Radiation protection and safety measures are essential to ensure adequate quality & safety in radiotherapy (RT). Side effects are systematically mitigated through optimisation and individualisation. Breast cancer (Bca) is the most common cancer in women in Europe, leading to millions of BCa survivors in Europe; this group is projected to increase in the next decades. Randomised trials established the role of RT following breast surgery. Careful RT planning and delivery allow targeting the breast while minimising the dose to organs at risk. Yet, some doses unavoidably reach the lung and the heart, putting BCa patients at risk for severe cardiac and pulmonary disease and second cancers. Quantitative personalised risk scores for late severe cardiac/pulmonary disease/second cancers following RT would reveal opportunities to further mitigate the risk of side effects at the individual patient level. Risk scores would provide a quantitative guide when shaping the patients’ follow-up and screening program to assess the risk or presence of subclinical diseases. Within TETRIS, we propose to design and test quantitative personalised low-level risk scores based on dose-response relationships already published in the literature and refined risk scores which merge patient-specific features (from imaging, genetics and transcriptomics) with RT dose. The project also proposes to explore the opportunities and challenges of applying digital twins (DTs) in RT safety. We will use the historical cohort to develop the prototypes and evaluate the gain we can have using digital twins for risk assessment instead of model-based risk scores. Using a prospective collection of detailed patient data, we will demonstrate the possibility of refining DTs, allowing a deeper description of the single patient and a broader description of the patient’s follow-up. The value of the refined DTs could shape investments in data collection and computational resources for radioprotection.

Cancer sequencing studies have extensively investigated the landscape of somatic mutations that drive tumor development, however the importance of germline variation for cancer susceptibility has been neglected. We hypothesize that for cancer types affecting a large proportion of the population, a shared set of genes with variants of different levels of penetrance leads to the clinical phenotype. While rare germline variants are not interrogated by array-based genome-wide association studies (GWAS), these can be effectively studied by whole-genome or whole-exome sequencing. Here, we propose in-depth pan-cancer analyses, which will be implemented as part of the International Cancer Genome Consortium (ICGC) initiative, as a model to develop and apply the necessary bioinformatics tools and pipelines to fully exploit the cancer-genome datasets, and to harness the diagnostic power of genome sequencing in day-to-day clinical practice. Our proposal addresses the full chain of computational and statistical tools that are needed for clinically relevant diagnosis and intervention, including discovery in large cohorts, validation of putative causal sites in model systems and development of targeted cancer-risk panels. The consortium combines complementary expertise to extend the computational discovery of novel variants that influence cancer susceptibility to intergenic and regulatory variants; to integrate genomic, molecular phenotype, biomarker and clinical data; and to develop novel statistical methods for variant association and eQTL analysis. The project will deal with essential aspects on how data are collected, stored, organized, integrated, analyzed and exploited in cancer genetic clinics. We aim to provide a concerted, cross-disciplinary framework for a better understanding, integration and use of cancer clinical data in the evaluation of the multitude of genetic variants and mutations involved in cancer susceptibility, for the direct benefit of cancer patients.