Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Resource
  • Published:

The landscape of cancer cell line metabolism


Despite considerable efforts to identify cancer metabolic alterations that might unveil druggable vulnerabilities, systematic characterizations of metabolism as it relates to functional genomic features and associated dependencies remain uncommon. To further understand the metabolic diversity of cancer, we profiled 225 metabolites in 928 cell lines from more than 20 cancer types in the Cancer Cell Line Encyclopedia (CCLE) using liquid chromatography–mass spectrometry (LC-MS). This resource enables unbiased association analysis linking the cancer metabolome to genetic alterations, epigenetic features and gene dependencies. Additionally, by screening barcoded cell lines, we demonstrated that aberrant ASNS hypermethylation sensitizes subsets of gastric and hepatic cancers to asparaginase therapy. Finally, our analysis revealed distinct synthesis and secretion patterns of kynurenine, an immune-suppressive metabolite, in model cancer cell lines. Together, these findings and related methodology provide comprehensive resources that will help clarify the landscape of cancer metabolism.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: The CCLE database enables quantitative metabolomic modeling in relation to genetic features.
Fig. 2: Systematic evaluations of metabolite associations with gene methylation patterns.
Fig. 3: Systematic evaluations of metabolite-dependency associations.
Fig. 4: Revealing amino acid metabolism auxotrophs by pooled cancer cell line screens.
Fig. 5: Therapeutic value of asparaginase in stomach and liver cancers.
Fig. 6: The landscape of kynurenine metabolism in the CCLE.

Similar content being viewed by others

Data availability

The CCLE metabolomic data and related analyses can be found in Supplementary Tables 1–4. Additional CCLE resources are available at: Source data of all western blots and DNA gels in this work are provided in Source Data Figures.


  1. Vander Heiden, M. G. et al. Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science 324, 1029–1034 (2009).

    Article  Google Scholar 

  2. Pavlova, N. N. & Thompson, C. B. The emerging hallmarks of cancer metabolism. Cell. Metab. 23, 27–47 (2016).

    Article  CAS  Google Scholar 

  3. Cairns, R. A., Harris, I. S. & Mak, T. W. Regulation of cancer cell metabolism. Nat. Rev. Cancer 11, 85–95 (2011).

    Article  CAS  Google Scholar 

  4. Vander Heiden, M. G. & Deberardinis, R. J. Understanding the intersections between metabolism and cancer biology. Cell 168, 657–669 (2017).

    Article  Google Scholar 

  5. Tennant, D. A., Durán, R. V. & Gottlieb, E. Targeting metabolic transformation for cancer therapy. Nat. Rev. Cancer 10, 267–277 (2010).

    Article  CAS  Google Scholar 

  6. McDonald, E. R. I. et al. Project DRIVE: a compendium of cancer dependencies and synthetic lethal relationships uncovered by large-Scale, deep RNAi screening. Cell 170, 577–592 (2017).

    Article  CAS  Google Scholar 

  7. Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603–607 (2012).

    Article  CAS  Google Scholar 

  8. Tsherniak, A. et al. Defining a cancer dependency map. Cell 170, 564–576 (2017).

    Article  CAS  Google Scholar 

  9. Ghandi, M. et al. Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature (2019).

  10. Xu, W. et al. Oncometabolite 2-hydroxyglutarate is a competitive inhibitor of a ketoglutarate-dependent dioxygenases. Cancer Cell. 19, 17–30 (2011).

    Article  CAS  Google Scholar 

  11. Dang, L. et al. Cancer-associated IDH1 mutations produce 2-hydroxyglutarate. Nature 462, 739–744 (2009).

    Article  CAS  Google Scholar 

  12. Shim, E. H. et al. L-2-hydroxyglutarate: An epigenetic modifier and putative oncometabolite in renal cancer. Cancer Discov. 4, 1290–1298 (2014).

    Article  CAS  Google Scholar 

  13. Wakabayashi, N. et al. Protection against electrophile and oxidant stress by induction of the phase 2 response: fate of cysteines of the Keap1 sensor modified by inducers. Proc. Natl Acad. Sci. USA 101, 2040–2045 (2004).

    Article  CAS  Google Scholar 

  14. Ohta, T. et al. Loss of Keap1 function activates Nrf2 and provides advantages for lung cancer cell growth. Cancer Res. 68, 1303–1309 (2008).

    Article  CAS  Google Scholar 

  15. Dey, P. et al. Genomic deletion of malic enzyme 2 confers collateral lethality in pancreatic cancer. Nature 542, 119–123 (2017).

