Cell lines

Parental cell lines were obtained from the Broad Institute-Novartis Institutes for BioMedical Research Cancer Cell Line Encyclopedia (CCLE) project10 before PRISM barcoding (see https://portals.broadinstitute.org/ccle for the original sources). For the follow-up studies, LS1034, HeLa and HEK293T cells were purchased from the ATCC. The REC1, SF295, OVISE, COLO 320, HT-29 and SNU-449 cell lines were provided by CCLE. Wild-type and ABCB1-overexpressing (pLX_317 vector) Kuramochi cell lines were gifts from E. Stover40. HeLa PDE3A CRISPR knockout cells (PDE3A−/− cells) were described previously18. LS1034, SF295, COLO 320, SNU-449 and OVISE cell lines derived with Cas9 were provided by the Cancer Dependency Map (Broad Institute). Short tandem repeat (STR) fingerprinting was performed by Genetica using the PowerPlex 16 HS system (Promega Corporation). STR profiles were compared with STR profiles reported by vendors and in literature. Misidentified cell lines or other STR conflicts are listed in Supplementary Table 9. These cell lines are flagged in the data files and are not shown by default in the website interface. Cell lines were confirmed to be negative for Mycoplasma using the MycoScope PCR Mycoplasma Detection Kit (Genlantis). The LS1034, REC1, OVISE, SF295, COLO 320, SNU-449 and Kuramochi cell lines were cultured in Roswell Park Memorial Institute (RPMI) medium (Thermo Fisher Scientific). HeLa cells were cultured using DMEM (Thermo Fisher Scientific). All media were supplemented with 10% heat-inactivated FCS (Sigma-Aldrich) and 1% penicillin-streptomycin G (Thermo Fisher Scientific) except for the HEK293T cell line, which was maintained without antibiotics.

PRISM screening

We made several improvements to the PRISM barcoding method described previously4. The assay employs a 24-nucleotide barcode stably introduced into cancer cell lines via lentiviral transduction. The barcode is located at the end of the blasticidin resistance gene and is expressed as an mRNA under the highly active PGK promoter. We adapted the mRNA capture and Luminex detection method developed for the L1000 gene expression assay41 to detect PRISM barcodes to improve throughput. In addition to using an mRNA-based readout, assay improvements included pooling cell lines according to doubling time similarity, collapsing lysate plates together before detection and adding a spike-in barcode control for amplification and detection.

The detailed PRISM assay protocol is available online at https://depmap.org/repurposing. Briefly, barcoded cell lines were pooled (25 cell lines per pool) based on doubling time and frozen into assay-ready vials. Vials were thawed and 1 pool was immediately plated per 384-well assay plate at 1,250 cells per well in triplicate. Cells were either treated the following morning with compounds by pin transfer (repurposing primary and secondary high-throughput screens) or plated directly onto assay-ready plates containing compounds (used for follow-up in the lower-scale MTS004 and MTS006 screens). After a 5-d incubation, cells were lysed. Lysate plates containing 1 pool of 25 cell lines each were then pooled together further to yield 1 (in the secondary screen) or 2 (in the primary screen) final detection pools for amplification and barcode measurement. For the secondary screen, a set of ten unique barcodes were spiked into each well before PCR to control for variation in PCR amplification and Luminex detection following lysate pooling.

Data processing

Luminex median fluorescence intensity (MFI) values were calculated as the median fluorescence values of all beads corresponding to a single PRISM barcode in a single technical replicate. MFI values were log 2 -transformed (logMFI) and subjected to two quality control steps. First, an ‘outlier pool filter’ was applied to remove probable screening artifacts (Extended Data Fig. 2). In each assay plate, logMFI values were median centered per cell line and each well was summarized by the median of these centered MFI values. Wells more than five median absolute deviations from the median across all wells from the same compound plate and plate location were removed. Second, a ‘control separation filter’ was applied. For each plate, cell lines with strictly standardized mean difference42 values <2 were excluded from the rest of the analysis (Extended Data Fig. 3). Strictly standardized mean difference values were calculated as:

$$\frac{{\left( {\mu _ - - \mu _ + } \right)}}{{\sqrt {\sigma _ - ^2 + \sigma _ + ^2} }}$$

where μ − /+ and σ − /+ stand for the medians and the median absolute deviations of the logMFI values computed over the negative/positive control wells for each cell line on each plate. The number of PRISM profile cell line replicates passing quality control is shown in Extended Data Fig. 4.

