CoChat
Sign Up
Cited by 175BRCA gene mutations in cancerGenomics and Rare DiseasesCancer Genomics and Diagnostics

The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.

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

Open access
Cheng, Donavan T., Prasad, Meera, Chekaluk, Yvonne, Benayed, Ryma, Sadowska, Justyna, Zehir, Ahmet, Syed, Aijazuddin, Wang, Yan Elsa, Somar, Joshua, Li, Yirong, Yelskaya, Zarina, Wong, Donna, Robson, Mark E., Offit, Kenneth, Berger, Michael F., Nafa, Khedoudja, Ladanyi, Marc, Zhang, Liying·2017·BMC Medical Genomics
DOI 10.1186/s12920-017-0271-4PMC PMC5437632
Open this paper in CoChat — free accountOpen PDF

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.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1186/s12920-017-0271-4

Cites

21 of 21 references verified0 citation errors · 0 not checkable

Statistics

Figures

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. 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 toolsCC BY · PubMed Central
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. 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 bpCC BY · PubMed Central
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. 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 variantsCC BY · PubMed Central
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
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 IndelsCC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Cited by

Sorted by citations

See all 175

Related

See all

Metadata

Authors
Cheng, D. T., Prasad, M., Chekaluk, Y., Benayed, R., Sadowska, J., Zehir, A., Syed, A., Wang, Y. E., Somar, J., Li, Y., Yelskaya, Z., Wong, D., Robson, M. E., Offit, K., Berger, M. F., Nafa, K., Ladanyi, M., & Zhang, L.
Affiliations
  • Memorial Sloan Kettering Cancer Center (US)
  • Brigham and Women's Hospital (US)
Corresponding author
  • Ladanyi, Marc
  • Zhang, Liying
ORCID
Venue
BMC Medical Genomics
Publisher
Springer Science and Business Media LLC
Published
2017-05-19
Volume
10
Issue
1
Pages
33-33
ISSN
1755-8794 (ISSN-L)
DOI
10.1186/s12920-017-0271-4
PMC
PMC5437632
OpenAlex
W2614654783
Cited by
158
References
21
Citations by year
2026: 9 · 2025: 19 · 2024: 12 · 2023: 23 · 2022: 21 · 2021: 20 · 2020: 22 · 2019: 16 · 2018: 13 · 2017: 2
FWCI
9.60
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • BMC Medical Genomics (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • eScholarship (California Digital Library) (submittedVersion)
  • eScholarship (California Digital Library) (submittedVersion, cc-by)
Funders
  • Memorial Sloan-Kettering Cancer Center (CA008748, P30 CA008748)
  • Geoffrey Beene Cancer Research Center (P30 CA008748)
  • Farmer Family Foundation
  • National Cancer Institute (P30CA008748)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › BRCA gene mutations in cancer
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
Keywords
MSK-IMPACT, germline variant detection, cancer predisposition syndromes, cancer susceptibility genes, gene panel testing, founder mutation, BRCA1, BRCA2, CHEK2, APC, TP53, TSC2, ATM, VHL, copy number variation, single nucleotide variants, hereditary cancer
MeSH terms
Tuberous Sclerosis Complex 2 Protein, DNA Mutational Analysis, Humans, Neoplasm Proteins, Neoplasms, Biomarkers, Tumor, Reproducibility of Results, Tumor Suppressor Protein p53, Germ-Line Mutation, BRCA1 Protein, Genetic Predisposition to Disease, Polymorphism, Single Nucleotide, BRCA2 Protein, Tumor Suppressor Proteins, Adenomatous Polyposis Coli Protein, Von Hippel-Lindau Tumor Suppressor Protein, DNA Copy Number Variations, Ataxia Telangiectasia Mutated Proteins, Checkpoint Kinase 2

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review