A major feature of metabolic reprogramming is the elevation of glycolysis (the so-called Warburg effect)1, a common characteristic of the most aggressive tumors that is evaluated in clinical practice by Positron Emission Tomography (PET). PET measures the avid uptake of the glucose analog tracer 2-[18F]fluoro-2-deoxy-D-glucose (FDG) by cancer cells, enabling imaging of primary tumors and metastasis with a limit of resolution of approximately 3-5 mm2.
In BC, high FDG-PET uptake has been recently proposed as a predictor of recurrence in ER-positive patients and of response to targeted therapy at early time points3-8. Moreover, TNBCs are characterized by elevated FDG uptake9 and, at least in pre-clinical models, depend on this metabolic adaptation for growth and survival10-13.
These promising perspectives are, however, preliminary and somewhat controversial. Current consensus is that there is no conclusive data indicating that staging of non-metastatic BC patients by FDG-PET produces a survival benefit; conversely, this technique might be useful in identifying sites of metastatic relapse. Even in these latter cases, however, there is concern about the overuse of FDG-PET due to: i) the high cost of the technique vs. its ascertained benefits, ii) the potential high rate of false positives, iii) the poor detection of metastases in patients with apparent early-stage disease. Consequently, FDG-PET is not routinely performed in the initial clinical staging of BC and its usage is largely confined to the metastatic setting.
From a molecular biology viewpoint, this problem might be resolved by developing a transcriptional gene signature capable of capturing the metabolic state of the primary tumor. Such a metabolic signature could be then used to stratify patients to test its potential clinical usefulness, especially in those cases in which available molecular tools are still wanting (need 1 above). In addition, genes of the signature might represent novel and innovative targets in those BCs that lack effective therapeutic options (need 2 above).
To implement the discovery of such a metabolic signature, we are faced with a “catch 22” situation. To perform molecular transcriptomic profiling, we need a case collection of patients stratified by FDG-PET (high vs. low uptake of FDG) in the primary tumor. Such clinical case collections, accompanied with the necessary patient follow-up, are, however, rare because FDG-PET is seldomly performed for the initial staging of non-metastatic BC. We are in a unique situation to address this issue, because of the availability at the European Institute of Oncology (IEO) in Milan of a large cohort of 600 BC patients who underwent PET imaging before entering therapeutic protocols, and for whom tumor-resection or biopsy material, as well as clinicopathological data are available. Within this cohort, we have selected 122 patients with the following characteristics:
- Patients with tumors of comparable size; mainly cT2 tumors, as defined by ultrasound, mammography and/or MRI, to avoid the partial volume effect that can alter PET parameter values in smaller lesions.
- Patients with unifocal disease, since in multifocal/multicentric disease cases exact matching between pathological reports and PET measurements is not possible.
- Patients belonging to two clearly distinguishable groups, based on their individual SUVmax value (i.e., a measurement of the maximum capacity of FDG uptake by the tumor): i) SUV-High (SUV-H, i.e., SUVmax>10; 58 cases), ii) SUV-Low (SUV-L, i.e., SUVmax <5; 64 cases). All other FDG-PET imaging parameters, such as Total Lesion Glycolysis, are related to the SUVmax value. Moreover, SUVmax has been proposed to predict response to neo-adjuvant pre-operatory chemotherapy14, thus representing an elective parameter that can be used to categorize high- or low-FDG uptake tumors.
Patients in these two subgroups (SUV-H and SUV-L) have been matched for age and, most importantly, for the Ki67 proliferation index, to exclude the potential impact of tumor growth rate on the metabolic status. Each subgroup has been further divided into two sets: i) a training set to generate the gene signature and ii) a validation set to test performance. Based on the mean SUVmax value in the two subgroups and their standard deviation, the minimal sample size required to achieve statistical significance is 22 patients in each set (with 5% significance, 90% power; www.sealedenvelope.com/power/continuous-superiority/). Because the sample size of the two sets of both subgroups (training vs. validation in SUV-H vs. SUV-L) already exceeds this value, we predict that our transcriptomic analysis will provide statistically significant results.
Our planned activities will consist of:
- Deriving and testing metabolic signature for its ability to predict disease outcome (risk of relapse) in BC patients, with particular emphasis on Luminal cases. We will employ a large cohort of ~2,500 BC patients, available at IEO, with at least 15 years of follow-up, that has already been used to validate transcriptional signatures predictive of metastasis15.
- By selecting genes of the signature with a hypothesis-driven approach, we will attempt to identify molecular targets able to intercept a subset of TNBCs characterized by elevated glycolysis and to test the effects of the interference of these genes, achieved by molecular genetics, on cancer-relevant phenotypes.
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