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Document name Authors Date
Causal inference models for the prediction of radio-induced toxicity after breast cancer radiotherapy. Benedetta Dionisi Ferrera, Tiziana Rancati, Maria Carmen De Santis, Wouter Van Amsterdam June 2025 Download Poster
The primary aim of the poster “Causal inference models for the prediction of radio-induced toxicity after breast cancer radiotherapy” is to apply causal inference models to gain a deeper understanding of the mechanisms underlying radiation-induced side effects following breast cancer radiotherapy. In particular, the project seeks to reconstruct the most comprehensive representation of the causal relationships between radiation dose and patient-specific characteristics, with the objective of identifying key factors contributing to normal tissue functional damage.
 

The poster offers a first glimpse into the project, presenting the initial steps in the development of a Directed Acyclic Graph (DAG) to investigate the causal relationships among variables involved in breast cancer treatment , with a particular focus on estimating the causal effect of skin dose on the risk of developing late fibrosis.

Document name Authors Date
Clinical Guidelines for Polygenic Risk Score in Predicting Radiation Toxicity in Breast Cancer. Jacqueline S. Peñaloza, Miguel E. Aguado-Barrera, Daniela Bavuso, Claudio Fiorino, David Gibon, Goretti Mallorquí Fernandez, Clara Marrone, Sandrine Pereira, Eva Onjukka, Victoria Reyes, Paolo Zunino, Ana Vega, Tiziana Rancati, Sara Gutiérrez-Enríquez. December 2025 Download Poster

This poster addresses the role of Polygenic Risk Scores (PRS) in predicting radiation-induced toxicity in breast cancer patients, within the context of personalised radiation oncology. Breast cancer remains a major global public health concern, and although radiotherapy significantly improves survival, it can also lead to severe long-term side effects, including cardiovascular and pulmonary toxicity and secondary cancers. Identifying patients at higher risk is therefore essential to improve radiation safety and personalise treatment.

The focus of the investigation is the development of clinical guidelines for integrating PRS into risk prediction models for radiotherapy-related toxicities. Using genotype data from the REQUITE cohort and published genome-wide association studies (GWAS), the study outlines a standardised pipeline for PRS calculation, quality control, population matching, and clinical transformation. Particular attention is given to the challenge of defining an appropriate reference population for PRS normalisation. Overall, the work supports the translational value of PRS as a key component of future digital twins for European breast cancer patients, enabling earlier surveillance and more personalised prevention strategies in radiation oncology.