Multiomic integration reveals subtype-specific predictors of neoadjuvant treatment response in breast cancer.

Neoadjuvant therapy has been widely used in breast cancer, but treatment response varies among individuals. We conducted multiomic profiling on tumor samples from 149 Chinese patients with breast cancer across ERHER2, ERHER2, and ERHER2 subtypes, categorizing outcomes as pathologic complete response (pCR;  = 81) or residual disease (RD;  = 68). We identified distinct molecular features linked to pCR in each subtype: elevated cell proliferation in patients with ERHER2 pCR, higher methylation in patients with ERHER2 RD, increased methylation in patients with ERHER2 RD, and hypermethylation in patients with ERHER2 RD. These findings were subsequently validated in independent datasets. By integrating clinical and multiomic data, we developed MOPCR, a subtype-specific machine learning model that outperformed single-omic approaches in predicting treatment response. MOPCR demonstrated potential generalizability across cohorts and provided preliminary stratification of patient subgroups with higher pCR probability, offering valuable insights for precision cancer management.