Localized metabolomic gradients in patient-derived xenograft models of glioblastoma
© 2019 American Association for Cancer Research. 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...
| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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American Association for Cancer Research (AACR)
2021
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| Online Access: | https://hdl.handle.net/1721.1/133604 |
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| author | Randall, Elizabeth C Lopez, Begoña GC Peng, Sen Regan, Michael S Abdelmoula, Walid M Basu, Sankha S Santagata, Sandro Yoon, Haejin Haigis, Marcia C Agar, Jeffrey N Tran, Nhan L Elmquist, William F White, Forest M Sarkaria, Jann N Agar, Nathalie YR |
| author_facet | Randall, Elizabeth C Lopez, Begoña GC Peng, Sen Regan, Michael S Abdelmoula, Walid M Basu, Sankha S Santagata, Sandro Yoon, Haejin Haigis, Marcia C Agar, Jeffrey N Tran, Nhan L Elmquist, William F White, Forest M Sarkaria, Jann N Agar, Nathalie YR |
| author_sort | Randall, Elizabeth C |
| collection | MIT |
| description | © 2019 American Association for Cancer Research. 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. |
| first_indexed | 2024-09-23T13:17:54Z |
| format | Article |
| id | mit-1721.1/133604 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T13:17:54Z |
| publishDate | 2021 |
| publisher | American Association for Cancer Research (AACR) |
| record_format | dspace |
| spelling | mit-1721.1/1336042021-10-28T03:54:05Z Localized metabolomic gradients in patient-derived xenograft models of glioblastoma Randall, Elizabeth C Lopez, Begoña GC Peng, Sen Regan, Michael S Abdelmoula, Walid M Basu, Sankha S Santagata, Sandro Yoon, Haejin Haigis, Marcia C Agar, Jeffrey N Tran, Nhan L Elmquist, William F White, Forest M Sarkaria, Jann N Agar, Nathalie YR © 2019 American Association for Cancer Research. 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. 2021-10-27T19:53:46Z 2021-10-27T19:53:46Z 2020 2021-09-10T17:33:34Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/133604 en 10.1158/0008-5472.CAN-19-0638 Cancer Research Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf American Association for Cancer Research (AACR) PMC |
| spellingShingle | Randall, Elizabeth C Lopez, Begoña GC Peng, Sen Regan, Michael S Abdelmoula, Walid M Basu, Sankha S Santagata, Sandro Yoon, Haejin Haigis, Marcia C Agar, Jeffrey N Tran, Nhan L Elmquist, William F White, Forest M Sarkaria, Jann N Agar, Nathalie YR Localized metabolomic gradients in patient-derived xenograft models of glioblastoma |
| title | Localized metabolomic gradients in patient-derived xenograft models of glioblastoma |
| title_full | Localized metabolomic gradients in patient-derived xenograft models of glioblastoma |
| title_fullStr | Localized metabolomic gradients in patient-derived xenograft models of glioblastoma |
| title_full_unstemmed | Localized metabolomic gradients in patient-derived xenograft models of glioblastoma |
| title_short | Localized metabolomic gradients in patient-derived xenograft models of glioblastoma |
| title_sort | localized metabolomic gradients in patient derived xenograft models of glioblastoma |
| url | https://hdl.handle.net/1721.1/133604 |
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