Multimodal Machine Learning–Based Clinical and Genomic Prognostic Factors for Progression-Free Survival in Metastatic Breast Cancer Using Phase III MONALEESA Trials

Abstract Purpose Cyclin-dependent kinase 4/6 inhibitors in combination with endocrine therapy have become widely adopted as a first-line treatment approach for hormone receptor-positive (HR+)/human epidermal growth factor receptor 2-negative (HER2−) advanced breast cancer (ABC). Through a collaboration between Novartis and the US Food and Drug Administration, we integrated multimodal data from phase III MONALEESA trials to identify novel prognostic factors for HR+/HER2−ABC. Experimental Design Baseline clinical and genomics data were collected from patients in the MONALEESA-2, −3, and −7 trials. Circulating tumor DNA (ctDNA) from plasma samples and gene expression in archival tumor biopsies were analyzed. Machine learning models were trained ( n = 1,355) on progression-free survival (PFS) using multimodal features via fusion models. In a test set ( n = 382), prognostic performances were evaluated using the Kaplan-Meier, Cox, and Weibull models. Results The late fusion model achieved a prognostic performance (C-index: 0.727, 95% CI: 0.641–0.799) slightly outperforming early fusion (C-index: 0.718, 95% CI: 0.631–0.791) and single-modality models. Model-based risk stratification identified distinct low- and high-risk groups (PFS HR, 0.28; 95% CI: 0.20–0.38). Among the top 40 features for progression risk (18 clinical, 20 gene expression, and 2 ctDNA), these were associated with higher risk: liver involvement, higher histological grades, early relapse; lower STC2 and RBBP8 expressions and higher FBX05 expression; and higher tumor mutational burden and ctDNA purity. Conclusions Integrating clinical and genomic modalities improved progression risk prediction compared with single-modality models, while highlighting salient features that inform the underlying biology and support hypothesis generation.