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

Study Overview
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Brief Summary
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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
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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.

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

Conditions
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Breast Cancer

Participation Criteria
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Eligibility Criteria
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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.

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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Female

Study Plan
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How is the study designed?
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What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 | 
| 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 | 
| 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 | 
| 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 | 
| 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 | 
| 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 | 
| 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 | 
| 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 | 
| 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 | 
| 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 | 
| 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 | 
| 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 | 
| 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 | 
| 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 | 
| 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 | 
| 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 | 
| 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 | 
| 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 | 
| 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 | 
| 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 | 
| 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 | 
| 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 | 
| 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 |