Abstract 7560: Microsatellite instability (MSI) detection using flow-based sequencing

Abstract Background: Microsatellite instability (MSI) is an important genomic biomarker associated with various cancers and hereditary conditions. Accurate and efficient classification of MSI status is essential for diagnosis, prognosis, and treatment decisions. Different approaches for testing MSI have been presented in the literature, including NGS-based methods which either rely on comparison with a matched control sample, a set of reference control samples, or the reference genome (no control). In this study, we explore the feasibility of accurately identifying MSI status using the UG100 platform, which utilizes flow-based sequencing chemistry. Since MSI detection is typically based on long homopolymers, we examined here whether the flow-based sequencing data can be effectively used for MSI classification. Methods: We performed whole genome sequencing (WGS) on a cohort of 24 cell lines with known MSI status, as well as on 15 tumor tissue samples, encompassing various cancer types, including colon, lung, bladder, and endometrial cancer. To determine the MSI status, polyA/T homopolymer loci were considered. We measured the MSI status in these loci using two methods: 1) Counting short indel variants detected using somatic Deep Variant, in comparison with matched blood normal, and 2) a novel MSI score based on the difference of the polyA/T homopolymer length, in comparison to the reference genome. We assessed the ability to evaluate the score for the full sequenced coverage (~100X) as well as when limiting to exome boundaries, and when sampling down to 1X coverage. Results and Conclusions: We established the performance of the MSI score on the cell lines with known MSI status. The score was verified on cell line mixtures (simulating lower tumor purity) as well as on clinical samples from various tissue sources, including colon samples annotated as mismatch-repair deficient. We demonstrate the ability to use the cost-effective UG100 sequencing combined with the two methodologies to identify MSI-high samples in these scenarios. Citation Format: Gila Lithwick-Yanai, Hila Benjamin, Jorge Cano-Nistal, Shir Geisler, Ariel Jaimovich, Nika Iremadze, Dror Kessler, Ilya Soifer, Danit Lebanony, Keren Ben Simhon, Shlomit Gilad, Eti Meiri, Doron Lipson, Yosef E. Maruvka. Microsatellite instability (MSI) detection using flow-based sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7560.