    Article  CAS  Google Scholar 

  16. Meyers, R. M. et al. Computational correction of copy number effect improves specificity of CRISPR-Cas9 essentiality screens in cancer cells. Nat. Genet. 49, 1779–1784 (2017).

    Article  CAS  Google Scholar 

  17. Indiveri, C., Tonazzi, A., Prezioso, G. & Palmieri, F. Kinetic characterization of the reconstituted carnitine carrier from rat liver mitochondria. Biochim. Biophys. Acta 1065, 231–238 (1991).

    Article  CAS  Google Scholar 

  18. Yang, W. S. et al. Regulation of ferroptotic cancer cell death by GPX4. Cell 156, 317–331 (2014).

    Article  CAS  Google Scholar 

  19. Yu, C. et al. High-throughput identification of genotype-specific cancer vulnerabilities in mixtures of barcoded tumor cell lines. Nat. Biotechnol. 34, 419–423 (2016).

    Article  CAS  Google Scholar 

  20. Mezrich, J. D. et al. An Interaction between kynurenine and the aryl hydrocarbon receptor can generate regulatory T cells. J. Immunol. 185, 3190–3198 (2010).

    Article  CAS  Google Scholar 

  21. Uyttenhove, C. et al. Evidence for a tumoral immune resistance mechanism based on tryptophan degradation by indoleamine 2,3-dioxygenase. Nat. Med. 9, 1269–1274 (2003).

    Article  CAS  Google Scholar 

  22. Munn, D. H. & Mellor, A. L. Indoleamine 2,3 dioxygenase and metabolic control of immune responses. Trends Immunol. 34, 137–143 (2013).

    Article  CAS  Google Scholar 

  23. Brochez, L., Chevolet, I. & Kruse, V. The rationale of indoleamine 2,3-dioxygenase inhibition for cancer therapy. Eur. J. Cancer 76, 167–182 (2017).

    Article  CAS  Google Scholar 

  24. Jain, M. et al. Metabolite profiling identifies a key role for glycine in rapid cancer cell proliferation. Science 336, 1040–1044 (2012).

    Article  CAS  Google Scholar 

  25. Long, G. V. Epacadostat (E) plus pembrolizumab (P) versus pembrolizumab alone in patients (pts) with unresectable or metastatic melanoma: results of the phase 3 ECHO-301/KEYNOTE-252 study. J. Clin. Oncol. 36, abstr. 108 (2018).

  26. Narta, U. K., Kanwar, S. S. & Azmi, W. Pharmacological and clinical evaluation of L-asparaginase in the treatment of leukemia. Crit. Rev. Oncol. Hematol. 61, 208–221 (2007).

    Article  Google Scholar 

  27. Hettmer, S. et al. Functional genomic screening reveals asparagine dependence as a metabolic vulnerability in sarcoma. eLife 4, 1–17 (2015).

    Article  Google Scholar 

  28. Knott, S. R. V. et al. Asparagine bioavailability governs metastasis in a model of breast cancer. Nature 554, 378–381 (2018).

    Article  CAS  Google Scholar 

  29. Townsend, M. K. et al. Reproducibility of metabolomic profiles among men and women in 2 large cohort studies. Clin. Chem. 59, 1657–1667 (2013).

    Article  CAS  Google Scholar 

Download references


The authors would like to thank E. Gelfand for assistance with project management. We acknowledge J. Jane-Valbuena, S. Gill, V. Viswanathan, L. Dailey, and R. Almeida for helpful discussions. The PRISM team including J. Roth, S. Bender, K. Gore, C. Zhu, and J. Fonseka provided valuable help for the pooled CCLE cancer cell line screening at the Broad Institute. We thank Dana-Farber/Harvard Cancer Center in Boston, MA for the use of the Specialized Histopathology Core, which provided histology and immunohistochemistry service. Dana-Farber/Harvard Cancer Center is supported in part by an NCI Cancer Center Support Grant #NIH 5 P30 CA06516. The CCLE project was supported by grants from Novartis and R35 Outstanding Investigator Award from the National Cancer Institute (L.A.G.) and U01 CA176058 (W.C.H.). This work was supported in part by the NCI’s Cancer Target Discovery and Development (CTD2) Network (grant number U01 CA217848, awarded to S.L.S.). H. Li is a fellow in the Herchel Smith graduate fellowship program.

