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. ClinicalTrials.gov identifier: NCT02725684.

[1]  Yufeng Shen,et al.  Next-Generation Sequencing of Pulmonary Sarcomatoid Carcinoma Reveals High Frequency of Actionable MET Gene Mutations. , 2016, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[2]  A. Wilm,et al.  LoFreq: a sequence-quality aware, ultra-sensitive variant caller for uncovering cell-population heterogeneity from high-throughput sequencing datasets , 2012, Nucleic acids research.

[3]  Thomas R. Gingeras,et al.  STAR: ultrafast universal RNA-seq aligner , 2013, Bioinform..

[4]  Theresa Zhang,et al.  Personalized genomic analyses for cancer mutation discovery and interpretation , 2015, Science Translational Medicine.

[5]  S. Gabriel,et al.  Pan-cancer patterns of somatic copy-number alteration , 2013, Nature Genetics.

[6]  Sarah C. Ayling,et al.  The Ensembl gene annotation system , 2016, Database J. Biol. Databases Curation.

[7]  Richard Durbin,et al.  Sequence analysis Fast and accurate short read alignment with Burrows – Wheeler transform , 2009 .

[8]  Daniel A. Haber,et al.  Epidermal growth factor receptor mutations in lung cancer , 2007, Nature Reviews Cancer.

[9]  Donavan T. Cheng,et al.  Precision medicine at Memorial Sloan Kettering Cancer Center: clinical next-generation sequencing enabling next-generation targeted therapy trials. , 2015, Drug discovery today.

[10]  Benjamin J. Raphael,et al.  Mutational landscape and significance across 12 major cancer types , 2013, Nature.

[11]  Mark Rosenzweig,et al.  Activation of MET via diverse exon 14 splicing alterations occurs in multiple tumor types and confers clinical sensitivity to MET inhibitors. , 2015, Cancer discovery.

[12]  Laxmi Parida,et al.  Watson for Genomics: Moving Personalized Medicine Forward. , 2016, Trends in cancer.

[13]  C. Perou,et al.  Allele-specific copy number analysis of tumors , 2010, Proceedings of the National Academy of Sciences.

[14]  A. Sivachenko,et al.  Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples , 2013, Nature Biotechnology.

[15]  T. Wurdinger,et al.  Identification of a novel MET mutation in high-grade glioma resulting in an auto-active intracellular protein , 2015, Acta Neuropathologica.

[16]  Giorgio Valle,et al.  The Gene Ontology in 2010: extensions and refinements , 2009, Nucleic Acids Res..

[17]  Wendy S. W. Wong,et al.  Strelka: accurate somatic small-variant calling from sequenced tumor-normal sample pairs , 2012, Bioinform..

[18]  C. Baker,et al.  Genome Sequencing of Autism-Affected Families Reveals Disruption of Putative Noncoding Regulatory DNA. , 2016, American journal of human genetics.

[19]  Cheng Li,et al.  Adjusting batch effects in microarray expression data using empirical Bayes methods. , 2007, Biostatistics.

[20]  Thomas Zichner,et al.  DELLY: structural variant discovery by integrated paired-end and split-read analysis , 2012, Bioinform..

[21]  F. Cavalli,et al.  PTEN loss of expression predicts cetuximab efficacy in metastatic colorectal cancer patients , 2007, British Journal of Cancer.

[22]  M. DePristo,et al.  The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. , 2010, Genome research.

[23]  J. Backer.  The regulation of class IA PI 3-kinases by inter-subunit interactions. , 2010, Current topics in microbiology and immunology.

[24]  M. Schatz,et al.  Accurate detection of de novo and transmitted indels within exome-capture data using micro-assembly , 2014, Nature Methods.

[25]  S. Gabriel,et al.  Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. , 2010, Cancer cell.

[26]  Sandrine Dudoit,et al.  GC-Content Normalization for RNA-Seq Data , 2011, BMC Bioinformatics.

[27]  Gabor T. Marth,et al.  A global reference for human genetic variation , 2015, Nature.

[28]  Wei Shi,et al.  featureCounts: an efficient general purpose program for assigning sequence reads to genomic features , 2013, Bioinform..

[29]  Kai Ye,et al.  Pindel: a pattern growth approach to detect break points of large deletions and medium sized insertions from paired-end short reads , 2009, Bioinform..

[30]  Michael C. Rusch,et al.  CREST maps somatic structural variation in cancer genomes with base-pair resolution , 2011, Nature Methods.

[31]  Martin Vingron,et al.  Detecting genomic indel variants with exact breakpoints in single- and paired-end sequencing data using SplazerS , 2012, Bioinform..

[32]  B. Peters,et al.  Somatic mutations in p85alpha promote tumorigenesis through class IA PI3K activation. , 2009, Cancer cell.

[33]  R. Wilson,et al.  BreakDancer: An algorithm for high resolution mapping of genomic structural variation , 2009, Nature Methods.

[34]  Mark D. Johnson,et al.  Copy number variation detection in whole-genome sequencing data using the Bayesian information criterion , 2011, Proceedings of the National Academy of Sciences.

[35]  Sohrab P. Shah,et al.  TITAN: inference of copy number architectures in clonal cell populations from tumor whole-genome sequence data , 2014, Genome research.

[36]  Pablo Cingolani,et al.  © 2012 Landes Bioscience. Do not distribute. , 2022 .

[37]  Mingming Jia,et al.  COSMIC: mining complete cancer genomes in the Catalogue of Somatic Mutations in Cancer , 2010, Nucleic Acids Res..

[38]  Benjamin M. Good,et al.  Organizing knowledge to enable personalization of medicine in cancer , 2014, Genome Biology.

[39]  Joshua F. McMichael,et al.  Optimizing cancer genome sequencing and analysis. , 2015, Cell systems.

[40]  Lei Shang,et al.  Whole-genome sequencing is more powerful than whole-exome sequencing for detecting exome variants , 2014, Proceedings of the National Academy of Sciences.

[41]  Martin L. Miller,et al.  Mutational landscape determines sensitivity to PD-1 blockade in non–small cell lung cancer , 2015, Science.