Molecular classification of geriatric breast cancer displays distinct senescent subgroups of prognostic significance.

Elderly breast cancer presents distinct biological characteristics and clinical treatment responses compared to younger counterparts. According to National Comprehensive Cancer Network (NCCN) guidelines, a Comprehensive Geriatric Assessment (CGA) is recommended to develop specialized treatment plans and evaluate treatment efficacy in elderly cancer patients, based on physiological classification. However, the investigation into molecular classification in older cancer patients, which contributes to predict prognosis and guide therapeutic responses, is still lacking. In this study, we identify two subgroups displaying distinct senescent clusters among geriatric breast cancer patients through multi-omics analysis. Utilizing various machine learning algorithms to classify these two senescent clusters, we develop a comprehensive scoring model called 'Sene_Signature'. This model exhibits a better prognosis prediction for geriatric breast cancer patients than other established methods. For clinical therapeutic response prediction, Sene_Signature is shown to be correlated with tumor immune cell infiltration, which is supported by single-cell transcriptomics, and our RNA sequencing and pathological data. Furthermore, we identify increased drug responsiveness in patients with a high Sene_Signature to treatments targeting the EGFR and cell cycle pathways. Additionally, we establish a user-friendly web platform (http://zhaoliminlab.cn:8080/GBC/index.jsp) facilitating investigators in assessing potential patient Sene_Signature and treatment responses for elderly breast cancer patients based on their own sequencing data. In conclusion, we introduce a novel Sene_Signature as a means to evaluate prognosis and therapeutic response in elderly breast cancer patients. This offers a potential molecular classification that aids in the pre-treatment assessment of geriatric breast cancer.