A total of 1,448 compounds were selected for secondary eight-point dose–response testing based on reproducibility, predictability, selectivity and compound availability (Supplementary Table 2). For the secondary screen only, ten unique barcodes were spiked into each well of each plate after cell lysis. Normalized MFI values were computed by taking the ratio of each logMFI value to the median logMFI of the inert barcodes in each well. For data produced before the spike-in protocol was introduced, normalized MFI values were set equal to MFI values.

Fold change values were calculated as the ratio of normalized MFI to the median of the normalized MFI from dimethylsulfoxide (DMSO)-treated negative controls for each cell line on each plate. The batch effects produced from variable detection and assay conditions were then removed using ComBat (located in the sva package; we used version 3.30.1 of sva)43. ComBat was run over each treatment condition separately by considering the log 2 -transformed fold change values as the probes and the pool-replicate combinations as the batches. The corrected log fold change values were then median collapsed for each cell line, screen, source plate and well combination. We labeled cell lines as sensitive to a treatment if the median-collapsed fold change was <0.3.

Dose–response analysis

Dose–response relationships were obtained by fitting four-parameter logistic curves to viability values for each compound and cell line using the R package drc (version 3.0-1). Following the practice of Smirnov et al.44, the upper asymptote of the logistic curves was fixed at 1 and the viability values were fitted as a function of drug concentration according to:

$${\it{V}}\left( c \right) = {\it{E}}_\infty + \frac{{1 - E_\infty }}{{1 + {\mathrm{e}}^{\mathrm{HS}\left( {c - \mathrm{EC}_{{50}}} \right)}}}$$

where all concentrations are in the natural logarithm scale. IC 50 values were defined as the concentration c at which V(c) = 0.5. Additionally, the dose–response area under the curve (AUC) was calculated using the normalized integral:

$${{\mathrm{AUC}}} = \frac{{\mathop {\smallint }

olimits_{c_{\mathrm{min}}}^{c_{\mathrm{max}}} V\left( c \right){\mathrm{d}}c}}{{c_{\mathrm{max}} - c_{\mathrm{min}}}}$$

This formulation puts AUC values on a scale between 0 and 1 for curves with lower asymptotes <1, where lower AUC values indicate increased sensitivity to treatment.

Biomarker discovery

To generate predictive biomarkers, we adopted the ATLANTIS predictive models9 and trained multiple models for each PRISM profile. ATLANTIS is a tailored nonlinear regression model for gene dependency prediction based on the baseline characteristics of cancer cell lines. More specifically, ATLANTIS is an efficient implementation of a conditional inference forest45 with additional weighting and iterative feature selection steps. Implementation details have been published previously9 and the code is available on a public repository (https://github.com/cancerdatasci/atlantis).

For each dose-wise log fold change profile, 14 ATLANTIS models are trained (1 model per feature set). Feature sets include baseline cell line omics, genetic dependencies and experimental confounders. All feature sets are listed in Supplementary Table 10.

Next, the predictive performance of each model was assessed based on the Pearson correlation between out-of-bag model predictions and the response variable. Models with Pearson correlations greater than 0.2 were strong models. The relative importance of each feature (mean decrease in accuracy) was computed by ATLANTIS for each model. The most important feature of each strong predictive model is presented as a potential predictive biomarker or strongly associated phenotype. The comprehensive list of biomarkers is available at https://depmap.org/repurposing.

