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Related Concept Videos

Next-generation Sequencing03:00

Next-generation Sequencing

The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features.

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Related Experiment Video

Updated: May 11, 2026

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing
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A deep multiple instance learning framework improves microsatellite instability detection from tumor next generation

John Ziegler1,2, Jaclyn F Hechtman1,3, Satshil Rana1

  • 1Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.

Nature Communications
|January 2, 2025
PubMed
Summary
This summary is machine-generated.

A new deep learning tool, MiMSI, accurately identifies microsatellite instability (MSI) in cancer genomes, even with low tumor purity. This improves biomarker discovery for immune checkpoint inhibitor treatment selection.

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Comparative Lesions Analysis Through a Targeted Sequencing Approach
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Area of Science:

  • Genomics
  • Cancer Biology
  • Bioinformatics

Background:

  • Microsatellite instability (MSI) is a key cancer genomic phenotype and FDA-approved biomarker for immune checkpoint inhibitor therapy.
  • Next-generation sequencing (NGS) data can identify MSI, but low tumor purity in clinical samples challenges existing algorithms.

Purpose of the Study:

  • To develop a sensitive MSI classifier that overcomes the limitations of low tumor purity in clinical samples.
  • To improve the accuracy of MSI detection for guiding cancer treatment decisions.

Main Methods:

  • Developed MiMSI, a deep neural network-based MSI classifier utilizing a multiple instance learning framework.
  • Trained MiMSI on a dataset including low tumor purity MSI cases.
  • Compared MiMSI performance against MSISensor using targeted NGS data from challenging clinical samples and a prospective cohort.

Main Results:

  • MiMSI demonstrated superior sensitivity (0.895) and auROC (0.971) compared to MSISensor (sensitivity: 0.67; auROC: 0.907) on challenging cases.
  • MiMSI significantly outperformed MSISensor in low purity samples in a prospective cohort (P = 8.244e-07).

Conclusions:

  • MiMSI offers a robust and sensitive method for MSI detection, particularly in low tumor purity samples.
  • This advancement can enhance the clinical utility of MSI as a biomarker for immunotherapy selection.