Guidelines for Validation of Next-Generation Sequencing-Based Oncology Panels: A Joint Consensus Recommendation of the Association for Molecular Pathology and College of American Pathologists

Abstract

Next-generation sequencing (NGS) methods for cancer testing have been rapidly adopted by clinical laboratories. To establish analytical validation best practice guidelines for NGS gene panel testing of somatic variants, a working group was convened by the Association of Molecular Pathology with liaison representation from the College of American Pathologists. These joint consensus recommendations address NGS test development, optimization, and validation, including recommendations on panel content selection and rationale for optimization and familiarization phase conducted before test validation; utilization of reference cell lines and reference materials for evaluation of assay performance; determining of positive percentage agreement and positive predictive value for each variant type; and requirements for minimal depth of coverage and minimum number of samples that should be used to establish test performance characteristics. The recommendations emphasize the role of laboratory director in using an error-based approach that identifies potential sources of errors that may occur throughout the analytical process and addressing these potential errors through test design, method validation, or quality controls so that no harm comes to the patient. The recommendations contained herein are intended to assist clinical laboratories with the validation and ongoing monitoring of NGS testing for detection of somatic variants and to ensure high quality of sequencing results.

Figure 1

High-level comparison of target enrichment workflow for amplicon and capture hybridization NGS assays. A: Nucleic acid is extracted and quantified. The DNA is sheared and repaired to generate fragments of uniform size distribution, and the fragment size can be monitored by gel electrophoresis or Agilent Bioanalyzer. B: Amplification-based assays: Target enrichment in amplification-based assays consists of PCR amplification of the desired region using primers. A tiled amplicon approach is depicted in which primers are designed to generate multiple overlapping amplicons of the same region to avoid allele dropout. The sequencing reads generated will have the same start and stop coordinates dictated by the primer design. C: Hybridization capture–based assays: Target enrichment in hybridization capture–based assays uses long biotinylated oligonucleotide probes complementary to a region of interest. Probes hybridize to target regions contained within larger fragments of DNA. As a result, regions flanking the target will also be isolated and sequenced. Targeted fragments are isolated using streptavidin magnetic beads, followed by washing, elution, amplification, and sequencing. The sequencing reads from these molecules will have unique start and stop coordinates when aligned to a reference, allowing identification and removal of PCR duplicates. D: Quality control: The size distribution pattern of the individual and pooled libraries are quality controlled using Agilent TapeStation and quantified using a Spectramax microplate reader. Example images as visualized using an Agilent Bioanalyzer and Spectramax microplate reader are shown. QC, quality control.

Figure 2

Determining depth of sequence. Given an allele burden of 5% and 250 read depth, the binomial distribution of true positives (TPs) can be calculated. Also, given a sequence error rate of 1%, the binomial distribution of false-positive (FP) results can also be calculated and shown to overlap the true positive distribution. The overlap of true-positive and false-positive distributions should be considered when determining minimum depth of sequence needed to reliably detect a given allele burden.

Figure 3

Determining the minimum number of reads: CI versus tolerance interval (TI). Determination of the CI and tolerance interval for minimum read depth (average of 275 reads with an SD of 50). The lower CI determines with 95% confidence the lower level of the average across the population. As the sample size increases, this estimate improves. The tolerance interval determines with 95% confidence the minimum number of reads above which 95% of the population will fall. The CI can be used to predict the average performance of a population, and tolerance interval can be used to predict the performance of a given sample.