🤖 AI Summary
This study addresses the challenge of improving molecular subtype characterization and clinical outcome prediction in breast cancer by jointly modeling protein sequence semantics and quantitative expression levels. We propose a novel integrative framework that fuses protein sequence embeddings—generated by ProtGPT2—with transcriptomic or proteomic expression data to construct biologically interpretable, discriminative multi-omics features. The method combines ensemble K-means clustering, XGBoost classification, PPI network analysis, and feature importance ranking to enable fine-grained subtype stratification and mechanistic insight extraction. It identifies key protein modules—including KMT2C, CLASP2, and MYO1B—that coordinately regulate hormonal signaling, cytoskeletal remodeling, and drug resistance pathways. In survival prediction and biomarker status classification tasks, our approach achieves F1-scores of 0.88 and 0.87, respectively—significantly outperforming conventional expression-only baselines.
📝 Abstract
Breast cancer's complexity and variability pose significant challenges in understanding its progression and guiding effective treatment. This study aims to integrate protein sequence data with expression levels to improve the molecular characterization of breast cancer subtypes and predict clinical outcomes. Using ProtGPT2, a language model designed for protein sequences, we generated embeddings that capture the functional and structural properties of proteins sequence. These embeddings were integrated with protein expression level to form enriched biological representations, which were analyzed using machine learning methods like ensemble K-means for clustering and XGBoost for classification. Our approach enabled successful clustering of patients into biologically distinct groups and accurately predicted clinical outcomes such as survival and biomarkers status, achieving high performance metrics, notably an F1 score of 0.88 for survival and 0.87 for biomarkers status prediction. Feature importance analysis identified KMT2C, CLASP2, and MYO1B as key proteins involved in hormone signaling, cytoskeletal remodeling, and therapy resistance in hormone receptor-positive and triple-negative breast cancer, with potential influence on breast cancer subtype behavior and progression. Furthermore, protein-protein interaction networks and correlation analyses revealed functional interdependencies among proteins that may influence breast cancer subtype behavior and progression. These findings suggest that integrating protein sequence and expression data provides valuable insights into tumor biology and has significant potential to enhance personalized treatment strategies in breast cancer care.