Toward genomic personalization of breast cancer radiotherapy: foundations, challenges, and a roadmap for clinical...

Personalizing radiotherapy dose in breast cancer remains a major unmet need, as current treatment paradigms rely on uniform prescriptions that overlook interpatient variability in intrinsic radiosensitivity. Over the past decade, transcriptome-based biomarkers such as the Radiosensitivity Index (RSI) and its radiobiological extension, the Genomic-Adjusted Radiation Dose (GARD), have emerged as promising tools capable of quantifying this biological heterogeneity and linking it to expected therapeutic effectiveness. Retrospective clinical studies across diverse breast cancer cohorts have consistently demonstrated that RSI and GARD correlate with locoregional control, identify radioresistant subgroups that may benefit from dose escalation, and reveal radiosensitive tumors for which de-escalation may be safely explored. These findings challenge the assumption that radiation response is uniform within histological or molecular subtypes and highlight the opportunity for biologically tailored dosing. Yet despite early evidence, translation into clinical practice remains limited. Key barriers include the absence of prospective validation, heterogeneous analytic pipelines for RNA sequencing and RSI computation, uncertainty regarding optimal biomarker timing in the neoadjuvant era, and sensitivity of bulk transcriptomic assays to spatial and microenvironmental heterogeneity. Addressing these challenges will require standardization, consensus on clinically meaningful GARD thresholds, and coordinated international efforts to define methodological and regulatory pathways. Emerging approaches in radiomics, digital pathology, and multimodal artificial intelligence may further refine radiosensitivity assessment and reduce reliance on invasive sampling. As the field progresses, genomic personalization of radiotherapy has the potential to transform breast cancer management by replacing one-size-fits-all prescriptions with biologically informed dose adaptation aimed at maximizing tumor control while minimizing toxicity.