Compound killing selectivity

To assess for selective killing activity, the bimodality coefficient8 for each median-collapsed log fold change PRISM profile was computed for each compound as follows:

$$\frac{{g^2 + 1}}{{{\it{k}} + 3\left( {n - 1} \right)^2/\left( {n - 2} \right)\left( {n - 3} \right)}}$$

where n is the number of samples (cell lines), g is the sample skewness and k is the sample excess kurtosis. Note that a larger bimodality coefficient implies a highly skewed (large magnitude of g) but light-tailed (small kurtosis) distribution.

Computation of AUC values for cross-dataset comparison

Secondary PRISM Repurposing data were compared to CTD2 (v.2.0, accessed 15 December 2015) and GDSC available through the PharmacoGX package (version 1.12.0)17,44. For the PRISM Repurposing and GDSC datasets, dose–response curves were fitted as described earlier. CTD2 provides dose–response curve parameters and curves were not refitted. The scope of the comparison was limited to compounds screened in all three datasets with a minimum overlapping fourfold dose range across the datasets. Dose–response curves were computed for each compound-cell line dataset combination using all available doses. AUC values were calculated over the shared dose range (curves were not refitted). The complete table of the published/recomputed dose–response parameters and AUC values are given in Supplementary Table 11.

Assessment of noise in the PRISM Repurposing dataset

The s.e.m. of the inferred log fold change viability values was calculated to estimate the amount of noise in the PRISM screen. We assumed that (spike-in) normalized logMFI values have a cell line-specific additive noise with a constant variance σ2 across treatments. An s.e.m. propagation analysis (see section 9.3 of Chatfield46), gives the following:

$$\sigma _{{{\mathrm{log}}}\,{{\mathrm{fold\ change}}} {{\mathrm{}}}} = \sqrt {\frac{{\sigma _{{{\mathrm{treatment}}}}^2 + \sigma _{{{\mathrm{control}}}}^2}}{{{{n}}_{\mathrm{rep}}}}} \approx \sigma \sqrt {\frac{{1 + 1/n_{{{\mathrm{control}}}}}}{{n_{\mathrm{rep}}}}}$$

where n rep is the number of replicates (3 for PRISM Repurposing), n control is the number of negative control wells in a given plate (32 in the standard PRISM assay format) and σ log fold change is the s.e.m. estimate; σ was estimated separately for each cell line and plate using normalized, negative-control log 2 MFI values. σ estimates for the same cell line were median-collapsed and used to calculate the σ log fold change . This procedure was applied separately to each PRISM Repurposing screen. In MTS006, DMSO-only plates were used.

Projection of viability profiles to two dimensions

The log viability profiles from the primary screen were embedded into a two-dimensional manifold using the UMAP7 algorithm and are visualized in Fig. 2a. Cell lines that were missing more than 10% of their viability values (failed in the quality-control steps) were removed and the remaining missing values were imputed using the R package FastImputation (version 2.0)47. UMAP was applied to the resulting data, using the cosine distance metric and the following configuration parameters: 7 nearest neighbors; 0.5 minimum distance; 2 components; and 200 training epochs. The rest of the parameters were set to the defaults provided by the R package umap (version 0.2.3.1). For the visualization, we filtered out compounds with an average Pearson replicate correlation below 0.25. Mechanisms of action that include fewer than 20 compounds are shown in translucent gray.

Comparison to genetic loss-of-function screens

Linear models were fitted to test the association between the primary collapsed log fold change profiles of each drug and the DepMap Avana CRISPR knockout gene effect scores using the lmFit function from the limma R package (version 3.38.3) with default parameters48. P values were corrected within each dose profile for multiple hypotheses using the Benjamini–Hochberg method. For primary data, a single profile was selected to test for each drug. The procedure was repeated for secondary collapsed log fold change profiles. A single dose series was selected for each drug.

