Validation of a Next-Generation Sequencing Assay for Clinical Molecular Oncology

Abstract

Currently, oncology testing includes molecular studies and cytogenetic analysis to detect genetic aberrations of clinical significance. Next-generation sequencing (NGS) allows rapid analysis of multiple genes for clinically actionable somatic variants. The WUCaMP assay uses targeted capture for NGS analysis of 25 cancerassociated genes to detect mutations at actionable loci. We present clinical validation of the assay and a detailed framework for design and validation of similar clinical assays. Deep sequencing of 78 tumor specimens (!1000Â average unique coverage across the capture region) achieved high sensitivity for detecting somatic variants at low allele fraction (AF). Validation revealed sensitivities and specificities of 100% for detection of single-nucleotide variants (SNVs) within coding regions, compared with SNP array sequence data (95% CI Z 83.4e100.0 for sensitivity and 94.2e100.0 for specificity) or whole-genome sequencing (95% CI Z 89.1e100.0 for sensitivity and 99.9e100.0 for specificity) of HapMap samples. Sensitivity for detecting variants at an observed 10% AF was 100% (95% CI Z 93.2e100.0) in HapMap mixes. Analysis of 15 masked specimens harboring clinically reported variants yielded concordant calls for 13/13 variants at AF of !15%. The WUCaMP assay is a robust and sensitive method to detect somatic variants of clinical significance in molecular oncology laboratories, with reduced time and cost of genetic analysis allowing for strategic patient management. (J Mol Diagn 2014, 16: 89e105; http://dx.doi.org/10.1016/j.jmoldx.2013 Traditional approaches to the genetic characterization of clinical oncology specimens include cytogenetic analysis, fluorescence in situ hybridization (FISH), and molecular studies of single genes. These methodologies are complementary to each other and generate information of diagnostic and prognostic relevance. However, as new insight is gained into the complexities of cancer at the molecular level, the need emerges to obtain a more detailed cancer genetic profile for improved patient management. As illustrated by recent studies, identifying DNA mutations in cancer may aid in understanding clonal evolution, 1 risk stratification, 2 and therapeutic strategies. 3,4 With the advent of next-generation sequencing (NGS), a more complete biological characterization of a tumor can be attained at the molecular level. 5

Feasibility of Low-Throughput Next Generation Sequencing for Germline DNA Screening

“…Overall, 99.39% of 2635 replicate variants were detected in all 3 replicates at a sensitivity of 99.42%, specificity of 99.99%, PPV of 99.86%, and NPV) of 99.94% from 7500 shared interrogations between NGS and array analysis. These frequencies are similar to reproducibility rates of 95.6%-98.8% (21,22 ), sensitivities of 95.9%-99.4% (21,23,24 ), and specificity of 100% (21 ) reported in studies using higher-throughput NGS formats, indicating an equivalence in analytical performance between modes. Notably, 11/16 (69%) replicate variants that were detected inconsistently had allele frequencies Յ0.15, strand biases ϾϪ35, or genotype quality scores Յ80 ( Fig.…”

Standards and Guidelines for the Interpretation and Reporting of Sequence Variants in Cancer

“…These databases are frequently used to filter out variants that are deemed polymorphic/benign based on an arbitrary cutoff of minor allele frequency (MAF). 14,32 There is no standardized cutoff for MAF to be used for eliminating polymorphic or benign variants. In the absence of paired normal tissue, the work group recommends using 1% (0.01) as a primary cutoff, which is also commonly used across many clinical laboratories.…”

Review of Clinical Next-Generation Sequencing

“…This is especially important for any oncology assay in which tumor percentage and heterogeneity affect the allele frequency, but it is also relevant for the ability to reliably detect mosaicism in an assay to detect inherited disorders. 31,80,81 During the validation, metrics should be defined to assess the quality of a test run and criteria for repeated testing established. 61,[82][83][84] These metrics may include cutoffs for the insert sizes after library preparation; criteria for assessing adequate target enrichment; library concentration parameters for various steps; expected performance of controls; and metrics for sequencing performance such as clustering, base and mapping quality scores, error rates, GC bias, transition/ transversion ratios, total number of sequencing reads, and coverage.…”