. 2019 May;25(5):850-860.

doi: 10.1038/s41591-019-0404-8. Epub 2019 May 8.

The landscape of cancer cell line metabolism

Haoxin Li^{1

2}, Shaoyang Ning³, Mahmoud Ghandi¹, Gregory V Kryukov¹, Shuba Gopal¹, Amy Deik¹, Amanda Souza¹, Kerry Pierce¹, Paula Keskula¹, Desiree Hernandez¹, Julie Ann⁴, Dojna Shkoza⁴, Verena Apfel⁵, Yilong Zou¹, Francisca Vazquez¹, Jordi Barretina⁴, Raymond A Pagliarini⁴, Giorgio G Galli⁵, David E Root¹, William C Hahn^{1

2}, Aviad Tsherniak¹, Marios Giannakis^{1

2}, Stuart L Schreiber^{1

6}, Clary B Clish¹, Levi A Garraway^{1

2}, William R Sellers^{7

8}

Affiliations

¹ Broad Institute of Harvard and MIT, Cambridge, MA, USA.
² Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.
³ Department of Statistics, Harvard University, Cambridge, MA, USA.
⁴ Novartis Institutes for Biomedical Research, Cambridge, MA, USA.
⁵ Novartis Institutes for Biomedical Research, Basel, Switzerland.
⁶ Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA.
⁷ Broad Institute of Harvard and MIT, Cambridge, MA, USA. [email protected].
⁸ Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. [email protected].

PMID: 31068703
PMCID: PMC6629041
DOI: 10.1038/s41591-019-0404-8

The landscape of cancer cell line metabolism

Haoxin Li et al. Nat Med. 2019 May.

. 2019 May;25(5):850-860.

doi: 10.1038/s41591-019-0404-8. Epub 2019 May 8.

Authors

Affiliations

¹ Broad Institute of Harvard and MIT, Cambridge, MA, USA.
² Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.
³ Department of Statistics, Harvard University, Cambridge, MA, USA.
⁴ Novartis Institutes for Biomedical Research, Cambridge, MA, USA.
⁵ Novartis Institutes for Biomedical Research, Basel, Switzerland.
⁶ Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA.
⁷ Broad Institute of Harvard and MIT, Cambridge, MA, USA. [email protected].
⁸ Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. [email protected].

PMID: 31068703
PMCID: PMC6629041
DOI: 10.1038/s41591-019-0404-8

Abstract

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.

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Figures

**Extended Data Fig. 1 ∣. Quality control of the CCLE metabolomic dataset.**
a, Metabolites ordered by estimated coefficients of variance (CV). b, Heatmap 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. Those cell lines with corresponding mutations are labeled as 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 ± SD). Each dot represents a cell line. The p-values were calculated based on two-sample t-tests (two-sided). **c-h**, Volcano plots comparing 225 profiled metabolites based on the mutational status of *EGFR* (c), *KRAS* (d), *NRAS* (e), *TP53* (f), *PTEN* (g), *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 **i-k**, 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 in each plot.

**Extended Data Fig. 3 ∣. Influences of *SLC25A20* methylation on acylcarnitine metabolism.**
**a-j**, 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 independently twice 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 ± SEM, n=2 cell culture replicates, two-sample t-test, two sided). After 13 days, 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 types. 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 3 independent cell culture replicates) cultured in RPMI media containing 0.1 μM glutamine for 6 days (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).

**Extended Data Fig. 5 ∣. Evaluation of asparaginase therapeutic value *in vivo*.**
a, Surgically removed SNU719 tumors after asparaginase treatment or vehicle control treatment (2 tumors per nude mouse). b, Relative mouse body weight changes in the duration of asparaginase treatment (3000 units/kg, 5 times a week) or vehicle control (n = 5 nude mice per condition, mean ± SEM). 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 while solid circles indicate methylated CpG. This experiment was repeated once with 4 technical replicates for each different tumor sample.

**Extended Data Fig. 6 ∣. Kynurenine metabolism in the CCLE.**
a, Waterfall plots showing the fold changes of pooled CCLE lines (n=554, median of 3 independent cell culture replicates) cultured in RPMI media containing 10 μM or 100 μM kynurenine for 6 days (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 heatmap 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<10⁻¹⁵ and p=2.5*10⁻¹¹ respectively). Cell lines (n=913) are represented as points with color-coded kynurenine levels.

