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

Breast Cancer Clinical Trial

— (Precise-RTox)  

Official title:

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

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT06114589
• Other study ID #: CRO-2022-29
• Status: Recruiting
• Start date: August 25, 2022
• Est. completion date: June 30, 2026

Study information

• Verified date: November 2023
• Source: Centro di Riferimento Oncologico - Aviano
• Contact: Lorenzo Vinante, MD
• Phone: +390434659855
• Email: lorenzo.vinante[@]cro.it
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 420
• Est. completion date: June 30, 2026
• Est. primary completion date: June 30, 2026
• Gender: Female
• Age group: 18 Years and older

Eligibility

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.

Related Conditions & MeSH terms

• Breast Cancer

Locations

Country	Name	City	State
Italy	Centro di Riferimento Oncologico (CRO) di Aviano - IRCCS	Aviano	Pordenone

Sponsors (1)

Lead Sponsor	Collaborator
Centro di Riferimento Oncologico - Aviano

Country where clinical trial is conducted

Italy, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Generation of a predictive model for actinic fibrosis.	Identification of a predictive model of actinic fibrosis in the breast, with sensitivity of at least 75% and specificity of 90%. Fibrosis is defined as grade =2 (CTCAE v 4.0) or skin induration as grade =2 defined according to CTCAE v 4.0 .	up to 2 years after start of treatment
Secondary	Generation of a predictive model for acute skin toxicity	Sensitivity of a model combining different variables to predict acute skin toxicity defined according to CTCAE scale v4.0 as dermatitis grade =2 or ulceration of the skin of grade =2	up to 2 years after start of treatment
Secondary	Generation of a predictive model for late skin toxicity	Sensitivity of a model combining different variables to predict late skin toxicity defined according to CTCAE scale v4.0 as grade 2 telangiectasia or grade 2 hyperpigmentation	up to 2 years after start of treatment
Secondary	Generation of a predictive model for acute pain	Sensitivity of a model combining different variables to predict acute pain of grade =2 defined according to CTCAE scale v4.0	up to 2 years after start of treatment
Secondary	Generation of a predictive model for chronic pain	Sensitivity of a model combining different variables to predict chronic pain grade =2 defined according to CTCAE scale v4.0	up to 2 years after start of treatment
Secondary	Generation of a predictive model for fatigue	Sensitivity of a model combining different variables to predict fatigue of grade =2 defined according to CTCAE scale v4.0	up to 2 years after start of treatment
Secondary	Generation of a predictive model for lymphedema	Sensitivity of a model combining different variables to predict ipsilateral limb lymphedema of grade =2 defined according to CTCAE v4.0	up to 2 years after start of treatment
Secondary	Generation of a predictive model for hypothyroidism	Sensitivity of a model combining different variables to predict hypothyroidism of grade =2 defined according to CTCAE v4.0	up to 2 years after start of treatment
Secondary	Generation of a predictive model for contra-lateral breast cancer	Sensitivity of a model combining different variables to predict secondary neoplasia to the contra-lateral breast according to CTCAE v4.0	up to 2 years after start of treatment
Secondary	Generation of a predictive model for cardiotoxicity	Sensitivity of a model combining different variables to predict cardiotoxicity defined as reduction at echocardiography of Global Longitudinal Strain (GLS) =10% compared to baseline	up to 2 years after start of treatment
Secondary	Generation of a predictive model for cardiotoxicity	Sensitivity of a model combining different variables to predict grade =2 cardiovascular events defined according to CTCAE v4.0	up to 2 years after start of treatment
Secondary	Generation of a predictive model for aesthetic outcome	Sensitivity of a model combining different variables to predict aesthetic outcome defined as fair/poor, according to Harvard score	up to 2 years after start of treatment
Secondary	Generation of a predictive model for acute skin toxicity	Specificity of a model combining different variables to predict acute skin toxicity defined according to CTCAE scale v4.0 as dermatitis grade =2 or ulceration of the skin of grade =2	up to 2 years after start of treatment
Secondary	Generation of a predictive model for late skin toxicity	Specificity of a model combining different variables to predict late skin toxicity defined according to CTCAE scale v4.0 as grade 2 telangiectasia or grade 2 hyperpigmentation	up to 2 years after start of treatment
Secondary	Generation of a predictive model for acute pain	Specificity of a model combining different variables to predict acute pain of grade =2 defined according to CTCAE scale v4.0	up to 2 years after start of treatment
Secondary	Generation of a predictive model for chronic pain	Specificity of a model combining different variables to predict chronic pain of grade =2 defined according to CTCAE scale v4.0	up to 2 years after start of treatment
Secondary	Generation of a predictive model for fatigue	Specificity of a model combining different variables to predict fatigue of grade =2 defined according to CTCAE scale v4.0	up to 2 years after start of treatment
Secondary	Generation of a predictive model for lymphedema	Specificity of a model combining different variables to predict ipsilateral limb lymphedema of grade =2 defined according to CTCAE v4.0	up to 2 years after start of treatment
Secondary	Generation of a predictive model for hypothyroidism	Specificity of a model combining different variables to predict hypothyroidism of grade =2 defined according to CTCAE v4.0	up to 2 years after start of treatment
Secondary	Generation of a predictive model for contra-lateral breast cancer	Specificity of a model combining different variables to predict secondary neoplasia to the contra-lateral breast according to CTCAE v4.0	up to 2 years after start of treatment
Secondary	Generation of a predictive model for cardiotoxicity	Specificity of a model combining different variables to predict cardiotoxicity defined as reduction at echocardiography of Global Longitudinal Strain (GLS) =10% compared to baseline	up to 2 years after start of treatment
Secondary	Generation of a predictive model for cardiotoxicity	Specificity of a model combining different variables to predict grade =2 cardiovascular events defined according to CTCAE v4.0	up to 2 years after start of treatment
Secondary	Generation of a predictive model for aesthetic outcome	Specificity of a model combining different variables to predict aesthetic outcome defined as fair/poor, according to Harvard score	up to 2 years after start of treatment
Secondary	Comparison between toxicity risk in treatment plans using protons or photons	Difference in frequency of high risk toxicity between treatment plans using protons or photons	up to 2 years after start of treatment