Personalized radiotherapy: incorporating cellular response to irradiation in personalized treatment planning to minimize radiation toxicity

In this project FRRB finances Partner number 5: Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milano. The Principal Investigator responsible of the project is Dr. Tiziana Rancati.

FRRB also finances partner number 7: Politecnico di Milano. The Principal Investigator is Dr. Paolo Zunino.

Pathology of interest:

All tumors treated with radiotherapy

Area of research:


Start date:

May 2019

End date:

April 2022


€ 498.880,00

Project partners:

German Cancer Research Center (DKFZ) Heidelberg

LXRepair - La Tronche, France

Vall d'Hebron Institute of Oncology-VHIO

Fundación Pública Galega Medicina Xenómica (FPGMX)

Fondazione IRCCS Istituto Nazionale dei Tumori (INT)


Politecnico di Milano (POLIMI)


The aim of this collaborative translational project is to personalize radiotherapy (RT) treatment for cancer patients by incorporating information from improved predictive models for RT-induced toxicity based on data from multiple biomarkers of individual radiosensitivity into treatment planning systems (TPS). This combines the (pre-)clinical research (Module 1A/B) with Module 2B “Towards application in health care”. The goal is to tailor RT at the individual patient level by minimizing toxicity while maximizing tumour control.

We will use and extend data from a prospective patient cohort established in the REQUITE project (, with breast and prostate cancer patients and standardized collection of clinical, dosimetric and toxicity data, biobank and a minimum of two years follow-up.

For RADprecise, biological stratification will be enhanced by newly generated information from ATM nucleoshuttling assay and transcriptomics/microRNA sequence analysis (using available PAXgene tubes). Follow-up of the patient cohort will be extended to 4-5 years to allow scoring of long-term RT-related adverse effects and to collect new blood and skin samples for the ATM nucleoshuttling assay.

RADprecise will use parametric models and machine learning to integrate the new biological data as well as already available genomics data to develop prediction models for RT-related adverse effects, which will be validated in an independent sample. Algorithms will be integrated into planning systems for biologically extended TPS. Health economics analyses will be conducted.

RADprecise is interdisciplinary, including leading clinical scientists from academia and health research, Small & Medium Enterprises as well as patient advocates. Most collaborators have already cooperated on related topics, which provide the basis for RADprecise to take biological optimisation into the clinic.