Open AccessPopulation bias in somatic measurement of microsatellite instability status
Author(s) -
Saul Michelle,
Poorman Kelsey,
Tae Hongseok,
Vanderwalde Ari,
Stafford Phillip,
Spetzler David,
Korn Wolfgang M.,
Gatalica Zoran,
Swensen Jeff
Publication year - 2020
Publication title -
cancer medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.403
H-Index - 53
ISSN - 2045-7634
DOI - 10.1002/cam4.3294
Subject(s) - microsatellite , microsatellite instability , false positive paradox , dna mismatch repair , computational biology , population , biology , robustness (evolution) , genome , genetics , computer science , dna , medicine , dna repair , artificial intelligence , gene , allele , environmental health
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.