Research projects

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; 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:

  1. 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.
  2. 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.



  1. DeBerardinis, RJ and Chandel, NS. Fundamentals of cancer metabolism. 2016. Sci Adv 2, e1600200, doi:10.1126/sciadv.1600200.
  2. Moses, W. W. Fundamental Limits of Spatial Resolution in PET. 2011. Nucl Instrum Methods Phys Res A 648 Supplement 1, S236-S240, doi:10.1016/j.nima.2010.11.092. 
  3. Groheux D, Giacchetti S, Moretti JL, Porcher R, Espié M, Lehmann-Che J, de Roquancourt A, Hamy AS, Cuvier C, Vercellino L and Hindié E. Correlation of high 18F-FDG uptake to clinical, pathological and biological prognostic factors in breast cancer. 2011. Eur J Nucl Med Mol Imaging 38, 426-435, doi:10.1007/s00259-010-1640-9.
  4. Bos R, van Der Hoeven JJ, van Der Wall E, van Der Groep P, van Diest PJ, Comans EF, Joshi U, Semenza GL, Hoekstra OS, Lammertsma AA and Molthoff CF. Biologic correlates of (18)fluorodeoxyglucose uptake in human breast cancer measured by positron emission tomography. 2001. J Clin Oncol 20, 379-387, doi:10.1200/JCO.2002.20.2.379.
  5. Groheux D, Hindié E, Giacchetti S, Delord M, Hamy AS, de Roquancourt A, Vercellino L, Berenger N, Marty M and Espié M. Triple-negative breast cancer: early assessment with 18F-FDG PET/CT during neoadjuvant chemotherapy identifies patients who are unlikely to achieve a pathologic complete response and are at a high risk of early relapse. 2012. J Nucl Med 53, 249-254, doi:10.2967/jnumed.111.094045.
  6. Groheux D, Majdoub M, Sanna A, de Cremoux P, Hindié E, Giacchetti S, Martineau A, de Roquancourt A, Merlet P, Visvikis D, Resche-Rigon M, Hatt M and Espié M. Early Metabolic Response to Neoadjuvant Treatment: FDG PET/CT Criteria according to Breast Cancer Subtype. 2015. Radiology 277, 358-371, doi:10.1148/radiol.2015141638.
  7. Groheux D, Martineau A, Teixeira L, Espié M4, de Cremoux P, Bertheau P, Merlet P and Lemarignier C. ¹⁸ FDG-PET/CT for predicting the outcome in ER+/HER2- breast cancer patients: comparison of clinicopathological parameters and PET image-derived indices including tumor texture analysis. 2017. Breast Cancer Res 19, 3, doi:10.1186/s13058-016-0793-2.
  8. de Cremoux P, Biard L, Poirot B, Bertheau P, Teixeira L, Lehmann-Che J, Bouhidel FA, Merlet P, Espié M, Resche-Rigon M, Sotiriou C and Groheux D. ¹⁸ FDG-PET/CT and molecular markers to predict response to neoadjuvant chemotherapy and outcome in HER2-negative advanced luminal breast cancers patients. 2018. Oncotarget 9, 16343-16353, doi:10.18632/oncotarget.24674.
  9. Basu S, Chen W, Tchou J, Mavi A, Cermik T, Czerniecki B, Schnall M and Alavi A. Comparison of triple-negative and estrogen receptor-positive/progesterone receptor-positive/HER2-negative breast carcinoma using quantitative fluorine-18 fluorodeoxyglucose/positron emission tomography imaging parameters: a potentially useful method for disease characterization. 2008. Cancer 112, 995-1000, doi:10.1002/cncr.23226.
  10. Palaskas N, Larson SM, Schultz N, Komisopoulou E, Wong J, Rohle D, Campos C, Yannuzzi N, Osborne JR, Linkov I, Kastenhuber ER, Taschereau R, Plaisier SB, Tran C, Heguy A, Wu H, Sander C, Phelps ME, Brennan C, Port E, Huse JT, Graeber TG and Mellinghoff IK. 18F-fluorodeoxy-glucose positron emission tomography marks MYC-overexpressing human basal-like breast cancers. 2011. Cancer Res 71, 5164-5174, doi:10.1158/0008-5472.CAN-10-4633.
  11. McCleland ML, Adler AS, Shang Y, Hunsaker T, Truong T, Peterson D, Torres E, Li L, Haley B, Stephan JP, Belvin M, Hatzivassiliou G, Blackwood EM, Corson L, Evangelista M, Zha J and Firestein R. An integrated genomic screen identifies LDHB as an essential gene for triple-negative breast cancer. 2012. Cancer Res 72, 5812-5823, doi:10.1158/0008-5472.CAN-12-1098.
  12. Zhang C, Liu J, Liang Y, Wu R, Zhao Y, Hong X, Lin M, Yu H, Liu L, Levine AJ, Hu W and Feng Z. Tumour-associated mutant p53 drives the Warburg effect. 2013. Nat Commun 4, 2935, doi:10.1038/ncomms3935.
  13. Dong C, Yuan T, Wu Y, Wang Y, Fan TW, Miriyala S, Lin Y, Yao J, Shi J, Kang T, Lorkiewicz P, St Clair D, Hung MC, Evers BM and Zhou BP. Loss of FBP1 by Snail-mediated repression provides metabolic advantages in basal-like breast cancer. 2013. Cancer Cell 23, 316-331, doi:10.1016/j.ccr.2013.01.022.
  14. Groheux D, Mankoff D, Espié M and Hindié, E. ¹⁸ F-FDG PET/CT in the early prediction of pathological response in aggressive subtypes of breast cancer: review of the literature and recommendations for use in clinical trials. 2016. Eur J Nucl Med Mol Imaging 43, 983-993, doi:10.1007/s00259-015-3295-z.
  15. Pece S, Disalvatore D, Tosoni D, Vecchi M, Confalonieri S, Bertalot G, Viale G, Colleoni M, Veronesi P, Galimberti V and Di Fiore PP. Identification and clinical validation of a multigene assay that interrogates the biology of cancer stem cells and predicts metastasis in breast cancer: A retrospective consecutive study. 2019. EBioMedicine 42:352-362, doi: 10.1016/j.ebiom.2019.02.036.

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