Marcel van Herk

Purpose: Genome-wide association studies are the gold standard for identifying SNP associated with rectal toxicity after prostate cancer radiotherapy. However, they often neglect the radiotherapy dose distribution, which is a key contributor to toxicity risk. Here, we combined rectal dose surface maps with genetic data to identify rectal regions in which variants influence dose–toxicity relationships. Experimental Design: Data were analyzed from 1,293 patients with prostate cancer from the REQUITE study. Deep learning rectum contouring ensured consistent segmentation, and rectum lengths were standardized to generate two-dimensional dose surface maps. Patients were categorized based on the presence of risk alleles for three candidate SNP (rs1801516, rs17055178, and rs17630638). Propensity score matching accounted for age, rectal volume, prostate volume, and hormone therapy. Voxel-wise Cox proportional hazards models with permutation testing assessed dose–toxicity associations. Results: Voxel-wise Cox proportional hazards models revealed significant (P < 0.05) dose–toxicity associations in risk allele carriers for all SNP for bowel urgency. Risk regions were consistently in the lower posterior rectum. Higher risk of acute bowel control was identified among carriers of the risk allele for rs17630638. For this SNP, carriers of the risk allele showed a higher risk for late rectal bleeding but a reduced risk for acute rectal bleeding. Conclusions: This study identified genotype-driven toxicity patterns using spatial dose mapping. By revealing consistent high-risk rectal regions, this approach strengthens the link between genomics and radiotherapy planning. Importantly, modern radiotherapy planning makes it feasible to reduce dose in genetically sensitive patients and move toward more personalized treatment.

Supplementary Figure S2. Illustration of the data that is used to derive the ΔR1, HFMRI and HVMRI parameters.

Supplementary Figure S8. Treatment-induced changes in OE-MRI biomarkers relative to baseline (BL).

Supplementary Figure S4. Motion correction effect on ΔR1 time-series.