Biologically Informed Prediction of Response to Neoadjuvant Chemotherapy using Routine Clinical Data in Breast Cancer

BackgroundEarly and accurate prediction of pathological complete response (pCR) is essential for personalizing neoadjuvant chemotherapy (NACT) in invasive breast cancer. However, most high-performing predictive models rely on costly, multi-modal data that are not routinely available in standard clinical practice. PurposeTo develop and validate Breast Cancer Biological Multi-modal Information Transfer for Response Prediction Model (BC-BioMIXER), a biologically informed predictive model that transfers multi-omics-derived knowledge to routine clinical data, enabling accurate prediction of pathological complete response prior to neoadjuvant chemotherapy initiation. Material and MethodsBC-BioMIXER was developed in a multi-modality cohort of 648 patients with invasive breast cancer (T2-4, any N, M0) incorporating transcriptomic, proteomic, MRI, and clinical data. The model was externally validated in three independent cohorts (total N = 830), including one multi-modality cohort, one clinical trial cohort, and one contemporary real-world cohort. All patients received NACT followed by surgery. The framework employs a teacher-student knowledge-transfer paradigm in which a multi-omics teacher model learns biologically integrated representations that are subsequently transferred to a student model using only routine clinical data. Predictive performance for pCR was benchmarked against a multi-modality reference model and evaluated across cohorts, receptor-defined subgroups (HER2 and hormone receptor [HR]), and treatment groups (NACT with or without immune checkpoint inhibitors [ICI]). Prognostic value was assessed using distant recurrence-free survival (DRFS). The potential to inform immunotherapy decision-making was explored by comparing DRFS between NACT + ICI and NACT-alone groups within model-predicted pCR and non-pCR subgroups. ResultsBC-BioMIXER achieved pCR prediction performance comparable to the multi-modality benchmark (AUC 0.82 vs. 0.85; p = 0.271) and demonstrated consistent discrimination across all validation cohorts (AUCs 0.82, 0.81, and 0.80; all p < 0.001). Patients predicted to achieve pCR experienced significantly improved 3-year DRFS (HR = 0.36; 95% CI, 0.20-0.67; p < 0.001). In patients treated with NACT + ICI, BC-BioMIXER showed numerically superior pCR prediction compared with PD-L1 expression alone (AUC 0.84 vs. 0.72; p = 0.08). Notably, within the model-predicted non-pCR subgroup, patients receiving NACT + ICI had significantly inferior DRFS compared with those receiving NACT alone (HR = 2.70; p = 0.032), whereas no significant difference was observed in the predicted pCR subgroup. ConclusionBC-BioMIXER translates multi-omics-derived biological knowledge into a robust, routine-data-based predictive tool for breast cancer NACT. Its consistent validation across evolving clinical settings and its potential to inform personalized immunotherapy strategies highlight a step toward scalable and accessible precision oncology. HighlightsO_LIBrings multi-omics power to routine clinical practice: Through cross-modality knowledge transfer, BC-BioMIXER leverages transcriptomic and proteomic data during training to enable highly accurate pCR prediction using only standard MRI and clinical variables (AUC 0.82 vs. 0.85 for full multi-modality benchmark, p=0.271). C_LIO_LIConsistently strong and generalizable performance: Validated in three independent cohorts (total N=830), the model maintained robust pCR discrimination (AUC 0.80-0.82, all p<0.001) across receptor subtypes (HR/HER2) and treatment regimens, including with or without immune checkpoint inhibitors. C_LIO_LIGuides personalized immunotherapy de-escalation: In HER2-negative patients predicted as non-pCR, adding ICI to neoadjuvant chemotherapy was associated with significantly worse distant recurrence-free survival (HR 2.70, p=0.032) compared to chemotherapy alone. This effect was not seen in the predicted pCR group, suggesting the model may help identify patients unlikely to benefit from additional immunotherapy. C_LI