# An Algorithmic Approach for Quantitative Determination of Microsatellite Status in NGS-Based Cancer Diagnostics

## TL;DR

This paper introduces a new NGS-based framework for accurately and flexibly determining microsatellite instability in cancer, which could improve patient selection for immunotherapy.

## Contribution

A transparent, adaptable NGS framework for quantitative microsatellite analysis with high concordance to established methods.

## Key findings

- The method showed 100% concordance with PCR and 90.32% with IHC in determining microsatellite status.
- The framework allows quantitative assessment of microsatellite alterations and flexible threshold definition.
- It provides a robust foundation for clinical implementation and further validation in larger cohorts.

## Abstract

Determining microsatellite instability is an essential component of modern cancer diagnostics, particularly for identifying patients who may benefit from immunotherapy. While Next Generation Sequencing offers the possibility to assess microsatellite status alongside other molecular markers, its clinical use is often limited. In this study, we present a transparent and adaptable framework for microsatellite analysis. The approach was validated against established diagnostic methods and showed high agreement. Beyond binary classification, it enables a quantitative assessment of microsatellite alterations and flexible threshold definition. This framework may facilitate the integration of sequencing-based analysis into routine diagnostics.

Purpose: To develop a transparent and adaptable methodological framework for the analysis of microsatellite status using Next Generation Sequencing (NGS), addressing current limitations in clinical implementation. Methods: Microsatellite status was assessed using NGS with a custom-designed panel. The approach was validated against polymerase chain reaction (PCR) and immunohistochemistry (IHC) results in a cohort of 32 patients with various tumor entities. A Python-based analysis pipeline was developed to process raw sequencing data and quantify mutational burden within microsatellite regions. Results: The proposed method demonstrated 100% concordance with PCR and 90.32% concordance with IHC results. The framework enabled quantitative assessment of microsatellite instability. Conclusions: This proof-of-concept study demonstrates reliable determination of microsatellite status. The transparent and panel-adaptable framework offers flexibility for clinical implementation and provides a robust foundation for further validation in larger cohorts and across diverse tumor entities.