Interpretable systems biomarkers predict response to immune-checkpoint inhibitors

Óscar Lapuente-Santana, Maisa van Genderen, Peter A.J. Hilbers, Francesca Finotello, Federica Eduati (Corresponding author)

Research output: Contribution to journalArticleAcademicpeer-review

40 Citations (Scopus)
88 Downloads (Pure)

Abstract

Cancer cells can leverage several cell-intrinsic and -extrinsic mechanisms to escape immune system recognition. The inherent complexity of the tumor microenvironment, with its multicellular and dynamic nature, poses great challenges for the extraction of biomarkers of immune response and immunotherapy efficacy. Here, we use RNA-sequencing (RNA-seq) data combined with different sources of prior knowledge to derive system-based signatures of the tumor microenvironment, quantifying immune-cell composition and intra- and intercellular communications. We applied multi-task learning to these signatures to predict different hallmarks of immune responses and derive cancer-type-specific models based on interpretable systems biomarkers. By applying our models to independent RNA-seq data from cancer patients treated with PD-1/PD-L1 inhibitors, we demonstrated that our method to Estimate Systems Immune Response (EaSIeR) accurately predicts therapeutic outcome. We anticipate that EaSIeR will be a valuable tool to provide a holistic description of immune responses in complex and dynamic systems such as tumors using available RNA-seq data.
Original languageEnglish
Article number100293
JournalPatterns
Volume2
Issue number8
Early online date30 Jun 2021
DOIs
Publication statusPublished - 13 Aug 2021

Keywords

  • Immuno-oncology
  • Systems Biology
  • Tumor Microenvironment
  • Precision oncology
  • Biomarkers
  • RNA-seq
  • Machine Learning
  • systems biology
  • precision oncology
  • biomarkers
  • machine learning
  • immuno-oncology
  • DSML 3: Development/Pre-production: Data science output has been rolled out/validated across multiple domains/problems
  • tumor microenvironment

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