Cancer cells convert nutrients into energy differently compared to healthy cells. This difference in metabolism allows them to grow and divide more quickly and sometimes to migrate to different areas of the body. The environment around cancer cells – known as the tumor microenvironment – contains a variety of different cells and blood vessels, which are bathed in interstitial fluid. This microenvironment provides nutrients for the cancer cells to metabolize, and therefore influences how well a tumor grows and how it might respond to treatment.

Recent advances with techniques such as mass spectrometry, which can measure the chemical composition of a substance, have allowed scientists to measure nutrient levels in the tumor microenvironments of mice. However, it has been more difficult to conduct such studies in humans, as well as to compare the tumor microenvironment to the healthy tissue the tumors arose from.

Abbott, Ali, Reinfeld et al. aimed to fill this gap in knowledge by using mass spectrometry to measure the nutrient levels in the tumor microenvironment of 55 patients undergoing surgery to remove kidney tumors. Comparing the type and levels of nutrients in the tumor interstitial fluid, the neighboring healthy kidney and the blood showed that nutrients in the tumor and healthy kidney were more similar to each other than those in the blood. For example, both the tumor and healthy kidney interstitial fluids contained less glucose than the blood. However, the difference between nutrient composition in the tumor and healthy kidney interstitial fluids was insignificant, suggesting that the healthy kidney and its tumor share a similar environment.

Taken together, the findings indicate that kidney cancer cells must adapt to the nutrients available in the kidney, rather than changing what nutrients are available in the tissue. Future studies will be required to investigate whether this finding also applies to other types of cancer. A better understanding of how cancer cells adapt to their environments may aid the development of drugs that aim to disrupt the metabolism of tumors.

Nutrient availability within the tumor microenvironment (TME) can influence cancer progression, therapeutic response, and metastasis (Muir and Vander Heiden, 2018; Muir et al., 2017; Abbott et al., 2023; Cantor et al., 2017; Vande Voorde et al., 2019; Davidson et al., 2016; Gui et al., 2016; Ferraro et al., 2021; Jin et al., 2020; Ngo et al., 2020; Faubert et al., 2020; Rossiter et al., 2021). How metabolite availability is regulated in the TME has been a topic of extensive research (Apiz Saab and Muir, 2023; Sullivan et al., 2019; Lyssiotis and Kimmelman, 2017; Sullivan and Vander Heiden, 2019; Reinfeld et al., 2021); however, a comprehensive analysis of what nutrients are found in cancer tissue interstitial fluid, and how this deviates from nutrients available in the corresponding normal tissue, has not been conducted. Thus, whether global tumor nutrient availability is actively modified by tumor cells, the accompanying immune, endothelial, and stromal cells, or predominantly influenced by the conditions of the originating resident tissue, is not known. Better understanding the determinants of nutrient availability in the TME will inform efforts to understand preferences for specific cancers to develop in specific tissues (Faubert et al., 2020; Bergers and Fendt, 2021; Mosier et al., 2021), and could enable better matching of patients with cancer therapies whose efficacy is influenced by what nutrients are present in tumor tissue (Muir and Vander Heiden, 2018; Muir et al., 2017; Abbott et al., 2023; Cantor et al., 2017; Vande Voorde et al., 2019; Rossiter et al., 2021).

Surgical management of human renal cell carcinoma (RCC) allows for the simultaneous sampling of tumor interstitial fluid (TIF), adjacent healthy kidney tissue interstitial fluid, and plasma collected from patients undergoing nephrectomy. This afforded the opportunity to assess metabolites across these different samples and assess how nutrients found in tumor tissue relate to those found in corresponding normal tissue and blood. Notably, a number of distinct types of primary renal cancers, driven by a diverse set of genetic programs, arise from the kidney (Linehan et al., 2010; Linehan, 2012). This study was unselected with regard to tumor histology, although the diversity of tumors offered a further opportunity to examine the range of nutrient variation across kidney tumors versus normal tissue, as well as among tumors with a variety of histologies. Supported by mass spectrometry-based quantification of polar metabolites and lipids in normal and cancerous tissue interstitial fluid and plasma, we find that nutrient availability in TIF closely mirrors that of interstitial fluid in adjacent normal kidney tissue (kidney interstitial fluid, KIF), but that nutrients found in both interstitial fluid compartments differ from those found in plasma. This analysis suggests that the nutrients in kidney tissue differs from those found in blood, and that nutrients found in kidney tumors are largely dictated by factors shared with normal kidney tissue. We additionally present metabolite measurements in these conditions as a resource to support further study and modeling of the local environment of RCC and normal kidney physiology.

We isolated interstitial fluid from RCC tumors (TIF) and from adjacent non-tumor bearing kidney tissue (KIF), then performed quantitative polar metabolomics (n = 104 metabolites) and lipidomics (n = 210 metabolites) of these fluids alongside plasma collected from patients with kidney cancer at the time of nephrectomy (Figure 1A; Supplementary files 1–3). Principle component analysis (PCA) of polar and lipid metabolite levels measured in these samples revealed some differences between TIF and KIF, but these tissue analytes principally clustered distinctly from plasma (Figure 1B,C), highlighting that the nutrients found in circulation differ extensively from those found in tissue interstitial fluid. In particular, lipid features were tightly concordant between KIF and TIF, in contrast to plasma where lipid components have widespread variation.

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We also performed PCA of polar and lipid metabolite levels measured in material derived from the subset of patients with clear cell RCC (ccRCC), as this is the most common subtype of RCC (Linehan, 2012; Muglia and Prando, 2015) and represented the largest fraction of the patient samples available for analysis. We found similar clustering patterns for ccRCC metabolite levels (Figure 1—figure supplement 1A,B) as those found when we analyzed the data collected from all patient-derived material. Further analysis by t- and chi-squared tests revealed that levels of most polar metabolites do not significantly differ between TIF and KIF (Figure 1D, Figure 1—figure supplement 1C). These same analyses revealed a difference in the number of statistically different metabolites when comparing TIF and KIF lipid composition, but these differences were fewer than when comparing the lipid composition of either TIF or KIF compartments to plasma (Figure 1E, Figure 1—figure supplement 1D). Taken together, these data suggest that metabolites found in TIF and KIF are largely similar based on these measurable analytes, although there are individual polar and lipid metabolites that differ between TIF and KIF (Figure 2A,B, Figure 2—figure supplement 1A,B). These data support the notion that the human RCC tumor nutrient environment is primarily driven by factors shared with kidney tissue, rather than being extensively modified by cancer cells or other factors unique to the tumor.

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Focusing on differences between TIF and KIF using data derived from the entire patient dataset, we found several metabolites to be elevated in TIF compared to KIF that agree with known metabolic characteristics of RCC. These include increased levels of 2-hydroxyglutarate (Shim et al., 2014; Wang et al., 2021), kynurenine (Wettersten et al., 2015; Ganti et al., 2012; Lucarelli et al., 2017), and glutathione (Wettersten et al., 2015; Hakimi et al., 2016; Figure 2C), all consistent with prior reports and known biology of RCC. Of note, we observed depletion of glucose and elevation of lactate in TIF compared to plasma, again consistent with known increases in glucose fermentation to lactate in this tumor type (Kaushik et al., 2022; Masson and Ratcliffe, 2014; Kaelin, 2008; Courtney et al., 2018; Sanchez and Simon, 2018; Figure 2D). However, the levels of glucose and lactate measured in TIF were similar to those measured in KIF, arguing that glucose was not further depleted in TIF compared to KIF (Figure 2D). These data challenge the notion that cells in the TME are necessarily glucose starved (Burgess and Sylven, 1962; Gullino et al., 1964; Hirayama et al., 2009; Urasaki et al., 2012; Ho et al., 2015), as the average glucose concentration is near or above the threshold identified as limiting for proliferation in a range of cancer cell lines (Birsoy et al., 2014). It is important to note that the levels of glucose measured in bulk TIF may not reflect local depletion in subregions of the tumor. Regardless, these findings fit with other data from RCC, as well as from other tumor types, demonstrating that while glucose availability is reduced in the TME relative to blood, it is not completely depleted (Sullivan et al., 2019; Reinfeld et al., 2021; Siska et al., 2017). This finding is somewhat unexpected, given that VHL loss in ccRCC leads to increased HIF1α-driven expression of glucose transporters and glycolytic enzymes (Linehan et al., 2010; Wettersten et al., 2015; Hakimi et al., 2016; Mandriota et al., 2002; Semenza, 2009; Rathmell et al., 2018), but exchange of glucose between blood and interstitial fluid may be sufficient to prevent glucose depletion to levels that are less than those found in normal kidney tissue. Of note, these data do not necessarily argue glucose metabolism in tumors, or in malignant cancer cells within the tumor, is similar to that of normal tissue, only that availability of glucose across the regions of tissue sampled is similar. Indeed, evidence for increased glucose metabolism in some forms of human RCC is evident from [¹⁸F]Fluorodeoxyglucose (FDG)-positron emission tomography scans (Reinfeld et al., 2021; Hou et al., 2021; Lee et al., 2017; Wang et al., 2012); however, FDG-glucose uptake is variable in the more common ccRCC subtye and can display regional variation (Brooks et al., 2016). In vivo tracing studies of isotope-labeled glucose have demonstrated distinct metabolic fluxes in ccRCC tumors compared to matched adjacent kidney tissue at the time of surgery (Courtney et al., 2018). Lastly, these data are notable with respect to the finding that ccRCC silences the key gluconeogenic enzyme fructose-1,6-bisphosphatase 1 (Li et al., 2014), as it suggests that loss of gluconeogenesis in cancer cells has minimal effect on local glucose availability.

