Comparing sequencing assays and human-machine analyses in actionable genomics for glioblastoma

Objective: To analyze a glioblastoma tumor specimen with 3 different platforms and compare potentially actionable calls from each.

Methods: 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.

Results: 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.

Conclusions: 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.


Other Contributors

Kazimierz O. Wrzeszczynski, PhD*, Mayu O. Frank, NP, MS*, Takahiko Koyama, PhD*, Kahn Rhrissorrakrai, PhD*, Nicolas Robine, PhD, Filippo Utro, PhD, Anne-Katrin Emde, PhD, Bo-Juen Chen, PhD, Kanika Arora, MS, Minita Shah, MS, Vladimir Vacic, PhD, Raquel Norel, PhD, Erhan Bilal, PhD, Ewa A. Bergmann, MSc, Julia L. Moore Vogel, PhD, Jeffrey N. Bruce, MD, Andrew B. Lassman, MD, Peter Canoll, MD, PhD, Christian Grommes, MD, Steve Harvey, BS, Laxmi Parida, PhD, Vanessa V. Michelini, BS, Michael C. Zody, PhD, Vaidehi Jobanputra, PhD, Ajay K. Royyuru, PhD and Robert B. Darnell, MD, PhD