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  1. Home
  3. Machine Learning Algorithm For The Detection Of Tumor Microsatellite Instability Based On Multiomics Biomarkers.
  1. Home
  3. Machine Learning Algorithm For The Detection Of Tumor Microsatellite Instability Based On Multiomics Biomarkers.

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Related Experiment Video

Multiomics Analysis of TMEM200A as a Pan-Cancer Biomarker
07:47

Multiomics Analysis of TMEM200A as a Pan-Cancer Biomarker

Published on: September 15, 2023

Machine Learning Algorithm for the Detection of Tumor Microsatellite Instability Based on Multiomics Biomarkers.

Kyle C Strickland1,2, Zachary D Wallen1, Sarabjot Pabla3

  • 1Labcorp, Durham, NC.

JCO Clinical Cancer Informatics
|June 25, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

A machine learning model accurately predicts microsatellite instability (MSI) status in cancers using next-generation sequencing data. This approach improves diagnostic precision for immunotherapy eligibility, especially in indeterminate cases.

Related Experiment Videos

Multiomics Analysis of TMEM200A as a Pan-Cancer Biomarker
07:47

Multiomics Analysis of TMEM200A as a Pan-Cancer Biomarker

Published on: September 15, 2023

Area of Science:

  • Oncology
  • Genomics
  • Bioinformatics

Background:

  • Accurate microsatellite instability (MSI) classification is crucial for guiding immunotherapy selection in advanced cancers.
  • Current MSI detection methods can exhibit variability, potentially leading to missed diagnoses and delayed treatment.
  • Next-generation sequencing (NGS) offers a platform for developing complementary MSI screening approaches.

Purpose of the Study:

  • To develop and validate a machine learning (ML) model for predicting MSI status in colorectal cancer (CRC) using multiomics data.
  • To assess the model's performance and translatability across different cancer types, including uterine and gastric cancers.
  • To investigate the utility of the ML model in identifying MSI-high cases among those with indeterminate status.

Main Methods:

  • Analysis of NGS data from 2,756 CRC patients, including single-nucleotide variants (SNVs), copy-number variants (CNVs), and immune-related gene expression.
  • Training ML algorithms on 70% of the CRC cohort, utilizing tumor mutation burden (TMB) and Boruta-selected features.
  • Testing the trained models on independent CRC cohorts, The Cancer Genome Atlas (TCGA) datasets (COAD/READ), and uterine and gastric cancer cases.

Main Results:

  • The developed ML model demonstrated strong predictive performance across multiple cancer cohorts, with high specificity and negative predictive value in CRC.
  • Sensitivity ranged from 78% in uterine cancer to near-perfect in other cohorts.
  • Among indeterminate cases in CRC and uterine cancer, 15% were reclassified as likely MSI-high, with a significant proportion showing loss of MMR proteins (MLH1/PMS2).

Conclusions:

  • The ML model accurately predicts MSI status in colorectal and uterine cancers using multiomics NGS data, independent of direct microsatellite analysis.
  • This approach enhances diagnostic accuracy for MSI-high tumors, particularly in indeterminate cases.
  • The findings support the potential of ML-driven multiomics analysis to improve patient selection for immunotherapy and ensure timely access to treatment.