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
Most deaths from cancer are explained by metastasis, and yet large-scale metastasis research has been impractical due to the complexity of in vivo models. Here, we introduce an in vivo barcoding strategy capable of determining the metastatic potential of human cancer cell lines in murine xenografts at scale. We validated the robustness, scalability and reproducibility of the method, and applied it to 500 cell lines 1 , 2 spanning 21 solid cancer types. We created a first-generation Metastasis Map (MetMap) that reveals organ-specific patterns of metastasis and allows relating those patterns to clinical and genomic features. We demonstrated the utility of MetMap by exploring the molecular basis of breast cancers capable of metastasizing to the brain - a principal cause of death in these patients. We found that breast cancers capable of metastasizing to the brain had unexpected evidence of altered lipid metabolism. Perturbing lipid metabolism curbed brain metastasis development, suggesting a therapeutic strategy to combat the disease and demonstrating the utility of MetMap as a public resource to support metastasis research.
Vorinostat is a histone deacetylase inhibitor that induces differentiation, growth arrest, and/or apoptosis of malignant cells both in vitro and in vivo and has shown clinical responses in f30% of patients with advanced mycosis fungoides and Sézary syndrome cutaneous T-cell lymphoma (CTCL). The purpose of this study was to identify biomarkers predictive of vorinostat response in CTCL using preclinical model systems and to assess these biomarkers in clinical samples. The signal transducer and activator of transcription (STAT) signaling pathway was evaluated. The data indicate that persistent activation of STAT1, STAT3, and STAT5 correlate with resistance to vorinostat in lymphoma cell lines. Simultaneous treatment with a pan-Janus-activated kinase inhibitor resulted in synergistic antiproliferative effect and down-regulation of the expression of several antiapoptotic genes. Immunohistochemical analysis of STAT1 and phosphorylated tyrosine STAT3 (pSTAT3) in skin biopsies obtained from CTCL patients enrolled in the vorinostat phase IIb trial showed that nuclear accumulation of STAT1 and high levels of nuclear pSTAT3 in malignant T cells correlate with a lack of clinical response. These results suggest that deregulation of STAT activity plays a role in vorinostat resistance in CTCL, and strategies that block this pathway may improve vorinostat response. Furthermore, these findings may be of prognostic value in predicting the response of CTCL patients to vorinostat. [Cancer Res 2008;68(10):3785-94]
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