MSIsensor-pro: Fast, Accurate, and Matched-normal-sample-free Detection of Microsatellite Instability

Area of Science:

• Oncology
• Bioinformatics
• Genomics

Background:

• Microsatellite instability (MSI) is a crucial biomarker for cancer prognosis and therapy selection.
• Existing experimental and computational methods for MSI detection have limitations, including time-consumption, sample type restrictions (e.g., leukemia, FFPE, xenografts), and the need for matched normal samples.

Purpose of the Study:

• To develop an open-source, single-sample MSI scoring method (MSIsensor-pro) for broader research and clinical utility.
• To enable accurate MSI detection without the requirement of matched normal samples.

Main Methods:

• Developed MSIsensor-pro, incorporating a multinomial distribution model to quantify polymerase slippages.
• Implemented a discriminative site selection method for MSI detection independent of matched normal samples.
• Evaluated MSIsensor-pro's performance across various sequencing depths and tumor purities.

Main Results:

• MSIsensor-pro demonstrated ultrafast, accurate, and robust performance in MSI calling.
• The method significantly outperformed existing leading methods in both accuracy and computational efficiency.
• Successfully enabled MSI detection in diverse sample types, overcoming limitations of previous approaches.

Conclusions:

• MSIsensor-pro provides a powerful and versatile tool for MSI detection in research and clinical applications.
• The open-source nature and improved performance make MSIsensor-pro a valuable asset for cancer biomarker analysis.
• Addresses critical unmet needs in MSI detection for challenging sample types.