Abstract

Changes in cellular gene expression in response to small-molecule or genetic perturbations have yielded signatures that can connect unknown mechanisms of action (MoA) to ones previously established. We hypothesized that differential basal gene expression could be correlated with patterns of small-molecule sensitivity across many cell lines to illuminate the actions of compounds whose MoA are unknown. To test this idea, we correlated the sensitivity patterns of 481 compounds with 19,000 basal transcript levels across 823 different human cancer cell lines and identified selective outlier transcripts. This process yielded many novel mechanistic insights, including the identification of activation mechanisms, cellular transporters and direct protein targets. We found that ML239, originally identified in a phenotypic screen for selective cytotoxicity in breast cancer stem-like cells, most likely acts through activation of fatty acid desaturase 2 (FADS2). These data and analytical tools are available to the research community through the Cancer Therapeutics Response Portal.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

References

  1. 1.

    , , & Principles of early drug discovery. Br. J. Pharmacol. 162, 1239–1249 (2011).

  2. 2.

    , , & Target identification and mechanism of action in chemical biology and drug discovery. Nat. Chem. Biol. 9, 232–240 (2013).

  3. 3.

    & How were new medicines discovered? Nat. Rev. Drug Discov. 10, 507–519 (2011).

  4. 4.

    et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102, 15545–15550 (2005).

  5. 5.

    et al. Functional discovery via a compendium of expression profiles. Cell 102, 109–126 (2000).

  6. 6.

    et al. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313, 1929–1935 (2006).

  7. 7.

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

  8. 8.

    et al. An interactive resource to identify cancer genetic and lineage dependencies targeted by small molecules. Cell 154, 1151–1161 (2013).

  9. 9.

    et al. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature 483, 570–575 (2012).

  10. 10.

    et al. Formation of 17-allylamino-demethoxygeldanamycin (17-AAG) hydroquinone by NAD(P)H:quinone oxidoreductase 1: role of 17-AAG hydroquinone in heat shock protein 90 inhibition. Cancer Res. 65, 10006–10015 (2005).

  11. 11.

    et al. Comparison and validation of genomic predictors for anticancer drug sensitivity. J. Am. Med. Inform. Assoc. 20, 597–602 (2013).

  12. 12.

    The NCI60 human tumour cell line anticancer drug screen. Nat. Rev. Cancer 6, 813–823 (2006).

  13. 13.

    et al. Inconsistency in large pharmacogenomic studies. Nature 504, 389–393 (2013).

  14. 14.

    et al. Connecting small molecules with similar assay performance profiles leads to new biological hypotheses. J. Biomol. Screen. 19, 771–781 (2014).

  15. 15.

    et al. NAMPT is the cellular target of STF-31-like small-molecule probes. ACS Chem. Biol. 9, 2247–2254 (2014).

  16. 16.

    , , & Comparison of chemical inhibitors of antiapoptotic Bcl-2-family proteins. Cell Death Differ. 13, 1419–1421 (2006).

  17. 17.

    et al. STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res. 41, D808–D815 (2013).

  18. 18.

    et al. Birinapant (TL32711), a bivalent SMAC mimetic, targets TRAF2-associated cIAPs, abrogates TNF-induced NF-κB activation, and is active in patient-derived xenograft models. Mol. Cancer Ther. 13, 867–879 (2014).

  19. 19.

    et al. Putative DNA/RNA helicase Schlafen-11 (SLFN11) sensitizes cancer cells to DNA-damaging agents. Proc. Natl. Acad. Sci. USA 109, 15030–15035 (2012).

  20. 20.

    et al. The selectivity of austocystin D arises from cell-line-specific drug activation by cytochrome P450 enzymes. J. Nat. Prod. 74, 567–573 (2011).

  21. 21.

    , , , & Selective, competitive and mechanism-based inhibitors of human cytochrome P450 2J2. Arch. Biochem. Biophys. 464, 155–168 (2007).