Compounds for confirmatory studies

Paclitaxel (catalog no. S1150), tyrphostin AG-1296 (catalog no. S8024), anagrelide HCl (catalog no. S3172), levonorgestrel (catalog no. S1727), deoxycorticosterone acetate (catalog no. S4243), drospirenone (catalog no. S1377) and norethindrone (catalog no. S4040) were purchased from Selleck Chemicals. Dofequidar fumarate (catalog no. SML0938), N,N′-Bis(salicylidene)-o-phenylenediamine vanadium(IV) oxide complex (catalog no. 68541) and vanadium(IV) oxide sulfate hydrate (catalog no. 233706) were purchased from Sigma-Aldrich. Tepoxalin (catalog no. T103205) was purchased from Toronto Research Chemicals and WuXi AppTec (custom synthesis). DCEBIO (catalog no. 1422) and zardaverine (catalog no. 1046) were purchased from Tocris. Disulfiram (HY-B0240), bortezomib (HY-10227) and tanaproget (HY-15606) were purchased from MedChemExpress. BMOV (FB18735) was purchased from Carbosynth. Tetrathiomolybdate (catalog no. AC389530010) was purchased from Thermo Fisher Scientific. Danazol (catalog no. 1500220) was purchased from Microsource. Gestrinone (catalog no. Prestw-1267) was purchased from Prestwick.

Synthesis of RWJ20142

Reactions were monitored by thin-layer chromatography with 0.25 mm Merck precoated silica gel plates (60 F 254 ) and Waters Alliance HT LC/MS system (Waters 2998 UV/Visible Detector, Waters ACQUITY SQD mass spectrometer and Waters e2795 Sample Manager) using a Waters CORTECS C18 column (3 × 30 mm2, 2.7 μm particle size). Additional parameters were: solvent gradient, 97% A at 0 min, 5% A at 1.75 min, 97% A at 2.28 min, total 2.60 min; solvent A, water (MILLIQ) + 0.01% formic acid (Sigma-Aldrich); solvent B, acetonitrile (EMD Millipore) + 0.01% formic acid; flow rate, 1.75 ml min−1. Purification of reaction products was carried out by flash chromatography using CombiFlash Rf with Isco RediSep Rf High Performance Gold (Teledyne ISCO) or SiliaSep High Performance (SiliCycle) columns (4, 12, 24, 40, 80 or 120 g). 1H nuclear magnetic resonance and 13C nuclear magnetic resonance spectra were obtained using a 400 Ascend (Bruker). Chemical shifts are reported relative to chloroform (δ = 7.24) for 1H nuclear magnetic resonance.

In a 100 ml oven-dried flask, a solution of lithium hexamethyldisilazide (10 ml, 10 mmol, 1.0 M in tetrahydrofuran (THF)) was added dropwise to a solution of 1-(4-chlorophenyl)ethanone (1.10 g, 7.14 mmol) in dry THF (20 ml) at −78 °C under argon atmosphere. After 1 h, a solution of dihydrofuran-2,5-dione (0.86 g, 8.52 mmol) in dry THF (10 ml) was added at −78 °C, stirred for 30 min and then warmed up to room temperature. After 2 h, the reacting mixture was quenched with water, acidified with 1 N HCl (pH 2), then extracted with dichloromethane (3 × 10 ml2). The combined organic layers were dried over Na 2 SO 4 , filtered and concentrated. The residue was purified by column chromatography on silica gel (10–50% ethanol ethylacetate in hexanes) to afford 6-(4-chlorophenyl)-4,6-dioxohexanoic acid (550 mg, 31% yield). LC–MS: MS (ESINeg) m/z = 253 [M-H]−.

In a 50 ml flask, a solution of (4-methoxyphenyl)hydrazine hydrochloride (0.37 g, 2.12 mmol), 6-(4-chlorophenyl)-4,6-dioxohexanoic acid (0.49 g, 1.93 mmol) and triethylamine (0.31 ml, 2.31 mmol) in methanol (30 ml) was stirred overnight at room temperature. The reaction was quenched with 5% HCl aqueous solution (pH 2), then extracted with dichloromethane (3 × 10 ml2). The combined organic layers were dried over Na 2 SO 4 , filtered and concentrated. The residue was purified by column chromatography on silica gel (20–60% ethanol ethylacetate in hexanes) to afford 3-[5-(4-chlorophenyl)-1-(4-methoxyphenyl)pyrazol-3-yl]propanoic acid (285 mg, 42% yield). LC–MS: MS (ESINeg) m/z = 355 [M-H]−. 1H nuclear magnetic resonance (400 MHz, chloroform-D) δ = 10.25 (brs, 1 H), 7.27 (d, J = 8.5 Hz, 2 H), 7.20–7.12 (m, 4 H), 6.87 (d, J = 8.9 Hz, 2 H), 6.35 (s, 1 H), 3.82 (s, 3H), 3.07 (t, J = 7.5 Hz, 2 H), 2.83 (t, J = 7.5 Hz, 2H). Compound purity >97% was quantified by LC–MS.