Recent studies have found that arginine is depleted to very low levels in murine pancreatic cancer TIF (Sullivan et al., 2019; Apiz Saab et al., 2023; Lee et al., 2023), a phenomenon facilitated by myeloid-derived arginase activity (Apiz Saab et al., 2023). Of note, arginine is not significantly depleted in KIF or TIF from RCC patients compared to plasma, although levels of related urea cycle metabolites are reduced in TIF and KIF compared to plasma (Figure 2E). These findings suggest that myeloid cell infiltrates in RCC are not sufficient to lower arginine levels, and this observation may relate to the responsiveness of RCC to T-cell immune checkpoint blockade therapy (Santoni et al., 2018). These data are also notable with respect to the observation that primary RCC tumors commonly downregulate ASS1 expression and urea cycle activity (Yoon et al., 2007; Perroud et al., 2009; Ochocki et al., 2018; Khare et al., 2021), implying that the amount of arginine in KIF or TIF is sufficient to support cancer cell proliferation without the requirement for de novo arginine synthesis involving ASS1 upregulation. To this point, a recent study found that metastatic kidney cells in the lung upregulated ASS1 expression due to the lower availability of arginine in the lung compared to the kidney (Sciacovelli et al., 2022).

Given that distinctive lipid metabolism alterations are known to be associated with RCC (Heravi et al., 2022; Gebhard et al., 1987; Saito et al., 2016; Riscal et al., 2021; Valera and Merino, 2011), we probed our lipidomics dataset to investigate changes in lipid composition within RCC TIF compared to KIF and plasma. We found cholesterol levels in both TIF and KIF were notably lower when compared to plasma (Figure 2F), aligning with prior studies observing a roughly fivefold depletion of VLDL, LDL, and HDL cholesterol in tissue interstitial fluid versus plasma (Vessby et al., 1987; Dabbagh and Frei, 1995; Parini et al., 2006). Although cholesterol levels were dramatically lower compared to plasma, we did detect a roughly threefold elevation in cholesterol and cholesteryl esters in TIF compared to KIF (Figure 2G,H), which is intriguing given work showing ccRCC suppresses de novo cholesterol biosynthesis leading to a dependence on exogenous cholesterol import to meet metabolic demands (Riscal et al., 2021). These findings may argue that alterations in local lipid metabolism may be a feature of RCC to supply cancer cells with elevated cholesterol in the TME and raises questions about the dynamic regulation of cholesterol metabolism involving non-cancer cells in these tumors.

Lastly, we examined the composition of plasma from the RCC patients undergoing nephrectomy, and compared the polar metabolite levels to polar metabolite levels in plasma collected from healthy adult donors, and to polar metabolites levels in plasma from non-small cell lung cancer (NSCLC) patients (Figure 2—figure supplement 2A–D). Among the metabolites analyzed, intermediates in nucleotide metabolism stood out as being different between samples. Cystine concentration was also noted to be approximately twofold higher in plasma from patients with RCC compared to levels in plasma from healthy individuals based on the measurements here and available data from the Human Metabolome Database (HMDB; Wishart et al., 2022; Figure 2—figure supplement 3A). This finding demonstrates that there are influences from the tumor on the composition of nutrients in the circulating plasma. Although the RCC patients were thought to have localized disease, whether this influence is due to metabolic demands or contributions within the TME from circulating tumor cells, or as a consequence of undiagnosed metastasis, cannot be determined. The cystine finding was specifically notable because cystine levels have been demonstrated to influence sensitivity to glutaminase inhibitors (Muir et al., 2017), and RCC was a tumor type that showed early responses in glutaminase inhibitor clinical trials (Lee et al., 2022). The concentration of cystine in RCC plasma was comparable to that of NSCLC plasma, which suggests this may be a more common feature of tumor physiology than previously appreciated.

Since the plasma samples collected from both the RCC and NSCLC patients were collected following an overnight fast prior to surgery, whereas the healthy donor samples were collected from non-fasted donors, we considered the possibility that the fasting status of patients during the collection process may impact the concentration of circulating cystine. To explore this further, we collected fed and fasted plasma from a healthy donor and found a 1.6-fold increase in circulating cystine in the fasted state compared to the fed state (Figure 2—figure supplement 3B). This observation offers a plausible explanation for at least a component of the higher cystine concentration in RCC or NSCLC plasma, but a larger trial, including plasma from non-fasted cancer patients, would be needed to verify whether cystine levels are affected by fasting status. Moreover, the observed rise in cystine concentration due to fasting raises an intriguing possibility that therapies influenced by nutrient levels, such as glutaminase inhibitors (Muir et al., 2017), may also be influenced in part by a patient’s diet or fed/fasted state (Wilinski et al., 2019; LaBarre et al., 2021), which could have effects on therapeutic efficacy. Nevertheless, while considering the influence of fasting status on metabolite availability is important, other differences in plasma metabolite levels were unique to either RCC or NSCLC that cannot be explained by blood being collected in a fed or fasted state, such as the buildup of glutathione or uric acid in RCC compared to NSCLC plasma. Whether these other metabolite changes in blood have any biological or clinical significance will require further study.

Assessment of nutrient availability in other tumor types and tissues is needed to determine whether the similarity between nutrient availability in RCC and normal kidney tissue is a general feature of cancers, and examination of metastatic tumors compared to local tissue is also needed. Nevertheless, it is provocative to consider that general tissue homeostatic nutrient maintenance mechanisms are preserved in tumors, and that cancer cells may retain some dependence on nutrients defined by the adjacent origin tissue. To this end, it is notable that the metabolites found in RCC TIF differ from those reported in mouse pancreatic ductal adenocarcinoma (PDAC) TIF (Sullivan et al., 2019; Apiz Saab et al., 2023), and from those found in interstitial fluid derived from mouse brain and mammary fat pad tissue (Ferraro et al., 2021). Findings from analysis of PDAC TIF from tumors implanted in different tissue sites also noted differences in metabolite levels (Sullivan et al., 2019), supporting the notion that cancer must adapt to a specific tissue nutrient environment rather than determine the majority of nutrients present. These findings may suggest that each tissue represents a unique nutrient environment driven my interactions between resident non-cancer cell types, with potential implications for cancer metastasis. That is, this model raises the possibility that accessing a tissue nutrient environment that shares features with the primary cancer site is needed to support proliferation of metastatic cancer cells, and may contribute to the stereotyped pattern of metastases associated with cancers arising in specific tissues (Riihimäki et al., 2018). These findings may also present a challenge for therapies targeting nutrient dependencies in patients with metastatic tumors given that available metabolites could differ in each organ of metastasis.

