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Population bias in somatic measurement of microsatellite instability status

Microsatellite instability (MSI) is a key secondary effect of a defective DNA mismatch repair mechanism resulting in incorrectly replicated microsatellites in many malignant tumors. Historically, MSI detection has been performed by fragment analysis (FA) on a panel of representative genomic markers....

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Autores principales: Saul, Michelle, Poorman, Kelsey, Tae, Hongseok, Vanderwalde, Ari, Stafford, Phillip, Spetzler, David, Korn, Wolfgang M., Gatalica, Zoran, Swensen, Jeff
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7476819/
https://www.ncbi.nlm.nih.gov/pubmed/32644297
http://dx.doi.org/10.1002/cam4.3294
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author Saul, Michelle
Poorman, Kelsey
Tae, Hongseok
Vanderwalde, Ari
Stafford, Phillip
Spetzler, David
Korn, Wolfgang M.
Gatalica, Zoran
Swensen, Jeff
author_facet Saul, Michelle
Poorman, Kelsey
Tae, Hongseok
Vanderwalde, Ari
Stafford, Phillip
Spetzler, David
Korn, Wolfgang M.
Gatalica, Zoran
Swensen, Jeff
author_sort Saul, Michelle
collection PubMed
description Microsatellite instability (MSI) is a key secondary effect of a defective DNA mismatch repair mechanism resulting in incorrectly replicated microsatellites in many malignant tumors. Historically, MSI detection has been performed by fragment analysis (FA) on a panel of representative genomic markers. More recently, using next‐generation sequencing (NGS) to analyze thousands of microsatellites has been shown to improve the robustness and sensitivity of MSI detection. However, NGS‐based MSI tests can be prone to population biases if NGS results are aligned to a reference genome instead of patient‐matched normal tissue. We observed an increased rate of false positives in patients of African ancestry with an NGS‐based diagnostic for MSI status utilizing 7317 microsatellite loci. We then minimized this bias by training a modified calling model that utilized 2011 microsatellite loci. With these adjustments 100% (95% CI: 89.1% to 100%) of African ancestry patients in an independent validation test were called correctly using the updated model. This poses not only a significant technical improvement but also has an important clinical impact on directing immune checkpoint inhibitor therapy.
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spelling pubmed-74768192020-09-11 Population bias in somatic measurement of microsatellite instability status Saul, Michelle Poorman, Kelsey Tae, Hongseok Vanderwalde, Ari Stafford, Phillip Spetzler, David Korn, Wolfgang M. Gatalica, Zoran Swensen, Jeff Cancer Med Cancer Prevention Microsatellite instability (MSI) is a key secondary effect of a defective DNA mismatch repair mechanism resulting in incorrectly replicated microsatellites in many malignant tumors. Historically, MSI detection has been performed by fragment analysis (FA) on a panel of representative genomic markers. More recently, using next‐generation sequencing (NGS) to analyze thousands of microsatellites has been shown to improve the robustness and sensitivity of MSI detection. However, NGS‐based MSI tests can be prone to population biases if NGS results are aligned to a reference genome instead of patient‐matched normal tissue. We observed an increased rate of false positives in patients of African ancestry with an NGS‐based diagnostic for MSI status utilizing 7317 microsatellite loci. We then minimized this bias by training a modified calling model that utilized 2011 microsatellite loci. With these adjustments 100% (95% CI: 89.1% to 100%) of African ancestry patients in an independent validation test were called correctly using the updated model. This poses not only a significant technical improvement but also has an important clinical impact on directing immune checkpoint inhibitor therapy. John Wiley and Sons Inc. 2020-07-09 /pmc/articles/PMC7476819/ /pubmed/32644297 http://dx.doi.org/10.1002/cam4.3294 Text en © 2020 Caris Life Sciences. Cancer Medicine published by John Wiley & Sons Ltd. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Cancer Prevention
Saul, Michelle
Poorman, Kelsey
Tae, Hongseok
Vanderwalde, Ari
Stafford, Phillip
Spetzler, David
Korn, Wolfgang M.
Gatalica, Zoran
Swensen, Jeff
Population bias in somatic measurement of microsatellite instability status
title Population bias in somatic measurement of microsatellite instability status
title_full Population bias in somatic measurement of microsatellite instability status
title_fullStr Population bias in somatic measurement of microsatellite instability status
title_full_unstemmed Population bias in somatic measurement of microsatellite instability status
title_short Population bias in somatic measurement of microsatellite instability status
title_sort population bias in somatic measurement of microsatellite instability status
topic Cancer Prevention
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7476819/
https://www.ncbi.nlm.nih.gov/pubmed/32644297
http://dx.doi.org/10.1002/cam4.3294
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