Synthesis of BEOV

Vanadyl sulfate trihydrate (25 g, 115 mmol) dissolved in 25 ml of water was added to ethyl maltol (43.4 g, 310 mmol) dissolved in 125 ml of hot water under argon; the resulting solution was heated gently with stirring for 30 min. The pH was adjusted very slowly to 8.5 by adding NaOH (12.7 g, 319 mmol) in 10 ml of water. The resulting mixture was refluxed for 2 h and then allowed to cool to room temperature. The dark blue–gray solid was collected by vacuum filtration, washed with cold water and dried in a vacuum to produce BEOV with a yield of 88%. The compound was 93% pure by LC–MS.

Cloning

The pXPR_003 and pXPR_023 vectors were acquired from the Genetic Perturbation Platform (GPP; Broad Institute). The oligonucleotides for sgRNA design were generated using Broad GPP sgRNA guide generator resource (https://portals.broadinstitute.org/gpp/public/analysis-tools/sgrna-design) and the respective oligonucleotides were synthesized by Integrated DNA Technologies. To clone the sgRNAs into either the pXPR_003 guide-only or pXPR_023 all-in-one CRISPR lentiviral expression systems, we followed the protocol available on the GPP website (https://portals.broadinstitute.org/gpp/public/resources/protocols). The CRISPR sgRNA sequences are shown in Supplementary Table 12.

Antibodies and western blot

The following antibodies were used: polyclonal rabbit anti-PDE3A (1:1,000; catalog no. A302-740A; Bethyl Laboratories); monoclonal mouse anti-V5 (1:5,000; catalog no. R960-25; Thermo Fisher Scientific); monoclonal rabbit anti-MDR1/ABCB1 (clone D3H1Q) (1:1,000; catalog no. 12683; Cell Signaling Technology); monoclonal mouse anti-β-actin (clone 8H10D10) (1:1,000; catalog no. 3700; Cell Signaling Technology); and monoclonal mouse anti-SLC26A2 (clone 3F6) (1:1,000 dilution; catalog no. H00001836-M04; Novus Biologicals). Cells were lysed with radioimmunoprecipitation assay buffer (CHAPS buffer substituted for Extended Data Fig. 8a) supplemented by protease and phosphatase inhibitors (Sigma-Aldrich). For the ABCB1 blots, proteins were transferred onto a nitrocellulose membrane using a Mini Trans-Blot Electrophoretic Transfer Cell (Bio-Rad Laboratories) in Tris-glycine buffer (Bio-Rad Laboratories) with 10% methanol for 4 h at 60 V and 4 °C. Membranes were blocked in Odyssey Blocking Buffer (LI-COR) for 1 h and probed overnight with primary antibodies. The following day, membranes were washed and probed with IRDye secondary antibodies (catalog nos. 926-68020 and 926-32211, each at a 1:5,000 dilution; LI-COR). For the PDE3A and V5 blots, different secondary antibodies were used (926-32210 and 926-68020, each at a 1:10,000 dilution; LI-COR). Blot images were collected with the Odyssey CLx imager (LI-COR).

