Comparison of unsupervised machine-learning methods to identify metabolomic signatures in patients with localized breast cancer
Jocelyn Gal, Caroline Bailleux, David Chardin, Thierry Pourcher, Julia Gilhodes, Lun Jing, Jean‐Marie Guigonis, Jean-Marc Ferrero, G. Milano, Baharia Mograbi, Patrick Brest, Yann Chateau, Olivier Humbert, Emmanuel Chamorey
Abstract
survival analysis revealed a significant difference for 5-year predicted OS between the 3 clusters. Further pathway analysis using the 449 selected metabolites showed significant differences in amino acid and glucose metabolism between BC histologic subtypes. Our results provide proof-of-concept for the use of unsupervised ML metabolomics enabling stratification and personalized management of BC patients. The design of novel computational methods incorporating ML and bioinformatics techniques should make available tools particularly suited to improving the outcome of cancer treatment and reducing cancer-related mortalities.