We acknowledge some caveats with how to interpret these findings. Quantification of steady-state nutrient levels does not directly reflect nutrient use, as nutrient consumption and release rates cannot be derived from these steady-state measurements. An increase in levels of a metabolite in tissue relative to blood must represent some local production in the tissue, while a decrease in the levels of a metabolite relative to blood must represent some local consumption, however a lack of change does not mean a metabolite is not involved in metabolism in that tissue. Exchange of metabolites between blood and tissues may be faster than the rate of consumption or production, and we also did not consider differences between arterial and venous blood metabolites. Moreover, that KIF was collected from non-tumor renal tissue in specimens that were resected from patients with RCC raises the possibility that the levels of at least some of the metabolites measured in normal kidney were affected by the nearby tumor, or the fact that the patient had a cancer. Ideally, metabolites measured in KIF from healthy individuals would be compared with those measured in interstitial fluid from kidney tumors and adjacent normal tissues, although obtaining KIF from healthy patients was not possible for this study. Additionally, we also note that analysis of interstitial fluid will not take into account local variation in nutrient levels across different tissue regions (Pan et al., 2016; Okegawa et al., 2017; Wang et al., 2022a; Miller et al., 2023; Wang et al., 2022b). Microenvironmental differences will be averaged in the pooled collection method employed in this study. Known tumor heterogeneity, including the presence of both cancer and non-cancer cell types, are likely to drive gradients in nutrient availability within tumors (Morandi et al., 2016; Lyssiotis and Kimmelman, 2017). Lastly, the processing methodology employed to collect interstitial fluid may itself lead to some cell lysis and release of intracellular metabolites, which would affect the measured metabolite concentrations. Despite the limitations, this dataset provides a resource for future studies of RCC and normal kidney metabolism. The findings also emphasize that metabolites levels measured in one cancer type cannot necessarily be extrapolated to reflect which nutrients are found in the TME of other tumors. Ongoing efforts to understand how nutrient availability varies across different cancers may inform therapeutic strategies that take into account both tumor genetics and unique nutrient environments in tissues (Abbott et al., 2023).

All data generated or analyzed during the student are included with the manuscript and supporting files.

1. 1. Wishart DS
   2. Guo A
   3. Oler E
   4. Wang F
   5. Anjum A
   6. Peters H
   7. Dizon R
   8. Sayeeda Z
   9. Tian S
   10. Lee BL
   11. Berjanskii M
   12. Mah R
   13. Yamamoto M
   14. Jovel J
   15. Torres-Calzada C
   16. Hiebert-Giesbrecht M
   17. Lui VW
   18. Varshavi D
   19. Varshavi D
   20. Allen D
   21. Arndt D
   22. Khetarpal N
   23. Sivakumaran A
   24. Harford K
   25. Sanford S
   26. Yee K
   27. Cao X
   28. Budinski Z
   29. Liigand J
   30. Zhang L
   31. Zheng J
   32. Mandal R
   33. Karu N
   34. Dambrova M
   35. Schiöth HB
   36. Greiner R
   37. Gautam V

   (2022) HMDB5.0

   ID HMDB 5.0. HMDB 5.0: the Human Metabolome Database for 2022.

   http://www.hmdb.ca/

1. 1. Abbott KL
   2. Ali A
   3. Casalena D
   4. Do BT
   5. Ferreira R
   6. Cheah JH
   7. Soule CK
   8. Deik A
   9. Kunchok T
   10. Schmidt DR
   11. Renner S
   12. Honeder SE
   13. Wu M
   14. Chan SH
   15. Tseyang T
   16. Stoltzfus AT
   17. Michel SLJ
   18. Greaves D
   19. Hsu PP
   20. Ng CW
   21. Zhang CJ
   22. Farsidjani A
   23. Kent JR
   24. Madariaga MLL
   25. Gramatikov IMT
   26. Matheson NJ
   27. Lewis CA
   28. Clish CB
   29. Rees MG
   30. Roth JA
   31. Griner LM
   32. Muir A
   33. Auld DS
   34. Vander Heiden MG

   (2023) Screening in serum-derived medium reveals differential response to compounds targeting metabolism

   Cell Chemical Biology 30:1156–1168.

   https://doi.org/10.1016/j.chembiol.2023.08.007

   • PubMed
   • Google Scholar
2. 1. Apiz Saab JJ
   2. Dzierozynski LN
   3. Jonker PB
   4. AminiTabrizi R
   5. Shah H
   6. Menjivar RE
   7. Scott AJ
   8. Nwosu ZC
   9. Zhu Z
   10. Chen RN
   11. Oh M
   12. Sheehan C
   13. Wahl DR
   14. Pasca di Magliano M
   15. Lyssiotis CA
   16. Macleod KF
   17. Weber CR
   18. Muir A

   (2023) Pancreatic tumors exhibit myeloid-driven amino acid stress and upregulate arginine biosynthesis

   eLife 12:e81289.

   https://doi.org/10.7554/eLife.81289

   • PubMed
   • Google Scholar
3. 1. Apiz Saab JJ
   2. Muir A

   (2023) Tumor interstitial fluid analysis enables the study of microenvironment-cell interactions in cancers

   Current Opinion in Biotechnology 83:102970.

   https://doi.org/10.1016/j.copbio.2023.102970

   • PubMed
   • Google Scholar
4. 1. Bergers G
   2. Fendt SM

   (2021) The metabolism of cancer cells during metastasis

   Nature Reviews. Cancer 21:162–180.

   https://doi.org/10.1038/s41568-020-00320-2

   • PubMed
   • Google Scholar
5. 1. Birsoy K
   2. Possemato R
   3. Lorbeer FK
   4. Bayraktar EC
   5. Thiru P
   6. Yucel B
   7. Wang T
   8. Chen WW
   9. Clish CB
   10. Sabatini DM

   (2014) Metabolic determinants of cancer cell sensitivity to glucose limitation and biguanides

   Nature 508:108–112.

   https://doi.org/10.1038/nature13110

   • PubMed
   • Google Scholar
6. 1. Brooks SA
   2. Khandani AH
   3. Fielding JR
   4. Lin W
   5. Sills T
   6. Lee Y
   7. Arreola A
   8. Milowsky MI
   9. Wallen EM
   10. Woods ME
   11. Smith AB
   12. Nielsen ME
   13. Parker JS
   14. Lalush DS
   15. Rathmell WK

   (2016) Alternate metabolic programs define regional variation of relevant biological features in renal cell carcinoma progression

   Clinical Cancer Research 22:2950–2959.

   https://doi.org/10.1158/1078-0432.CCR-15-2115

   • PubMed
   • Google Scholar
7. 1. Burgess EA
   2. Sylven B

   (1962)

   Glucose, lactate, and lactic dehydrogenase activity in normal interstitial fluid and that of solid mouse tumors

   Cancer Research 22:581–588.

   • PubMed
   • Google Scholar
8. 1. Cantor JR
   2. Abu-Remaileh M
   3. Kanarek N
   4. Freinkman E
   5. Gao X
   6. Louissaint A
   7. Lewis CA
   8. Sabatini DM

   (2017) Physiologic medium rewires cellular metabolism and reveals uric acid as an endogenous inhibitor of UMP synthase

   Cell 169:258–272.

   https://doi.org/10.1016/j.cell.2017.03.023

   • PubMed
   • Google Scholar
9. 1. Courtney KD
   2. Bezwada D
   3. Mashimo T
   4. Pichumani K
   5. Vemireddy V
   6. Funk AM
   7. Wimberly J
   8. McNeil SS
   9. Kapur P
   10. Lotan Y
   11. Margulis V
   12. Cadeddu JA
   13. Pedrosa I
   14. DeBerardinis RJ
   15. Malloy CR
   16. Bachoo RM
   17. Maher EA

   (2018) Isotope tracing of human clear cell renal cell carcinomas demonstrates suppressed glucose oxidation in vivo

   Cell Metabolism 28:793–800.