**Fig.1 ∣. The CCLE database enables quantitative metabolomic modeling in relation to genetic features.**
a, 928 cancer cell lines from more than 20 major tissues of origin were profiled for the abundance of 225 metabolites. The number of cell lines is annotated based on the tissues of origin. b, Schematic summarizing the workflow of metabolite profiling. c, Heatmap of 225 clustered metabolites (Y axis) and their associations with selected genetic features (X axis). T-statistics were calculated based on linear regression for each metabolite paired with each feature across all cell lines conditioned on the major lineages and were used to represent the regression coefficients scaled by standard deviations. Examples mentioned in the text are magnified and shown in black boxes. CN, copy number. d, 2HG and the top correlated mutations among all mutational features. Cell lines are shown as lines and ordered by increasing levels of 2HG. Those cell lines with corresponding mutations are labeled as 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). e, Cancer cell lines with outlier levels of 2HG have specific *IDH1/2* mutations. f, Malate levels and a heatmap representation of top correlated copy number alterations among all copy number features. The features are color-coded to indicate various copy number alterations (log₂ scale) and are standardized for plotting. The reported test statistics and p-values are based on the significance tests of genetic feature regression coefficients (cell line n=912, two-sided t-tests). g, Schematic of the genomic locus containing *ME2*, *ELAC1*, and *SMAD4*.

**Fig.2 ∣. Systematic evaluations of metabolite associations with gene methylation patterns.**
a, Heatmap of 225 clustered metabolites (Y axis) and their associations with selected gene methylation features (X axis). b, Oleylcarnitine (an example of long-chain acylcarnitines) and the top correlated features among all methylation features. The reported test statistics and p-values are based on the significance tests of DNA methylation feature regression coefficients (cell line n=811, two-sided t-tests). c, Scatter plot comparing *SLC25A20* DNA methylation levels with its mRNA levels in selected lineages. **d-g,** Scatter plots comparing *SLC25A20* mRNA levels with different acylcarnitines: myristoylcarnitine (d), palmitoylcarnitine (e), stearoylcarnitine (f), and oleycarnitine (g). The q-values were calculated based on the significance test of Pearson correlations (two-sided) with multiple hypothesis testing correction. h, Scatter plot comparing *PYCR1* DNA methylation levels with its mRNA transcripts in hematopoietic cell lines. i, Scatter plot comparing *PYCR1* mRNA transcripts with proline levels in hematopoietic cell lines. j, Scatter plot comparing *GPT2* DNA methylation levels with its mRNA transcripts in hematopoietic cell lines. k, Scatter plot comparing *GPT2* mRNA transcripts with alanine levels in hematopoietic cell lines. For h-k, the p-values were calculated based on the significance test of Pearson correlations (two-sided).

**Fig.3 ∣. Systematic evaluations of metabolite-dependency associations.**
a, Heatmap of 225 clustered metabolites (Y axis) and their associations with top 3000 gene dependencies (CERES scores) (X axis). The two distinct lipid groups revealed by clustering are highlighted. TAG, triacylglycerol. **b-e**, T-statistics based on selected metabolites (b, reduced glutathione, c, oxidized glutathione, d, NADP⁺, e, asparagine) and gene dependencies (CERES). Each point represents a gene knockout (KO). The statistical test was based on linear regression conditioned on major lineage types (cell line n=455). f, Heatmap showing relative levels of ordered TAG species in 928 cell lines. PUFA^high and PUFA^low cell lines are selected by two-sample t-test (two-sided p<0.05) and are indicated by black lines below the heatmap. **g-h**, Volcano plots comparing the phosphatidylcholine (g) and cholesterol ester (h) species in the PUFA^high (n=315) versus PUFA^low (n=325) cell lines. Each point represents a metabolite and is colored by the ratio of carbon-carbon double bonds to the acyl chain number. i, Volcano plot comparing the differential dependencies in the PUFA^high (n=315) versus PUFA^low (n=325) cell lines. The dependency scores (CERES) used in comparison indicate cell line sensitivity in response to gene knockout (smaller values suggest greater sensitivity). For g-i, the q-values were calculated based on two-sample t-tests (two-sided) with multiple hypothesis testing correction.

**Fig.4 ∣. Revealing amino acid metabolism auxotrophs by pooled cancer cell line screens.**
a, Scatter plot comparing *ASNS* DNA methylation levels with *ASNS* mRNA levels in all cell lines. b, Schematic summarizing the workflow of pooled cancer cell line screens. **c-e**, Waterfall plots showing the fold changes of pooled CCLE lines (n=554, median of 3 independent cell culture replicates) cultured in RPMI media containing 0.1 μM asparagine (c), 0.1 μM arginine + 1 mM L-citrulline (precursor required for arginine synthesis) (d), 0.1 μM glutamine (e) for 6 days (normalized to control). For c-e, the p-values were calculated based on the significance test of Pearson correlations (two-sided).

