OCT 12, 2017 06:00 AM PDT
Inference of gene signatures associated with response and resistance to targeted and immunotherapies
Presented at the Cancer Research & Oncology 2017 Virtual Event
CONTINUING EDUCATION (CME/CE/CEU) CREDITS: CME | P.A.C.E. CE | Florida CE
1 1 31

Speakers:
  • Professor of Statistics, Biostatistics and Computational Biology, Dana Farber Cancer Institute/Harvard University
    Biography
      Dr. X. Shirley Liu is Professor of Statistics, Biostatistics and Computational Biology at Harvard University, and Director of the Center of Functional Cancer Epigenetics at Dana-Farber Cancer Institute. Her research focuses on algorithm development and integrative modeling of high throughput genomic data to understand the specificity and function of gene expression regulators in tumour development, progression, drug response and resistance. In computational biology, her laboratory developed widely used algorithms for transcription factor motif finding, ChIP-chip/seq, DNase-seq, and CRISPR screen data analysis. In epigenetics, she and colleagues identified the chromatin signature of embryonic pluripotency, and were the pioneers to use the dynamics of nucleosomes, histone marks, and DNase hypersensitivity to predict driving transcription factors and cis-elements in a biological process.

    Abstract:

    We developed two computational methods, CARE and TIDE, to predict response and resistance to targeted therapies and immunotherapies. CARE infers gene signatures of targeted therapy response based on finding genes interacting with the drug target genes from CCLE, CGP and CTRP compound screens on cancer cell lines. CARE shows superior performance on predicting patient response to targeted therapy and chemotherapy, and predicted novel synergistic drug combinations that were experimentally validated. TIDE infers tumor immune dysfunction and exclusion based on finding genes from TCGA gene expression data that interact with tumor infiltrating CD8 T cells to impact patient survival. Interestingly, the TIDE signatures computed from clinical data without immunotherapies can reliably predict the clinical response of melanoma patients for both anti-PD1 and anti-CTLA4 therapies, with higher accuracy than mutation load and other biomarkers.


    Show Resources
    Loading Comments...