   https://doi.org/10.1016/j.cmet.2018.07.020

   • PubMed
   • Google Scholar
10. 1. Dabbagh AJ
    2. Frei B

    (1995) Human suction blister interstitial fluid prevents metal ion-dependent oxidation of low density lipoprotein by macrophages and in cell-free systems

    The Journal of Clinical Investigation 96:1958–1966.

    https://doi.org/10.1172/JCI118242

    • PubMed
    • Google Scholar
11. 1. Davidson SM
    2. Papagiannakopoulos T
    3. Olenchock BA
    4. Heyman JE
    5. Keibler MA
    6. Luengo A
    7. Bauer MR
    8. Jha AK
    9. O’Brien JP
    10. Pierce KA
    11. Gui DY
    12. Sullivan LB
    13. Wasylenko TM
    14. Subbaraj L
    15. Chin CR
    16. Stephanopolous G
    17. Mott BT
    18. Jacks T
    19. Clish CB
    20. Vander Heiden MG

    (2016) Environment impacts the metabolic dependencies of ras-driven non-small cell lung cancer

    Cell Metabolism 23:517–528.

    https://doi.org/10.1016/j.cmet.2016.01.007

    • PubMed
    • Google Scholar
12. 1. Faubert B
    2. Solmonson A
    3. DeBerardinis RJ

    (2020) Metabolic reprogramming and cancer progression

    Science 368:eaaw5473.

    https://doi.org/10.1126/science.aaw5473

    • PubMed
    • Google Scholar
13. 1. Ferraro GB
    2. Ali A
    3. Luengo A
    4. Kodack DP
    5. Deik A
    6. Abbott KL
    7. Bezwada D
    8. Blanc L
    9. Prideaux B
    10. Jin X
    11. Posada JM
    12. Chen J
    13. Chin CR
    14. Amoozgar Z
    15. Ferreira R
    16. Chen IX
    17. Naxerova K
    18. Ng C
    19. Westermark AM
    20. Duquette M
    21. Roberge S
    22. Lindeman NI
    23. Lyssiotis CA
    24. Nielsen J
    25. Housman DE
    26. Duda DG
    27. Brachtel E
    28. Golub TR
    29. Cantley LC
    30. Asara JM
    31. Davidson SM
    32. Fukumura D
    33. Dartois VA
    34. Clish CB
    35. Jain RK
    36. Vander Heiden MG

    (2021) Fatty acid synthesis is required for breast cancer brain metastasis

    Nature Cancer 2:414–428.

    https://doi.org/10.1038/s43018-021-00183-y

    • PubMed
    • Google Scholar
14. 1. Ganti S
    2. Taylor SL
    3. Abu Aboud O
    4. Yang J
    5. Evans C
    6. Osier MV
    7. Alexander DC
    8. Kim K
    9. Weiss RH

    (2012) Kidney tumor biomarkers revealed by simultaneous multiple matrix metabolomics analysis

    Cancer Research 72:3471–3479.

    https://doi.org/10.1158/0008-5472.CAN-11-3105

    • PubMed
    • Google Scholar
15. 1. Gebhard RL
    2. Clayman RV
    3. Prigge WF
    4. Figenshau R
    5. Staley NA
    6. Reesey C
    7. Bear A

    (1987)

    Abnormal cholesterol metabolism in renal clear cell carcinoma

    Journal of Lipid Research 28:1177–1184.

    • PubMed
    • Google Scholar
16. 1. Gui DY
    2. Sullivan LB
    3. Luengo A
    4. Hosios AM
    5. Bush LN
    6. Gitego N
    7. Davidson SM
    8. Freinkman E
    9. Thomas CJ
    10. Vander Heiden MG

    (2016) Environment dictates dependence on mitochondrial complex I for NAD+ and aspartate production and determines cancer cell sensitivity to metformin

    Cell Metabolism 24:716–727.

    https://doi.org/10.1016/j.cmet.2016.09.006

    • PubMed
    • Google Scholar
17. 1. Gullino PM
    2. ClarkLARK SH
    3. GranthamRANTHAM FH

    (1964)

    The interstitial fluid of solid tumors

    Cancer Research 24:780–794.

    • PubMed
    • Google Scholar
18. 1. Hakimi AA
    2. Reznik E
    3. Lee C-H
    4. Creighton CJ
    5. Brannon AR
    6. Luna A
    7. Aksoy BA
    8. Liu EM
    9. Shen R
    10. Lee W
    11. Chen Y
    12. Stirdivant SM
    13. Russo P
    14. Chen YB
    15. Tickoo SK
    16. Reuter VE
    17. Cheng EH
    18. Sander C
    19. Hsieh JJ

    (2016) An integrated metabolic atlas of clear cell renal cell carcinoma

    Cancer Cell 29:104–116.

    https://doi.org/10.1016/j.ccell.2015.12.004

    • PubMed
    • Google Scholar
19. 1. Heravi G
    2. Yazdanpanah O
    3. Podgorski I
    4. Matherly LH
    5. Liu W

    (2022) Lipid metabolism reprogramming in renal cell carcinoma

    Cancer Metastasis Reviews 41:17–31.

    https://doi.org/10.1007/s10555-021-09996-w

    • PubMed
    • Google Scholar
20. 1. Hirayama A
    2. Kami K
    3. Sugimoto M
    4. Sugawara M
    5. Toki N
    6. Onozuka H
    7. Kinoshita T
    8. Saito N
    9. Ochiai A
    10. Tomita M
    11. Esumi H
    12. Soga T

    (2009) Quantitative metabolome profiling of colon and stomach cancer microenvironment by capillary electrophoresis time-of-flight mass spectrometry

    Cancer Research 69:4918–4925.

    https://doi.org/10.1158/0008-5472.CAN-08-4806

    • PubMed
    • Google Scholar
21. 1. Ho P-C
    2. Bihuniak JD
    3. Macintyre AN
    4. Staron M
    5. Liu X
    6. Amezquita R
    7. Tsui Y-C
    8. Cui G
    9. Micevic G
    10. Perales JC
    11. Kleinstein SH
    12. Abel ED
    13. Insogna KL
    14. Feske S
    15. Locasale JW
    16. Bosenberg MW
    17. Rathmell JC
    18. Kaech SM

    (2015) Phosphoenolpyruvate is a metabolic checkpoint of anti-tumor T cell responses

    Cell 162:1217–1228.

    https://doi.org/10.1016/j.cell.2015.08.012

    • PubMed
    • Google Scholar
22. 1. Hou G
    2. Zhao D
    3. Jiang Y
    4. Zhu Z
    5. Huo L
    6. Li F
    7. Cheng W

    (2021) Clinical utility of FDG PET/CT for primary and recurrent papillary renal cell carcinoma

    Cancer Imaging 21:25.

    https://doi.org/10.1186/s40644-021-00393-8

    • PubMed
    • Google Scholar
23. 1. Jin X
    2. Demere Z
    3. Nair K
    4. Ali A
    5. Ferraro GB
    6. Natoli T
    7. Deik A
    8. Petronio L
    9. Tang AA
    10. Zhu C
    11. Wang L
    12. Rosenberg D
    13. Mangena V
    14. Roth J
    15. Chung K
    16. Jain RK
    17. Clish CB
    18. Vander Heiden MG
    19. Golub TR

    (2020) A metastasis map of human cancer cell lines

    Nature 588:331–336.

    https://doi.org/10.1038/s41586-020-2969-2

    • PubMed
    • Google Scholar
24. 1. Kaelin WG

    (2008) The von Hippel-Lindau tumour suppressor protein: O2 sensing and cancer

    Nature Reviews. Cancer 8:865–873.

    https://doi.org/10.1038/nrc2502

    • PubMed
    • Google Scholar
25. Software

    1. Kassambara A
    2. Mundt F

    (2020) Factoextra: extract and visualize the results of multivariate data analyses

    R Package Version.

