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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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Integration of multiple machine learning approaches develops a gene mutation-based classifier for accurate

Run Shi1, Jing Sun2,3, Zhaokai Zhou4

  • 1Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.

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Predicting cancer immune checkpoint blockade (ICB) response is crucial. This study developed a novel Gene mutation-based Predictive Signature (GPS) using machine learning, outperforming existing methods for accurate patient stratification and improved outcomes.

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Area of Science:

  • Computational Biology and Bioinformatics
  • Cancer Immunology and Immunotherapy
  • Genomics and Precision Medicine

Background:

  • Current biomarkers for immune checkpoint blockade (ICB) response, such as PD-(L)1 expression and tumor mutation burden (TMB), have limitations.
  • There is a critical need for more reliable predictive methods for ICB therapy in cancer patients.
  • Machine learning approaches offer potential for identifying novel predictive signatures from genomic data.

Purpose of the Study:

  • To identify key nonsynonymous mutations significantly correlated with ICB response using machine learning.
  • To develop and validate a novel classifier, the Gene mutation-based Predictive Signature (GPS), for predicting ICB response.
  • To investigate the tumor immunogenicity, immune responses, and tumor microenvironment (TME) in relation to GPS classification.

Main Methods:

  • Applied multiple machine learning algorithms to nonsynonymous mutation data to identify predictive features.
  • Developed the Gene mutation-based Predictive Signature (GPS) classifier for patient categorization based on predicted ICB response.
  • Validated GPS performance in independent patient cohorts using multi-omics analysis, multiplex immunohistochemistry (mIHC), and ex-vivo organoid co-culture models.

Main Results:

  • Identified key mutations strongly correlated with ICB response through machine learning analysis.
  • The developed GPS classifier demonstrated superior performance compared to conventional predictors in independent validation cohorts.
  • Multi-omics and mIHC analyses revealed distinct tumor immunogenicity and TME characteristics across different GPS groups in lung adenocarcinoma (LUAD).
  • Ex-vivo models confirmed differential responses of GPS-defined patient samples to ICB therapy.

Conclusions:

  • The Gene mutation-based Predictive Signature (GPS) is a robust and accurate classifier for predicting immune checkpoint blockade response.
  • GPS offers a promising tool to improve patient selection for ICB therapy, potentially reducing costs and testing times.
  • This approach facilitates clinical implementation and personalized treatment strategies in cancer immunotherapy.