The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Microsatellite Instability Detection by Next Generation Sequencing
Abstract
BACKGROUND: Microsatellite instability (MSI) is a useful phenotype in cancer diagnosis and prognosis. Nevertheless, methods to detect MSI status from next generation DNA sequencing (NGS) data are underdeveloped. METHODS: We developed an approach to detect the MSI phenotype using NGS (mSINGS). The method was used to evaluate mononucleotide microsatellite loci that were incidentally sequenced after targeted gene enrichment and could be applied to gene or exome capture panels designed for other purposes. For each microsatellite locus, the number of differently sized repeats in experimental samples were quantified and compared to a population of normal controls. Loci were considered unstable if the experimental number of repeats was statistically greater than in the control population. MSI status was determined by the fraction of unstable microsatellite loci. RESULTS: We examined data from 324 samples generated using targeted gene capture assays of 3 different sizes, ranging from a 0.85-Mb to a 44-Mb exome design and incorporating from 15 to 2957 microsatellite markers. When we compared mSING results to MSI-PCR as a gold standard for 108 cases, we found the approach to be both diagnostically sensitive (range of 96.4% to 100% across 3 panels) and specific (range of 97.2% to 100%) for determining MSI status. The fraction of unstable microsatellite markers calculated from sequencing data correlated with the number of unstable loci detected by conventional MSI-PCR testing. CONCLUSIONS: NGS data can enable highly accurate detection of MSI, even from limited capture designs. This novel approach offers several advantages over existing PCR-based methods.
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.
Integrity signals
Registry
Cites
Statistics
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.
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
The Sequence Alignment/Map format and SAMtools
Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R., & 1000 Genome Project Data Processing Subgroup (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp352 · Cited by 62,041
Comprehensive molecular characterization of human colon and rectal cancer
The Cancer Genome Atlas Network (2012) · Nature · doi:10.1038/nature11252 · Cited by 8,499
Ribic, C. M., Sargent, D. J., Moore, M. J., Thibodeau, S. N., French, A. J., Goldberg, R. M., Hamilton, S. R., Laurent-Puig, P., Gryfe, R., Shepherd, L. E., et al. (2003) · New England Journal of Medicine · doi:10.1056/nejmoa022289 · Cited by 2,160
Cited by
Microsatellite Instability in Colorectal Cancer
Boland, C. R., & Goel, A. (2010) · Gastroenterology · doi:10.1053/j.gastro.2009.12.064 · Cited by 2,456
Pan-tumor genomic biomarkers for PD-1 checkpoint blockade–based immunotherapy
Cristescu, R., Mogg, R., Ayers, M., Albright, A., Murphy, E., Yearley, J., Sher, X., Liu, X. Q., Lu, H., Nebozhyn, M., et al. (2018) · Science · doi:10.1126/science.aar3593 · Cited by 2,301
Immunotherapy in colorectal cancer: Rationale, challenges and potential
Ganesh, K., Stadler, Z. K., Cercek, A., Mendelsohn, R. B., Shia, J., Segal, N. H., & Diaz, L. A. (2019) · Nature Reviews Gastroenterology & Hepatology · doi:10.1038/s41575-019-0126-x · Cited by 1,954
Classification and characterization of microsatellite instability across 18 cancer types
Hause, R. J., Pritchard, C. C., Shendure, J., & Salipante, S. J. (2016) · Nature Medicine · doi:10.1038/nm.4191 · Cited by 991
Self-citationAntonarakis, E. S., Piulats, J. M., Gross-Goupil, M., Goh, J., Ojamaa, K., Hoimes, C. J., Vaishampayan, U., Berger, R., Sezer, A., Alanko, T., et al. (2020) · Journal of Clinical Oncology · doi:10.1200/jco.19.01638 · Cited by 736
Metadata
- Authors
- Salipante, S. J., Scroggins, S. M., Hampel, H. L., Turner, E. H., & Pritchard, C. C.
- Venue
- Clinical Chemistry
- Publisher
- Oxford University Press (OUP)
- Published
- 2014-09-01
- Volume
- 60
- Issue
- 9
- Pages
- 1192-1199
- ISSN
- 0009-9147 (ISSN-L)
- 1530-8561
- OpenAlex
- W2156021518
- Cited by
- 399
- References
- 3
- FWCI
- 10.69
- Type
- journal article
- Language
- en
- Open access
- closed
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Microsatellite Instability Detection by Next Generation Sequencing. https://app.cochat.ai/papers/10.1373/clinchem.2014.223677
- The paper
- Salipante, S. J., Scroggins, S. M., Hampel, H. L., Turner, E. H., & Pritchard, C. C. (2014). Microsatellite Instability Detection by Next Generation Sequencing. Clinical Chemistry. https://doi.org/10.1373/clinchem.2014.223677
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.