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. 2017 Jul 26;5(1):82–86.e3. doi: 10.1016/j.cels.2017.06.002

CancerGD: A Resource for Identifying and Interpreting Genetic Dependencies in Cancer

Stephen Bridgett 1, James Campbell 2, Christopher J Lord 2, Colm J Ryan 1,3,∗
PMCID: PMC5531859  PMID: 28711281

Summary

Genes whose function is selectively essential in the presence of cancer-associated genetic aberrations represent promising targets for the development of precision therapeutics. Here, we present CancerGD, a resource that integrates genotypic profiling with large-scale loss-of-function genetic screens in tumor cell lines to identify such genetic dependencies. CancerGD provides tools for searching, visualizing, and interpreting these genetic dependencies through the integration of functional interaction networks. CancerGD includes different screen types (siRNA, shRNA, CRISPR), and we describe a simple format for submitting new datasets.

Keywords: genetic interactions, synthetic lethality, cancer, precision medicine, RNAi, CRISPR, network biology

Graphical Abstract

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Highlights

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    Integrating loss-of-function screens with sequencing identifies genetic dependencies

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    CancerGD facilitates searching and visualizing of dependencies from multiple sources

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    CancerGD aids the interpretation of dependencies by integrating interaction networks

We present a resource that integrates molecular profiling with loss-of-function screens to identify genetic dependencies in cancer cell lines.

Main Text

The ability to inhibit tumors in molecularly defined cohorts of patients is a cornerstone of precision cancer treatment. A successful approach has been the development of drugs that inhibit proteins specifically required in tumors harboring aberrations in recurrently altered cancer driver genes (Luo et al., 2009). For example, oncogene addiction effects, such as the increased sensitivity of ERBB2 (HER2)-amplified breast tumors to ERBB2 inhibitors (Hynes and Lane, 2005), can be clinically exploited, as can non-oncogene addiction effects, such as the synthetic lethal relationship between BRCA1/BRCA2 mutations and PARP inhibitors (Lord et al., 2015). To identify additional cancer genetic dependencies (CGDs) that may ultimately be exploited therapeutically, multiple groups have performed large-scale loss-of-function genetic screens in panels of tumor cell lines (Brough et al., 2011b, Campbell et al., 2016, Cheung et al., 2011, Cowley et al., 2014, Marcotte et al., 2012, Marcotte et al., 2016, Wang et al., 2017). Integrating the results of these screens with molecular profiling data creates hypothesis-generating resources where the hypotheses are of the form “tumor cells with a mutation in gene X are sensitive to inhibition of gene Y.” These hypotheses are typically tested in subsequent experiments—for example, in larger panels of cell lines, using orthogonal mechanisms of gene inhibition, and/or in mouse models—to ensure they are not statistical or experimental artifacts. Recent examples of novel CGDs discovered through genetic screening approaches include an increased sensitivity of ARID1A mutant cell lines to inhibition of the ARID1A paralog ARID1B (Helming et al., 2014), of PTEN mutant breast tumor cell lines to inhibition of the mitotic checkpoint kinase TTK (Brough et al., 2011b), and of MYC-amplified breast tumor cell lines to inhibition of multiple distinct splicing components (Hsu et al., 2015).

Although the results of loss-of-function screens are typically made publically available, their integration with genotypic data remains challenging for those without bioinformatics skills. Sequencing and copy-number data must be processed to identify likely functional alterations, cell line names matched between different data sources, and statistical analysis performed to identify associations between the alteration of driver genes and an increased sensitivity to inhibition of target genes. To address these challenges, we have developed CancerGD (www.cancergd.org), a resource that integrates multiple loss-of-function screens (Campbell et al., 2016, Cowley et al., 2014, Marcotte et al., 2012, Marcotte et al., 2016, Wang et al., 2017) with genotype data (Forbes et al., 2015, Iorio et al., 2016, Yang et al., 2013) to identify CGDs associated with a panel of cancer driver genes (Figure 1).

Figure 1.

Figure 1

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CancerGD Overview

Loss-of-function screens from multiple sources are integrated with exome and copy-number profiles from the GDSC resource. Cell lines are annotated according to the mutational status of a panel of driver genes (see Table S1). Statistical analysis is then performed to identify associations between the presence of driver gene alterations and sensitivity to reagents targeting specific genes. These CGDs are filtered such that only those with nominal significance (p < 0.05) and moderate common language effect sizes (≥65%) are retained. Finally, all CGDs are annotated according to whether they occur between driver-target pairs with known functional relationships (STRING) and whether there is an inhibitor available for the target gene (DGIdb).

