Dataset Information

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

ABSTRACT: 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. CLINICALTRIALSGOV IDENTIFIER:NCT02725684.

SUBMITTER: Wrzeszczynski KO

PROVIDER: S-EPMC5506390 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

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

Wrzeszczynski Kazimierz O KO Frank Mayu O MO Koyama Takahiko T Rhrissorrakrai Kahn K Robine Nicolas N Utro Filippo F Emde Anne-Katrin AK Chen Bo-Juen BJ Arora Kanika K Shah Minita M Vacic Vladimir V Norel Raquel R Bilal Erhan E Bergmann Ewa A EA Moore Vogel Julia L JL Bruce Jeffrey N JN Lassman Andrew B AB Canoll Peter P Grommes Christian C Harvey Steve S Parida Laxmi L Michelini Vanessa V VV Zody Michael C MC Jobanputra Vaidehi V Royyuru Ajay K AK Darnell Robert B RB

Neurology. Genetics 20170711 4

<h4>Objective</h4>To analyze a glioblastoma tumor specimen with 3 different platforms and compare potentially actionable calls from each.<h4>Methods</h4>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 ...[more]

PMID: 28740869

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Project description:BackgroundBreast cancer is one of the leading causes of cancer-related deaths in women, and there is a demand in developing an Asian-based genetic profiling database for breast cancer in improving the treatment response. This study aimed to determine molecular alternations and identify potential therapeutic targets by analyzing the genetic profiling from a cohort of Taiwanese breast cancers using a commercialized next-generation sequencing (NGS) targeted panel.MethodsThe study population comprised a broad spectrum of breast cancer patients in Taiwan, including Group 1: planned to receive first-line surgery and followed by adjuvant therapy, or early relapse within three years, Group 2: planned to receive first-line neoadjuvant therapy and followed by surgery, and Group 3: de novo stage IV, or stage IV with recurrence beyond three years. Molecular profiles were determined using Thermo Fisher™ Oncomine™ Comprehensive Assay version 3 (TMO comprehensive assay) from Formalin-Fixed Paraffin-Embedded (FFPE) tissues. Level of actionability was evaluated with the ESMO Scale of clinical actionability of molecular targets (ESCAT).ResultsA total of 380 TMO comprehensive assays were conducted on 372 patients, and we presented targeted sequencing analyses of Tier I: alteration-drug match associated with improved outcome in clinical trials including ERBB2 amplification, BRCA1/2 germline mutation, PIK3CA mutation, and NTRK translocation, and Tier II: antitumor activity associated with the matched alteration-drug but lack of prospective outcome data including PTEN loss, ESR1 mutation, AKT1 mutation, and ERBB2 mutation, and Tier III: matched drug-alteration that led to clinical benefit in another tumor type including MDM2 amplification, and ERBB3 mutation. Among them, 249 (66%) showed at least one actionable alternation based on the ESCAT criteria. The most frequent impacted genes (all variants combined within each sample) were PIK3CA (38%), followed by ERBB2 (23%), ESR1 (10%), AKT1 (6%), and BRCA2 (5%), and the remaining rare variants (less than 5% of assayed cohort) were BRCA1 (3%), MDM2 (2.2%), and ERBB3 (1.1%).ConclusionTargeted sequencing of actionable genes is believed to provide clinical applicability and substantial benefits for Taiwanese breast cancer patients. A valid scale of clinical actionability should be adopted for precision medicine practice under multidisciplinary molecular tumor board.

Dataset Information

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

Publications

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

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