Discovering the anticancer potential of non-oncology drugs by systematic viability profiling

Abstract

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...

Post-perturbational transcriptional signatures of cancer cell line vulnerabilities

“…To this end, we leveraged data generated by the Connectivity Map using the L1000 assay, which measures the expression levels of 978 landmark genes following perturbation and computationally infers the remainder of the transcriptome. In parallel, we integrated drug sensitivity data from three sources: the PRISM Repurposing dataset [3], the Genomics of Drug Sensitivity in Cancer resource (GDSC) [8,1,21,22,11], and the Cancer Target Discovery and Development database (CTD2) [22,1,21] (Methods). Together, these viability datasets have screened thousands of small molecules in hundreds of cell lines.…”

“…In particular, both studies find the transcription-viability relationship to be robust across perturbation types and time points, and both demonstrate the ability to predict long-term sensitivity from post-perturbation expression profiles. Our approach extends their analysis by incorporating the recently published PRISM Repurposing drug sensitivity data [3], which allowed us to more thoroughly examine the expression-viability associations that are specific to individual perturbations. As demonstrated in the Results section, these individual signatures are crucial not only for understanding the different mechanisms underlying viability effects, but also for the development of pharmacodynamic markers of response that could be used in clinical applications.…”

“…PRISM viability data Drug response data were collected by the PRISM Repurposing project at the Broad Institute [3], and were downloaded from the Cancer Dependency Map portal https://depmap.org/portal/ download/. The 19Q3 PRISM Repurposing Primary Screen data were used.…”

Global computational alignment of tumor and cell line transcriptional profiles

“…Tumor-derived cell line models have been a cornerstone of cancer research for decades. The genomic and molecular features of over a thousand cancer cell line models have now been deeply characterized 1 , and recent efforts are systematically mapping their genetic [2][3][4] and chemical 5 vulnerabilities. These datasets are thus providing new opportunities to identify potential therapeutic targets and connect these vulnerabilities with measurable biomarkers that can be used to develop precision cancer approaches 2,5 .…”

“…Comparisons based on information-rich gene expression profiles are a promising alternative 20 , given their demonstrated utility for resolving clinically relevant tumor (sub)types [21][22][23][24][25] , as well as predicting genetic 2 and chemical vulnerabilities of cancer cells 5,26 . However, a key challenge is that gene expression measurements from bulk tumor biopsy samples are confounded by the presence of stromal and immune cell populations not found in cell lines, often comprising a substantial fraction of the cellular makeup of each sample 27,28 .…”