Prediction in Silico of Pathological Response in a Prospective Cohort Study of Early Breast Cancer Patients (NEOEPICURE)

Prediction in Silico of Pathological Response in a Prospective Cohort Study of Early Breast Cancer Patients

Prediction in Silico of Pathological Response in a Prospective Cohort Study of Early Breast Cancer Patients

Breast cancer (BC) is the most common cancer in women in France with nearly 58,500 new cases and 12,150 deaths estimated in 2018 . Two major achievements have been made in the last five years for breast cancer patients. The first is therapeutic with the approval of immune checkpoint inhibitors in advanced and early triple-negative BC (TNBC) and the impressive efficacy of new antibody-drug conjugated in all BC subtypes. The second is conceptual with the generalization of adaptive therapeutic strategies guided by pathological responses after neoadjuvant therapy in early TNBC, HER2+, HR+ and BRCA mutated breast cancer. This new paradigm in the treatment of cancer patients completely redefined prognostic factors that were previously established with conventional approaches Pathological response remains a major prognostic factor especially for TNBC and HER2 early breast cancer. However, this parameter is evaluated at the end of neoadjuvant treatment and for patients with residual disease, the prognosis remains poor despite some adaptative strategies. Our project is to integrate massive and heterogeneous data concerning the disease (clinical and biological data, imaging and histological results (with multi-omics data)) and patient's environment, personal and familial history. These data are multiple and have dynamic interactions overtime. With the help of mathematical units with biological competences and scientific collaborations, our project is to improve the prediction of treatment response, based on clinical and molecular heterogeneous big data investigation. The main objective of this project is to set up a clinicobiological database prospectively by collecting prospective clinical, biological, pathological and multi-omic data from 300 Patients with early BC treated at the ICO in order to define an algorithm of individual decision for the prediction of the response to this treatment.

Neo adjuvant chemotherapy, Multi omic analysis, Predilection in silico, Prospective clinico database, Surgery

Standard drug: Neoadjuvant treatment : Weekly paclitaxel + carboplatin + pembrolizumab (TCP) followed by EC90 *4 pembrolizumab Procedure: supplementary biopsy at inclusion + centralized blood samples and questionnaires at evaluation visite

Standard drug: EC100 followed by docetaxel + trastuzumab or EC100 followed by paclitaxel + trastuzumab Docetaxel + carboplatin + trastuzumab *6 folowed by trastuzumab alone Procedure: supplementary biopsy at inclusion + centralized blood samples and questionnaires at evaluation visite

Standard drug: EC100 paclitaxel or EC100 docetaxel Procedure: supplementary biopsy at inclusion + centralized blood samples and questionnaires at evaluation visite

Food inquiry + food-frequency questionnaire + physical activity questionnaire will be performed at inclusion

The accurancy predictive parameters for complete pathological response (pCR) after neoadjuvant chemotherapy

The primary endpoint is the predictive yield of complete pathological response (pCR) after the neoaduvant treatment

Inclusion Criteria: Written informed consent obtained from the patient prior to performing any protocol-related procedures, including screening biopsy, blood samples and questionnaires 18 years old or at time of written consent Patient with histologically confirmed breast cancer Absence of metastatic disease Patient requiring neoadjuvant chemotherapy Performance status ≤ 2 (according to WHO criteria) Indication of any systemic therapeutic strategy can be performed alongside this current cohort in accordance with national and / or international recommendations. Patient is willing and able to comply with the protocol for the duration of the study including undergoing treatment and scheduled visits and examinations including follow up. Patient must be affiliated to a Social Health Insurance For patients taking part in the RTW WP: working patients at time of diagnostic and in sick leave at time of inclusion Exclusion Criteria: Other malignancy treated within the last 5 years (except non-melanoma skin cancer or in situ carcinoma of the cervix) Non epithelial breast cancer Coagulopathy or other pathology that contraindicates biopsy procedures Pregnant or nursing patient Individual deprived of liberty or placed under the authority of a tutor Impossibility to submit to the medical follow-up of this clinical trial for geographical, social or psychological reasons For patients taking part in the RTW WP: patient in an "self employed" or "interim" employment situation For patients taking part in the RTW WP: Patients working part-timeProcedures for withdrawal of incorrectly enrolled patients are presented in Section 7.5.