Integration of Dose Surface Maps and Genetic Data Identifies the Lower Posterior Rectum as a Key Region for Toxicity after Prostate Cancer Radiotherapy
Year: 2025
Type: article
Abstract: 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.
Source: Clinical Cancer Research
Authors: Artemis Bouzaki, Eliana Vasquez Osorio, Sarah Kerns, Marcel van Herk, Andrew Morris, Jane Shortall, David Azria, Marie-Pierre Farcy-Jacquet, Jenny Chang-Claude, Ananya Choudhury, Alison Dunning, Maarten Lambrecht, Barbara Avuzzi, Dirk De Ruysscher, Petra Seibold, Elena Sperk, Christopher Talbot, Ana Vega, Liv Veldeman, Adam Webb, Barry Rosenstein, Catharine West, Tiziana Rancati, Eliana Gioscio, Alan McWilliam
Institutions: University of Manchester, The Christie NHS Foundation Trust, Medical College of Wisconsin, Inserm, Institut de Recherche en Cancérologie de Montpellier, Institut Regional du Cancer de Montpellier, Universität Hamburg, German Cancer Research Center, University Medical Center Hamburg-Eppendorf, University Cancer Center Hamburg, University of Cambridge, Universitair Ziekenhuis Leuven, KU Leuven, Fondazione IRCCS Istituto Nazionale dei Tumori, Maastricht University Medical Centre, Maastricht University, Maastro Clinic, Heidelberg University, Medizinische Fakultät Mannheim, University of Leicester, Centre for Biomedical Network Research on Rare Diseases, Fundación Pública Galega de Medicina Xenómica, Instituto de Investigación Sanitaria de Santiago, Ghent University Hospital, Ghent University, Icahn School of Medicine at Mount Sinai
Language: English
Subfield: Radiation
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: hybrid
Funders: Fundación Cellex, Pancreatic Cancer UK, Centres de Recerca de Catalunya, Cancer Research UK, National Institute for Health and Care Research, Prostate Cancer UK, European Commission, Fundación Científica Asociación Española Contra el Cáncer, Generalitat de Catalunya, Vedecká Grantová Agentúra MŠVVaŠ SR a SAV, Cancer Research UK Manchester Centre, National Institutes of Health, Seventh Framework Programme, Manchester Biomedical Research Centre, Instituto de Salud Carlos III, Agencia Estatal de Investigación, NIHR Leicester Biomedical Research Centre
Awards: Radiogenómica: identificación de biomarcadores a través de métodos basados en transcriptómica, epigenómica e integración de multiómicas, G1483213741, G1496791142, G1534170958, G1767572923, G1798610363, G181676207, G1875743708, G2070489050, G2262748287, G2544851460, G3005324705, G3480869486, G3655515678, G3681454997, G3958022989, G396409819, G3965165411, G4042783231, G425668148, Radioterapia personalizada: genómica para predecir respuesta y efectos adversos al tratamiento en pacientes con cáncer de próstata y seguimiento a 10 años., G4762558655, G5144603555, Personalised radiotherapy: Making treatment kinder and more effective, G516662629, Validating predictive models of radiotherapy toxicity to improve quality-of-life and reduce side-effects in cancer survivors, G5374892841, G5677030604, Personalized radiotherapy: incorporating cellular response to irradiation in personalized treatment planning to minimize radiation toxicity, G5752270261, G5763065391, G6537094549, G6610840220, G6656126374, G704912930, G7084143925, G7805419838, G8041854674, G8269116634, G8652648143, G8807413798, G8996256810, G988107449
OpenAlex ID: W4415105545