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:IntroductionWhole genome sequencing (WGS) of bacterial isolates can be used to identify antimicrobial resistance (AMR) genes. Previous studies have shown that genotype-based AMR has variable accuracy for predicting carbapenem resistance in carbapenem-resistant Enterobacterales (CRE); however, the majority of these studies used short-read platforms (e.g. Illumina) to generate sequence data. In this study, our objective was to determine whether Oxford Nanopore Technologies (ONT) long-read WGS would improve detection of carbapenem AMR genes with respect to short-read only WGS for nine clinical CRE samples. We measured the minimum inhibitory breakpoint (MIC) using two phenotype assays (MicroScan and ETEST) for six antibiotics, including two carbapenems (meropenem and ertapenem) and four non-carbapenems (gentamicin, ciprofloxacin, cefepime, and trimethoprim/sulfamethoxazole). We generated short-read data using the Illumina NextSeq and long-read data using the ONT MinION. Four assembly methods were compared: ONT-only assembly; ONT-only assembly plus short-read polish; ONT + short-read hybrid assembly plus short-read polish; short-read only assembly.ResultsConsistent with previous studies, our results suggest that the hybrid assembly produced the highest quality results as measured by gene completeness and contig circularization. However, ONT-only methods had minimal impact on the detection of AMR genes and plasmids compared to short-read methods, although, notably, differences in gene copy number differed between methods. All four assembly methods showed identical presence/absence of the blaKPC-2 carbapenemase gene for all samples. The two phenotype assays showed 100% concordant results for the non-carbapenems, but only 65% concordance for the two carbapenems. The presence/absence of AMR genes was 100% concordant with AMR phenotypes for all four non-carbapenem drugs, although only 22%-50% sensitivity for the carbapenems.ConclusionsOverall, these findings suggest that the lack of complete correspondence between CRE AMR genotype and phenotype for carbapenems, while concerning, is independent of sequencing platform/assembly method.

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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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