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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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Multimodal Approach Predicts Relapse upon Cessation of Immune Checkpoint Inhibitors in Advanced Melanoma.

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Researchers identified key biomarkers, including TGFBR1 expression and a digital pathology system, to predict relapse after stopping immune checkpoint inhibitors (ICI) for advanced melanoma. This aids in safely discontinuing treatment for eligible patients.

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Area of Science:

  • Oncology
  • Immunotherapy
  • Digital Pathology

Background:

  • Immune checkpoint inhibitors (ICI) offer durable responses in advanced melanoma.
  • A lack of predictive algorithms hinders safe discontinuation of ICI therapy.
  • Identifying patients suitable for treatment cessation is crucial for managing advanced melanoma.

Purpose of the Study:

  • To develop a predictive model for relapse after discontinuing ICI in advanced melanoma.
  • To identify biomarkers for risk stratification upon ICI cessation.
  • To establish an algorithm for safe discontinuation of ICI therapy.

Main Methods:

  • A multimodal approach combining clinical data, AI analysis of H&E-stained whole-slide images, and gene expression signatures.
  • Univariable and multivariable Cox regression analyses to identify predictive factors for relapse.
  • Development of a Multivariate Adaptive Regression Spline (MARS) model for predictive accuracy assessment.

Main Results:

  • Best overall response, six gene mRNA expression, tumor cell density (TCD), and lymphocyte to plasma cell ratio (LYM/PC) predicted relapse.
  • TGFBR1 expression and a digital pathology-based prognostic system were independently associated with relapse.
  • The MARS model achieved an 84.6% predictive accuracy for relapse after ICI discontinuation.

Conclusions:

  • Identified prognostic markers are explainable and implementable in routine clinical practice.
  • These markers facilitate risk stratification for patients considering ICI cessation.
  • The findings support personalized treatment strategies in advanced melanoma management.