Genomic DNA (gDNA) PCR and next-generation sequencing to quantify CRISPR editing frequency

To confirm efficient CRISPR cutting at the target loci, PCR primers were designed flanking the sgRNA cut site by 75–100 base pairs (bp) on either side. The human gDNA PCR primer sequences corresponding to the regions targeted by MDR1, SLC26A2 and MTF1 sgRNAs are shown in Supplementary Table 13. gDNA was isolated from knockout cell lines using the Gentra Puregene Kit (QIAGEN) and amplified with the primers to yield an amplicon roughly 150–250 bp in length using Herculase II Fusion Polymerase (Agilent Technologies). The PCR protocol involved a 2-min denaturation at 95 °C, followed by 24 cycles of 95 °C for 2 min, 55 °C for 2 min and 72 °C for 1 min. PCR samples were purified and submitted for the next-generation CRISPR sequencing assay at the Massachusetts General Hospital DNA Core. Knockout efficiency was assessed by the percentage of reads containing a frameshift caused by an indel compared to the total read count. Results for each guide were averaged across primer sets with successful amplification.

Cellular viability assays

Cells were seeded at a density of 2,000 cells per well (1,000 cells per well for the HeLa cell line) in a 96-well clear bottom black microplate (Corning). The following day, compounds were dispensed using the D300e Digital Dispenser (Tecan). Following incubation, viability was assessed by CellTiter-Glo (Promega Corporation). Luminescence was measured using an EnVision plate reader (PerkinElmer; EnVision Manager 1.13.3009.1401). Independent replicate wells were averaged and normalized to vehicle control. Dose curves were generated using Prism 8 (GraphPad Software).

PDE3A coimmunoprecipitation assay

The PDE3A immunoprecipitation and western blotting of coprecipitated SLFN12-V5 protein experiments were performed as described previously18. Briefly, HeLa cells were plated onto 15 cm plates at 3 × 106 cells per plate and transfected the next day with 15 µg of pLX307-SLFN12 plasmid (clone TRCN0000476272) using FuGENE 6 (Promega Corporation) at a 4:1 ratio. Roughly 72 h posttransfection, cells were treated with 10 µM of the repurposing hit compounds or 1 µM of DNMDP or 1 µM anagrelide for 6 h. Cells were collected and lysed with a modified radioimmunoprecipitation assay buffer (150 mM NaCl, 10% glycerol, 50 mM Tris-Cl pH 8.0, 50 mM MgCl 2 , 1% NP-40 detergent), supplemented protease and phosphatase inhibitors. Immunoprecipitation was performed using 2 mg of total protein lysates and 1 µg of anti-PDE3A antibody at 4 °C overnight, followed by incubation with 7.5 µl each of Protein A and Protein G Dynabeads (10001D and 10003D; Thermo Fisher Scientific) at 4 °C for 1 h. Beads were washed with lysis buffer and proteins were eluted with 30 μl of lithium dodecyl sulfate–polyacrylamide gel electrophoresis loading buffer.

PDE3A and PDE3B enzyme activity assays

A commercial fluorescence polarization assay was performed using recombinant PDE3A and PDE3B by BPS Bioscience. Compounds were tested in duplicate at 9 concentrations in a half-logarithmic dilution series (top concentration of 10 µM) with a final DMSO concentration of 1%. The enzymatic reactions were conducted at room temperature for 60 min in a 50 µl mixture containing phosphodiesterase assay buffer, 100 nM FAM-cAMP, a PDE enzyme and the test compound. After the enzymatic reaction, 100 µl of a binding solution (1:100 dilution of the binding agent with the binding agent diluent) was added to each mix and the reaction was performed at room temperature for 15 min. Fluorescence intensity was measured at an excitation of 485 nm and an emission of 528 nm using an Infinite M1000 microplate reader (Tecan). Fluorescence intensity was converted to fluorescence polarization using the Magellan v.6 software (Tecan).

Differential scanning fluorimetry (thermal shift) analysis of compounds binding to PDE3A

The gene for PDE3A (residues 677–1,141) was codon-optimized for Escherichia coli expression (GeneArt; Thermo Fisher Scientific) and cloned into an expression vector that attached an N-terminal polyhistidine sequence followed by a Tobacco Etch Virus protease cleavage site. The protein was expressed in E. coli and purified by affinity and size-exclusion chromatography. The polyhistidine sequence was removed by the Tobacco Etch Virus protease. PDE3A (5 µM) was incubated for 20 min at room temperature with 100 µM of each compound. The reaction buffer was 20 mM HEPES, pH 7.4, 150 mM NaCl, 500 µM TCEP, 5 mM MgCl 2 and 1% DMSO. After incubation, SYPRO orange (Thermo Fisher Scientific) was added to give a final concentration of 10× relative to stock concentrate. Protein was tested using the LightCycler 480 (Roche Life Science). The temperature was increased from 25 to 95 °C using a gradient of 0.06 °C s−1.

