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A somatic reference standard for cancer genome sequencing.

ABSTRACT: Large-scale multiplexed identification of somatic alterations in cancer has become feasible with next generation sequencing (NGS). However, calibration of NGS somatic analysis tools has been hampered by a lack of tumor/normal reference standards. We thus performed paired PCR-free whole genome sequencing of a matched metastatic melanoma cell line (COLO829) and normal across three lineages and across separate institutions, with independent library preparations, sequencing, and analysis. We generated mean mapped coverages of 99X for COLO829 and 103X for the paired normal across three institutions. Results were combined with previously generated data allowing for comparison to a fourth lineage on earlier NGS technology. Aggregate variant detection led to the identification of consensus variants, including key events that represent hallmark mutation types including amplified BRAF V600E, a CDK2NA small deletion, a 12 kb PTEN deletion, and a dinucleotide TERT promoter substitution. Overall, common events include >35,000 point mutations, 446 small insertion/deletions, and >6,000 genes affected by copy number changes. We present this reference to the community as an initial standard for enabling quantitative evaluation of somatic mutation pipelines across institutions.

SUBMITTER: Craig DW

PROVIDER: S-EPMC4837349 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature

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A somatic reference standard for cancer genome sequencing.

Craig David W DW Nasser Sara S Corbett Richard R Chan Simon K SK Murray Lisa L Legendre Christophe C Tembe Waibhav W Adkins Jonathan J Kim Nancy N Wong Shukmei S Baker Angela A Enriquez Daniel D Pond Stephanie S Pleasance Erin E Mungall Andrew J AJ Moore Richard A RA McDaniel Timothy T Ma Yussanne Y Jones Steven J M SJ Marra Marco A MA Carpten John D JD Liang Winnie S WS

Scientific reports 20160420

Large-scale multiplexed identification of somatic alterations in cancer has become feasible with next generation sequencing (NGS). However, calibration of NGS somatic analysis tools has been hampered by a lack of tumor/normal reference standards. We thus performed paired PCR-free whole genome sequencing of a matched metastatic melanoma cell line (COLO829) and normal across three lineages and across separate institutions, with independent library preparations, sequencing, and analysis. We generat ...[more]

PMID: 27094764

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Project description:BACKGROUND:'Precision oncology' can ensure the best suitable treatment at the right time by tailoring treatment towards individual patient and comprehensive tumour characteristics. In current molecular pathology, diagnostic tests which are part of the standard of care (SOC) only cover a limited part of the spectrum of genomic changes, and often are performed in an iterative way. This occurs at the expense of valuable patient time, available tissue sample, and interferes with 'first time right' treatment decisions. Whole Genome Sequencing (WGS) captures a near complete view of genomic characteristics of a tumour in a single test. Moreover, WGS facilitates faster implementation of new treatment relevant biomarkers. At present, WGS mainly has been applied in study settings, but its performance in a routine diagnostic setting remains to be evaluated. The WIDE study aims to investigate the feasibility and validity of WGS-based diagnostics in clinical practice. METHODS:1200 consecutive patients in a single comprehensive cancer centre with (suspicion of) a metastasized solid tumour will be enrolled with the intention to analyse tumour tissue with WGS, in parallel to SOC diagnostics. Primary endpoints are (1) feasibility of implementation of WGS-based diagnostics into routine clinical care and (2) clinical validation of WGS by comparing identification of treatment-relevant variants between WGS and SOC molecular diagnostics. Secondary endpoints entail (1) added clinical value in terms of additional treatment options and (2) cost-effectiveness of WGS compared to SOC diagnostics through a Health Technology Assessment (HTA) analysis. Furthermore, the (3) perceived impact of WGS-based diagnostics on clinical decision making will be evaluated through questionnaires. The number of patients included in (experimental) therapies initiated based on SOC or WGS diagnostics will be reported with at least 3 months follow-up. The clinical efficacy is beyond the scope of WIDE. Key performance indicators will be evaluated after every 200 patients enrolled, and procedures optimized accordingly, to continuously improve the diagnostic performance of WGS in a routine clinical setting. DISCUSSION:WIDE will yield the optimal conditions under which WGS can be implemented in a routine molecular diagnostics setting and establish the position of WGS compared to SOC diagnostics in routine clinical care.

Project description:BackgroundDriven by high throughput next generation sequencing technologies and the pressing need to decipher cancer genomes, computational approaches for detecting somatic single nucleotide variants (sSNVs) have undergone dramatic improvements during the past 2 years. The recently developed tools typically compare a tumor sample directly with a matched normal sample at each variant locus in order to increase the accuracy of sSNV calling. These programs also address the detection of sSNVs at low allele frequencies, allowing for the study of tumor heterogeneity, cancer subclones, and mutation evolution in cancer development.MethodsWe used whole genome sequencing (Illumina Genome Analyzer IIx platform) of a melanoma sample and matched blood, whole exome sequencing (Illumina HiSeq 2000 platform) of 18 lung tumor-normal pairs and seven lung cancer cell lines to evaluate six tools for sSNV detection: EBCall, JointSNVMix, MuTect, SomaticSniper, Strelka, and VarScan 2, with a focus on MuTect and VarScan 2, two widely used publicly available software tools. Default/suggested parameters were used to run these tools. The missense sSNVs detected in these samples were validated through PCR and direct sequencing of genomic DNA from the samples. We also simulated 10 tumor-normal pairs to explore the ability of these programs to detect low allelic-frequency sSNVs.ResultsOut of the 237 sSNVs successfully validated in our cancer samples, VarScan 2 and MuTect detected the most of any tools (that is, 204 and 192, respectively). MuTect identified 11 more low-coverage validated sSNVs than VarScan 2, but missed 11 more sSNVs with alternate alleles in normal samples than VarScan 2. When examining the false calls of each tool using 169 invalidated sSNVs, we observed >63% false calls detected in the lung cancer cell lines had alternate alleles in normal samples. Additionally, from our simulation data, VarScan 2 identified more sSNVs than other tools, while MuTect characterized most low allelic-fraction sSNVs.ConclusionsOur study explored the typical false-positive and false-negative detections that arise from the use of sSNV-calling tools. Our results suggest that despite recent progress, these tools have significant room for improvement, especially in the discrimination of low coverage/allelic-frequency sSNVs and sSNVs with alternate alleles in normal samples.

Dataset Information

A somatic reference standard for cancer genome sequencing.

Publications

A somatic reference standard for cancer genome sequencing.

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