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Related Experiment Video

Updated: Aug 19, 2025

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies
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Improved NGS-based detection of microsatellite instability using tumor-only data.

Ana Claudia Marques1, Carole Ferraro-Peyret2,3, Frederic Michaud1

  • 1SOPHiA GENETICS, Saint-Sulpice, Switzerland.

Frontiers in Oncology
|December 5, 2022
PubMed
Summary

Microsatellite instability (MSI) detection is crucial for predicting response to cancer immunotherapy. A new method, MSIdetect, accurately identifies MSI in various cancer types, even with limited tumor samples, improving patient selection for treatment.

Keywords:
MSIMicrosatellite instabilityMismatch Repair deficiencymicrosatellitenext-generating sequencingpan-cancertumor-only sequencing

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Area of Science:

  • Oncology
  • Molecular Diagnostics
  • Genomics

Background:

  • Microsatellite instability (MSI) is a key biomarker for mismatch repair deficiency (dMMR) and predicts response to immune checkpoint inhibitor (ICI) therapy.
  • Current MSI detection methods face limitations in sensitivity and applicability across diverse cancer types and sample properties, especially when only tumor tissue is available.
  • These limitations hinder the widespread clinical adoption of MSI testing and patient identification for ICI therapy.

Purpose of the Study:

  • To introduce MSIdetect, a novel next-generation sequencing (NGS)-based solution for accurate MSI detection across various cancer types.
  • To address the challenges of MSI detection in tumor-only samples and samples with low tumor content or instability.
  • To validate the performance of MSIdetect in a diverse set of clinical FFPE samples.

Main Methods:

  • MSIdetect utilizes a novel approach modeling indel burden and tumor content on read coverage in specific homopolymer regions.
  • The method was validated on 139 Formalin-Fixed Paraffin-Embedded (FFPE) clinical samples, including colorectal, endometrial, glioma, and sebaceous tumors.
  • Performance was assessed using tumor-only data and compared against results from tumor-normal matched pairs.

Main Results:

  • MSIdetect demonstrated high accuracy with 100% specificity and 96.3% sensitivity across tested cancer types.
  • The method showed sensitivity even in samples with low tumor content and limited microsatellite instability.
  • Results from MSIdetect using tumor-only data strongly correlated with those from tumor-normal matched pairs (R=0.988).

Conclusions:

  • MSIdetect offers an accurate and robust solution for NGS-based MSI detection, overcoming limitations of existing methods.
  • Its high accuracy across diverse cancer types and effectiveness with tumor-only samples facilitate broader clinical implementation.
  • The adoption of MSIdetect can increase the identification of patients likely to benefit from immune checkpoint inhibitor therapy.