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Volume 5, Issue 2 (Suppl)
Transcriptomics, an open access journal
ISSN: 2329-8936
Molecular Biology 2017
August 31-September 01, 2017
2
nd
International Conference on
August 31-September 01, 2017 Philadelphia, USA
Molecular Biology, Nucleic Acids &
Molecular Medicine
Analyzing cancer genomics using watson for genomics with structural variants
Takahiko Koyama
IBM Research, USA
Statement of the Problem:
As sequencing cost declines, more cancer patients have their tumor samples sequenced to seek for
optimum treatments. However, analyzing genomic data requires considerable expertise and efforts. Besides, reports should be
generated in clinically relevant time frame, without errors, with objectivity, and in comprehensive manner. It is difficult to meet
those criteria in ever increasing flood of new publications, clinical trial information on investigational drugs, and more high-
resolution data. As an example of high resolution data, structural variant data is available. However, so far, fusion proteins such
as BCR-ABL, EML4-ALK receives attentions; other types of structural variants such as simple disrupts, exon skippings, intron
retentions, and internal tandem duplications are underestimated.
Methodology & Theoretical Orientation:
Watson for Genomics (WfG) takes variants, copy number alterations, and gene
expressions as inputs to generate a report in automated manner. WfG performs molecular profile to identify driver mutations
and drug response biomarkers followed by drug analysis including pathway analysis. Structural variants module is being
developed to accommodate structural variant data.
Findings:
WfG generates reports with high recall rates in drug recommendation compared with human experts using whole
genome samples from collaborators such as New York Genome Center and British Columbia Cancer Agency. Structural
variants such as EGFR vIII, MET exon skipping event, and tumor suppressor gene disruptions are successfully captured along
with de novo events. WfG provides cancer communities with up to date knowledge to benefit their patients.
tkoyama@us.ibm.comTranscriptomics 2017, 5:2 (Suppl)
DOI: 10.4172/2329-8936-C1-013