EV-PREDICT
L’Ente Capofila del progetto è IRCCS Istituti Clinici Scientifici Maugeri SpA Società Benefit. Il Responsabile Scientifico del progetto è il Professor Fabio Corsi.
| Patologia: | Tumore al seno |
| Area Tematica: | Medicina di Precisione e Innovazione tecnologica |
| Data di Inizio Progetto: | 26.01.2026 |
| Data di Fine Progetto: | 25.01.2029 |
| Finanziamento: | € 1.840.000,00 |
| Partenariato | IRCCS Istituti Clinici Scientifici Maugeri SpA Società Benefit ASST Fatebenefratelli Sacco Università degli Studi di Brescia SCITEC-CNR – Istituto di Scienze e Tecnologie Chimiche – Consiglio Nazionale delle Ricerche |
Triple negative breast cancer (TNBC) is the most aggressive subtype of breast cancer and its treatment is a challenge. Neoadjuvant chemotherapy +/- immunotherapy (NAT), is a preferred option in TNBC >1 cm, aiming not only to reduce tumor size and explore sensitivity to therapy, but also to achieve a complete pathological response (pCR), which should be considered a surrogate for prognosis. Currently, the choice of NAT is based on tumor biology, size, grade, and lymph node involvement. However, these criteria are often not adequate and sufficient to predict individual response.
In order to improve a precision medicine approach, we will develop an innovative non-invasive tool integrating new biomarkers transported in blood by extracellular vesicles (EV) through artificial intelligence methods.
Circulating EV will be analyzed throughout NAT by multiple approaches and omics technologies to identify EV signatures predictive of response to NAT in TNBC. EV from plasma, primary tumor tissue and culture medium of patient-derived organoids (PDO) will be sorted and characterized from the same patients to point out tumor-specific biomarkers that best refine the predictive EV signature. PDO will be also tested for sensitivity to different drugs that could be used to tailor treatments for TNBC.
This research will allow an important step forward in the personalization of TNBC treatment, by offering a non-invasive avenue for identifying predictive circulating biomarkers in tumor-derived EV.