Non-oncology drugs are a source of previously unappreciated anti-cancer activity

Abstract

Anti-cancer uses of non-oncology drugs have been found on occasion, but such discoveries have been serendipitous and rare. We sought to create a public resource containing the growth inhibitory activity of 4,518 drugs tested across 578 human cancer cell lines. To accomplish this, we used PRISM, which involves drug treatment of molecularly barcoded cell lines in pools. Relative barcode abundance following treatment thus reflects cell line viability. We found that an unexpectedly large number of non-oncology drugs selectively inhibited subsets of cancer cell lines. Moreover, the killing activity of the majority of these drugs was predictable based on the molecular features of the cell lines. Follow-up of several of these compounds revealed novel mechanisms. For example, compounds that kill by inducing PDE3A-SLFN12 complex formation; vanadium-containing compounds whose killing is dependent on the sulfate transporter SLC26A2; the alcohol dependence drug disulfiram, which kills cells with low expression of metallothioneins; and the anti-inflammatory drug tepoxalin, whose killing is dependent on high expression of the multi-drug resistance gene ABCB1. These results illustrate the potential of the PRISM drug repurposing resource as a starting point for new oncology therapeutic development. The resource is available at https://depmap.org.

Division of labor between YAP and TAZ in non-small cell lung cancer

“…YAP-differential and TAZ-differential drug sensitivity analysis To identify YAP or TAZ distinct associations with drug response, we utilized drug sensitivity profiles (PRISM) of 4,518 drugs tested across 47 LUAD cancer cell lines [38]. We identified drugs that showed differential response between cell lines with high expression of YAP (top 33%) compared to those with low YAP expression (bottom 33%), and likewise for TAZ.…”

“…Thus, in agreement with their distinct biological roles in lung cancer cells, YAP and TAZ also affect differentially the sensitivity to specific anti-cancer agents. [38], binned according to YAP or TAZ expression levels; lower cell viability implies greater sensitivity to Taxol. P = p-value determined by Wilcoxon rank-sum test.…”

Multiplexed single-cell profiling of post-perturbation transcriptional responses to define cancer vulnerabilities and therapeutic mechanism of action

“…These included 8 targeted cancer therapies with known mechanisms, 4 pan-lethal compounds that broadly kill most cell lines, and one tool compound (BRD-3379) with unknown MoA, which was found to induce strong selective killing in a high-throughput screen. We compared our scRNA-seq based phenotyping to long-term viability responses measured for these drugs and cell lines from the genomics of drug sensitivity in cancer (GDSC) screening dataset 4,7 , as well as data generated using the PRISM assay 16,19 (see Methods).…”

“…4a,b). We employed a statistical modeling approach relating the (average) transcriptional changes measured in each cell line to their viability response in the drug sensitivity data from GDSC and PRISM 4,16,19 . Specifically, we decompose the change in expression of each gene into two components: a viability-independent response component (β 0 ) characterizing the response of completely insensitive cell lines, and a viabilityrelated response component (β 1 ) characterizing the difference between sensitive and insensitive cell lines ( Fig.…”

“…However, the response to perturbation is very often context specific, reflecting the interaction between the perturbation and a cell's particular genomic or functional features. For example, targeted drugs may elicit responses only in cell lines harboring particular oncogenic mutations, or expressing certain genes, making observed results specific to the particular cell line models chosen 4,6,7,15,16 . More generally, the inability to efficiently measure transcriptional responses across diverse cell contexts has limited our understanding of how perturbation effects differ across the broad range of genomic and molecular cell states, which could be critical for predicting the therapeutic response of patient tumors.…”