Personalized Breast Cancer Therapy Optimization Using Deep Q-Learning and TCGA Data

Breast cancer therapy is challenged by tumor heterogeneity and limited personalization. This study presents a Deep Q-Learning (DQN) model trained on The Cancer Genome Atlas (TCGA) data to optimize personalized treatment plans for breast cancer patients. The model achieved an average tumor reduction of 129.71 mm, a minimum of 95.01 mm, and a maximum of 150.0 mm (indicating complete tumor elimination in some simulated cases), with side effects below 0.0802. Genetic differentiation for key markers (TP53, KRAS, BRAF) ranged from 9.53 to 12.96, enabling targeted therapy adjustments. These results demonstrate a promising framework for precision oncology, with potential to guide future clinical trials while maintaining minimal side effects.