Anticancer uses of non-oncology drugs have occasionally been found, but such discoveries have been serendipitous. We sought to create a public resource containing the growth-inhibitory activity of 4,518 drugs tested across 578 human cancer cell lines. We used PRISM (profiling relative inhibition simultaneously in mixtures), a molecular barcoding method, to screen drugs against cell lines in pools. An unexpectedly large number of non-oncology drugs selectively inhibited subsets of cancer cell lines in a manner predictable from the molecular features of the cell lines. Our findings include compounds that killed by inducing phosphodiesterase 3A-Schlafen 12 complex formation, vanadium-containing compounds whose killing depended on the sulfate transporter SLC26A2, the alcohol dependence drug disulfiram, which killed cells with low expression of metallothioneins, and the anti-inflammatory drug tepoxalin, which killed via the multidrug resistance protein ATP-binding cassette subfamily B member 1 (ABCB1). The PRISM drug repurposing resource (https://depmap.org/repurposing) is a starting point to develop new oncology therapeutics, and more rarely, for potential direct clinical translation. NATURE CANCER | VOL 1 | FeBRUARY 2020 | 235-248 | www.nature.com/natcancer 235 ResouRce NATuRE CANCER the remaining compounds being either chemotherapeutics (2%) or targeted oncology agents (21%).Screening results. We employed a 2-stage screening strategy whereby drugs were first screened in triplicate at a single dose (2.5 µM); 1,448 drugs screening positives were then rescreened in triplicate in an eight-point dose-response ranging from 10 µM to 610 pM ( Fig. 1c and Supplementary Table 2). Interestingly, most active compounds (774 out of 1,448, 53%) were originally developed for non-oncology clinical indications (Fig. 1d). The primary and secondary screening datasets are available on the Cancer Dependency Map portal (https://depmap.org/repurposing) and figshare (https://doi.org/10.6084/m9.figshare.9393293; Extended Data Figs. 1-4). We compared the PRISM results to two gold standard datasets: GDSC (ref. 2 ) and CTD 2 (ref. 3 ). The three datasets shared 84 compounds tested on a median of 236 common cell lines, yielding 16,650 shared data points. The PRISM dataset had a similar degree of concordance to GDSC and CTD 2 (Pearson correlations of 0.60 and 0.61, respectively over all shared data points), as the GDSC and CTD 2 datasets had to each other (Pearson correlation 0.62) (Extended Data Fig. 5a). The three datasets remained similarly concordant when the analysis was restricted to data points showing evidence of anticancer activity (Extended Data Fig. 5b). We conclude that, despite differences in assay format, sources of compounds 5 and sources of cell lines 6 , the PRISM Repurposing dataset is similarly robust compared to existing pharmacogenomic datasets.At the level of individual compound dose-responses, we note that the PRISM Repurposing dataset tends to be somewhat noisier, with a higher standard error estimated from vehicle contr...

Cultured cell lines are the workhorse of cancer research, but it is unclear to what extent they recapitulate the cellular heterogeneity observed among malignant cells in tumors. To address this, we used multiplexed single cell RNA-seq to profile ~200 cancer cell lines from 22 cancer types. We uncovered 12 expression programs that are recurrently heterogeneous within many cancer cell lines. These programs are associated with diverse biological processes including cell cycle, senescence, stress and interferon responses, epithelial-mesenchymal transition, and protein maturation and degradation. Notably, most of these recurrent programs of heterogeneity recapitulate those recently observed within human tumors. The similarity to tumors allowed us to prioritize specific cell lines as model systems of cellular heterogeneity. We used two such models

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Assays to study cancer cell responses to pharmacologic or genetic perturbations are typically restricted to using simple phenotypic readouts such as proliferation rate. Information-rich assays, such as gene-expression profiling, have generally not permitted efficient profiling of a given perturbation across multiple cellular contexts. Here, we develop MIX-Seq, a method for multiplexed transcriptional profiling of post-perturbation responses across a mixture of samples with single-cell resolution, using SNP-based computational demultiplexing of singlecell RNA-sequencing data. We show that MIX-Seq can be used to profile responses to chemical or genetic perturbations across pools of 100 or more cancer cell lines. We combine it with Cell Hashing to further multiplex additional experimental conditions, such as posttreatment time points or drug doses. Analyzing the high-content readout of scRNA-seq reveals both shared and context-specific transcriptional response components that can identify drug mechanism of action and enable prediction of long-term cell viability from shortterm transcriptional responses to treatment.

ERAP1 is an endoplasmic reticulum-resident zinc aminopeptidase that plays an important role in the immune system by trimming peptides for loading onto major histocompatibility complex proteins. Here, we report discovery of the first inhibitors selective for ERAP1 over its paralogues ERAP2 and IRAP. Compound 1 (N-(N-(2-(1H-indol-3-yl)­ethyl)­carbamimidoyl)-2,5-difluorobenzenesulfonamide) and compound 2 (1-(1-(4-acetylpiperazine-1-carbonyl)­cyclohexyl)-3-(p-tolyl)­urea) are competitive inhibitors of ERAP1 aminopeptidase activity. Compound 3 (4-methoxy-3-(N-(2-(piperidin-1-yl)-5-(trifluoromethyl)­phenyl)­sulfamoyl)­benzoic acid) allosterically activates ERAP1’s hydrolysis of fluorogenic and chromogenic amino acid substrates but competitively inhibits its activity toward a nonamer peptide representative of physiological substrates. Compounds 2 and 3 inhibit antigen presentation in a cellular assay. Compound 3 displays higher potency for an ERAP1 variant associated with increased risk of autoimmune disease. These inhibitors provide mechanistic insights into the determinants of specificity for ERAP1, ERAP2, and IRAP and offer a new therapeutic approach of specifically inhibiting ERAP1 activity in vivo.