Development of a Horizontal Data Integration Classifier for Noninvasive Early Diagnosis of Breast Cancer

This is a translational no-profit study. Our proposal aims at creating a noninvasive Horizontal Data Integration (HDI) classifier for early diagnosis of breast cancer, with the final goal of avoiding in most cases useless biopsies of suspect cases encountered during radiological screening. Women with radiologically identified lesions, BIRADS-3/4/5, smaller than 2 cm by radiological assessment (i.e., radiological T1), will be enrolled and invited to donate peripheral blood samples (35 ml) and urine samples (50 ml). Radiological images as well as demographic and anatomopathological data will be collected. Objective of this project is to develop a HDI classifier enabling early noninvasive diagnosis of breast cancer with similar accuracy compared to breast biopsies. Such classifier will be developed based on the correlation between the molecular profile of peripheral blood (ctDNA, proteins, exosomes) and urine (ctDNA) collected at T0 (baseline, before diagnostic biopsy) and bioptic diagnosis. The assessment of the profile of peripheral blood (ctDNA, proteins, exosomes) and urine (ctDNA) at two time points for diagnosed pT1 breast cancers (T0: baseline, before biopsy; T1: after diagnosis of pT1 breast cancer) will allow us to distinguish between tumor- and host-specific molecular alterations in connection with the presence/absence of breast cancer.