  22. 22.

    et al. Dynamic chromatin modification sustains epithelial-mesenchymal transition following inducible expression of Snail-1. Cell Rep. 5, 1679–1689 (2013).

  23. 23.

    et al. Selective toxicity of the tricyclic thiophene NSC 652287 in renal carcinoma cell lines: differential accumulation and metabolism. Biochem. Pharmacol. 57, 1283–1295 (1999).

  24. 24.

    et al. Human sulfotransferases and their role in chemical metabolism. Toxicol. Sci. 90, 5–22 (2006).

  25. 25.

    et al. The “survivin suppressants” NSC 80467 and YM155 induce a DNA damage response. Cancer Chemother. Pharmacol. 70, 207–212 (2012).

  26. 26.

    et al. YM155, a novel small-molecule survivin suppressant, induces regression of established human hormone-refractory prostate tumor xenografts. Cancer Res. 67, 8014–8021 (2007).

  27. 27.

    et al. The solute carrier SLC35F2 enables YM155-mediated DNA damage toxicity. Nat. Chem. Biol. 10, 768–773 (2014).

  28. 28.

    , , & P-glycoprotein plays a role in the oral absorption of BMS-387032, a potent cyclin-dependent kinase 2 inhibitor, in rats. Cancer Chemother. Pharmacol. 55, 110–116 (2005).

  29. 29.

    et al. Expression of multidrug resistance protein-related genes in lung cancer: correlation with drug response. Clin. Cancer Res. 5, 673–680 (1999).

  30. 30.

    et al. Discovery of small-molecule enhancers of reactive oxygen species that are nontoxic or cause genotype-selective cell death. ACS Chem. Biol. 8, 923–929 (2013).

  31. 31.

    et al. Selective blockade of 2-arachidonoylglycerol hydrolysis produces cannabinoid behavioral effects. Nat. Chem. Biol. 5, 37–44 (2009).

  32. 32.

    et al. Monoacylglycerol lipase regulates a fatty acid network that promotes cancer pathogenesis. Cell 140, 49–61 (2010).

  33. 33.

    et al. Identification of a selective small molecule inhibitor of breast cancer stem cells. Bioorg. Med. Chem. Lett. 22, 3571–3574 (2012).

  34. 34.

    & Transitions between epithelial and mesenchymal states: acquisition of malignant and stem cell traits. Nat. Rev. Cancer 9, 265–273 (2009).

  35. 35.

    et al. Core epithelial-to-mesenchymal transition interactome gene-expression signature is associated with claudin-low and metaplastic breast cancer subtypes. Proc. Natl. Acad. Sci. USA 107, 15449–15454 (2010).

  36. 36.

    Metabolism of highly unsaturated n-3 and n-6 fatty acids. Biochim. Biophys. Acta 1486, 219–231 (2000).

  37. 37.

    et al. Identification and characterization of a novel delta6/delta5 fatty acid desaturase inhibitor as a potential anti-inflammatory agent. Biochem. Pharmacol. 55, 1045–1058 (1998).

  38. 38.

    Lipid peroxidation of membrane phospholipids generates hydroxy-alkenals and oxidized phospholipids active in physiological and/or pathological conditions. Chem. Phys. Lipids 157, 1–11 (2009).

  39. 39.

    , & Ether-linked lipids and their bioactive species. in Biochemistry of Lipids, Lipoproteins, and Membranes edn. 4 (eds. Vance, D.E. & Vance, J.E.) 233–262 (Elsevier, 2002).

  40. 40.

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

  41. 41.

    , , & FADS2 function loss at the cancer hotspot 11q13 locus diverts lipid signaling precursor synthesis to unusual eicosanoid fatty acids. PLoS One 6, e28186 (2011).

  42. 42.

    , & Epithelial-to-mesenchymal transition rewires the molecular path to PI3K-dependent proliferation. Cancer Discov. 4, 186–199 (2014).

