Multi-omic-based classification for identifying optimal neoadjuvant treatment strategies for high-risk early-stage...

Neoadjuvant therapies are essential for managing high-risk early-stage breast cancer, but their effectiveness is limited, necessitating the exploration of the optimal neoadjuvant treatment strategy based on innovative subtypes. Given the heterogeneity inherent in breast cancer, there is growing need for identifying novel molecular subtypes predictive of treatment response through multi-omic analyses. A comprehensive analysis was performed using data from 142 high-risk early breast cancer patients, including genomic, transcriptomic, proteomic, and phosphoproteomic profiles. The molecular subtypes were explored based on the biological characteristics and responses to neoadjuvant treatments. The results were also validated in the TCGA-breast cancer cohort and external dataset. Three molecular subtypes were identified, each associated with different optimal treatment strategies. The immune-activated (IA) subtype displayed a significantly higher pathologic complete response (pCR) rate when treated with platinum-based neoadjuvant regimens, and exhibited heightened immune cell infiltration. The vesicular transport pathway-activated (VT) subtype, characterized by vesicular transport pathway activation, showed a favorable pCR rate to anthracycline-based neoadjuvant chemotherapy. In contrast, the kinase activation (KA) subtype demonstrated limited responsiveness to both platinum and anthracycline-based treatments and featured enrichment in non-canonical TGF-β signaling, MAPK/ATM kinase activation, and angiogenic signatures. A seven-gene classifier linked to non-canonical TGF-β signaling was created to identify the KA subtype, achieving an area under the curve value of 90%. The drug vulnerability of breast cancer cells within the KA subtype indicated potential therapeutic effectiveness of ATR/ATM inhibitors and anti-angiogenic agents. This study defined three novel molecular subtypes in high-risk early-stage breast cancer patients and provided optimal therapeutic treatment strategies based on these subtypes. Therefore, important insights about intrinsic biological properties can form the basis for new cancer treatment strategies.