    https://CRAN.R-project.org/package=factoextra
26. 1. Kaushik AK
    2. Tarangelo A
    3. Boroughs LK
    4. Ragavan M
    5. Zhang Y
    6. Wu C-Y
    7. Li X
    8. Ahumada K
    9. Chiang J-C
    10. Tcheuyap VT
    11. Saatchi F
    12. Do QN
    13. Yong C
    14. Rosales T
    15. Stevens C
    16. Rao AD
    17. Faubert B
    18. Pachnis P
    19. Zacharias LG
    20. Vu H
    21. Cai F
    22. Mathews TP
    23. Genovese G
    24. Slusher BS
    25. Kapur P
    26. Sun X
    27. Merritt M
    28. Brugarolas J
    29. DeBerardinis RJ

    (2022) In vivo characterization of glutamine metabolism identifies therapeutic targets in clear cell renal cell carcinoma

    Science Advances 8:eabp8293.

    https://doi.org/10.1126/sciadv.abp8293

    • PubMed
    • Google Scholar
27. 1. Khare S
    2. Kim LC
    3. Lobel G
    4. Doulias PT
    5. Ischiropoulos H
    6. Nissim I
    7. Keith B
    8. Simon MC

    (2021) ASS1 and ASL suppress growth in clear cell renal cell carcinoma via altered nitrogen metabolism

    Cancer & Metabolism 9:40.

    https://doi.org/10.1186/s40170-021-00271-8

    • PubMed
    • Google Scholar
28. 1. LaBarre JL
    2. Hirschfeld E
    3. Soni T
    4. Kachman M
    5. Wigginton J
    6. Duren W
    7. Fleischman JY
    8. Karnovsky A
    9. Burant CF
    10. Lee JM

    (2021) Comparing the fasting and random-fed metabolome response to an oral glucose tolerance test in children and adolescents: implications of sex, obesity, and insulin resistance

    Nutrients 13:3365.

    https://doi.org/10.3390/nu13103365

    • PubMed
    • Google Scholar
29. 1. Lee C-H
    2. Gundem G
    3. Lee W
    4. Chen Y-B
    5. Cross JR
    6. Dong Y
    7. Redzematovic A
    8. Mano R
    9. Wei EY
    10. Cheng EH
    11. Srinivasan R
    12. Oschwald D
    13. Hakimi AA
    14. Dunphy MP
    15. Linehan WM
    16. Papaemmanuil E
    17. Hsieh JJ

    (2017) Persistent severe hyperlactatemia and metabolic derangement in lethal SDHB-Mutated metastatic kidney cancer: clinical challenges and examples of extreme warburg effect

    JCO Precision Oncology 1:PO.16.00007.

    https://doi.org/10.1200/PO.16.00007

    • PubMed
    • Google Scholar
30. 1. Lee C-H
    2. Motzer R
    3. Emamekhoo H
    4. Matrana M
    5. Percent I
    6. Hsieh JJ
    7. Hussain A
    8. Vaishampayan U
    9. Liu S
    10. McCune S
    11. Patel V
    12. Shaheen M
    13. Bendell J
    14. Fan AC
    15. Gartrell BA
    16. Goodman OB
    17. Nikolinakos PG
    18. Kalebasty AR
    19. Zakharia Y
    20. Zhang Z
    21. Parmar H
    22. Akella L
    23. Orford K
    24. Tannir NM

    (2022) Telaglenastat plus everolimus in advanced renal cell carcinoma: a randomized, double-blinded, placebo-controlled, phase II ENTRATA trial

    Clinical Cancer Research 28:3248–3255.

    https://doi.org/10.1158/1078-0432.CCR-22-0061

    • PubMed
    • Google Scholar
31. 1. Lee M-S
    2. Dennis C
    3. Naqvi I
    4. Dailey L
    5. Lorzadeh A
    6. Ye G
    7. Zaytouni T
    8. Adler A
    9. Hitchcock DS
    10. Lin L
    11. Hoffman MT
    12. Bhuiyan AM
    13. Barth JL
    14. Machacek ME
    15. Mino-Kenudson M
    16. Dougan SK
    17. Jadhav U
    18. Clish CB
    19. Kalaany NY

    (2023) Ornithine aminotransferase supports polyamine synthesis in pancreatic cancer

    Nature 616:339–347.

    https://doi.org/10.1038/s41586-023-05891-2

    • PubMed
    • Google Scholar
32. 1. Li B
    2. Qiu B
    3. Lee DSM
    4. Walton ZE
    5. Ochocki JD
    6. Mathew LK
    7. Mancuso A
    8. Gade TPF
    9. Keith B
    10. Nissim I
    11. Simon MC

    (2014) Fructose-1,6-bisphosphatase opposes renal carcinoma progression

    Nature 513:251–255.

    https://doi.org/10.1038/nature13557

    • PubMed
    • Google Scholar
33. 1. Linehan WM
    2. Srinivasan R
    3. Schmidt LS

    (2010) The genetic basis of kidney cancer: a metabolic disease

    Nature Reviews. Urology 7:277–285.

    https://doi.org/10.1038/nrurol.2010.47

    • PubMed
    • Google Scholar
34. 1. Linehan WM

    (2012) Genetic basis of kidney cancer: role of genomics for the development of disease-based therapeutics

    Genome Research 22:2089–2100.

    https://doi.org/10.1101/gr.131110.111

    • PubMed
    • Google Scholar
35. 1. Lucarelli G
    2. Rutigliano M
    3. Ferro M
    4. Giglio A
    5. Intini A
    6. Triggiano F
    7. Palazzo S
    8. Gigante M
    9. Castellano G
    10. Ranieri E
    11. Buonerba C
    12. Terracciano D
    13. Sanguedolce F
    14. Napoli A
    15. Maiorano E
    16. Morelli F
    17. Ditonno P
    18. Battaglia M

    (2017) Activation of the kynurenine pathway predicts poor outcome in patients with clear cell renal cell carcinoma

    Urologic Oncology 35:461.

    https://doi.org/10.1016/j.urolonc.2017.02.011

    • Google Scholar
36. 1. Lyssiotis CA
    2. Kimmelman AC

    (2017) Metabolic interactions in the tumor microenvironment

    Trends in Cell Biology 27:863–875.

    https://doi.org/10.1016/j.tcb.2017.06.003

    • PubMed
    • Google Scholar
37. 1. Mandriota SJ
    2. Turner KJ
    3. Davies DR
    4. Murray PG
    5. Morgan NV
    6. Sowter HM
    7. Wykoff CC
    8. Maher ER
    9. Harris AL
    10. Ratcliffe PJ
    11. Maxwell PH

    (2002) HIF activation identifies early lesions in VHL kidneys: evidence for site-specific tumor suppressor function in the nephron

    Cancer Cell 1:459–468.

    https://doi.org/10.1016/s1535-6108(02)00071-5

    • PubMed
    • Google Scholar
38. 1. Masson N
    2. Ratcliffe PJ

    (2014) Hypoxia signaling pathways in cancer metabolism: the importance of co-selecting interconnected physiological pathways

    Cancer & Metabolism 2:3.

    https://doi.org/10.1186/2049-3002-2-3

    • PubMed
    • Google Scholar
39. 1. Miller A
    2. York EM
    3. Stopka SA
    4. Martínez-François JR
    5. Hossain MA
    6. Baquer G
    7. Regan MS
    8. Agar NYR
    9. Yellen G

    (2023) Spatially resolved metabolomics and isotope tracing reveal dynamic metabolic responses of dentate granule neurons with acute stimulation

    Nature Metabolism 5:1820–1835.

    https://doi.org/10.1038/s42255-023-00890-z

    • PubMed
    • Google Scholar
40. 1. Morandi A
    2. Giannoni E
    3. Chiarugi P

    (2016) Nutrient exploitation within the tumor-stroma metabolic crosstalk

    Trends in Cancer 2:736–746.

    https://doi.org/10.1016/j.trecan.2016.11.001

    • PubMed
    • Google Scholar
41. 1. Mosier JA
    2. Schwager SC
    3. Boyajian DA
    4. Reinhart-King CA

    (2021) Cancer cell metabolic plasticity in migration and metastasis

    Clinical & Experimental Metastasis 38:343–359.

    https://doi.org/10.1007/s10585-021-10102-1

    • PubMed
    • Google Scholar
42. 1. Muglia VF
    2. Prando A

    (2015) Renal cell carcinoma: histological classification and correlation with imaging findings

