Evaluation of host and tumor related metabolic asset as possible predictive and prognostic factors in early breast cancer patients candidate to neoadjuvant therapy

BACKGROUND: Neoadjuvant therapy is increasingly used for breast cancer. Achievement of pathological complete response is a strong predictor, although suboptimal, of long-term outcome and offers opportunity for adjuvant treatment modulation based on prognosis. Therefore, the development of more reliable surrogates of long-term outcome is urgently needed to improve patient selection and treatment options. In this context, the assessment of both host and tumor related metabolic asset might improve treatment individualization. AIM: In this project we aim to evaluate metabolomics and metabolic/immune gene-expression signatures as integrated tools to predict the outcome in breast cancer patients treated with neoadjuvant therapy. EXPERIMENTAL DESIGN: The study will enroll a multicentric prospective cohort of 100 stage I-III breast cancer patients receiving neoadjuvant therapy. Metabolic related biomarkers will be tested: 1. plasma metabolomics will be assessed through mass spectrometry analysis at multiple timepoints, 2. metabolic/immune gene-expression signatures assessed on baseline tumor samples. Association of these biomarkers with treatment response and long-term outcome will be tested and integrated with clinico-pathological features to develop a multivariable refined predictive and prognostic algorithm. EXPECTED RESULTS AND IMPACT: This project will contribute to the development of a comprehensive prognostic/predictive model, to properly select patients that: 1) may benefit from modulation of treatment in the neoadjuvant segment (by early identification of responders vs non-responders), 2) may require additional treatments in the adjuvant setting (by predicting long-term prognosis). This biomarker-driven approach will improve prognostic stratification and pave the way to design future strategies of treatment personalization, ultimately allowing to optimize and rationalize the positioning of available resources.