A transcriptomic interrogation of the metabolic status of breast cancers for patient stratification and identification of novel therapeutic targets

Despite substantial improvements in breast cancer (BC) detection and management, it remains the leading cause of cancer-death in women worldwide. Only by improving our molecular understanding of BC, will we be able to effectively manage and treat patients and ultimately cure this disease. This research proposal is centered on metabolic alterations in BC and how they can be exploited to address pressing clinical needs; namely: 1) Stratification tools for therapeutic decisions, particularly for the prediction of distant recurrence and therapy response in Luminal-type BCs, which remain at persistent risk of recurrence for at least 15-20 years 2) Targetable alterations in the aggressive triple-negative BCs (TNBCs) that lack treatment options There is good reason to believe that investigations of metabolic reprogramming in BC might boost our understanding of the disease and ultimately improve patient management. Metabolic reprogramming is a hallmark of cancer; in particular, the elevation of glycolysis is a common theme that has multiple effects: i) it alters the bioenergetics potential of tumor cells, ii) it generates metabolites for anabolic reprogramming, iii) it improves the redox balance to sustain survival under stressful conditions. Elevated glycolysis occurs in a large fraction of BCs, especially TNBCs, which depend on this metabolic drift for proliferation and display unique gene ontologies of growth factor signaling, glycolysis and gluconeogenesis. At the clinical level, the enhanced glycolytic state of BC can be detected through the avid uptake of glucose, as measured by positron emission tomography (PET). Through the availability of a unique cohort of BC patients who underwent PET before any treatment, we have developed a 16-gene signature (the “PET signature”) that correlates with the metabolic status of BC and predicts prognosis. The first objective of this proposal is to reduce into practice this signature, by developing a risk algorithm on a vast case-cohort of BCs. We have also shown that the PET signature powerfully synergizes with another risk predictor, the StemPrintER signature, which interrogates the cancer stem cell content of BCs. The synergy of the two signatures is especially intriguing, since they address crucial aspects of BC biology that have thus far been neglected in the development of prognostic/predictive risk algorithms. Indeed, the currently available risk algorithms are mostly based on proliferation-/hormone receptor-related genes. Thus, our second objective is to integrate the PET and StemPrintER signatures and benchmark the individual and combined signatures against available BC predictors. Finally, we will investigate the PET signature genes through high-resolution biological and metabolic studies, to understand their contribution to the biology of aggressive BCs. The third objective should lead to the identification of candidate therapeutic targets, especially relevant for TNBCs that lack treatment options.