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Review
. 2018 Nov:191:178-189.
doi: 10.1016/j.pharmthera.2018.06.014. Epub 2018 Jun 25.

More than fishing for a cure: The promises and pitfalls of high throughput cancer cell line screens

Alexander Ling  1 , Robert F Gruener  2 , Jessica Fessler  3 , R Stephanie Huang  4
Affiliations
Review

More than fishing for a cure: The promises and pitfalls of high throughput cancer cell line screens

Alexander Ling et al. Pharmacol Ther. 2018 Nov.

Abstract

High-throughput screens in cancer cell lines (CCLs) have been used for decades to help researchers identify compounds with the potential to improve the treatment of cancer and, more recently, to identify genomic susceptibilities in cancer via genome-wide shRNA and CRISPR/Cas9 screens. Additionally, rich genomic and transcriptomic data of these CCLs has allowed researchers to pair this screening data with biological features, enabling efforts to identify biomarkers of treatment response and gene dependencies. In this paper, we review the major CCL screening efforts and the large datasets these screens have made available. We also assess the CCL screens collectively and include a resource with harmonized CCL and compound identifiers to facilitate comparisons across screens. The CCLs in these screens were found to represent a wide range of cancer types, with a strong correlation between the representation of a cancer type and its associated mortality. Patient ages and gender distributions of CCLs were generally as expected, with some notable exceptions of female underrepresentation in certain disease types. Also, ethnicity information, while largely incomplete, suggests that African American and Hispanic patients may be severely underrepresented in these screens. Nearly all genes were targeted in the genetic perturbations screens, but the compounds used for the drug screens target less than half of known cancer drivers, likely reflecting known limitations in our drug design capabilities. Finally, we discuss recent developments in the field and the promise they hold for enabling future screens to overcome previous limitations and lead to new breakthroughs in cancer treatment.

Keywords: Biomarkers; Cancer; Cell lines; Drug screens; Genetic perturbation screens; Pharmacogenomics.

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Conflict of interest statement

Conflict of Interest Statement:

The authors declare that there are no conflicts of interest.

Figures

Figure 1.
Figure 1.. Screened CCLs Correlate with Cancer Fatality while Capturing Age, Gender, and Ethnicity to Varying Extents.
Data for this figure is included in Table S2. A) Correlation is shown between cancer mortality (obtained from Siegel et al., 2017) and the number of unique cell lines screened from each cancer type. Cancer type was determined by bioinformatic and manual curation using Cellosaurus, the BioSample database, COSMIC, or annotations provided by the datasets themselves. Only cell lines with available screening results are included. B-D) As with part A, age of collection, gender, and ethnicity for screened CCLs were determined by bioinformatic and manual curation using Cellosaurus, the BioSample database, and COSMIC. Part B shows the number of unique cell lines collected from patients at given ages, while parts C and D show the distribution of genders and ethnicities respectively for screened cell lines from each cancer type.
Figure 2.
Figure 2.. Targets and Clinical Stage of Compounds in CCL Screens.
Data for this figure is included in Tables S2, S4 and S5. Compounds used in Figure 2 are the 1,207 unique compounds from the 14 CCL drug screens reviewed in this paper with targeted information from the CCL screens or the Broad DRH. A) The clinical stage distribution for the drugs with current clinical trial information from Broad DRH (1149 of the 1207). B) Shows the ten most commonly targeted genes and the number of unique compounds against them. Table S4 contains the complete list of all the genes and their targeted frequency. C) Shows the 10 most commonly targeted pathways in MSigDB’s Canonical Pathway Gene Set (C2:CP) based on the number of unique compounds whose gene information in Table S4 indicate the compound impacts at least one gene target in that pathway. The full list of pathways and the number of genes and compounds that impact the pathway can also be found in Table S4.
Figure 3.
Figure 3.. Composition and Overlap of CCL Screens.
A) Cell line tissue type vs. dataset. Tissue type was determined by bioinformatic and manual curation using Cellosaurus, the BioSample database, COSMIC, or annotations provided by the datasets themselves, and then similar cancer types were grouped in the broad groups shown. The data for this figure is included in Table S2. B) Heatmap of cell line overlap between reviewed studies. Overlap is based on data from Table S2, with each color scale being relevant to the amount of overlap each column study has with the study in that row. C) Heatmap of compound overlap between reviewed drug screen studies. Overlap is based on data from Table S3, with each color scale corresponding to the amount of overlap each column study has with the study in that row. Note that the 8,000 diversity-oriented synthesis molecules tested in PRISM are excluded in this plot.
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