Correlating chemical sensitivity and basal gene expression reveals mechanism of action - PubMed (original) (raw)

doi: 10.1038/nchembio.1986. Epub 2015 Dec 14.

Brinton Seashore-Ludlow 1 2, Jaime H Cheah 1 2, Drew J Adams 1 2, Edmund V Price 1 2, Shubhroz Gill 1, Sarah Javaid 3, Matthew E Coletti 1, Victor L Jones 1, Nicole E Bodycombe 1 2, Christian K Soule 1 2, Benjamin Alexander 1, Ava Li 1, Philip Montgomery 1, Joanne D Kotz 1, C Suk-Yee Hon 1, Benito Munoz 1, Ted Liefeld 1 2, Vlado Dančík 1, Daniel A Haber 3, Clary B Clish 1, Joshua A Bittker 1, Michelle Palmer 1 2, Bridget K Wagner 1, Paul A Clemons 1, Alykhan F Shamji 1, Stuart L Schreiber 1

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Correlating chemical sensitivity and basal gene expression reveals mechanism of action

Matthew G Rees et al. Nat Chem Biol. 2016 Feb.

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.

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Conflict of interest statement

COMPETING FINANCIAL INTERESTS

The authors declare no competing financial interests.

Figures

Figure 1

Figure 1. Correlating Gene Expression and CCL Sensitivity Data Illuminates Known Small-Molecule Mechanisms of Action

(a) Calculation of _z_-scored Pearson correlation coefficients between small-molecule sensitivity data, expressed as areas under concentration-response curves (AUCs), with basal gene-expression measurements, expressed as log2 robust-multi-array-average values. We examined 18,543 correlation coefficients of transcript levels to ABT-199 sensitivity (black), and 481 correlation coefficients of small molecules to BCL2 expression (gray) across non-hematopoietic and lymphoid (non-HL) CCLs. Box-and-whisker plot outlier points represent Tukey outliers (1.5 × interquartile range). (b) Distribution of _z_-scored Pearson correlation coefficients between 660 annotated small-molecule–target pairs (green) across all CCLs, non-HL CCLs, and HL CCLs compared to random sampling of correlation coefficients (black). Dashed lines represent two-tailed Bonferroni-corrected significance (|z| = 3.96). (c–e) Expression–sensitivity correlations for target–pathway connections (blue), including (c) the Smac mimetic birinapant; (d) the nucleoside analogues clofarabine and gemcitabine, the IKKβ inhibitor PF-184, and the transcript SLFN11; and (e) the statin simvastatin and the transcript HMGCS1. The blue dashed line represents the low-AUC (left) cutoff for the robust _z_-score of birinapant AUCs.

Figure 2

Figure 2. MoA Analysis Reveals New Mechanisms of Small-Molecule Metabolism

(a) Expression–sensitivity correlations for the compound austocystin D and CYP2J2 expression. Reactive functionalities are depicted with a red arrow. (b) Cytotoxicity of austocystin D and a selective CYP2J2 inhibitor (CYP2J2i) across renal CCLs with different expression levels of CYP2J2, and effects of co-treatment of austocystin D with CYP2J2i. Cell viability values are normalized to vehicle-only (DMSO) treatment, with each point representing the mean of n = 2 independent experiments with 2 technical replicates each. (c) Cytotoxicity of austocystin D in MCF7-ER-Snail-16SA cells either induced to undergo epithelial-to-mesenchymal transition (blue) or vehicle-treated (black). Each point is the mean of n = 2 technical replicates. Two independent inductions are shown. (d) Expression–sensitivity correlations for the compound RITA and SULT1A1 expression. (e) Protein expression of SULT1A1 and sensitivity to RITA of six renal CCLs. CCLs described in ref. as capable (*) or incapable (‡) of metabolizing RITA into a cytotoxic form are indicated. Each point is mean ± s.d. for n = 3 independent experiments. For uncropped gel image, see Supplementary Figure 8b. (f) Sulfotransferase enzyme activity of varying amounts of recombinant SULT1A1 at a fixed concentration of RITA in the presence of the sulfate donor 3′-phosphoadenosine-5′-phosphosulfate. Each point is mean ± s.d. for n = 3 independent experiments.

Figure 3

Figure 3. MoA Analysis Reveals Small-Molecule Transport Mechanisms

(a) Expression–sensitivity correlations for YM-155 and SLC35F2. (b) Effects of SLC35F2 overexpression in NB1 cells, or of SLC35F2 knockdown in 22RV1 cells, on YM-155 cytotoxicity. Each point represents the mean of n = 2 independent experiments with 3 (22RV1) –4 (NB1) technical replicates each. (c) Difference in 8-point AUC values between 22RV1-shlacZ_1 and 22RV1-shSLC35F2_3 cells for 439 small molecules tested in duplicate. (d) Expression–sensitivity correlations for multidrug resistance genes implicated by MoA analysis. (e) Effects of co-treatment with DMSO, the ABCB1 inhibitor CP-100356, or the ABCB1 inhibitor elacridar on NSC23766 cytotoxicity in SKNDZ cells. (f) Effects of co-treatment with DMSO or the ABCC1 inhibitor MK-571 on BRD5468 cytotoxicity in MALME3M cells. (g) Expression–sensitivity correlations for BRD5468 and MGLL, and effects of co-treatment with DMSO or the MGLL inhibitor JZL184 on BRD5468 cytotoxicity in COLO800 cells. For (e–g), each point is mean ± s.d. for n = 3 independent experiments.

Figure 4

Figure 4. MoA Analysis Identifies and Illuminates the Basis for a Requirement for FADS2 Activity in ML239 Cytotoxicity

(a) Correlation of PC2 score excluding HL CCLs (PC_B2) with sensitivity to all small molecules. (b) Expression–sensitivity correlations for ML239 and FADS2. (c) Effects of FADS2 knockdown on ML239 cytotoxicity in NCIH661 LCLC cells. Each point represents the mean of n = 2 independent experiments. (d) Change in levels of 183 cellular lipids in NCIH661 cells upon 24-hour treatment with 2 μM ML239. Bars represent mean ± s.e.m. for n=9. (e) Significantly changed lipid species from (d) upon 24-hour treatment with ML239, 2 μM SC-26196, or both (p < 0.001, two-way ANOVA with Bonferroni correction). Bars represent mean ± s.e.m. for n=9 for DMSO, ML239, and SC-26196, and n=3 for co-treatment.

Figure 5

Figure 5. Large numbers of CCLs are required to identify MoA

(a) Significance of connection between austocystin D AUC and CYP2J2 expression (black), and ML239 AUC and FADS2 expression (blue) as a function of CCL number in non-HL CCLs. Red, Bonferroni-corrected _z_-score cutoff (|z| > 4.79) for random sampling of CCLs. (b) Simulation of whether connections to 43 small molecules listed in Supplementary Table 1 could be identified using smaller numbers of CCLs. Depicted are the fraction (red trace) of connections that were statistically significant relative to the null distribution after Bonferroni correction for multiple hypothesis testing. Correlations one standard deviation above or below the mean are in gray.

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