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. 2014 Sep 30:1:140035.
doi: 10.1038/sdata.2014.35. eCollection 2014.

Parallel genome-scale loss of function screens in 216 cancer cell lines for the identification of context-specific genetic dependencies

Glenn S Cowley  1 , Barbara A Weir  2 , Francisca Vazquez  2 , Pablo Tamayo  1 , Justine A Scott  1 , Scott Rusin  1 , Alexandra East-Seletsky  1 , Levi D Ali  1 , William Fj Gerath  1 , Sarah E Pantel  1 , Patrick H Lizotte  1 , Guozhi Jiang  1 , Jessica Hsiao  1 , Aviad Tsherniak  1 , Elizabeth Dwinell  1 , Simon Aoyama  1 , Michael Okamoto  1 , William Harrington  1 , Ellen Gelfand  1 , Thomas M Green  1 , Mark J Tomko  1 , Shuba Gopal  1 , Terence C Wong  1 , Hubo Li  3 , Sara Howell  1 , Nicolas Stransky  4 , Ted Liefeld  1 , Dongkeun Jang  1 , Jonathan Bistline  1 , Barbara Hill Meyers  1 , Scott A Armstrong  5 , Ken C Anderson  6 , Kimberly Stegmaier  7 , Michael Reich  1 , David Pellman  3 , Jesse S Boehm  1 , Jill P Mesirov  1 , Todd R Golub  1 , David E Root  1 , William C Hahn  8
Affiliations

Parallel genome-scale loss of function screens in 216 cancer cell lines for the identification of context-specific genetic dependencies

Glenn S Cowley et al. Sci Data. .

Erratum in

  • Sci Data. 2014;1:140044. Wong, Terrence C [corrected to Wong, Terence C]

Abstract

Using a genome-scale, lentivirally delivered shRNA library, we performed massively parallel pooled shRNA screens in 216 cancer cell lines to identify genes that are required for cell proliferation and/or viability. Cell line dependencies on 11,000 genes were interrogated by 5 shRNAs per gene. The proliferation effect of each shRNA in each cell line was assessed by transducing a population of 11M cells with one shRNA-virus per cell and determining the relative enrichment or depletion of each of the 54,000 shRNAs after 16 population doublings using Next Generation Sequencing. All the cell lines were screened using standardized conditions to best assess differential genetic dependencies across cell lines. When combined with genomic characterization of these cell lines, this dataset facilitates the linkage of genetic dependencies with specific cellular contexts (e.g., gene mutations or cell lineage). To enable such comparisons, we developed and provided a bioinformatics tool to identify linear and nonlinear correlations between these features.

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Conflict of interest statement

The Achilles Consortium is composed of representatives from Novartis, Lilly, Pfizer and EMD- Serono contributed funding to generate these data.

Figures

Figure 1
Figure 1. Schematic representation of the schema used for pooled shRNA screening.
Figure 2
Figure 2. Assessment of data accuracy using DNA pools containing known relative proportions of DNA.
Two 45,000-shRNA pools were created by combining 4 subsets of the shRNA library plasmids (labeled in black, red, green, and blue) in a 1:1:1:1 ratio of concentrations for the ‘Reference pool’ and in a 1:4:16:64 ratio for the ‘Dilution pool.’ The observed separation of the 4 subsets of shRNAs according to their known relative proportions in the 2 pools illustrates the ability of (a) NGS and (b) Affymetrix arrays to deconvolve the pooled shRNA library.
Figure 3
Figure 3. Comparison of pooled screen measurements from sequencing deconvolution against individual shRNA proliferation tests.
The relative abundance (fold change values) of 350 shRNAs measured from sequencing deconvolution of four OVCAR-8 replicates (y-axis) are plotted against the relative abundance of OVCAR-8 cells (x-axis) infected with each shRNA encoded in a GFP+ plasmid, measured at 7 days post infection in the competition assay . The circled dot indicates the median value, boxes represent the 25th to 75th percentile and whiskers extend to the full range of the data for those 4 replicates.
Figure 4
Figure 4. Evaluation of batch effect from differences in screening conditions.
The first principal component (x-axis) was plotted against the second principal component (y-axis) using the shRNA-level data for all 216 cell lines. Each point is an individual cell line, and is colored by (a) cancer type, (b) screener, (c) observed infection rate of each screen, (d) date of the PCR reaction, and (e) observed cell representation of each screen. Ellipses are drawn around colored groups with greater than 5 examples, to aid in visualization.
Figure 5
Figure 5. Assessment of reproducibility by measuring intra- and inter-replicate correlation.
(a) A boxplot of correlation between replicates (y-axis) plotted for each cell line (x-axis) shows the range of replicate-replicate correlations. The circled dot indicates the median value, boxes represent the 25th to 75th percentile and whiskers extend to the full range of the data not considered outliers for each cell line. A line indicating the threshold for passing quality control is in red. Histograms of (b) all intra-replicate correlations and (c) all inter-replicate (non-replicate) correlations show overall that replicate correlations are higher than non-replicate correlations. Colors indicate the percentile of signal in the initial DNA reference pool.
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