Comprehensive detection of germline variants by MSK-IMPACT, a clinical diagnostic platform for solid tumor molecular oncology and concurrent cancer predisposition testing

Abstract

Background: The growing number of Next Generation Sequencing (NGS) tests is transforming the routine clinical diagnosis of hereditary cancers. Identifying whether a cancer is the result of an underlying disease-causing mutation in a cancer predisposition gene is not only diagnostic for a cancer predisposition syndrome, but also has significant clinical implications in the clinical management of patients and their families.

Methods: Here, we evaluated the performance of MSK-IMPACT (Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets) in detecting genetic alterations in 76 genes implicated in cancer predisposition syndromes. Output from hybridization-based capture was sequenced on an Illumina HiSeq 2500. A custom analysis pipeline was used to detect single nucleotide variants (SNVs), small insertions/deletions (indels) and copy number variants (CNVs).

Results: MSK-IMPACT detected all germline variants in a set of 233 unique patient DNA samples, previously confirmed by previous single gene testing. Reproducibility of variant calls was demonstrated using inter- and intra- run replicates. Moreover, in 16 samples, we identified additional pathogenic mutations other than those previously identified through a traditional gene-by-gene approach, including founder mutations in BRCA1, BRCA2, CHEK2 and APC, and truncating mutations in TP53, TSC2, ATM and VHL.

Conclusions: This study highlights the importance of the NGS-based gene panel testing approach in comprehensively identifying germline variants contributing to cancer predisposition and simultaneous detection of somatic and germline alterations.

Fig. 1

a: The MSK-IMPACT workflow. MSK-IMPACT is performed as a matched tumor/normal test at our institution, allowing for concurrent identification of somatic mutations in the tumor sample and inherited germline variants in the subset of 76 cancer relevant genes. b: The validation approach. DNA samples that were previously tested positive for a pathogenic or likely pathogenic variant were identified and blinded for the validation. The samples were tested through the MSK-IMPACT pipeline. Three different types of variants (SNVs, indels and CNVs) were called using various analysis tools

Fig. 2

Distribution of sequence coverage. a exons of canonical transcripts of 76 cancer predisposition genes within the MSK-IMPACT panel, b intronic regions flanking targeted exons (50 bp). c Average sequence coverage decreases with increasing distance from the exon-intron boundary (black line), while the fraction of intronic regions flanking the exons that maintain a minimum of 50× coverage (red line) drops off sharply as the size of the flanking regions exceed 100 bp. Dotted line indicates 50 bp

Fig. 3

Number of exonic and non-coding mutations identified per sample. a Exonic and b Non-coding mutations identified per sample tested in the validation study, shown with ranges in a box-and-whisker plot. Distributions are also shown for variants grouped by pathogenicity classification: pathogenic and likely pathogenic = Class_4_5, VUS = Class 3, likely benign and benign = Class_1_2. Pathogenicity classifications are a combination of known pathogenicity determinations for the expected variants, and pathogenicity estimates for incidental variants

Fig. 4

Distribution of expected variants vs. incidental pathogenic variants. Oncoprint shows the distribution of expected variants (red) vs. incidental pathogenic variants (blue) across 233 unique samples used for the validation of germline SNVs and Indels