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Integrating genetic polymorphisms and clinical data to develop predictive models for skin toxicity in breast cancer radiation therapy

Ester Aguado-Flor, Victoria M. Reyes, Víctor Navarro, Meritxell Mollà, Miguel E. Aguado-Barrera, Manuel Altabas, David Azria, Adinda Baten, Celine Bourgier, Renée Bultijnck (UGent) , et al.
(2025) BREAST. 82.
Author
Organization
Abstract
Background: We aim to develop and validate predictive models for acute and late skin toxicity in breast cancer (BC) patients undergoing radiation therapy (RT). Models incorporate a genetic profile-comprising candidate single nucleotide polymorphisms (SNPs) in non-coding RNAs and previously reported toxicity-associated variants-combined with clinical variables. Methods: The study involved 1979 BC patients monitored for two to eight years post-RT in a multi-centre study. We assessed acute (oedema/erythema) and late (atrophy/fibrosis) toxicity using logistic regression and Cox proportional hazards models. The cohort was divided into training and validation datasets. Results: Six SNPs demonstrated to be predictors of acute (rs13116075, rs12565978, rs72550778 and rs7284767) and late toxicity (rs16837908 and rs61764370) either in the training or validation cohort. However, none of these SNPs were consistently associated with toxicity across both stages of analysis. The rs13116075, rs12565978 and rs16837908 were previously reported to be associated with RT toxicity. In the validation phase, SNP-based models showed limited predictive ability, with AUC values of 0.49 and c-index of 0.54 for acute and late toxicity, respectively. Models incorporating either clinical variables alone or in combination with SNPs achieved similar AUC and c-index values of similar to 0.60 for acute and late toxicity, respectively. However, the combined model exhibited the highest predictive accuracy for acute and late toxicity, both in the training and the validation cohorts. Conclusions: Our findings highlight the importance of combining clinical data with genetic markers to enhance the accuracy of models predicting RT toxicity in BC.
Keywords
Breast cancer, Radiation therapy-induced side-effects, Literature candidate polymorphisms' validation, Predictive models, Non-coding RNA polymorphisms, INTENSITY-MODULATED RADIOTHERAPY, PROSTATE-CANCER, RADIOGENOMICS, COHOR

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Citation

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MLA
Aguado-Flor, Ester, et al. “Integrating Genetic Polymorphisms and Clinical Data to Develop Predictive Models for Skin Toxicity in Breast Cancer Radiation Therapy.” BREAST, vol. 82, 2025, doi:10.1016/j.breast.2025.104506.
APA
Aguado-Flor, E., Reyes, V. M., Navarro, V., Mollà, M., Aguado-Barrera, M. E., Altabas, M., … REQUITE Consortium, [missing]. (2025). Integrating genetic polymorphisms and clinical data to develop predictive models for skin toxicity in breast cancer radiation therapy. BREAST, 82. https://doi.org/10.1016/j.breast.2025.104506
Chicago author-date
Aguado-Flor, Ester, Victoria M. Reyes, Víctor Navarro, Meritxell Mollà, Miguel E. Aguado-Barrera, Manuel Altabas, David Azria, et al. 2025. “Integrating Genetic Polymorphisms and Clinical Data to Develop Predictive Models for Skin Toxicity in Breast Cancer Radiation Therapy.” BREAST 82. https://doi.org/10.1016/j.breast.2025.104506.
Chicago author-date (all authors)
Aguado-Flor, Ester, Victoria M. Reyes, Víctor Navarro, Meritxell Mollà, Miguel E. Aguado-Barrera, Manuel Altabas, David Azria, Adinda Baten, Celine Bourgier, Renée Bultijnck, Jenny Chang-Claude, Maria Carmen De Santis, Alison M. Dunning, Laura Duran-Lozano, Rebecca M. Elliott, Marie-Pierre Farcy Jacquet, Carlotta Giandini, Alexandra Giraldo, Sheryl Green, Maarten Lambrecht, Carlos Lopez-Pleguezuelos, Christel Monten, Tiziana Rancati, Tim Rattay, Barry S. Rosenstein, Dirk De Ruysscher, Orland Diez, Petra Seibold, Elena Sperk, R Paul Symonds, Hilary Stobart, Ana Vega, Liv Veldeman, Guillermo Villacampa, Adam J. Webb, Caroline Weltens, Paolo Zunino, Christopher J. Talbot, Catharine M. West, Jordi Giralt, Sara Gutiérrez-Enríquez, Wilfried De Neve, Pieter Deseyne, Fréderic Duprez, Valerie Fonteyne, Yolande Lievens, Marcus Mareel, Piet Ost, Leen Paelinck, Giselle Post, Martijn Swimberghe, Marc Van Eijkeren, Annick Van Greveling, Tom Vercauteren, Katrien Vandecasteele, and [missing] REQUITE Consortium. 2025. “Integrating Genetic Polymorphisms and Clinical Data to Develop Predictive Models for Skin Toxicity in Breast Cancer Radiation Therapy.” BREAST 82. doi:10.1016/j.breast.2025.104506.
Vancouver
1.
Aguado-Flor E, Reyes VM, Navarro V, Mollà M, Aguado-Barrera ME, Altabas M, et al. Integrating genetic polymorphisms and clinical data to develop predictive models for skin toxicity in breast cancer radiation therapy. BREAST. 2025;82.
IEEE
[1]
E. Aguado-Flor et al., “Integrating genetic polymorphisms and clinical data to develop predictive models for skin toxicity in breast cancer radiation therapy,” BREAST, vol. 82, 2025.
@article{01K9ENBPR3HXF0C8H6S4C8JZNT,
  abstract     = {{Background: We aim to develop and validate predictive models for acute and late skin toxicity in breast cancer (BC) patients undergoing radiation therapy (RT). Models incorporate a genetic profile-comprising candidate single nucleotide polymorphisms (SNPs) in non-coding RNAs and previously reported toxicity-associated variants-combined with clinical variables.

