research
  • Publications
  • News & Awards
  • People
  • Ca-Genome Methods
  • Join the lab