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Neurol Genet. 2017 Jul 11;3(4):e164. doi: 10.1212/NXG.0000000000000164. eCollection 2017 Aug.

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

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New York Genome Center (K.O.W., M.O.F., N.R., A.-K.E., B.-J.C., K.A., M.S., V.V., E.A.B., J.L.M.V., M.C.Z., V.J., R.B.D.); IBM Thomas J. Watson Research Center (T.K., K.R., F.U., R.N., E.B., L.P., A.K.R.); Columbia University Medical Center (J.N.B., A.B.L., P.C., V.J.); Memorial Sloan-Kettering Cancer Center (C.G.), New York, NY; IBM Watson Health (S.H., V.V.M.), Boca Raton, FL; Laboratory of Molecular Neuro-Oncology (M.O.F., R.B.D.), and Howard Hughes Medical Institute (R.B.D.), The Rockefeller University, New York, NY. B.-J.C. is currently affiliated with Google, New York, NY. V.V. is currently affiliated with 23andMe, Inc., Mountain View, CA. E.A.B. is currently affiliated with Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany.



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.



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