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Evaluating Multiple Next-Generation Sequencing–Derived Tumor Features to Accurately Predict DNA Mismatch Repair Status by Romy Walker; Peter Georgeson; Khalid Mahmood; Jihoon E. Joo; Enes Makalic; Mark Clendenning; Julia Como; Susan Preston; Sharelle Joseland; Bernard J. Pope; Ryan A. Hutchinson; Kais Kasem; Michael D. Walsh; Finlay A. Macrae; Aung K. Win; John L. Hopper; Dmitri Mouradov; Peter Gibbs; Oliver M. Sieber; Dylan E. O'Sullivan; Darren R. Brenner; Steven Gallinger; Mark A. Jenkins; Christophe Rosty; Ingrid M. Winship; Daniel D. Buchanan is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

What is Evaluating Multiple Next-Generation Sequencing–Derived Tumor Features to Accurately Predict DNA Mismatch Repair Status about?

Identifying tumor DNA mismatch repair deficiency (dMMR) is important for precision medicine. Tumor features, individually and in combination, derived from whole-exome sequenced (WES) colorectal cancers (CRCs) and panel-sequenced CRCs, endometrial cancers (ECs), and sebaceous skin tumors (SSTs) were assessed for their accuracy in detecting dMMR. CRCs (n = 300) with WES, where mismatch repair status was determined by immunohistochemistry, were assessed for microsatellite instability (MSMuTect, MANTIS, MSIseq, and MSISensor), Catalogue of Somatic Mutations in Cancer tumor mutational signatures, and somatic mutation counts. A 10-fold cross-validation approach (100 repeats) evaluated the dMMR prediction accuracy for i) individual features, ii) Lasso statistical model, and iii) an additive feature combination approach. Panel-sequenced tumors (29 CRCs, 22 ECs, and 20 SSTs) were assessed for the top performing dMMR predicting features/models using these three approaches. For WES CRCs, 10 features provided >80% dMMR prediction accuracy, with MSMuTect, MSIseq, and MANTIS achieving ≥99% accuracy. The Lasso model achieved 98.3% accuracy. The additive feature approach, with three or more of six

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Author

Romy Walker; Peter Georgeson; Khalid Mahmood; Jihoon E. Joo; Enes Makalic; Mark Clendenning; Julia Como; Susan Preston; Sharelle Joseland; Bernard J. Pope; Ryan A. Hutchinson; Kais Kasem; Michael D. Walsh; Finlay A. Macrae; Aung K. Win; John L. Hopper; Dmitri Mouradov; Peter Gibbs; Oliver M. Sieber; Dylan E. O'Sullivan; Darren R. Brenner; Steven Gallinger; Mark A. Jenkins; Christophe Rosty; Ingrid M. Winship; Daniel D. Buchanan

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)