Integrative Survival Prediction in Breast Cancer Using Extracellular Matrix Protease Transcript Signatures and Clinical Variables: A Machine Learning Approach. - Babas, Rami; Vynios, Demitrios H.; Kompothrekas, Aristotelis; Boutsinas, Basilis; Karamanos, Nikos (Cancers, 2026)
Simple Summary: Breast cancer is among the most common malignancies globally, with incidence and mortality placing it at the forefront of oncological research. Patients with similar clinical profiles can experience markedly different outcomes, largely because traditional staging tools fail to capture molecular heterogeneity. This study addresses that gap by integrating transcriptional abundance of extracellular matrix proteases, specifically the MMP, ADAM, and ADAMTS families, with standard clinical variables to improve survival prediction. The models successfully identified "hidden high-risk" patterns in TCGA-BRCA. Independent testing in METABRIC showed statistically significant risk-group separation but only modest individual-level discrimination (external C-index = 0.581), indicating that the signature is promising but requires further platform-harmonized and prospective validation before clinical use. Background/Objectives: Traditional breast cancer prognostic tools relying on clinical staging often miss molecular heterogeneity, leading to divergent patient outcomes. Extracellular matrix (ECM) remodeling, driven by the Matrix Metalloproteinase (MMP), ADAM, and ADAMTS enzyme families, is critical to tumor progression. This study evaluates whether integrating ECM protease transcript abundance with standard clinical variables improves survival prediction accuracy and personalized risk stratification. Methods: Clinical and transcriptomic data from The Cancer Genome Atlas (TCGA) breast cancer cohort were analyzed. We integrated the protein-coding transcripts per million (pTPM) of top-ranked protease genes with standard clinical covariates (age, ordinal stage). Cox Proportional Hazards (CoxPH), penalized Cox (CoxNet), Random Survival Forest (RSF), and Gradient Boosting Survival (GBS) models were evaluated under a stratified 70/30 train–test split, followed by five-fold cross-validation. The locked final RSF model was then externally tested in METABRIC without retraining or risk-cutoff optimization. Results: Univariate screening identified ADAM15, MMP15, and ADAMTSL1 as global risk factors, whereas ADAMTS8 and MMP7 were protective. Prognostic signals were subtype-dependent. Integrated multivariable models outperformed transcript-only approaches in internal testing. The integrative RSF achieved the highest held-out discrimination (C-index = 0.797), outperforming a clinical-only Cox baseline trained on age and stage alone (C-index = 0.742, 95% CI 0.636–0.826). In METABRIC, the external C-index was 0.581 (95% CI 0.562–0.598), with significant survival separation across training-defined risk groups (log-rank p < 0.0001). Conclusions: ECM protease transcript profiles provide complementary prognostic information in TCGA-BRCA and show partial transportability to METABRIC. However, the modest external C-index indicates limited individual-level discrimination across platforms, so these candidate markers should be interpreted as hypothesis-generating and require further validation before clinical implementation.