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Related Concept Videos

Tumor Immunotherapy01:27

Tumor Immunotherapy

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Immunotherapy is a treatment that boosts or manipulates the immune system to fight diseases, including cancer. For instance, by stimulating an immune response through vaccinations against viruses that cause cancers, like hepatitis B virus and human papillomavirus, these diseases can be prevented. Nonetheless, some cancer cells can avoid the immune system due to their rapid mutation and division. The immune response to many cancers involves three phases: elimination, equilibrium, and escape.
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Using Machine Learning Algorithms to Predict Immunotherapy Response in Patients with Advanced Melanoma.

Paul Johannet1, Nicolas Coudray2,3, Douglas M Donnelly4

  • 1Department of Medicine, NYU Grossman School of Medicine, New York, New York.

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We developed a deep learning pipeline integrating histology and clinical data to predict immune checkpoint inhibitor (ICI) response in advanced melanoma, stratifying patients by progression risk.

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Area of Science:

  • Oncology
  • Computational Pathology
  • Immunotherapy

Background:

  • Biomarkers for immune checkpoint inhibitor (ICI) response are limited in clinical scalability.
  • Predicting treatment outcomes in advanced melanoma remains a challenge.

Purpose of the Study:

  • To develop and validate a computational pipeline for predicting ICI response using histology and clinical data.
  • To stratify patients into high-risk and low-risk groups for disease progression.

Main Methods:

  • A multivariable classifier integrating deep learning on histology images and clinical data was developed.
  • The pipeline was trained on a cohort from New York University and validated on a cohort from Vanderbilt University.
  • Performance was assessed using ROC curves, and patient stratification was validated using Kaplan-Meier curves for progression-free survival (PFS).

Main Results:

  • The classifier achieved an AUC of 0.800 (Aperio AT2) and 0.805 (Leica SCN400) in predicting response.
  • Patients classified as high-risk for progression demonstrated significantly worse PFS compared to low-risk patients (P=0.02-0.03).

Conclusions:

  • Histology slides and clinical data are valuable predictors of ICI treatment outcomes.
  • This validated approach shows potential for clinical integration to guide ICI therapy decisions.