Uncovering gene and cellular signatures of immune checkpoint response via machine learning and single-cell RNA-seq
Year: 2025
Type: article
Abstract: Immune checkpoint inhibitors have transformed cancer therapy. However, only a fraction of patients benefit from these treatments. The variability in patient responses remains a significant challenge due to the intricate nature of the tumor microenvironment. Here, we harness single-cell RNA-sequencing data and employ machine learning to predict patient responses while preserving interpretability and single-cell resolution. Using a dataset of melanoma-infiltrated immune cells, we applied XGBoost, achieving an initial AUC score of 0.84, which improved to 0.89 following Boruta feature selection. This analysis revealed an 11-gene signature predictive across various cancer types. SHAP value analysis of these genes uncovered diverse gene-pair interactions with non-linear and context-dependent effects. Finally, we developed a reinforcement learning model to identify the most informative single cells for predictivity. This approach highlights the power of advanced computational methods to deepen our understanding of cancer immunity and enhance the prediction of treatment outcomes.
Source: npj Precision Oncology
Authors: Asaf Pinhasi, Keren Yizhak
Institution: Technion – Israel Institute of Technology
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $4,090
OpenAlex ID: W4409118691