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...

Proteins that recognize specific DNA sequences play a central role in the regulation of transcription. The tremendous increase in structural information on protein-DNA complexes has uncovered a remarkable structural diversity in DNA binding folds, while at the same time revealing common themes in binding to target sites in the genome.

Pax5 regulates the B cell-specific expression of the mb-1 gene together with members of the Ets family of transcriptional activators. The Ets proteins on their own bind poorly to the Pax5/Ets binding site, but can be recruited to the site by cooperative interactions with Pax5. The structure of the ETS domain of Ets-1 and the paired domain of Pax5 bound to DNA reveals the molecular details of the selective recruitment of different Ets proteins by Pax5. Comparison with structures of Ets-1 alone bound to both high- and low-affinity DNA sites reveals that Pax5 alters the Ets-1 contacts with DNA. The ability of one protein to alter the DNA sequence-specific contacts of another provides a general mechanism for combinatorial regulation of transcription.