  43. 43.

    et al. An epithelial-mesenchymal transition gene signature predicts resistance to EGFR and PI3K inhibitors and identifies Axl as a therapeutic target for overcoming EGFR inhibitor resistance. Clin. Cancer Res. 19, 279–290 (2013).

  44. 44.

    et al. Structure of the DDB1-CRBN E3 ubiquitin ligase in complex with thalidomide. Nature 512, 49–53 (2014).

  45. 45.

    & Opposing effects of target overexpression reveal drug mechanisms. Nat. Commun. 5, 4296 (2014).

  46. 46.

    & Multiplex methods to assay mixed cell populations simultaneously. US Patent WO 2013138585 A1 (2013).

  47. 47.

    et al. Parallel genome-scale loss of function screens in 216 cancer cell lines for the identification of context-specific genetic dependencies. Sci. Data 1, 140035 (2014).

  48. 48.

    et al. Selective inhibitors of CYP2J2 related to terfenadine exhibit strong activity against human cancers in vitro and in vivo. J. Pharmacol. Exp. Ther. 329, 908–918 (2009).

  49. 49.

    et al. A public genome-scale lentiviral expression library of human ORFs. Nat. Methods 8, 659–661 (2011).

Download references

Acknowledgements

This work was supported by the US National Cancer Institute's Cancer Target Discovery and Development (CTD2) Network (grant number U01CA176152, awarded to S.L.S.). We acknowledge the following colleagues and centers for contributing compounds and for valuable critique: Boston University, J. Bradner, P. Brown, C. Chen, J. Clardy, CNIO (Spanish National Cancer Research Center), E.J. Corey, the Drug Synthesis and Chemistry Branch (Developmental Therapeutics Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute), Eutropics, J. Gutterman, E. Holson, Karyopharm, M. Meyerson, A. Myers, J. Porco, J. Qi, Sanford-Burnham, M. Serrano-Wu, M. Shair, B. Stockwell, L. Walensky, X. Wang and D. Zaharevitz. We thank A. Deik for conducting lipid profiling measurements; S. Chattopadhay, J. Law, G. Schaefer, M. Stewart, V. Viswanathan and other members of the Schreiber laboratory for advice and helpful discussions; S. Wang for helping curate the Informer Set; K. Emmith, J. Aseidu and the CSofT informatics group for development and support of cell-line and data-tracking software; A. Vrcic and the Broad Compound Management team for handling the Informer Set; J. Boehm, A. Tsherniak, A. Aguirre and the Broad Cancer Program for training and advice; the Broad Biological Samples Platform for providing CCLs; and L. Garraway and the Broad-Novartis Cancer Cell Line Encyclopedia (CCLE) team. S.L.S. is an Investigator of the Howard Hughes Medical Institute.

Author information

Author notes

    • Brinton Seashore-Ludlow
    • , Jaime H Cheah
    • , Drew J Adams
    • , Edmund V Price
    • , Nicole E Bodycombe
    • , Christian K Soule
    • , Ted Liefeld
    •  & Michelle Palmer

    Present addresses: Chemical Biology Consortium Sweden, Science for Life Laboratory Stockholm, Division of Translational Medicine and Chemical Biology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Solna, Sweden (B.S.L); Koch Institute for Cancer Research at MIT, Cambridge, Massachusetts, USA; Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA; Novartis Institutes for Biomedical Research, Emeryville, California, USA; Pfizer, Cambridge, Massachusetts, USA; University of California San Diego School of Medicine, La Jolla, California, USA; ImmunoGen, Waltham, Massachusetts, USA.

