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

This blog is part of our Paper Spotlight series, which features peer-reviewed research publications involving work done in Terra and highlights how the analysis methods were applied. 


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

By Steven M. Corsello, Rohith T. Nagari, Todd R. Golub et al., 2020

Nature Cancer, 1, pages 235–248 (2020) https://doi.org/10.1038/s43018-019-0018-6 

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.



What part of the work was done in Terra?

Excerpts from the paper’s Methods section:


 Transcriptional profiling by RNA-seq 

[…] Nucleic acid was sequenced using an Illumina NextSeq 500 PE75 instrument. Gene-level expression values were obtained from RNA-seq using the TOPMed RNA-seq pipeline (version 1). RSEM (version 1.3.0) was used to generate transcripts per million gene-level expression quantifications. These tools were run using the FireCloud and Terra platforms. Differential gene expression was calculated using the DESeq2 package (version 1.22.2).


Note: FireCloud is a project powered by the Terra platform and supported by the National Cancer Institute. It is one of the three Cloud Resources that provide access to cloud compute capabilities as part of the NCI’s Cancer Research Data Commons program. The FireCloud portal provides full access to Terra’s data and analysis capabilities.


How did they do it?

The authors used analysis workflows originally developed by the Genotype-Tissue Expression (GTEx) consortium as part of a seminal project to characterize variation in gene expression levels across individuals and diverse tissues of the human body. These workflows were since reimplemented in the Workflow Description Language (WDL) and shared in the Broad Methods Repository

Terra also supports importing workflows from Dockstore, a free and open source platform for sharing reusable and scalable analytical tools and workflows. 

The authors ran the workflows at scale using Terra’s workflow execution service

To try your hand at running a workflow in Terra, check out this Quickstart Tutorial Workspace


Appendix: Data and code availability



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