LS1034 CRISPR–Cas9 genome-wide knockout screen

The Brunello genome-scale sgRNA library was obtained from the Broad Institute GPP49. Virus was titrated to a goal infection efficiency of 0.3–0.6. LS1034-Cas9 cells were infected with Brunello virus in 12-well plates via centrifugation at 2,000 r.p.m. and 30 °C. The following day, cells were split into two replicate flasks and selected with 6 µg ml−1 puromycin for 7 d. After selection, replicates were seeded into 16 µM tepoxalin (Wuxi) or DMSO control. Cells were maintained at 37 °C and 5% CO 2 in CellSTACK 1,272 cm2 2-STACK flasks (Corning) in RPMI with 10% FCS. Cells were reseeded every 7 d at a minimum of 40 million cells per passage (to maintain approximately 500× library representation). Media and drugs were refreshed every 3–4 d for a total of 3 weeks. gDNA was isolated from cell pellets using the NucleoSpin Blood XL columns (MACHEREY-NAGEL). gDNA PCR and sequencing were performed by the Broad Institute GPP.

LS1034 CRISPR–dCas9 genome-wide activation screen

LS1034 cells were stably transduced with pXPR_109 to express dCas9-VP64 (ref. 50). Selective induction of CD45 and CD4 expression using control guides was confirmed by flow cytometry. The Calabrese B genome-scale virus library was obtained from the Broad Institute GPP. LS1034-dCas9-VP64 cells were infected via centrifugation as described earlier. The following day, cells were split into two replicates and reseeded. The next day, 6 µg ml−1 of puromycin was added. After selection for 6 d, replicates were split into DMSO or 16 µM tepoxalin (Wuxi) drug arms in duplicate and cultured for 2 weeks as described earlier. gDNA was isolated and sequenced as described earlier.

CRISPR screen analysis

Guides targeting multiple genes or with <50 reads in the plasmid DNA pool were filtered. Counts were normalized against total library size. The guide-level log 2 fold change was computed using the ratio between treatment versus vehicle control counts. The results from all guides targeting each gene were averaged. Statistical significance for each gene-level result was calculated using the MAGeCK-MLE method, using the suggested number of permutation rounds (10; ref. 51). Two-sided P values were corrected for multiple hypothesis testing using the Benjamini–Hochberg method.

Tepoxalin competition assay

LS1034-Cas9-firefly luciferase cells were cocultured in a 1:1 ratio with LS1034-Renilla luciferase cells, both infected with ABCB1 sgRNA or a cutting control sgRNA. Cells were treated with 16 µM tepoxalin (Wuxi) or vehicle. The coculture was passaged every 4 d and reseeded with drug. Luciferase activity was quantified using the Dual-Glo Luciferase Assay System (Promega Corporation) and measured using an EnVision plate reader. Data points were collected at day 0 and every 4 d until 12 d of treatment. The firefly to Renilla luminescence ratio was normalized to the initial day 0 measurement.

