Foundation Model-Based Recommendation of Optimal Neoadjuvant Therapy in Breast Cancer

Neoadjuvant therapy, involving treatment administered before surgery to shrink tumors, significantly impacts breast cancer management. However, current clinical approaches rely predominantly on limited clinical features, leading to suboptimal patient outcomes. To enhance therapeutic decision-making, we propose a novel foundation model-based recommendation framework (FDR) utilizing TabPFN, a deep learning model trained on extensive synthetic tabular data. Our method integrates multi-omics profiles with traditional clinical factors, enabling accurate counterfactual predictions for various drug combinations. Experimental results show that FDR markedly improves personalized treatment recommendations, resulting in a three-fold increase in recovery response rates. This study introduces the first multi-omics-informed neoadjuvant recommendation system, advancing precision oncology and demonstrating effectiveness even with limited patient data.