Comparing sequencing assays and human-machine analyses in actionable genomics for glioblastoma.
Kazimierz O Wrzeszczynski, Mayu O Frank, Takahiko Koyama, Kahn Rhrissorrakrai, Nicolas Robine, Filippo Utro, Anne-Katrin Emde, Bo-Juen Chen, Kanika Arora, Minita Shah, Vladimir Vacic, Raquel Norel, Erhan Bilal, Ewa A Bergmann, Julia L Moore Vogel, Jeffrey N Bruce, Andrew B Lassman, Peter Canoll, Christian Grommes, Steve Harvey, Laxmi Parida, Vanessa V Michelini, Michael C Zody, Vaidehi Jobanputra, Ajay K Royyuru, Robert B Darnell,
Neurology. Genetics, July 25, 2017
To analyze a glioblastoma tumor specimen with 3 different platforms and compare potentially actionable calls from each. Tumor DNA was analyzed by a commercial targeted panel. In addition, tumor-normal DNA was analyzed by whole-genome sequencing (WGS) and tumor RNA was analyzed by RNA sequencing (RNA-seq). The WGS and RNA-seq data were analyzed by a team of bioinformaticians and cancer oncologists, and separately by IBM Watson Genomic Analytics (WGA), an automated system for prioritizing somatic variants and identifying drugs. More variants were identified by WGS/RNA analysis than by targeted panels. WGA completed a comparable analysis in a fraction of the time required by the human analysts. The development of an effective human-machine interface in the analysis of deep cancer genomic datasets may provide potentially clinically actionable calls for individual patients in a more timely and efficient manner than currently possible. NCT02725684.
Pubmed Link: 28740869