    Radiologia Brasileira 48:166–174.

    https://doi.org/10.1590/0100-3984.2013.1927

    • PubMed
    • Google Scholar
43. 1. Muir A
    2. Danai LV
    3. Gui DY
    4. Waingarten CY
    5. Lewis CA
    6. Vander Heiden MG

    (2017) Environmental cystine drives glutamine anaplerosis and sensitizes cancer cells to glutaminase inhibition

    eLife 6:e27713.

    https://doi.org/10.7554/eLife.27713

    • PubMed
    • Google Scholar
44. 1. Muir A
    2. Vander Heiden MG

    (2018) The nutrient environment affects therapy

    Science 360:962–963.

    https://doi.org/10.1126/science.aar5986

    • PubMed
    • Google Scholar
45. 1. Ngo B
    2. Kim E
    3. Osorio-Vasquez V
    4. Doll S
    5. Bustraan S
    6. Liang RJ
    7. Luengo A
    8. Davidson SM
    9. Ali A
    10. Ferraro GB
    11. Fischer GM
    12. Eskandari R
    13. Kang DS
    14. Ni J
    15. Plasger A
    16. Rajasekhar VK
    17. Kastenhuber ER
    18. Bacha S
    19. Sriram RK
    20. Stein BD
    21. Bakhoum SF
    22. Snuderl M
    23. Cotzia P
    24. Healey JH
    25. Mainolfi N
    26. Suri V
    27. Friedman A
    28. Manfredi M
    29. Sabatini DM
    30. Jones DR
    31. Yu M
    32. Zhao JJ
    33. Jain RK
    34. Keshari KR
    35. Davies MA
    36. Vander Heiden MG
    37. Hernando E
    38. Mann M
    39. Cantley LC
    40. Pacold ME

    (2020) Limited environmental serine and glycine confer brain metastasis sensitivity to PHGDH inhibition

    Cancer Discovery 10:1352–1373.

    https://doi.org/10.1158/2159-8290.CD-19-1228

    • PubMed
    • Google Scholar
46. 1. Ochocki JD
    2. Khare S
    3. Hess M
    4. Ackerman D
    5. Qiu B
    6. Daisak JI
    7. Worth AJ
    8. Lin N
    9. Lee P
    10. Xie H
    11. Li B
    12. Wubbenhorst B
    13. Maguire TG
    14. Nathanson KL
    15. Alwine JC
    16. Blair IA
    17. Nissim I
    18. Keith B
    19. Simon MC

    (2018) Arginase 2 suppresses renal carcinoma progression via biosynthetic cofactor pyridoxal phosphate depletion and increased polyamine toxicity

    Cell Metabolism 27:1263–1280.

    https://doi.org/10.1016/j.cmet.2018.04.009

    • PubMed
    • Google Scholar
47. 1. Okegawa T
    2. Morimoto M
    3. Nishizawa S
    4. Kitazawa S
    5. Honda K
    6. Araki H
    7. Tamura T
    8. Ando A
    9. Satomi Y
    10. Nutahara K
    11. Hara T

    (2017) Intratumor heterogeneity in primary kidney cancer revealed by metabolic profiling of multiple spatially separated samples within tumors

    EBioMedicine 19:31–38.

    https://doi.org/10.1016/j.ebiom.2017.04.009

    • PubMed
    • Google Scholar
48. 1. Pan M
    2. Reid MA
    3. Lowman XH
    4. Kulkarni RP
    5. Tran TQ
    6. Liu X
    7. Yang Y
    8. Hernandez-Davies JE
    9. Rosales KK
    10. Li H
    11. Hugo W
    12. Song C
    13. Xu X
    14. Schones DE
    15. Ann DK
    16. Gradinaru V
    17. Lo RS
    18. Locasale JW
    19. Kong M

    (2016) Regional glutamine deficiency in tumours promotes dedifferentiation through inhibition of histone demethylation

    Nature Cell Biology 18:1090–1101.

    https://doi.org/10.1038/ncb3410

    • PubMed
    • Google Scholar
49. 1. Parini P
    2. Johansson L
    3. Bröijersén A
    4. Angelin B
    5. Rudling M

    (2006) Lipoprotein profiles in plasma and interstitial fluid analyzed with an automated gel-filtration system

    European Journal of Clinical Investigation 36:98–104.

    https://doi.org/10.1111/j.1365-2362.2006.01597.x

    • PubMed
    • Google Scholar
50. 1. Perroud B
    2. Ishimaru T
    3. Borowsky AD
    4. Weiss RH

    (2009) Grade-dependent proteomics characterization of kidney cancer

    Molecular & Cellular Proteomics 8:971–985.

    https://doi.org/10.1074/mcp.M800252-MCP200

    • PubMed
    • Google Scholar
51. 1. Rathmell WK
    2. Rathmell JC
    3. Linehan WM

    (2018) Metabolic pathways in kidney cancer: current therapies and future directions

    Journal of Clinical Oncology 36:3540–3546.

    https://doi.org/10.1200/JCO.2018.79.2309

    • Google Scholar
52. Software

    1. R Development Core Team

    (2021) R: A language and environment for statistical computing

    R Foundation for Statistical Computing, Vienna, Austria.

    https://www.R-project.org
53. 1. Reinfeld BI
    2. Madden MZ
    3. Wolf MM
    4. Chytil A
    5. Bader JE
    6. Patterson AR
    7. Sugiura A
    8. Cohen AS
    9. Ali A
    10. Do BT
    11. Muir A
    12. Lewis CA
    13. Hongo RA
    14. Young KL
    15. Brown RE
    16. Todd VM
    17. Huffstater T
    18. Abraham A
    19. O’Neil RT
    20. Wilson MH
    21. Xin F
    22. Tantawy MN
    23. Merryman WD
    24. Johnson RW
    25. Williams CS
    26. Mason EF
    27. Mason FM
    28. Beckermann KE
    29. Vander Heiden MG
    30. Manning HC
    31. Rathmell JC
    32. Rathmell WK

    (2021) Cell-programmed nutrient partitioning in the tumour microenvironment

    Nature 593:282–288.

    https://doi.org/10.1038/s41586-021-03442-1

    • PubMed
    • Google Scholar
54. 1. Riihimäki M
    2. Thomsen H
    3. Sundquist K
    4. Sundquist J
    5. Hemminki K

    (2018) Clinical landscape of cancer metastases

    Cancer Medicine 7:5534–5542.

    https://doi.org/10.1002/cam4.1697

    • PubMed
    • Google Scholar
55. 1. Riscal R
    2. Bull CJ
    3. Mesaros C
    4. Finan JM
    5. Carens M
    6. Ho ES
    7. Xu JP
    8. Godfrey J
    9. Brennan P
    10. Johansson M
    11. Purdue MP
    12. Chanock SJ
    13. Mariosa D
    14. Timpson NJ
    15. Vincent EE
    16. Keith B
    17. Blair IA
    18. Skuli N
    19. Simon MC

    (2021) Cholesterol auxotrophy as a targetable vulnerability in clear cell renal cell carcinoma

    Cancer Discovery 11:3106–3125.

    https://doi.org/10.1158/2159-8290.CD-21-0211

    • PubMed
    • Google Scholar
56. 1. Rossiter NJ
    2. Huggler KS
    3. Adelmann CH
    4. Keys HR
    5. Soens RW
    6. Sabatini DM
    7. Cantor JR

    (2021) CRISPR screens in physiologic medium reveal conditionally essential genes in human cells

    Cell Metabolism 33:1248–1263.

    https://doi.org/10.1016/j.cmet.2021.02.005

    • PubMed
    • Google Scholar
57. 1. Saito K
    2. Arai E
    3. Maekawa K
    4. Ishikawa M
    5. Fujimoto H
    6. Taguchi R
    7. Matsumoto K
    8. Kanai Y
    9. Saito Y

    (2016) Lipidomic signatures and associated transcriptomic profiles of clear cell renal cell carcinoma

    Scientific Reports 6:28932.

    https://doi.org/10.1038/srep28932

    • PubMed
    • Google Scholar
58. 1. Sanchez DJ
    2. Simon MC

    (2018) Genetic and metabolic hallmarks of clear cell renal cell carcinoma

    Biochimica et Biophysica Acta 1870:23–31.