**Fig.5 ∣. Therapeutic value of asparaginase in stomach and liver cancers.**
a, Methylation-specific PCR for *ASNS* CpG islands (a cropped gel image is shown). This experiment was repeated once. b, Bisulfite sequencing for *ASNS* methylation status in different cell lines. Open circles indicate unmethylated CpG while solid circles indicate methylated CpG. This experiment was repeated once with 4 technical replicates for each cell line sample. c, Cropped immunoblot of ASNS in representative stomach and liver cancer cell lines. Actin was used as the loading control. This experiment was repeated independently twice with similar results. d, Evaluation of asparagine depletion on the viability of selected stomach and liver cancer cell lines. Viabilities were quantified by Cell-Titer Glo 6 days after treatment (mean ± SEM, n=3 cell culture replicates). e, Volume measurements for tumors resulting from subcutaneous injection of 2313287 cells and SNU719 cells with 3000 units/kg asparaginase treatment or vehicle control (10 tumors from 5 nude mice per condition, mean ± SEM). The p-values were calculated based on the tumor volume difference between Day 21 and Day 1 using two-sample t-tests (two-sided). f, Immunostaining of ASNS (brown) in xenograft tumors expressing high (2313287) or low (SNU719) levels of ASNS treated with vehicle control or 3000 units/kg asparaginase 5 times a week for 3 weeks. Each subplot is representative of a different tumor. The immunostaining was repeated independently twice with similar results. Scale bar, 100 μm. g, Waterfall plots showing the *ASNS* mRNA levels related to its DNA methylation (probe: cg08114476) in the STAD cohort (n=372) and the LIHC cohort (n=371) in TCGA. Each line represents a tumor sample. The p-values were calculated based on the significance test of Pearson correlations (two-sided).

**Fig.6 ∣. The landscape of kynurenine metabolism in the CCLE.**
a, Kynurenine levels with medians across the CCLE cell lines grouped by tissues of origin. b, Scatter plot showing intracellular kynurenine levels compared to secreted kynurenine levels in the selected cell lines (discovery set: n=49; validation set: n=16). Two cell culture replicates were used for each cell line in the validation set. The p-values were calculated based on the significance test of Pearson correlations (two-sided). c, T-statistics based on all gene transcripts and the basal levels of kynurenine conditioned on the major lineages. Each point represents a gene. The statistical test was based on linear regression conditioned on major lineage types (cell line n=913). d, mRNA levels of *IDO1* and/or *TDO* correlate with kynurenine accumulation (Pearson correlation= 0.26 and 0.10 respectively; p<10⁻¹⁵ and p=0.002 respectively). Cell lines (n=913) are represented as points with color-coded kynurenine levels. e, Classification of cell lines with high kynurenine levels (the top 5% in the CCLE) based on their expression of IDO1 and TDO. Cell lines with IDO1/TDO mRNA transcripts lower than −2 (log₂ RPKM) are considered to lack sufficient expression. f, Classification of TCGA tumor samples based on IDO1/TDO mRNA transcripts. The samples with above average IDO1/TDO levels were defined to be high in expression. The cancer type short names are based on standard TCGA notations. g, Kynurenine secretion to media in response to epacadostat in cell lines with expression of IDO1, TDO or both (mean ± SEM, n=3 cell culture replicates). The detection limit of the ELISA assay is 0.1 μM.

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References

1. Vander Heiden MG et al. Understanding the Warburg effect : the metabolic requirements of cell proliferation. Science 324, 1029–1034 (2009). - PMC - PubMed
1. Pavlova NN & Thompson CB The emerging hallmarks of cancer metabolism. Cell Metabolism 23, 27–47 (2016). - PMC - PubMed
1. Cairns RA, Harris IS & Mak TW Regulation of cancer cell metabolism. Nature Reviews Cancer 11, 85–95 (2011). - PubMed
1. Vander Heiden MG & Deberardinis RJ Understanding the intersections between metabolism and cancer biology. Cell 168, 657–669 (2017). - PMC - PubMed
1. Tennant DA, Durán RV & Gottlieb E Targeting metabolic transformation for cancer therapy. Nature Reviews Cancer 10, 267–277 (2010). - PubMed

Reference for methods

1. Townsend MK et al. Reproducibility of metabolomic profiles among men and women in 2 large cohort studies. Clinical Chemistry 59, 1657–1667 (2013). - PMC - PubMed

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The landscape of cancer cell line metabolism

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The landscape of cancer cell line metabolism

Authors

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Abstract

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