Clinical trial NCT06114589: A Study to Develop Molecular Integrated Predictive Models of Breast Radio-toxicity (Precise-RTox)
Official title: An Observational Study to Develop Molecular Integrated Predictive Models of Breast Radio-toxicity (Precise-RTox)
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Breast Cancer
Lead sponsor: Centro di Riferimento Oncologico - Aviano
Sponsor class: OTHER
First posted: 2023-11-02
Enrollment: 420
Sex: FEMALE
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Women with distant non-metastatic breast cancer candidated to radiotherapy after conservative surgery

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.

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.

Primary outcomes:
measure: Generation of a predictive model for actinic fibrosis.
timeFrame: up to 2 years after start of treatment
Secondary outcomes:
measure: Generation of a predictive model for acute skin toxicity
timeFrame: up to 2 years after start of treatment
measure: Generation of a predictive model for late skin toxicity
timeFrame: up to 2 years after start of treatment
measure: Generation of a predictive model for acute pain
timeFrame: up to 2 years after start of treatment
measure: Generation of a predictive model for chronic pain
timeFrame: up to 2 years after start of treatment
measure: Generation of a predictive model for fatigue
timeFrame: up to 2 years after start of treatment
measure: Generation of a predictive model for lymphedema
timeFrame: up to 2 years after start of treatment
measure: Generation of a predictive model for hypothyroidism
timeFrame: up to 2 years after start of treatment
measure: Generation of a predictive model for contra-lateral breast cancer
timeFrame: up to 2 years after start of treatment
measure: Generation of a predictive model for cardiotoxicity
timeFrame: up to 2 years after start of treatment
measure: Generation of a predictive model for cardiotoxicity
timeFrame: up to 2 years after start of treatment
measure: Generation of a predictive model for aesthetic outcome
timeFrame: up to 2 years after start of treatment
measure: Generation of a predictive model for acute skin toxicity
timeFrame: up to 2 years after start of treatment
measure: Generation of a predictive model for late skin toxicity
timeFrame: up to 2 years after start of treatment
measure: Generation of a predictive model for acute pain
timeFrame: up to 2 years after start of treatment
measure: Generation of a predictive model for chronic pain
timeFrame: up to 2 years after start of treatment
measure: Generation of a predictive model for fatigue
timeFrame: up to 2 years after start of treatment
measure: Generation of a predictive model for lymphedema
timeFrame: up to 2 years after start of treatment
measure: Generation of a predictive model for hypothyroidism
timeFrame: up to 2 years after start of treatment
measure: Generation of a predictive model for contra-lateral breast cancer
timeFrame: up to 2 years after start of treatment
measure: Generation of a predictive model for cardiotoxicity
timeFrame: up to 2 years after start of treatment
measure: Generation of a predictive model for cardiotoxicity
timeFrame: up to 2 years after start of treatment
measure: Generation of a predictive model for aesthetic outcome
timeFrame: up to 2 years after start of treatment
measure: Comparison between toxicity risk in treatment plans using protons or photons
timeFrame: up to 2 years after start of treatment
Other outcomes:
Locations:
city: Aviano
country: Italy
facility: Centro di Riferimento Oncologico (CRO) di Aviano - IRCCS
state: Pordenone
Study officials:
affiliation: Centro di Riferimento Oncologico di Aviano (CRO) - IRCCS
name: Lorenzo Vinante, MD
role: PRINCIPAL_INVESTIGATOR
affiliation: Centro di Riferimento Oncologico di Aviano (CRO) - IRCCS
name: Barbara Belletti, PhD
role: PRINCIPAL_INVESTIGATOR
References: