Comparing two assays for clinical genomic profiling: the devil is in the data

Samples from patients with AML show high concordance in detection of mutations by NGS at local institutions vs central laboratories

Next-generation sequencing (NGS) to identify pathogenic mutations is an integral part of acute myeloid leukemia (AML) therapeutic decision-making. The concordance in identifying pathogenic mutations among different NGS platforms at different diagnostic laboratories has been studied in solid tumors but not in myeloid malignancies to date. To determine this interlaboratory concordance, we collected a total of 194 AML bone marrow or peripheral blood samples from newly diagnosed patients with AML enrolled in the Beat AML Master Trial (BAMT) at 2 academic institutions. We analyzed the diagnostic samples from patients with AML for the detection of pathogenic myeloid mutations in 8 genes (DNMT3A, FLT3, IDH1, IDH2, NPM1, TET2, TP53, and WT1) locally using the Hematologic Neoplasm Mutation Panel (50-gene myeloid indication filter) (site 1) or the GeneTrails Comprehensive Heme Panel (site 2) at the 2 institutions and compared them with the central results from the diagnostic laboratory for the BAMT, Foundation Medicine, Inc. The overall percent agreement was over 95% each in all 8 genes, with almost perfect agreement (κ > 0.906) in all but WT1, which had substantial agreement (κ = 0.848) when controlling for site. The minimal discrepancies were due to reporting variants of unknown significance (VUS) for the WT1 and TP53 genes. These results indicate that the various NGS methods used to analyze samples from patients with AML enrolled in the BAMT show high concordance, a reassuring finding given the wide use of NGS for therapeutic decision-making in AML.

Advancing NGS quality control to enable measurement of actionable mutations in circulating tumor DNA

The primary objective of the FDA-led Sequencing and Quality Control Phase 2 (SEQC2) project is to develop standard analysis protocols and quality control metrics for use in DNA testing to enhance scientific research and precision medicine. This study reports a targeted next-generation sequencing (NGS) method that will enable more accurate detection of actionable mutations in circulating tumor DNA (ctDNA) clinical specimens. To accomplish this, a synthetic internal standard spike-in was designed for each actionable mutation target, suitable for use in NGS following hybrid capture enrichment and unique molecular index (UMI) or non-UMI library preparation. When mixed with contrived ctDNA reference samples, internal standards enabled calculation of technical error rate, limit of blank, and limit of detection for each variant at each nucleotide position in each sample. True-positive mutations with variant allele fraction too low for detection by current practice were detected with this method, thereby increasing sensitivity.

A machine learning approach for somatic mutation discovery

Variability in the accuracy of somatic mutation detection may affect the discovery of alterations and the therapeutic management of cancer patients. To address this issue, we developed a somatic mutation discovery approach based on machine learning that outperformed existing methods in identifying experimentally validated tumor alterations (sensitivity of 97% versus 90 to 99%; positive predictive value of 98% versus 34 to 92%). Analysis of paired tumor-normal exome data from 1368 TCGA (The Cancer Genome Atlas) samples using this method revealed concordance for 74% of mutation calls but also identified likely false-positive and false-negative changes in TCGA data, including in clinically actionable genes. Determination of high-quality somatic mutation calls improved tumor mutation load-based predictions of clinical outcome for melanoma and lung cancer patients previously treated with immune checkpoint inhibitors. Integration of high-quality machine learning mutation detection in clinical next-generation sequencing (NGS) analyses increased the accuracy of test results compared to other clinical sequencing analyses. These analyses provide an approach for improved identification of tumor-specific mutations and have important implications for research and clinical management of cancer patients.

Performance of next-generation sequencing on small tumor specimens and/or low tumor content samples using a commercially available platform

Background

Next generation sequencing tests (NGS) are usually performed on relatively small core biopsy or fine needle aspiration (FNA) samples. Data is limited on what amount of tumor by volume or minimum number of FNA passes are needed to yield sufficient material for running NGS. We sought to identify the amount of tumor for running the PCDx NGS platform.

Methods

2,723 consecutive tumor tissues of all cancer types were queried and reviewed for inclusion. Information on tumor volume, success of performing NGS, and results of NGS were compiled. Assessment of sequence analysis, mutation calling and sensitivity, quality control, drug associations, and data aggregation and analysis were performed.

Results

6.4% of samples were rejected from all testing due to insufficient tumor quantity. The number of genes with insufficient sensitivity make definitive mutation calls increased as the percentage of tumor decreased, reaching statistical significance below 5% tumor content. The number of drug associations also decreased with a lower percentage of tumor, but this difference only became significant between 1–3%. The number of drug associations did decrease with smaller tissue size as expected. Neither specimen size or percentage of tumor affected the ability to pass mRNA quality control. A tumor area of 10 mm² provides a good margin of error for specimens to yield adequate drug association results.

Conclusions

Specimen suitability remains a major obstacle to clinical NGS testing. We determined that PCR-based library creation methods allow the use of smaller specimens, and those with a lower percentage of tumor cells to be run on the PCDx NGS platform.

Diagnostics based on nucleic acid sequence variant profiling: PCR, hybridization, and NGS approaches

Nucleic acid sequence variations have been implicated in many diseases, and reliable detection and quantitation of DNA/RNA biomarkers can inform effective therapeutic action, enabling precision medicine. Nucleic acid analysis technologies being translated into the clinic can broadly be classified into hybridization, PCR, and sequencing, as well as their combinations. Here we review the molecular mechanisms of popular commercial assays, and their progress in translation into in vitro diagnostics.