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

Area of Science:

• Oncology
• Genomics
• Molecular Diagnostics

Background:

• Microsatellite instability (MSI) is a key cancer biomarker.
• Current methods for MSI analysis have limitations in clinical settings.
• Next Generation Sequencing (NGS) offers potential for improved MSI detection.

Purpose of the Study:

• To develop a transparent and adaptable NGS-based framework for microsatellite status analysis.
• To address limitations hindering clinical implementation of MSI testing.
• To provide a flexible and robust method for MSI assessment.

Main Methods:

• Developed a custom NGS panel for microsatellite analysis.
• Validated the NGS approach against polymerase chain reaction (PCR) and immunohistochemistry (IHC).
• Created a Python-based pipeline for data processing and instability quantification.

Main Results:

• Achieved 100% concordance with PCR and 90.32% concordance with IHC.
• Demonstrated the framework's ability to quantitatively assess microsatellite instability.
• Confirmed the reliability of the NGS method in a cohort of 32 patients.

Conclusions:

• The study presents a reliable NGS-based method for determining microsatellite status.
• The developed framework is transparent, adaptable, and suitable for clinical use.
• This approach provides a foundation for larger validation studies across various cancer types.