Affiliations

  1. Broad Institute, Cambridge, Massachusetts, USA.

    • Matthew G Rees
    • , Brinton Seashore-Ludlow
    • , Jaime H Cheah
    • , Drew J Adams
    • , Edmund V Price
    • , Shubhroz Gill
    • , Matthew E Coletti
    • , Victor L Jones
    • , Nicole E Bodycombe
    • , Christian K Soule
    • , Benjamin Alexander
    • , Ava Li
    • , Philip Montgomery
    • , Joanne D Kotz
    • , C Suk-Yee Hon
    • , Benito Munoz
    • , Ted Liefeld
    • , Vlado Dančík
    • , Clary B Clish
    • , Joshua A Bittker
    • , Michelle Palmer
    • , Bridget K Wagner
    • , Paul A Clemons
    • , Alykhan F Shamji
    •  & Stuart L Schreiber
  2. Massachusetts General Hospital Cancer Center, Charlestown, Massachusetts, USA.

    • Sarah Javaid
    •  & Daniel A Haber

Authors

  1. Search for Matthew G Rees in:

  2. Search for Brinton Seashore-Ludlow in:

  3. Search for Jaime H Cheah in:

  4. Search for Drew J Adams in:

  5. Search for Edmund V Price in:

  6. Search for Shubhroz Gill in:

  7. Search for Sarah Javaid in:

  8. Search for Matthew E Coletti in:

  9. Search for Victor L Jones in:

  10. Search for Nicole E Bodycombe in:

  11. Search for Christian K Soule in:

  12. Search for Benjamin Alexander in:

  13. Search for Ava Li in:

  14. Search for Philip Montgomery in:

  15. Search for Joanne D Kotz in:

  16. Search for C Suk-Yee Hon in:

  17. Search for Benito Munoz in:

  18. Search for Ted Liefeld in:

  19. Search for Vlado Dančík in:

  20. Search for Daniel A Haber in:

  21. Search for Clary B Clish in:

  22. Search for Joshua A Bittker in:

  23. Search for Michelle Palmer in:

  24. Search for Bridget K Wagner in:

  25. Search for Paul A Clemons in:

  26. Search for Alykhan F Shamji in:

  27. Search for Stuart L Schreiber in:

Contributions

M.G.R. designed and executed validation experiments, analyzed and interpreted data, made the figures, and wrote the manuscript; B.S.-L. designed and executed validation experiments, analyzed and interpreted data, and wrote the manuscript; J.H.C. designed and executed the primary cell-line screen; D.J.A. designed and executed validation experiments and analyzed data; E.V.P. executed the primary cell-line screen; S.G. executed validation experiments; S.J. executed validation experiments and analyzed data; M.E.C. executed the primary cell-line screen; V.L.J. executed the primary cell-line screen; N.E.B. analyzed and interpreted primary cell-line screen data; C.K.S. executed the primary cell-line screen; B.A. developed the CTRP; A.L. analyzed and interpreted data; P.M. developed the CTRP; J.D.K. wrote the manuscript; C.S.-Y.H. designed the primary cell-line screen; B.M. designed the primary cell-line screen; T.L. developed the CTRP; V.D. analyzed and interpreted primary cell-line screen data; D.A.H. designed validation experiments; C.B.C. designed and executed lipid profiling experiments and analyzed data; J.A.B. designed the primary cell-line screen; M.P. designed the primary cell-line screen; B.K.W. executed validation experiments and wrote the manuscript; P.A.C. designed, analyzed and interpreted primary cell-line screening data, directed the study and wrote the manuscript; A.F.S. designed the primary cell-line screen, interpreted data, directed the study and wrote the manuscript; S.L.S. designed the primary cell-line screen, directed the study and wrote the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Paul A Clemons or Alykhan F Shamji or Stuart L Schreiber.

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Results, Supplementary Table 1 and Supplementary Figures 1–8.

Excel files

  1. 1.

    Supplementary Data Set 1

    The cancer cell-line panel. Description of the cancer cell lines (CCLs) profiled in this experiment, including annotations from the CCLE1 for lineage and histology and experimental growth conditions (including media composition).

  2. 2.

    Supplementary Data Set 2

    The small-molecule informer set. Description of the small-molecule informer set profiled in this experiment, including concentrations used, protein target or activity, source and vendor information, and structure.

  3. 3.

    Supplementary Data Set 3

    Area-under-curve values for 481 small molecules across 823 unique CCLs.