Tepoxalin cell permeability and stability assays

Compounds were incubated in medium alone or with 1 million cells ml−1 at 37 °C with gentle shaking for 3 h. Following incubation, cell samples were centrifuged at 500g for 5 min and washed twice with cold PBS. Cells were resuspended in 130 µl of water. Samples were sonicated and centrifuged at 3,000g for 15 min at 20 °C; 5 µl of supernatant was combined with 45 µl of cell media, 50 µl of water and 50 µl of acetonitrile containing internal standard. Samples were centrifuged again and a final 100 µl aliquot was transferred to a 96-well plate for analysis. Samples were analyzed on an ultra-performance liquid chromatography–tandem mass spectrometry system consisting of an ACQUITY UPLC I-Class FTN (Waters) and Triple Quad 4500 System (SCIEX) with compounds detected by positive mode multiple reaction monitoring detection. Mobile phase A consisted of water with 0.1% formic acid (catalog no. 33015-1L; Honeywell), while mobile phase B consisted of acetonitrile with 0.1% formic acid. The gradient ran from 10 to 95% B over 0.8 min at a flow rate of 0.9 ml min−1. An ACQUITY BEH C18 1.7 m, 2.1 × 50 mm2 column (Waters) was used with column temperature maintained at 65 °C. Sample concentrations were determined using a standard curve and dilution quality-control samples prepared in a surrogate matrix. The Analyst v.1.6.2 software was used for integration and calculation determination. For the stability study, 1 µM of tepoxalin was added to PBS, RPMI with 10% FCS or acetonitrile in duplicate and measured by mass spectrometry with time points prepared at 0, 24, 48 and 72 h. The mass spectrometer was run in positive mode using multiple reaction monitoring detection for tepoxalin and the internal standard (75 nM midazolam).

Tepoxalin drug synergy/antagonism assays

Tepoxalin or paclitaxel were added in a dose–response matrix to LS1034 or REC1 cells, and viability was assessed by CellTiter-Glo. For the drug combination viability data, we first normalized CellTiter-Glo measurements by the median over DMSO wells on each plate. We then used the R package synergyfinder (version 1.8.0)52 to estimate Bliss synergy scores across all dose combinations, applying synergyfinder’s default baseline correction method. Synergy values for each drug combination and cell line were summarized by the synergy score with the highest magnitude across dose combinations (maximum synergy). We verified that similar results were obtained qualitatively using other synergy models (for example, Loewe, highest single agent and zero interaction potency) and methods for aggregating synergy scores across dose combinations (for example, averaging).

ABCB1 activity assays

The ABCB1 antagonism assay was performed by Eurofins using published methods53. Briefly, MDR1-MDCK cells were incubated with test compounds and calcein acetoxymethyl. Change in calcein acetoxymethyl concentration was assessed by fluorescence measurement. A second assay, MDR1-MDCK cell permeability, was performed by Cyprotex. Briefly, loperamide, a known MDR1 substrate, was added to the apical side of a cell monolayer and transport to the basal side was quantified over 60 min. Inhibition of MDR1-mediated transport of loperamide was assessed by adding tepoxalin or positive control (verapamil).

Transcriptional profiling by RNA-seq

LS1034 cells were seeded in 12-well plates. The following day, cells were treated in triplicate with 12 µM tepoxalin or DMSO vehicle control for 6 h. Wild-type SF295 cells and SF295 cells containing Cas9 and sgRNAs targeting GFP or MTF1 were plated in triplicate on a 6-well plate and incubated overnight. RNA was isolated using the RNeasy Mini Kit (QIAGEN) with DNase treatment. RNA quality was confirmed by Bioanalyzer (Agilent Technologies). Library preparation was performed by the Molecular Biology Core Facility at the Dana-Farber Cancer Institute using the KAPA mRNA HyperPrep Kit (Roche). Nucleic acid was sequenced using an Illumina NextSeq 500 PE75 instrument. Gene-level expression values were obtained from RNA-seq using the TOPMed RNA-seq pipeline (version 1)54. RSEM (version 1.3.0) was used to generate transcripts per million gene-level expression quantifications. These tools were run using the FireCloud and Terra platforms55. Differential gene expression was calculated using the DESeq2 package (version 1.22.2)56.

Statistics and reproducibility

No statistical method was used to determine sample sizes. Compounds were plated for screening without regard to compound identity, but experiments were not randomized. No data were excluded from the analysis, except individual data points flagged as assay failures by the process described earlier. Statistical tests are described in the text and figure legends with their associated sample sizes. PRISM data were generated without the investigators’ knowledge of compound and cell line identities during screening. Investigators were not blind to compound and cell line identities during the analysis. Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Reporting Summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.