Implementation Of Watson Genomic Analytics Processing To Improve The Efficiency Of Interpreting Whole Genome Sequencing Data On Patients With Advanced Cancers.

e12549 Background: The personalized onco-genomics project (POG) at BCCA uses paired tumour/normal whole genome and transcriptome sequence information from a cancer patient to inform treatment options. Analysis and interpretation of this large and complex data set is difficult to complete within a clinically relevant time-frame of less than 10 days. As whole genome sequencing becomes more accessible there is a need to scale-up these interpretive processes but maintain a high degree of evidence and data integrity. We compare the analytic output of conventional human literature review and data compilation with that of Watson Genomic Analytics (WGA). Methods: The POG has biopsied, sequenced and analyzed over 100 metastatic cancers since 2012. Each case has whole genome and transcriptome sequencing and the data is mined to identify somatic mutations or disrupted signaling cascades that might be functionally critical for understanding driver events within an individualu0027s cancer or useful for rationalizing treat...