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MSIsensor-ct: microsatellite instability detection using cfDNA sequencing data.

Xinyin Han1,2, Shuying Zhang1,2, Daniel Cui Zhou3,4

  • 1Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China.

Briefings in Bioinformatics
|January 18, 2021
PubMed
Summary
This summary is machine-generated.

A new tool, MSIsensor-ct, detects microsatellite instability (MSI) from plasma cell-free DNA (cfDNA). This noninvasive approach accurately identifies MSI status, crucial for cancer prognosis and treatment selection.

Keywords:
MSIcfDNActDNAmachine learning

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Area of Science:

  • Oncology
  • Bioinformatics
  • Molecular Diagnostics

Background:

  • Microsatellite instability (MSI) is a key biomarker for predicting cancer outcomes and response to chemotherapy.
  • Current MSI detection methods often rely on tumor tissue, which can be difficult to obtain.
  • Plasma circulating cell-free DNA (cfDNA) offers a promising noninvasive alternative for MSI detection.

Purpose of the Study:

  • To develop and evaluate a bioinformatics tool for detecting MSI status from cfDNA sequencing data.
  • To establish a user-friendly and accurate method for noninvasive MSI assessment in cancer patients.

Main Methods:

  • Development of MSIsensor-ct, a machine learning-based bioinformatics tool.
  • Utilizing cfDNA sequencing data (BAM files) as input.
  • Validation on independent datasets with varying circulating tumor DNA (ctDNA) levels and sequencing depths.

Main Results:

  • MSIsensor-ct accurately detects MSI status from cfDNA.
  • Achieved 100% accuracy at a limit of detection of 0.05% ctDNA.
  • The tool demonstrated a stable MSIscore threshold of 20%.

Conclusions:

  • MSIsensor-ct provides a highly accurate and noninvasive method for MSI detection using cfDNA.
  • The tool's ease of use and integration potential facilitate its adoption in clinical settings.
  • This approach holds significant promise for improving cancer prognosis and guiding treatment decisions.