    https://doi.org/10.1016/j.bbcan.2018.06.003

    • Google Scholar
59. 1. Santoni M
    2. Massari F
    3. Di Nunno V
    4. Conti A
    5. Cimadamore A
    6. Scarpelli M
    7. Montironi R
    8. Cheng L
    9. Battelli N
    10. Lopez-Beltran A

    (2018) Immunotherapy in renal cell carcinoma: latest evidence and clinical implications

    Drugs in Context 7:212528.

    https://doi.org/10.7573/dic.212528

    • PubMed
    • Google Scholar
60. 1. Sciacovelli M
    2. Dugourd A
    3. Jimenez LV
    4. Yang M
    5. Nikitopoulou E
    6. Costa ASH
    7. Tronci L
    8. Caraffini V
    9. Rodrigues P
    10. Schmidt C
    11. Ryan DG
    12. Young T
    13. Zecchini VR
    14. Rossi SH
    15. Massie C
    16. Lohoff C
    17. Masid M
    18. Hatzimanikatis V
    19. Kuppe C
    20. Von Kriegsheim A
    21. Kramann R
    22. Gnanapragasam V
    23. Warren AY
    24. Stewart GD
    25. Erez A
    26. Vanharanta S
    27. Saez-Rodriguez J
    28. Frezza C

    (2022) Dynamic partitioning of branched-chain amino acids-derived nitrogen supports renal cancer progression

    Nature Communications 13:7830.

    https://doi.org/10.1038/s41467-022-35036-4

    • PubMed
    • Google Scholar
61. 1. Semenza GL

    (2009) Regulation of cancer cell metabolism by hypoxia-inducible factor 1

    Seminars in Cancer Biology 19:12–16.

    https://doi.org/10.1016/j.semcancer.2008.11.009

    • PubMed
    • Google Scholar
62. 1. Shim E-H
    2. Livi CB
    3. Rakheja D
    4. Tan J
    5. Benson D
    6. Parekh V
    7. Kho E-Y
    8. Ghosh AP
    9. Kirkman R
    10. Velu S
    11. Dutta S
    12. Chenna B
    13. Rea SL
    14. Mishur RJ
    15. Li Q
    16. Johnson-Pais TL
    17. Guo L
    18. Bae S
    19. Wei S
    20. Block K
    21. Sudarshan S

    (2014) L-2-Hydroxyglutarate: an epigenetic modifier and putative oncometabolite in renal cancer

    Cancer Discovery 4:1290–1298.

    https://doi.org/10.1158/2159-8290.CD-13-0696

    • PubMed
    • Google Scholar
63. 1. Siska PJ
    2. Beckermann KE
    3. Mason FM
    4. Andrejeva G
    5. Greenplate AR
    6. Sendor AB
    7. Chiang YCJ
    8. Corona AL
    9. Gemta LF
    10. Vincent BG
    11. Wang RC
    12. Kim B
    13. Hong J
    14. Chen CL
    15. Bullock TN
    16. Irish JM
    17. Rathmell WK
    18. Rathmell JC

    (2017) Mitochondrial dysregulation and glycolytic insufficiency functionally impair CD8 T cells infiltrating human renal cell carcinoma

    JCI Insight 2:e93411.

    https://doi.org/10.1172/jci.insight.93411

    • PubMed
    • Google Scholar
64. 1. Sullivan MR
    2. Danai LV
    3. Lewis CA
    4. Chan SH
    5. Gui DY
    6. Kunchok T
    7. Dennstedt EA
    8. Vander Heiden MG
    9. Muir A

    (2019) Quantification of microenvironmental metabolites in murine cancers reveals determinants of tumor nutrient availability

    eLife 8:e44235.

    https://doi.org/10.7554/eLife.44235

    • PubMed
    • Google Scholar
65. 1. Sullivan MR
    2. Vander Heiden MG

    (2019) Determinants of nutrient limitation in cancer

    Critical Reviews in Biochemistry and Molecular Biology 54:193–207.

    https://doi.org/10.1080/10409238.2019.1611733

    • PubMed
    • Google Scholar
66. 1. Urasaki Y
    2. Heath L
    3. Xu CW

    (2012) Coupling of glucose deprivation with impaired histone H2B monoubiquitination in tumors

    PLOS ONE 7:e36775.

    https://doi.org/10.1371/journal.pone.0036775

    • PubMed
    • Google Scholar
67. 1. Valera VA
    2. Merino MJ

    (2011) Misdiagnosis of clear cell renal cell carcinoma

    Nature Reviews. Urology 8:321–333.

    https://doi.org/10.1038/nrurol.2011.64

    • PubMed
    • Google Scholar
68. 1. Vande Voorde J
    2. Ackermann T
    3. Pfetzer N
    4. Sumpton D
    5. Mackay G
    6. Kalna G
    7. Nixon C
    8. Blyth K
    9. Gottlieb E
    10. Tardito S

    (2019) Improving the metabolic fidelity of cancer models with a physiological cell culture medium

    Science Advances 5:eaau7314.

    https://doi.org/10.1126/sciadv.aau7314

    • PubMed
    • Google Scholar
69. 1. Vessby B
    2. Gustafson S
    3. Chapman MJ
    4. Hellsing K
    5. Lithell H

    (1987)

    Lipoprotein composition of human suction-blister interstitial fluid

    Journal of Lipid Research 28:629–641.

    • PubMed
    • Google Scholar
70. 1. Wang HY
    2. Ding HJ
    3. Chen JH
    4. Chao CH
    5. Lu YY
    6. Lin WY
    7. Kao CH

    (2012) Meta-analysis of the diagnostic performance of [18F]FDG-PET and PET/CT in renal cell carcinoma

    Cancer Imaging 12:464–474.

    https://doi.org/10.1102/1470-7330.2012.0042

    • PubMed
    • Google Scholar
71. 1. Wang H
    2. Wang L
    3. Zheng Q
    4. Lu Z
    5. Chen Y
    6. Shen D
    7. Xue D
    8. Jiang M
    9. Ding L
    10. Zhang J
    11. Wu H
    12. Xia L
    13. Qian J
    14. Li G
    15. Lu J

    (2021) Oncometabolite L-2-hydroxyglurate directly induces vasculogenic mimicry through PHLDB2 in renal cell carcinoma

    International Journal of Cancer 148:1743–1755.

    https://doi.org/10.1002/ijc.33435

    • PubMed
    • Google Scholar
72. 1. Wang G
    2. Heijs B
    3. Kostidis S
    4. Mahfouz A
    5. Rietjens RGJ
    6. Bijkerk R
    7. Koudijs A
    8. van der Pluijm LAK
    9. van den Berg CW
    10. Dumas SJ
    11. Carmeliet P
    12. Giera M
    13. van den Berg BM
    14. Rabelink TJ

    (2022a) Analyzing cell-type-specific dynamics of metabolism in kidney repair

    Nature Metabolism 4:1109–1118.

    https://doi.org/10.1038/s42255-022-00615-8

    • PubMed
    • Google Scholar
73. 1. Wang L
    2. Xing X
    3. Zeng X
    4. Jackson SR
    5. TeSlaa T
    6. Al-Dalahmah O
    7. Samarah LZ
    8. Goodwin K
    9. Yang L
    10. McReynolds MR
    11. Li X
    12. Wolff JJ
    13. Rabinowitz JD
    14. Davidson SM

    (2022b) Spatially resolved isotope tracing reveals tissue metabolic activity

    Nature Methods 19:223–230.

    https://doi.org/10.1038/s41592-021-01378-y

    • PubMed
    • Google Scholar
74. 1. Wettersten HI
    2. Hakimi AA
    3. Morin D
    4. Bianchi C
    5. Johnstone ME
    6. Donohoe DR
    7. Trott JF
    8. Aboud OA
    9. Stirdivant S
    10. Neri B
    11. Wolfert R
    12. Stewart B
    13. Perego R
    14. Hsieh JJ
    15. Weiss RH

