Localized metabolomic gradients in patient-derived xenograft models of glioblastoma

Elizabeth C. Randall, Begoña G.C. Lopez, Sen Peng, Michael S. Regan, Walid M. Abdelmoula, Sankha S. Basu, Sandro Santagata, Haejin Yoon, Marcia C. Haigis, Jeffrey N. Agar, Nhan L. Tran, William F. Elmquist, Forest M. White, Jann N. Sarkaria, Nathalie Y.R. Agar

Research output: Contribution to journalArticlepeer-review

55 Scopus citations


Glioblastoma (GBM) is increasingly recognized as a disease involving dysfunctional cellular metabolism. GBMs are known to be complex heterogeneous systems containing multiple distinct cell populations and are supported by an aberrant network of blood vessels. A better understanding of GBM metabolism, its variation with respect to the tumor microenvironment, and resulting regional changes in chemical composition is required. This may shed light on the observed heterogeneous drug distribution, which cannot be fully described by limited or uneven disruption of the blood-brain barrier. In this work, we used mass spectrometry imaging (MSI) to map metabolites and lipids in patient-derived xenograft models of GBM. A data analysis workflow revealed that distinctive spectral signatures were detected from different regions of the intracranial tumor model. A series of long-chain acylcarnitines were identified and detected with increased intensity at the tumor edge. A 3D MSI dataset demonstrated that these molecules were observed throughout the entire tumor/normal interface and were not confined to a single plane. mRNA sequencing demonstrated that hallmark genes related to fatty acid metabolism were highly expressed in samples with higher acylcarnitine content. These data suggest that cells in the core and the edge of the tumor undergo different fatty acid metabolism, resulting in different chemical environments within the tumor. This may influence drug distribution through changes in tissue drug affinity or transport and constitute an important consideration for therapeutic strategies in the treatment of GBM. SIGNIFICANCE: GBM tumors exhibit a metabolic gradient that should be taken into consideration when designing therapeutic strategies for treatment. See related commentary by Tan and Weljie, p. 1231.

Original languageEnglish (US)
Pages (from-to)1258-1267
Number of pages10
JournalCancer Research
Issue number6
StatePublished - Mar 15 2020

Bibliographical note

Funding Information:
This work was funded by NIH U54 CA210180 MIT/Mayo Physical Science Oncology Center for Drug Distribution and Drug Efficacy in Brain Tumors, and by the Dana-Farber Cancer Institute PLGA Fund (9616692). N.Y.R. Agar receives support from the Ferenc Jolesz National Center for Image Guided Therapy NIH P41-EB-015898. N.Y.R. Agar also receives support from NIH R01CA201469. E.C. Randall received an NIH R25 (R25 CA-89017) Fellowship in partnership with the Ferenc

Funding Information:
S. Santagata is a consultant (paid consultant) for Rarecyte. J.N. Sarkaria reports receiving a commercial research grant from Novartis, Basilea, Cavion, Curtana, Forma, AbbVie, Actuate, Boehringer Ingelheim, Bayer, Celgene, Cible, Mitochon, Genentech, Wayshine, Sanofi, Beigene, Lilly, Glaxo-Smith-Kline, Peloton, Glionova, and Bristol-Myers Squibb Pharmaceuticals. N.Y.R. Agar is a scientific advisor (paid consultant) for BayesianDx and has unpaid consultant/advisory board relationship with Bruker. No potential conflicts of interest were disclosed by the other authors.

Publisher Copyright:
© 2019 American Association for Cancer Research.


  • Brain Neoplasms
  • Glioblastoma
  • Heterografts
  • Humans
  • Mass Spectrometry
  • Tumor Microenvironment

PubMed: MeSH publication types

  • Comment
  • Research Support, Non-U.S. Gov't
  • Journal Article
  • Research Support, N.I.H., Extramural


Dive into the research topics of 'Localized metabolomic gradients in patient-derived xenograft models of glioblastoma'. Together they form a unique fingerprint.

Cite this