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Analytical Validation of the Next-Generation Sequencing Assay for a Nationwide Signal-Finding Clinical Trial: Molecular Analysis for Therapy Choice Clinical Trial.

ABSTRACT: The National Cancer Institute-Molecular Analysis for Therapy Choice (NCI-MATCH) trial is a national signal-finding precision medicine study that relies on genomic assays to screen and enroll patients with relapsed or refractory cancer after standard treatments. We report the analytical validation processes for the next-generation sequencing (NGS) assay that was tailored for regulatory compliant use in the trial. The Oncomine Cancer Panel assay and the Personal Genome Machine were used in four networked laboratories accredited for the Clinical Laboratory Improvement Amendments. Using formalin-fixed paraffin-embedded clinical specimens and cell lines, we found that the assay achieved overall sensitivity of 96.98% for 265 known mutations and 99.99% specificity. High reproducibility in detecting all reportable variants was observed, with a 99.99% mean interoperator pairwise concordance across the four laboratories. The limit of detection for each variant type was 2.8% for single-nucleotide variants, 10.5% for insertion/deletions, 6.8% for large insertion/deletions (gap ?4 bp), and four copies for gene amplification. The assay system from biopsy collection through reporting was tested and found to be fully fit for purpose. Our results indicate that the NCI-MATCH NGS assay met the criteria for the intended clinical use and that high reproducibility of a complex NGS assay is achievable across multiple clinical laboratories. Our validation approaches can serve as a template for development and validation of other NGS assays for precision medicine.

SUBMITTER: Lih CJ

PROVIDER: S-EPMC5397672 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

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Analytical Validation of the Next-Generation Sequencing Assay for a Nationwide Signal-Finding Clinical Trial: Molecular Analysis for Therapy Choice Clinical Trial.

Lih Chih-Jian CJ Harrington Robin D RD Sims David J DJ Harper Kneshay N KN Bouk Courtney H CH Datta Vivekananda V Yau Jonathan J Singh Rajesh R RR Routbort Mark J MJ Luthra Rajyalakshmi R Patel Keyur P KP Mantha Geeta S GS Krishnamurthy Savitri S Ronski Karyn K Walther Zenta Z Finberg Karin E KE Canosa Sandra S Robinson Hayley H Raymond Amelia A Le Long P LP McShane Lisa M LM Polley Eric C EC Conley Barbara A BA Doroshow James H JH Iafrate A John AJ Sklar Jeffrey L JL Hamilton Stanley R SR Williams P Mickey PM

The Journal of molecular diagnostics : JMD 20170207 2

The National Cancer Institute-Molecular Analysis for Therapy Choice (NCI-MATCH) trial is a national signal-finding precision medicine study that relies on genomic assays to screen and enroll patients with relapsed or refractory cancer after standard treatments. We report the analytical validation processes for the next-generation sequencing (NGS) assay that was tailored for regulatory compliant use in the trial. The Oncomine Cancer Panel assay and the Personal Genome Machine were used in four ne ...[more]

PMID: 28188106

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Project description:BackgroundThe Oncotype DX Prostate Cancer Assay is a multi-gene RT-PCR expression assay that was developed for use with fixed paraffin-embedded (FPE) diagnostic prostate needle biopsies containing as little as 1 mm of prostate tumor in the greatest dimension. The assay measures expression of 12 cancer-related genes representing four biological pathways and 5 reference genes which are algorithmically combined to calculate the Genomic Prostate Score (GPS). This biopsy-based assay has been analytically and subsequently clinically validated as a predictor of aggressive prostate cancer. The aim of this study was to validate the analytical performance of the Oncotype DX Prostate Cancer Assay using predefined acceptance criteria.ResultsThe lowest quartile of RNA yields from prostate needle biopsies (six 5 ?m sections) was between 19 and 34 ng. Analytical validation of the process requiring as little as 5 ng of RNA met all pre-defined acceptance criteria. Amplification efficiencies, analytical sensitivity, and accuracy of gene assays were measured by serially diluting an RNA sample and analyzing features of the linear regression between RNA expression measured by the crossing point (Cp) versus the log2 of the RNA input per PCR assay well. Gene assays were shown to accurately measure expression over a wide range of inputs (from as low as 0.005 ng to 320 ng). Analytical accuracy was excellent with average biases at qPCR inputs representative of patient samples <9.7% across all assays while amplification efficiencies were within ±6% of the median. Assessments of reproducibility and precision were performed by testing 10 prostate cancer RNA samples over multiple instruments, reagent lots, operators, days (precision), and RNA input levels (reproducibility) using appropriately parameterized linear mixed models. The standard deviations for analytical precision and reproducibility were 1.86 and 2.11 GPS units (100-unit scale) respectively.ConclusionsThe Oncotype DX Prostate Cancer Assay, a clinical RT-PCR assay specifically designed for use with prostate needle biopsies, has been analytically validated using very limited RNA inputs. The assay requirements and analytical performance will provide physicians with test results from a robust and reliable assay which will enable improved treatment decisions for men diagnosed with early-stage prostate cancer.

Project description:Next-generation sequencing (NGS) enables clinical microbiology assays such as molecular typing of bacterial isolates which is now routinely applied for infection control and epidemiology. Additionally, feasibility for NGS-based identification of antimicrobial resistance (AMR) markers as well as genetic prediction of antibiotic susceptibility testing results has been demonstrated. Various bioinformatics approaches enabling NGS-based clinical microbiology assays exist, but standardized, computationally efficient and scalable sample-to-results workflows including validated quality control parameters are still lacking. Bioinformatics analysis workflows based on k-mers have been shown to allow for fast and efficient analysis of large genomics data sets as obtained from microbial sequencing applications. We here demonstrate applicability of k-mer based clinical microbiology assays for whole-genome sequencing (WGS) including variant calling, taxonomic identification, bacterial typing as well as AMR marker detection. The wet-lab and dry-lab workflows were developed and validated in line with Clinical Laboratory Improvement Act (CLIA) guidelines for laboratory-developed tests (LDTs) on multi-drug resistant ESKAPE pathogens. The developed k-mer based workflow demonstrated ?99.39% repeatability, ?99.09% reproducibility and ?99.76% accuracy for variant calling and applied assays as determined by intra-day and inter-day triplicate measurements. The limit of detection (LOD) across assays was found to be at 20× sequencing depth and 15× for AMR marker detection. Thorough benchmarking of the k-mer based workflow revealed analytical performance criteria are comparable to state-of-the-art alignment based workflows across clinical microbiology assays. Diagnostic sensitivity and specificity for multilocus sequence typing (MLST) and phylogenetic analysis were 100% for both approaches. For AMR marker detection, sensitivity and specificity were 95.29 and 99.78% for the k-mer based workflow as compared to 95.17 and 99.77% for the alignment-based approach. Summarizing, results illustrate that k-mer based analysis workflows enable a broad range of clinical microbiology assays, potentially not only for WGS-based typing and AMR gene detection but also genetic prediction of antibiotic susceptibility testing results.

Dataset Information

Analytical Validation of the Next-Generation Sequencing Assay for a Nationwide Signal-Finding Clinical Trial: Molecular Analysis for Therapy Choice Clinical Trial.

Publications

Analytical Validation of the Next-Generation Sequencing Assay for a Nationwide Signal-Finding Clinical Trial: Molecular Analysis for Therapy Choice Clinical Trial.

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