CANCER PANOMICS: COMPUTATIONAL METHODS AND INFRASTRUCTURE FOR INTEGRATIVE ANALYSIS OF CANCER HIGH-THROUGHPUT “OMICS'DATA

摘要

Targeted cancer treatment is becoming the goal of newly developed oncology medicines and has already shown promise in some spectacular cases such as the case of BRAF kinase inhibitors in BRAF-mutant (e.g. V600E) melanoma.1 These developments are driven by the advent of high-throughput sequencing, which continues to drop in cost, and that has enabled the sequencing of the genome, transcriptome, and epigenome of the tumors of a large number of cancer patients in order to discover the molecular aberrations that drive the oncogenesis of several types of cancer.2 Applying these technologies in the clinic promises to transform cancer treatment by identifying therapeutic vulnerabilities of each patient tumor.3 These approaches will need to address the panomics of cancer - the integration of the complex combination of patient-specific characteristics that drive the development of each person's tumor and response to therapy. This in turn necessitates new computational methods to integrate large-scale "omics" data for each patient with their electronic medical records, and in the context of the results from large-scale pan-cancer research studies, to select the best therapy and/or clinical trial for the patient at hand.