Author information

Authors and Affiliations



L.A.G., W.R.S., C.B.C., and S.L.S. supervised the project. H.L., A.D., A.S., K.P., and V.A. performed experiments. P.K., D.H., J.A., and D.S. cultured the CCLE cancer cell lines. F.V., D.E.R., and A.T. contributed to the generation and processing of the genetic screen data. H.L., S.N., M. Ghandi, G.V.K., and S.G. contributed to the data analysis. H.L., G.G.G., J.B., R.A.P., Y.Z., M. Giannakis, W.C.H., S.L.S., and W.R.S. interpreted the results. H.L. and W.R.S. wrote the manuscript with critical reading and feedback from the other co-authors.

Corresponding author

Correspondence to William R. Sellers.

Ethics declarations

Competing interests

G.V.K. is an employee of and has equity in KSQ Therapeutics. G.V.K. is also a shareholder of Illumina and CRISPR Therapeutics. J.A., D.S., V.A., and G.G.G. are current Novartis employees. J.B. is a shareholder and former employee of Novartis. R.A.P. is a former employee of Novartis and a current employee of Celsius Therapeutics. W.C.H. is a consultant for Thermo-Fisher, Seer, AjuIB, MPM Capital, and KSQ Therapeutics and receives research funding from Deerfield Management. W.C.H. is a founder and has equity in KSQ Therapeutics. L.A.G. is a current employee at Eli Lilly and Company. A.T. is a consultant for Tango Therapeutics. M. Giannakis participated in an advisory board for Astrazeneca and receives research funding from Bristol-Myers Squibb. S.L.S. is a shareholder of Forma Therapeutics, a shareholder of and adviser to Jnana Therapeutics and Decibel Therapeutics, an adviser to Eisai, Inc. and F-Prime Capital Partners, and a Novartis Faculty Scholar. W.R.S. is a shareholder and former employee of Novartis. W.R.S. is a consultant and shareholder for Peloton Therapeutics and Ideaya Biosciences and is a consultant for Array, Astex, Ibsen, Servier, and Sanofi Pharmaceuticals and Epidarex Capital and receives research funding from Deerfield Management. H.L. and W.R.S. have a patent application based on this work. The other authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Quality control of the CCLE metabolomic dataset.

a, Metabolites ordered by estimated coefficients of variance (CV). b, Heat map showing the hierarchical clustering of the CCLE metabolomic data with annotated tissues of origin. CNS, central nervous system; UAT, upper aerodigestive tract. For each metabolite (log10 scale), the abundance was standardized for plotting. c, Ordered lineage effects of the 225 profiled metabolites. Each dot represents a metabolite. d, Phosphocreatine levels with medians across the CCLE cell lines grouped by tissues of origin. e, 1-methylnicotinamide levels with medians across the CCLE cell lines grouped by tissues of origin.

Extended Data Fig. 2 Selected mutation features in relation to metabolite abundances.

a, Reduced glutathione (GSH) levels and the top correlated mutations among all mutational features. Cell lines are shown as lines and ordered by increasing levels of GSH. Cell lines with corresponding mutations are labeled in black. The reported test statistics and P values are based on the significance tests of genetic feature regression coefficients (cell line n = 927, two-sided t tests). b, Swarm plot comparing GSH, GSSG, and NADP+ levels in cell lines with or without KEAP1 mutations (n = 72 and 855, respectively, mean ± s.d.). Each dot represents a cell line. The P values were calculated based on two-sample t tests (two-sided). ch, Volcano plots comparing 225 profiled metabolites based on the mutational status of EGFR (c), KRAS (d), NRAS (e), TP53 (f), PTEN (g), and TSC1/2 (h) in CCLE cell lines. Each point represents a metabolite and the red lines indicate a cutoff of q = 0.05. The statistical significance and effect sizes were calculated using linear regression models conditioned on major lineage types (two-sided t test). Note that cell lines with either PTEN mutation or deletion were defined as PTEN deficient. i, Scatter plot comparing ME2 copy number alterations with its mRNA levels in all cell lines. j, Scatter plot comparing ME2 mRNA levels with malate levels in all cell lines. k, Scatter plot comparing ME2 mRNA levels with ME3-knockout CERES scores. For ik, the P values were calculated based on the significance test of Pearson correlations (two-sided). The number of cell lines used for analysis is shown above each plot.