CancerGD currently facilitates the searching, visualization, and interpretation of CGDs (Figure 1) associated with 53 driver genes (Table S1). These genes were selected based on their identification as driver genes in multiple independent analyses (Campbell et al., 2016, Forbes et al., 2015, Vogelstein et al., 2013) and due to their alteration in at least three tumor cell lines featured in one or more of the included loss-of-function screens. Driver-gene-associated CGDs are identified both across cell lines from multiple histologies (Pan cancer) and within tumor cell lines arising from specific primary sites (e.g., Breast). With an intuitive search interface, it is thus possible to retrieve CGDs associated with ERBB2 amplification across cell lines from all tissue types or specifically associated with ERBB2 amplification in breast tumor models (Figure 2A). The data supporting every CGD can be visualized in an interactive boxplot (Figure 2B) and downloaded for reference.

Figure 2.

Figure 2

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Genetic Dependency Exploration and Visualization

(A) The principle view of the database. Each row represents a gene identified as a dependency associated with ERBB2 amplification in Campbell et al. (2016) across all tumor types (Pan cancer). Columns display experimental details along with the p value, common language effect size, and difference in median sensitivity score for each dependency. Genes identified as dependencies in multiple datasets are indicated in the Multiple Hit column. Genes with a known functional relationship to the driver gene (e.g., PIK3CA) are indicated in the String Interaction column, and drugs known to inhibit the target gene are indicated in the Inhibitors column. Toggles/search boxes permit easy filtering of interactions, e.g., to select only those genes with an associated inhibitor available. See also Figure S1 and the tutorial in Methods S1.

(B) Example boxplot showing an increased sensitivity of ERBB2-amplified cell lines to inhibition of MAP2K3. Each data point represents the sensitivity of a particular cell line to RNAi reagents targeting MAP2K3. Cell lines are grouped according to ERBB2 amplification status with the wild-type group on the left and amplified group on the right. Cell lines are colored according to site of origin and toggles on the right permit the hiding/showing of cell lines from specific sites. Hovering over a given data point provides the cell line's name, the primary site, and the score associated with the RNAi reagent in that cell line. An overlapped box-whisker plot displays the interquartile range and the median for each group. High-resolution PNG images for each boxplot can be downloaded along with a CSV file containing all of the data presented in the boxplot. Links to the target gene (MAP2K3) on external websites are provided at the bottom of the plot.

Aside from oncogene addiction effects (Luo et al., 2009), which represent a tiny minority of the dependencies stored in CancerGD, the mechanistic interpretation of CGDs remains challenging. Why would mutation of one gene result in an increased dependency upon another? In yeast, the interpretation of such relationships has been greatly aided by the integration of protein-protein interaction networks with genetic screens (Kelley and Ideker, 2005). Following a similar model, to aid the interpretation of CGDs in CancerGD, we integrate functional interactions from the STRING database (Szklarczyk et al., 2015). This facilitates the rapid identification of CGDs involving gene pairs with known functional relationships. For instance, in the Campbell et al. (2016) dataset ERBB2 amplification is associated with an increased dependency upon the ERBB2 protein interaction partners JAK2 and ERBB3, as well as the ERBB2 downstream effector PIK3CA (Figure 2A). Similarly in the Cowley et al. (2014) dataset loss or mutation of the BAF complex subunit ARID1A is associated with an increased dependency upon the ARID1A paralog and BAF complex member ARID1B (Helming et al., 2014). Such dependencies may make more promising candidates for follow-on experiments as they are supported by existing functional relationships in addition to the genetic association.

In addition to identifying known functional interactions between the driver gene and associated dependency, it can be helpful to understand the relationships between all of the CGDs associated with a given driver gene. For instance, we previously found that cell lines with a deletion or mutation of the tumor suppressor SMAD4 display a strong dependency upon the mitotic checkpoint kinase CHEK1 (Campbell et al., 2016). Considered in isolation, it is not clear whether this CGD relates to a specific function of CHEK1 or a more general sensitivity to inhibition of the mitotic checkpoint. However, by analyzing all of the dependencies associated with SMAD4, we found that they were densely connected on the protein interaction network and primarily involved in the mitotic checkpoint (Campbell et al., 2016), suggesting a more general sensitivity to perturbation of this pathway. To facilitate the identification of such pathway-level dependencies, CancerGD provides network visualizations of the functional interactions between CGDs associated with each driver gene (Figure S1).

In contrast to the results of drug-screening efforts in panels of tumor cell lines (Barretina et al., 2012, Basu et al., 2013, Daemen et al., 2013, Garnett et al., 2012, Iorio et al., 2016, Yang et al., 2013), the CGDs identified in loss-of-function screens include targets that have no inhibitors available and consequently may serve as the rationale for the development of new small-molecule inhibitors. To facilitate the identification of CGDs that may be more readily exploited with available inhibitors, CancerGD integrates drug-gene interaction relationships from DGIdb (Griffith et al., 2013).

