BackgroundAs a heterogeneous disease

breast cancer requires refined classification frameworks that can effectively guide targeted therapies. However

traditional methods fail to capture the comprehensive molecular insights needed for this purpose.MethodsTo comprehensively capture breast cancer heterogeneity

we employed integrative clustering that incorporates six molecular features from 670 breast cancer samples. Ten distinct clustering algorithms were combined to ensure robust subtype identification

and the identified subtypes were validated in four independent datasets. Subsequently

we constructed a survival support vector machine prognostic model based on key molecular features to enhance survival prediction and clinical applicability.ResultsFive novel subtypes were identified: consensus subtypes 1–5 (CS1–CS5). CS2 was an aggressive subtype with elevated TP53 mutation rates

high tumor mutational burden

and strong sensitivity to YM-155 and ispinesib. Conversely

CS5 exhibited stable genomics with enhanced nucleotide excision repair and favorable prognoses. CS2 and CS4 showed enriched immune checkpoint expression

indicating potential immunotherapy responsiveness

while CS1 and CS5 exhibited immune-cold profiles. The survival support vector machine model effectively predicted survival outcomes across independent datasets.ConclusionsThe refined breast cancer classification framework developed in this research uncovers new insights into molecular heterogeneity

enhances risk stratification

and enables the identification of promising therapeutic targets. The potential of this framework to optimize personalized treatment strategies warrants further clinical validation.