Extended Data Fig. 3 Influences of SLC25A20 methylation on acylcarnitine metabolism.

aj, Scatter plots comparing SLC25A20 mRNA transcripts with different metabolites: carnitine (a), acetylcarnitine (b), propionylcarnitine (c), malonylcarnitine (d), butyrylcarnitine/isobutyrylcarnitine (e), valerylcarnitine/isovalerylcarnitine/2-methylbutyroylcarnitine (f), hexanoylcarnitine (g), heptanoylcarnitine (h), lauroylcarnitine (i), arachidonylcarnitine (j). The q values were calculated based on significance test of Pearson correlations (two-sided) with multiple hypothesis testing correction.

Extended Data Fig. 4 Additional information regarding amino acid dependency.

a, Cropped immunoblot of ASNS in A2058 cells with or without dox-inducible ASNS knockdown (KD). Tubulin was used as the loading control. The experiment was repeated twice independently with similar results. b, Relative cell growth upon ASNS KD with or without rescue in the A2058 cell line grown in DMEM without asparagine (mean ± s.e.m., n = 2 cell culture replicates, two-sample t test, two-sided). After 13 d, the relative growth was quantified by standard crystal violet staining. PLK1 KD was used as a control. NEAA, non-essential amino acids. c, ASNS mRNA levels with medians across the CCLE lines grouped by cancer type. DLBCL, diffuse large B-cell lymphoma; CML, chronic myeloid leukemia; AML, acute myeloid leukemia; ALL, acute lymphoblastic leukemia. d, Scatter plot comparing ATF4 mRNA levels with ASNS mRNA levels in all cell lines. e, Schematic depicting part of the metabolic pathway of asparagine, arginine, and glutamine. f, Scatter plot comparing ASS1 DNA methylation levels with ASS1 mRNA levels in all cell lines. g, Scatter plot comparing GLUL DNA methylation levels with GLUL mRNA levels in all cell lines. For f,g, the P values were calculated based on the significance test of Pearson correlations (two-sided). The number of cell lines used for analysis is shown in the plot above. h, Waterfall plot showing the fold changes of pooled CCLE lines (n = 554, median of three independent cell culture replicates) cultured in RPMI medium containing 0.1 µM glutamine for 6 d (normalized to control). The colors show the associated cell doubling time. For h, the P value was calculated based on the significance test of Pearson correlations (two-sided).

Source data

Extended Data Fig. 5 Evaluation of asparaginase therapeutic value in vivo.

a, Surgically removed SNU719 tumors after asparaginase treatment or vehicle control treatment (two tumors per nude mouse). b, Relative mouse body weight changes in the duration of asparaginase treatment (3,000 units/kg, 5×/week) or vehicle control (n = 5 nude mice per condition, mean ± s.e.m.). c, Methylation-specific PCR for ASNS CpG islands in different tumor samples (a cropped gel image is shown). This experiment was repeated once. d, Bisulfite sequencing for ASNS methylation status in different tumor samples. Open circles indicate unmethylated CpG, and solid circles indicate methylated CpG. This experiment was repeated once with four technical replicates for each different tumor sample.

Source data

Extended Data Fig. 6 Kynurenine metabolism in the CCLE.

a, Waterfall plots showing the fold changes of pooled CCLE lines (n = 554, median of three independent cell culture replicates) cultured in RPMI media containing 10 µM or 100 µM kynurenine for 6 d (normalized to control). The colors show the associated kynurenine levels in cells. The P values were calculated based on the significance test of Pearson correlations (two-sided). b, Schematic depicting part of the tryptophan catabolism pathway. c, Kynurenine levels and a heat map representation of top correlated mRNA transcripts in each cell line. The feature values (log2 RPKM) were standardized for plotting. The reported test statistics and P values are based on the significance tests of mRNA feature regression coefficients (cell line n = 913, two-sided t tests). d, mRNA levels of IDO1 and/or IDO2 correlate with kynurenine accumulation (Pearson correlation = 0.26 and 0.22, respectively; two-sided P < 1 × 10−15 and P = 2.5 × 10−11, respectively). Cell lines (n = 913) are represented as points with color-coded kynurenine levels.

Supplementary information

Reporting Summary

Supplementary Tables 1–4

Supplementary Table 1, 2, 3, and 4

Source data

Source Data Fig. 5

Unprocessed western blots and gel image.

Source Data Extended Data Fig. 4

Unprocessed western blots.

Source Data Extended Data Fig. 5

Unprocessed gel image.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, H., Ning, S., Ghandi, M. et al. The landscape of cancer cell line metabolism. Nat Med 25, 850–860 (2019).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:


Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer