A Study to Develop Molecular Integrated Predictive Models of Breast Radio-toxicity (Precise-RTox)

NCT ID: NCT06114589

Last Updated: 2023-11-07

Basic Information

• Recruitment Status: RECRUITING
• Total Enrollment: 420 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2022-08-25
• Study Completion Date: 2026-06-30

Brief Summary

Breast radiation treatment is burdened by acute and chronic toxicities, in most cases mild. However, considering the excellent life expectancy of patients with breast cancer, maintaining a low toxicity profile is of primary importance in order to guarantee a satisfactory quality of life. The definition of the molecular and genetic variables related to radiotoxicity and their integration into predictive molecular signatures may allow the risk of toxicity to be individualized. This would provide the clinician with a useful tool in order to personalize the radiation treatment, thus being able to choose the best technique or schedule for each patient.

Detailed Description

Breast radiation treatment is burdened by acute and chronic toxicities, in most cases mild. However, considering the excellent life expectancy of patients with breast cancer, maintaining a low toxicity profile is of primary importance in order to guarantee a satisfactory quality of life. Currently there are numerous predictive models of toxicity (Normal Tissue Complication Probability, NTCP) which are based on dosimetric and sometimes also clinical data. To date, they do not include individual genetic variability. However, it is believed that inter-individual variability may be responsible for up to 40% of actinic toxicity. Multiparametric models that consider genetics, dose and clinical aspects probably better reflect the complexity of radiotoxicity than models that rely on a single parameter and it is possible to integrate such parameters using a machine learning approach. The definition of the molecular and genetic variables related to radiotoxicity and their integration into predictive molecular signatures would therefore allow the risk to be individualized. This would provide the clinician with a useful tool in order to personalize the radiation treatment, thus being able to choose the best technique or schedule for each patient.

Conditions

Breast Cancer

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Eligibility Criteria

Inclusion Criteria

• Age ≥18 years;
• Ability to express appropriate informed consent to treatment;
• Distant nonmetastatic breast cancer;
• Histology: infiltrating NST(no special type)/lobular carcinoma or ductal carcinoma in situ;
• Stage: pTis; pT1-3 pN1-3 M0;
• Hormone receptors, HER-2 status: Any;
• Breast-conserving surgery. Both the sentinel lymph node biopsy and axillary lymphadenectomy. Negative surgical margins.
• Candidates for postoperative radiation treatment.

Exclusion Criteria

• Refusal of radiotherapy treatment (i.e., absence of signed informed consent);
• Previous radiation therapy at the same site;
• Concomitant chemotherapy with anthracyclines or taxanes;
• Inability to maintain treatment position;
• Partial breast radiotherapy (PBI);
• Male breast cancer;
• Mastectomy surgery.

• Minimum Eligible Age: 18 Years
• Eligible Sex: FEMALE
• Accepts Healthy Volunteers: No

Sponsors

Centro di Riferimento Oncologico - Aviano

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Lorenzo Vinante, MD

Role: PRINCIPAL_INVESTIGATOR

Centro di Riferimento Oncologico di Aviano (CRO) - IRCCS

Barbara Belletti, PhD

Role: PRINCIPAL_INVESTIGATOR

Centro di Riferimento Oncologico di Aviano (CRO) - IRCCS

Locations

Centro di Riferimento Oncologico (CRO) di Aviano - IRCCS

Aviano, Pordenone, Italy

Site Status: RECRUITING

Countries

Italy

Central Contacts

Lorenzo Vinante, MD

Role: CONTACT

lorenzo.vinante@cro.it

+390434659855

Facility Contacts

Lorenzo Vinante, MD

Role: primary

lorenzo.vinante@cro.it

+390434659855

Other Identifiers

CRO-2022-29

Identifier Type: -

Identifier Source: org_study_id