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Targeted analyses for the treatment of breast cancer

Study by Unifi's CERM in the highlights of the journal Clinical Cancer Research

Locate at an early stage the possible recurrence of breast cancer in a non-invasive way. This is the path opened by the study published in Clinical Cancer Research, where it features among the highlights of the March issue.

The work is authored by an international team coordinated by Angelo Di Leo, of the Department of Medical Oncology "Sandro Pitigliani" at the Hospital of Prato, in collaboration with researchers from the Magnetic Resonance Center (CERM) of the University of Florence led by Claudio Luchinat ("Serum Metabolomic Profiles Identify ER-Positive Early Breast Cancer Patients at Increased Risk of Disease Recurrence in a Multicenter Population ", doi: 10.1158 / 1078-0432.CCR-16-1153).

The study, carried out with the collaboration of the research group of Richard Love of the International Breast Cancer Research Foundation, Madison (Wisconsin), will help identify the best treatment option for women with breast cancer at an early stage, in view of a personalized medicine.

"Many low-risk patients often receive unnecessary chemotherapy, thus increasing the risk of side effects during therapy," says Angelo Di Leo. "It is crucial to determine which patients are actually at high risk in order to treat and monitor them appropriately."

To this end, in the CERM laboratories, UNIFI researchers Claudio Luchinat, Alessia Vignoli and Leonardo Tenori have analysed with nuclear magnetic resonance (NMR) more than 700 serum samples from women involved in a Phase III clinical trial conducted by the International Breast Cancer Research Foundation.

The analysis made it possible to define two distinct metabolic profiles, which are characteristic of patients in the early and in the metastatic phases. The statistical model built on a subset of samples allowed us to distinguish between these two groups with an accuracy of 90%. In an independent group of oncological patients at an early stage, moreover, the statistical model was used to predict the risk of recurrence, on the assumption that a high-risk patient may have already micrometastases. With this approach, over 70% of patients at high or low risk of recurrence have been correctly identified.

"These results," says Claudio Luchinat, "are paving the way for the use NMR-based metabolomic analysis in oncology, providing a simple, non-invasive and inexpensive tool able to give relevant prognostic information for women with breast cancer."


Unifi, Leonardo Tenori, Claudio Luchinat, Alessia Vignoli

From left: Leonardo Tenori, Claudio Luchinat e Alessia Vignoli.

03 April 2017