Methods: The study involved 1979 BC patients monitored for two to eight years post-RT in a multi-centre study. We assessed acute (oedema/erythema) and late (atrophy/fibrosis) toxicity using logistic regression and Cox proportional hazards models. The cohort was divided into training and validation datasets.

Results: Six SNPs demonstrated to be predictors of acute (rs13116075, rs12565978, rs72550778 and rs7284767) and late toxicity (rs16837908 and rs61764370) either in the training or validation cohort. However, none of these SNPs were consistently associated with toxicity across both stages of analysis. The rs13116075, rs12565978 and rs16837908 were previously reported to be associated with RT toxicity. In the validation phase, SNP-based models showed limited predictive ability, with AUC values of 0.49 and c-index of 0.54 for acute and late toxicity, respectively. Models incorporating either clinical variables alone or in combination with SNPs achieved similar AUC and c-index values of similar to 0.60 for acute and late toxicity, respectively. However, the combined model exhibited the highest predictive accuracy for acute and late toxicity, both in the training and the validation cohorts.

Conclusions: Our findings highlight the importance of combining clinical data with genetic markers to enhance the accuracy of models predicting RT toxicity in BC.}},
  articleno    = {{104506}},
  author       = {{Aguado-Flor, Ester and Reyes, Victoria M. and Navarro, Víctor and Mollà, Meritxell and Aguado-Barrera, Miguel E. and Altabas, Manuel and Azria, David and Baten, Adinda and Bourgier, Celine and Bultijnck, Renée and Chang-Claude, Jenny and De Santis, Maria Carmen and Dunning, Alison M. and Duran-Lozano, Laura and Elliott, Rebecca M. and Farcy Jacquet, Marie-Pierre and Giandini, Carlotta and Giraldo, Alexandra and Green, Sheryl and Lambrecht, Maarten and Lopez-Pleguezuelos, Carlos and Monten, Christel and Rancati, Tiziana and Rattay, Tim and Rosenstein, Barry S. and De Ruysscher, Dirk and Diez, Orland and Seibold, Petra and Sperk, Elena and Symonds, R Paul and Stobart, Hilary and Vega, Ana and Veldeman, Liv and Villacampa, Guillermo and Webb, Adam J. and Weltens, Caroline and Zunino, Paolo and Talbot, Christopher J. and West, Catharine M. and Giralt, Jordi and Gutiérrez-Enríquez, Sara and De Neve, Wilfried and Deseyne, Pieter and Duprez, Fréderic and Fonteyne, Valerie and Lievens, Yolande and Mareel, Marcus and Ost, Piet and Paelinck, Leen and Post, Giselle and Swimberghe, Martijn and Van Eijkeren, Marc and Van Greveling, Annick and Vercauteren, Tom and Vandecasteele, Katrien and REQUITE Consortium, [missing]}},
  issn         = {{0960-9776}},
  journal      = {{BREAST}},
  keywords     = {{Breast cancer,Radiation therapy-induced side-effects,Literature candidate polymorphisms' validation,Predictive models,Non-coding RNA polymorphisms,INTENSITY-MODULATED RADIOTHERAPY,PROSTATE-CANCER,RADIOGENOMICS,COHOR}},
  language     = {{eng}},
  pages        = {{14}},
  title        = {{Integrating genetic polymorphisms and clinical data to develop predictive models for skin toxicity in breast cancer radiation therapy}},
  url          = {{http://doi.org/10.1016/j.breast.2025.104506}},
  volume       = {{82}},
  year         = {{2025}},
}
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