  4. 4.

    Supplementary Data Set 4

    All transcripts significantly correlated with small-molecule sensitivity across all CCLs, non-HL CCLs, and HL CCLs. A Bonferroni-corrected significance cutoff of |z| > 5.83, representing a two-tailed distribution with family-wise error rate α < 0.025 in each tail, was used.

  5. 5.

    Supplementary Data Set 5

    Significant lineage-specific correlation values. All significant values where the absolute value of the z-scored Pearson correlation was greater than the value across all CCLs and non-HL CCLs are included.

  6. 6.

    Supplementary Data Set 6

    Correlations for 660 annotated compound-target pairs across all CCLs, non-HL CCLs, and HL CCLs. For compound-target pairs, the transcript rank (of 18,543 transcripts), z-scored Pearson correlation, and significance (|z| > 3.96; Bonferroni-corrected, two-tailed distribution with family-wise error-rate α < 0.025 in each tail) are included.

  7. 7.

    Supplementary Data Set 7

    Expression-sensitivity correlations for TNFRSF12A with 481 small molecules across all CCLs. Included are the number of CCLs tested per small molecule; TNFRSFS12A transcript rank (see Supplementary Fig. 1b); the Pearson expression-sensitivity correlation; the z-scored Pearson correlation; minimum, median, mean, maximum, and standard deviation of z-scored correlations from permutation testing (65,536 permutations); results of Kolmogorov-Smirnov test for normality of permutation results; empirical P-value from permutation testing; and estimated P-value from the normal cumulative distribution function.

  8. 8.

    Supplementary Data Set 8

    Transcript loadings from principal component analyses (PCA). Results are from PC_A1 (first PC from PCA of the entire 18,543 transcript × 481 small molecule correlation matrix across all CCLs), PC_B1 (first PC from PCA of the correlation matrix excluding HL CCLs), and PC_B2 (second PC from PCA of the correlation matrix excluding HL CCLs). The number of small molecules to which each gene was significantly correlated (excluding HL CCLs) was calculated using a Bonferroni-corrected, two-tailed distribution with family-wise error-rate α < 0.025 in each tail (|z| > 5.83).

  9. 9.

    Supplementary Data Set 9

    Small-molecule scores from principal component analyses.

  10. 10.

    Supplementary Data Set 10

    GSEA results with PC_B2 gene loadings as the input using the C2 and C5 gene sets. See www.broadinstitute.org/gsea/doc/GSEAUserGuideTEXT.htm#_Viewing_Analysis_results for analysis description.

  11. 11.

    Supplementary Data Set 11

    Correlations across all 481 small molecules for 654 individual transcripts most correlated with response to at least one small molecule. Included are the number of CCLs tested per small molecule; transcript rank; the Pearson expression-sensitivity correlation; the z-scored Pearson correlation; minimum, median, mean, maximum, and standard deviation of z-scored correlations from permutation testing (≥16,384 permutations); results of Kolmogorov-Smirnov test for normality of permutation results; empirical P-value from permutation testing; and estimated P -value from the normal cumulative distribution function.

  12. 12.

    Supplementary Data Set 12

    GSEA results with austocystin D expression-sensitivity correlations (excluding HL CCLs) as the input using the C2 and C5 gene sets. See www.broadinstitute.org/gsea/doc/GSEAUserGuideTEXT.htm#_Viewing_Analysis_results for analysis description.

  13. 13.

    Supplementary Data Set 13

    Raw results from profiling cellular lipids in NCIH661 cells. Results are from 24-hour treatment with 2 μM ML239, 2 μM SC-26196, co-treatment, or DMSO, with individual replicates numbered across columns. Metabolite IDs match those from The Human Metabolome Database (www.hmdb.ca).

  14. 14.

    Supplementary Data Set 14

    Mapping of accession numbers for the file CCLE_Expression_Entrez_2012-10-18.res to the gene symbols used in this analysis.

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/nchembio.1986

Further reading