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Updated: May 16, 2025

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Identification of predictive subphenotypes for clinical outcomes using real world data and machine learning.

Weishen Pan1, Deep Hathi2, Zhenxing Xu1

  • 1Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, USA.

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|May 12, 2025
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This study introduces Graph-Encoded Mixture Survival (GEMS) to identify patient subphenotypes for predicting treatment response in advanced non-small cell lung cancer (aNSCLC). GEMS improves overall survival (OS) prediction and reveals distinct patient groups for personalized therapy.

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Area of Science:

  • Oncology
  • Bioinformatics
  • Machine Learning

Background:

  • Predicting treatment response is crucial but challenging due to patient heterogeneity.
  • Existing unsupervised machine learning methods for electronic health record (EHR) data lack guaranteed outcome coherence within patient clusters.
  • Identifying distinct patient subphenotypes is key to understanding and managing treatment variability.

Purpose of the Study:

  • To develop a machine learning framework, Graph-Encoded Mixture Survival (GEMS), for identifying predictive subphenotypes with coherent survival outcomes.
  • To apply GEMS to advanced non-small cell lung cancer (aNSCLC) patients receiving first-line immune checkpoint inhibitor (ICI) therapy.
  • To improve the prediction of overall survival (OS) and understand treatment response heterogeneity.

Main Methods:

  • Proposed Graph-Encoded Mixture Survival (GEMS), a novel machine learning framework.
  • Utilized a real-world dataset of advanced non-small cell lung cancer (aNSCLC) patients undergoing first-line immune checkpoint inhibitor (ICI) therapy.
  • Employed GEMS to identify distinct patient subphenotypes based on EHR data and predict overall survival (OS).

Main Results:

  • GEMS outperformed baseline methods in predicting overall survival (OS).
  • Identified three reproducible subphenotypes within the aNSCLC patient cohort.
  • These subphenotypes exhibited distinct baseline clinical characteristics and differential OS outcomes.

Conclusions:

  • GEMS effectively identifies predictive subphenotypes, addressing heterogeneity in treatment response.
  • The identified subphenotypes offer insights into treatment variability for advanced non-small cell lung cancer (aNSCLC) patients.
  • This approach has the potential to inform and personalize immune checkpoint inhibitor (ICI) therapy selection.