    (2015) Grade-dependent metabolic reprogramming in kidney cancer revealed by combined proteomics and metabolomics analysis

    Cancer Research 75:2541–2552.

    https://doi.org/10.1158/0008-5472.CAN-14-1703

    • PubMed
    • Google Scholar
75. 1. Wilinski D
    2. Winzeler J
    3. Duren W
    4. Persons JL
    5. Holme KJ
    6. Mosquera J
    7. Khabiri M
    8. Kinchen JM
    9. Freddolino PL
    10. Karnovsky A
    11. Dus M

    (2019) Rapid metabolic shifts occur during the transition between hunger and satiety in Drosophila melanogaster

    Nature Communications 10:4052.

    https://doi.org/10.1038/s41467-019-11933-z

    • PubMed
    • Google Scholar
76. 1. Wishart DS
    2. Guo A
    3. Oler E
    4. Wang F
    5. Anjum A
    6. Peters H
    7. Dizon R
    8. Sayeeda Z
    9. Tian S
    10. Lee BL
    11. Berjanskii M
    12. Mah R
    13. Yamamoto M
    14. Jovel J
    15. Torres-Calzada C
    16. Hiebert-Giesbrecht M
    17. Lui VW
    18. Varshavi D
    19. Varshavi D
    20. Allen D
    21. Arndt D
    22. Khetarpal N
    23. Sivakumaran A
    24. Harford K
    25. Sanford S
    26. Yee K
    27. Cao X
    28. Budinski Z
    29. Liigand J
    30. Zhang L
    31. Zheng J
    32. Mandal R
    33. Karu N
    34. Dambrova M
    35. Schiöth HB
    36. Greiner R
    37. Gautam V

    (2022) HMDB 5.0: the human metabolome database for 2022

    Nucleic Acids Research 50:D622–D631.

    https://doi.org/10.1093/nar/gkab1062

    • PubMed
    • Google Scholar
77. 1. Yoon CY
    2. Shim YJ
    3. Kim EH
    4. Lee JH
    5. Won NH
    6. Kim JH
    7. Park IS
    8. Yoon DK
    9. Min BH

    (2007) Renal cell carcinoma does not express argininosuccinate synthetase and is highly sensitive to arginine deprivation via arginine deiminase

    International Journal of Cancer 120:897–905.

    https://doi.org/10.1002/ijc.22322

    • PubMed
    • Google Scholar

Author details

1. Keene L Abbott

   1. Department of Biology, Massachusetts Institute of Technology, Cambridge, United States
   2. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, United States
   3. Broad Institute of MIT and Harvard, Cambridge, United States

   Contribution

   Conceptualization, Investigation, Methodology, Writing – original draft, Writing – review and editing

   Contributed equally with

   Ahmed Ali and Bradley I Reinfeld

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-6166-704X

2. Ahmed Ali

   1. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, United States
   2. Broad Institute of MIT and Harvard, Cambridge, United States

   Contribution

   Conceptualization, Investigation, Methodology, Writing – original draft, Writing – review and editing

   Contributed equally with

   Keene L Abbott and Bradley I Reinfeld

   Competing interests

   No competing interests declared

3. Bradley I Reinfeld

   1. Medical Scientist Training Program, Vanderbilt University, Nashville, United States
   2. Department of Medicine, Vanderbilt University Medical Center (VUMC), Nashville, United States
   3. Graduate Program in Cancer Biology, Vanderbilt University, Nashville, United States

   Contribution

   Conceptualization, Investigation, Methodology, Writing – original draft, Writing – review and editing

   Contributed equally with

   Keene L Abbott and Ahmed Ali

   Competing interests

   No competing interests declared

4. Amy Deik

   Broad Institute of MIT and Harvard, Cambridge, United States

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9687-0953

5. Sonu Subudhi

   Steele Laboratories of Tumor Biology, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, United States

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5937-1880

6. Madelyn D Landis

   Department of Medicine, Vanderbilt University Medical Center (VUMC), Nashville, United States

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

7. Rachel A Hongo

   Department of Medicine, Vanderbilt University Medical Center (VUMC), Nashville, United States

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

8. Kirsten L Young

   Department of Medicine, Vanderbilt University Medical Center (VUMC), Nashville, United States

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

9. Tenzin Kunchok

   Whitehead Institute for Biomedical Research, Cambridge, United States

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

10. Christopher S Nabel

    1. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, United States
    2. Department of Medicine, Massachusetts General Hospital, Boston, United States
    3. Harvard Medical School, Boston, United States

    Contribution

    Investigation, Writing – review and editing

    Competing interests

    Royalty and income from Cambridge Epigenetix and ThermoFisher and stock in Opko Health

11. Kayla D Crowder

    Whitehead Institute for Biomedical Research, Cambridge, United States

    Contribution

    Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-0861-6489

12. Johnathan R Kent

    Department of Surgery, University of Chicago Medicine, Chicago, United States

    Contribution

    Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

13. Maria Lucia L Madariaga

    Department of Surgery, University of Chicago Medicine, Chicago, United States

    Contribution

    Resources, Writing – review and editing

    Competing interests

    No competing interests declared

14. Rakesh K Jain

    Steele Laboratories of Tumor Biology, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, United States

    Contribution

    Resources, Writing – review and editing

    Competing interests

    Consultant/SAB fees from Cur, DynamiCure, Elpis, SPARC, SynDevRx; owns equity in Accurius, Enlight, SynDevRx; served on the Board of Trustees of Tekla Healthcare Investors, Tekla Life Sciences Investors, Tekla Healthcare Opportunities Fund, Tekla World Healthcare Fund, and received Research Grants from Boehringer Ingelheim and Sanofi; no funding or reagents from these organizations were used in the study

15. Kathryn E Beckermann

    Department of Medicine, Vanderbilt University Medical Center (VUMC), Nashville, United States

    Contribution

    Resources, Writing – review and editing

    Competing interests

    No competing interests declared

16. Caroline A Lewis

    Whitehead Institute for Biomedical Research, Cambridge, United States

    Present address

    UMass Chan Medical School, Program in Molecular Medicine, Worcester, United States

    Contribution

    Resources, Writing – review and editing

    Competing interests

    No competing interests declared

17. Clary B Clish

    Broad Institute of MIT and Harvard, Cambridge, United States

    Contribution

    Resources, Writing – review and editing

    Competing interests

    No competing interests declared

18. Alexander Muir

    Ben May Department of Cancer Research, University of Chicago, Chicago, United States

    Contribution

    Resources, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-3811-3054

19. W Kimryn Rathmell

    1. Department of Medicine, Vanderbilt University Medical Center (VUMC), Nashville, United States
    2. Vanderbilt Center for Immunobiology and Vanderbilt-Ingram Cancer Center, VUMC, Nashville, United States

    Contribution

    Conceptualization, Resources, Supervision, Funding acquisition, Methodology, Writing – original draft, Writing – review and editing

    Competing interests

    No competing interests declared

20. Jeffrey Rathmell

    1. Vanderbilt Center for Immunobiology and Vanderbilt-Ingram Cancer Center, VUMC, Nashville, United States
    2. Department of Pathology, Microbiology and Immunology, VUMC, Nashville, United States

    Contribution

    Conceptualization, Resources, Supervision, Funding acquisition, Methodology, Writing – original draft, Writing – review and editing

    For correspondence

    jeff.rathmell@vumc.org

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-4106-3396

21. Matthew G Vander Heiden

    1. Department of Biology, Massachusetts Institute of Technology, Cambridge, United States
    2. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, United States
    3. Broad Institute of MIT and Harvard, Cambridge, United States
    4. Dana-Farber Cancer Institute, Boston, United States

    Contribution

    Conceptualization, Resources, Supervision, Funding acquisition, Methodology, Writing – original draft, Writing – review and editing

    For correspondence

    mvh@mit.edu

    Competing interests

    Scientific advisor for Agios Pharmaceuticals, iTeos Therapeutics, Sage Therapeutics, Pretzel Therapeutics, Lime Therapeutics, Faeth Therapeutics, Droia Ventures, and Auron Therapeutics

    "This ORCID iD identifies the author of this article:" 0000-0002-6702-4192

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