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Biology-aware mutation-based deep learning for outcome prediction of cancer immunotherapy with immune checkpoint inhibitors.
- Source :
- NPJ Precision Oncology; 11/6/2023, Vol. 7 Issue 1, p1-10, 10p
- Publication Year :
- 2023
-
Abstract
- The response rate of cancer immune checkpoint inhibitors (ICI) varies among patients, making it challenging to pre-determine whether a particular patient will respond to immunotherapy. While gene mutation is critical to the treatment outcome, a framework capable of explicitly incorporating biology knowledge has yet to be established. Here we aim to propose and validate a mutation-based deep learning model for survival analysis on 1571 patients treated with ICI. Our model achieves an average concordance index of 0.59 ± 0.13 across nine types of cancer, compared to the gold standard Cox-PH model (0.52 ± 0.10). The "black box" nature of deep learning is a major concern in healthcare field. This model's interpretability, which results from incorporating the gene pathways and protein interaction (i.e., biology-aware) rather than relying on a 'black box' approach, helps patient stratification and provides insight into novel gene biomarkers, advancing our understanding of ICI treatment. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 2397768X
- Volume :
- 7
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- NPJ Precision Oncology
- Publication Type :
- Academic Journal
- Accession number :
- 173471694
- Full Text :
- https://doi.org/10.1038/s41698-023-00468-8