Published May 24, 2024
| Version v1
Journal article
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Metabolite profiling of human renal cell carcinoma reveals tissue-origin dominance in nutrient availability
Creators
-
Abbott, Keene L.1
- Ali, Ahmed1
- Reinfeld, Bradley I.2
-
Deik, Amy3
-
Subudhi, Sonu4
- Landis, Madelyn D.2
- Hongo, Rachel A.2
- Young, Kirsten L.2
- Kunchok, Tenzin5
- Nabel, Christopher S.1
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Crowder, Kayla D.5
- Kent, Johnathan R.6
- Madariaga, Maria Lucia L.6
- Jain, Rakesh K.4
- Beckermann, Kathryn E.2
- Lewis, Caroline A.5
- Clish, Clary B.3
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Muir, Alexander6
- Rathmell, W. Kimryn2
- Rathmell, Jeffrey2
- Vander Heiden, Matthew G.1
- 1. Massachusetts Institute of Technology
- 2. Vanderbilt University
- 3. Broad Institute
- 4. Harvard University
- 5. Whitehead Institute for Biomedical Research
- 6. University of Chicago
Description
The tumor microenvironment is a determinant of cancer progression and therapeutic efficacy, with nutrient availability playing an important role. Although it is established that the local abundance of specific nutrients defines the metabolic parameters for tumor growth, the factors guiding nutrient availability in tumor compared to normal tissue and blood remain poorly understood. To define these factors in renal cell carcinoma (RCC), we performed quantitative metabolomic and comprehensive lipidomic analyses of tumor interstitial fluid (TIF), adjacent normal kidney interstitial fluid (KIF), and plasma samples collected from patients. TIF nutrient composition closely resembles KIF, suggesting that tissue-specific factors unrelated to the presence of cancer exert a stronger influence on nutrient levels than tumor-driven alterations. Notably, select metabolite changes consistent with known features of RCC metabolism are found in RCC TIF, while glucose levels in TIF are not depleted to levels that are lower than those found in KIF. These findings inform tissue nutrient dynamics in RCC, highlighting a dominant role of non-cancer-driven tissue factors in shaping nutrient availability in these tumors.
Data availability
All data generated or analyzed during the student are included with the manuscript and supporting files.
The following previously published data sets were used:
Wishart DS Guo A Oler E Wang F Anjum A Peters H Dizon R Sayeeda Z Tian S Lee BL Berjanskii M Mah R Yamamoto M Jovel J Torres-Calzada C Hiebert-Giesbrecht M Lui VW Varshavi D Varshavi D Allen D Arndt D Khetarpal N Sivakumaran A Harford K Sanford S Yee K Cao X Budinski Z Liigand J Zhang L Zheng J Mandal R Karu N Dambrova M Schiöth HB Greiner R Gautam V (2022) HMDB5.0 ID HMDB 5.0. HMDB 5.0: the Human Metabolome Database for 2022. http://www.hmdb.ca/
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Additional details
Identifiers
- DOI
- 10.7554/eLife.95652.3
- Other
- oai:uchicago.tind.io:12443
Funding
- National Science Foundation
- DGE-1122374
- National Cancer Institute
- F31CA271787
- National Institutes of Health
- T32GM007287
- Howard Hughes Medical Institute
- Medical Research Fellowship
- National Cancer Institute
- F30CA247202
- National Institutes of Health
- T32GM007347
- American Association for Cancer Research
- National Institutes of Health
- U01-CA224348
- National Institutes of Health
- R01-CA259253
- National Institutes of Health
- R01-CA208205
- National Institutes of Health
- R01-NS118929
- National Institutes of Health
- U01CA261842
- Harvard University
- Nile Albright Research Foundation
- National Foundation for Cancer Research
- Jane's Trust Foundation
- Department of Defense
- Education Activity
- National Cancer Institute
- R01CA217987
- Massachusetts Institute of Technology
- National Cancer Institute
- R35CA242379
- National Cancer Institute
- P30CA1405141