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Mini Oral - NETs and endocrine tumours

1158MO - Metabolomic profile of advanced neuroendocrine cancer patients

Date

18 Sep 2020

Session

Mini Oral - NETs and endocrine tumours

Topics

Clinical Research

Tumour Site

Neuroendocrine Neoplasms

Presenters

Beatriz Soldevilla

Citation

Annals of Oncology (2020) 31 (suppl_4): S711-S724. 10.1016/annonc/annonc281

Authors

B. Soldevilla1, A. Lens-Pardo1, A. Lopez-Lopez2, C. Carretero-Puche1, A. La Salvia3, M.C. Riesco-Martinez4, P. Espinosa-Olarte3, Á. López-Gonzálvez2, C. Barbas2, R. Garcia-Carbonero5

Author affiliations

  • 1 Oncología Traslacional, Instituto de Investigación Sanitaria Hospital 12 de Octubre, 28041 - Madrid/ES
  • 2 Facultad Farmacia. Universidad San Pablo-ceu, Centro de Metabolomica y Bioanalisis (CEMBIO), 28660 - Boadilla del Monte/ES
  • 3 Medical Oncology, Hospital 12 de Octubre, Imas12, 28041 - Madrid/ES
  • 4 Dept. Medical Oncology, Hospital Universitario 12 de Octubre, Imas12, 28041 - Madrid/ES
  • 5 Medical Oncology Department, University Hospital 12 De Octubre, 28041 - Madrid/ES

Resources

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Abstract 1158MO

Background

NETs are heterogeneous tumours that have the unique capacity to secrete bioactive molecules that can cause specific clinical syndromes in ∼20% of cases (“functioning tumours”). In this context, and given the relevance of dysregulated metabolism in cancer, the aim of our study was to assess the metabolomic profile of NET pts to better understand metabolic dysregulation in this tumors and identify novel biomarkers of potential clinical use.

Methods

Multiplatform untargeted metabolomic profiling was performed in plasma of 77 pts with advanced GI and lung NETs, and of 68 control pts, matched per age, gender and IMC. Samples were analyzed by GC, CE and LC, coupled to MS. Differences between NETs and controls were performed by Univariate (MATLAB, t-Student (p≤0.05)) and Multivariate analysis (SIMCA15.0). Related pathways were explored by MPA/MSEA using Metaboanalyst 4.0. ROC and OPLS-DA were used to select metabolites with biomarker potential (AUC>0.85 or VIP>1). Logistic regression models were built to identify confounding clinical covariables. AdjAUCs were calculated with model probabilities of each diagnostic metabolite.

Results

We identified 155 differential compounds between NETs and controls, 14 of them by several techniques. The main biochemical groups of identified metabolites were amino acids (27.7%), fatty acids (16.1%), glycerophopholipids (14.1%), steroids (9.6%) and carbohydrates (3.8%). Specifically, we detected an increase of dipeptides and oxidized lipids in NETs, a decrease of carnitine levels and a rise of oxidized compounds derived from arachidonic acid (HETE). Differential metabolites were related with classical cancer pathways (apoptosis, cell cycle) and NET signalling (tryptophan metabolism, angiogenesis, mTOR). MPA/MSEA showed 32 novel enriched metabolic pathways in NETs, related with TCA cycle and with arginine, pyruvate or glutathione metabolism. Finally, OPLS-DA, ROC and LRM models showed 46 metabolites of diagnostic potential.

Conclusions

This study provides, for the first time, a comprehensive metabolic profile of NET pts. The study identified a reduced set of metabolites of potential diagnostic utility and also reveals new enriched metabolic pathways that may open new avenues of clinical research, including novel targets of therapy.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

Instituto de Investigación Sanitaria Hospital 12 de Octubre. Imas12.

Funding

Spanish National Taskforce on Neuroendocrine Tumors (GETNE), AECC (SPAIN). CAM (Progama de Empleo Juvenil (YEI), co-funded by European Union (ERDF/ESF, “Investing in your future). Instituto de Salud Carlos III and SEOM.

Disclosure

M.C. Riesco-Martinez: Speaker Bureau/Expert testimony: Roche; Advisory/Consultancy: Bayer; Advisory/Consultancy: Novartis; Travel/Accommodation/Expenses: Servier; Non-remunerated activity/ies: Incyte Bioscience. P. Espinosa-Olarte: Travel/Accommodation/Expenses: Ipsen; Travel/Accommodation/Expenses: Novartis; Travel/Accommodation/Expenses: Pfizer. R. Garcia-Carbonero: Honoraria (self), Advisory/Consultancy, Speaker Bureau/Expert testimony: AAA, Advanz Pharma, Bayer, BMS, HMP, Ipsen, Merck, Midatech Pharma, MSD, Novartis, PharmaMar, Pfizer, Pierre Fabre, Roche, Sanofi and Servier; Research grant/Funding (self): Pfizer, BMS; Research grant/Funding (institution): ARMO BioSciences, AstraZeneca, Pfizer, Novartis, Ipsen, Roche, Pharmacyclics, Boston Biomedicals, Merck, MSD, Amgen, Sanofi, Bayer, Bristol-Myers-Squibb, Boerhringer, Sysmex, Gilead Sciences, Servier, Adacap, VCN, Lilly, PharmaMar; Non-remunerated activity/ies: Member of the Executive Committee of the Spanish Neuroendocrine Tumor Cooperative Group (GETNE), Member of the Executive Committee of the European Society of Neuroendocrine Tumors (ENETS), Member of the Scientific Advisory Group for Oncology (SAG-O) of th; Non-remunerated activity/ies: Global PI of a clinical trial of Axitinib (Pfizer) in NETs; Global PI of a clinical trial of Nivolumab (BMS) and chemotherapy in NECs; Non-remunerated activity/ies: Member of the EORTC, ASCO, ESMO, SEOM, TTD, GEMCAD. All other authors have declared no conflicts of interest.

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