Integrative cross-platform analyses identify enhanced heterotrophy as a metabolic hallmark in glioblastoma
Authors: Pravin Kesarwani
Prakash Chinnaiyan
Peter Forsyth
Ichiro Nakano
Shiva Kant
Antony H Prabhu
Kamran Ahmed
Journal: Neuro-oncology (2019)
Discipline: NEUROSCIENCE & BEHAVIOR
Keywords: genomics; glioblastoma; integrative analyses; intratumoral heterogeneity; metabolomics.
Affiliations: Radiation Oncology, Beaumont Health, Royal Oak, Michigan., Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida., Neuro-oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida., Neurosurgery, University of Alabama, Birmingham, Alabama., Oakland University William Beaumont School of Medicine, Royal Oak, Michigan.

Abstract:  Background: Although considerable progress has been made in understanding molecular alterations driving gliomagenesis, the diverse metabolic programs contributing to the aggressive phenotype of glioblastoma remain unclear. The aim of this study was to define and provide molecular context to metabolic reprogramming driving gliomagenesis. Methods: Integrative cross-platform analyses coupling global metabolomic profiling with genomics in patient-derived glioma (low-grade astrocytoma [LGA; n = 28] and glioblastoma [n = 80]) were performed. Identified programs were then metabolomically, genomically, and functionally evaluated in preclinical models. Results: Clear metabolic programs were identified differentiating LGA from glioblastoma, with aberrant lipid, peptide, and amino acid metabolism representing dominant metabolic nodes associated with malignant transformation. Although the metabolomic profiles of glioblastoma and LGA appeared mutually exclusive, considerable metabolic heterogeneity was observed in glioblastoma. Surprisingly, integrative analyses demonstrated that O6-methylguanine-DNA methyltransferase methylation and isocitrate dehydrogenase mutation status were equally distributed among glioblastoma metabolic profiles. Transcriptional subtypes, on the other hand, tightly clustered by their metabolomic signature, with proneural and mesenchymal tumor profiles being mutually exclusive. Integrating these metabolic phenotypes with gene expression analyses uncovered tightly orchestrated and highly redundant transcriptional programs designed to support the observed metabolic programs by actively importing these biochemical substrates from the microenvironment, contributing to a state of enhanced metabolic heterotrophy. These findings were metabolomically, genomically, and functionally recapitulated in preclinical models. Conclusion: Despite disparate molecular pathways driving the progression of glioblastoma, metabolic programs designed to maintain its aggressive phenotype remain conserved. This contributes to a state of enhanced metabolic heterotrophy supporting survival in diverse microenvironments implicit in this malignancy. 

Retracted: No

PubPeer comments:
 Authors, I am not writing to criticize your paper, but to ask a salient question. I thank the authors for full disclosure of your metabolic profiling, which is sadly lacking from most metabolomics papers from other groups. My question: Your supplementary section https: www.ncbi.nlm.nih.gov pmc articles PMC6380409 Supplementary Methods. noy185_suppl_supplementary_methods.docx (41K) GUID: 8532DE2A-B57D-4F81-A310-D4089554F06E states the following; ![file] If I understand correctly, you have selected 757 metabolite signatures out of the thousands to be the most differentially expressed in GBM vs. low grade glioma. Of these signatures, 97 were xenobiotics - which you removed from the profile, and rightly so. But, that is an almost 13% rate of misidentified metabolites, since these should not appear in cells that were cultured in defined media. Not only that, I see a bunch of plant metabolites in the listing when I go through your dataset; Supplementary Table S1. noy185_suppl_supplementary_table_s1.xlsx (795K) GUID: 745E0AAD-DC77-4050-B7CE-431A0553634C Is this an issue with metabolomics in general? If so, how confident can we be with the rest of the data that were selected for analysis? If this is an issue in metabolomic analysis in general, I d appreciate if the authors point me to publications reviews textbooks that highlight the problem. I recall that genomics and proteomics of yesteryear had similar issues. It appears that metabolomics is no better? I see similar sets of xenobiotics and plant and microbial metabolites appear in datasets from other groups, but these are quietly brushed aside or never mentioned in the main publication, nor their fate described in a supplement. Your group has done the right thing and described your methodology exactly. I commend you for that. ,