It has previously been highlighted that many CGDs identified in one loss-of-function screen are not evident in additional datasets (Brough et al., 2011a, Downward, 2015). This could indicate that these CGDs are context specific (Ashworth et al., 2011) but can also be explained by a variety of technical factors. Different screens feature different coverage of gene libraries (e.g., kinome versus genome-wide), different coverage of cancer types (e.g., only melanoma in one versus only breast in another), and different coverage of driver genes (e.g., many BRAF mutant cell lines in one screen versus none in another). These technical factors can result in the identification of CGDs in one screen that cannot be observed in a second screen. Furthermore, in any given screen there may be false positives resulting from the off-target effects of gene-targeting reagents (Jackson and Linsley, 2010) and false negatives resulting from variation in the knockdown efficiencies of different gene-targeting reagents (Kaelin, 2012). There are thus a number of explanations for why a CGD observed in one dataset may not be evident in another. Nonetheless, the CGDs that are observed in multiple datasets may be of particular interest as they are perhaps less likely to result from the off-target effects of gene-targeting reagents and also less likely to be highly context specific. In CancerGD, we provide functionality to identify and filter those CGDs observed independently in multiple datasets.

CancerGD can incorporate datasets generated using different experimental and computational pipelines and is not restricted to loss-of-function screens generated using any specific method (shRNA/siRNA/CRISPR). The main requirement for inclusion is that a dataset must contain the results of screens in a panel of cell lines (a minimum of ten cell lines) and provide some quantitative measurement of the sensitivity of each cell line to the inhibition of each gene screened. Currently, the resource includes three genome-scale shRNA screens (Cowley et al., 2014, Marcotte et al., 2012, Marcotte et al., 2016), one kinome-wide siRNA screen (Campbell et al., 2016), and one genome-wide CRISPR screen (Wang et al., 2017). As additional screens become available, we will incorporate their results into the resource (see STAR Methods for instructions on how to format screens for easy inclusion in CancerGD).

A tutorial demonstrating the full functionality of CancerGD is provided in Supplemental Information. We believe that CancerGD will be a useful resource to aid a wider group of cancer researchers to benefit from the information generated in large-scale loss-of-function screens.

STAR★Methods

Key Resources Table

REAGENT or RESOURCE SOURCE IDENTIFIER
Deposited Data
Cell Line Copy Number Data Iorio et al., 2016 ftp://ftp.sanger.ac.uk/pub/project/cancerrxgene/releases/release-6.0/Gene_level_CN.xlsx
Cell Line Exome Data Iorio et al., 2016 http://www.cancerrxgene.org/gdsc1000/GDSC1000_WebResources//Data/suppData/TableS2C.xlsx
Achilles v2 shRNA data Cowley et al., 2014 https://ndownloader.figshare.com/files/3178886
COLT shRNA data Marcotte et al., 2012 http://dpsc.ccbr.utoronto.ca/cancer/datasets.html; RRID: SCR_006485
Wang et al., CRISPR data Wang et al., 2017 Table S3 in Wang et al.
Marcotte et al., 2016 Breast Cancer shRNA data Marcotte et al., 2016 https://github.com/neellab/bfg/blob/gh-pages/data/shrna/breast_zgarp.txt.zip?raw=true
Kinome siRNA data Campbell et al., 2016 Table S1B
STRING interactions Szklarczyk et al., 2015 http://string-db.org/download/protein.links.v10/9606.protein.links.v10.txt.gz; RRID: SCR_005223
Drug Target Information Wagner et al., 2016 http://dgidb.genome.wustl.edu/; RRID: SCR_006608
Gene names and synonyms Gray et al., 2015 ftp://ftp.ebi.ac.uk/pub/databases/genenames/new/tsv/hgnc_complete_set.txt; RRID: SCR_002827
Software and Algorithms
Django Django Software Foundation https://www.djangoproject.com/; RRID: SCR_012855
JQuery jQuery Foundation https://jquery.com/
Python version 3.4 Python Software Foundation https://www.python.org/; RRID: SCR_008394
R version 3.3.1 R Foundation for Statistical Computing https://www.r-project.org/; RRID: SCR_001905
Intercell Analysis Scripts Campbell et al., 2016 https://github.com/GeneFunctionTeam/kinase-dependency-profiling
CancerGD Python / Javascript / R / HTML code This study https://github.com/cancergenetics/cancergd
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Contact for Reagent and Resource Sharing

Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Colm J. Ryan (colm.ryan@ucd.ie).

Method Details

Genotype Data

Exome data for ∼1,000 cell lines are obtained from the GDSC resource (Iorio et al., 2016, Yang et al., 2013). We use this data to annotate ∼500 driver genes (Campbell et al., 2016) according to whether they feature likely functional alterations. For oncogenes we consider recurrent missense or recurrent in frame deletions/insertions to be likely functional alterations, where recurrence is defined as at least 3 previous mutations of a particular site in the COSMIC database (Forbes et al., 2015). In addition to recurrent missense or indel events, for tumor suppressors we consider that all nonsense, frameshift and splice-site mutations are likely functional alterations. For copy number analysis we use the gene level copy number scores from COSMIC for the same set of cell lines (which are derived from PICNIC analysis of Affymetrix SNP6.0 array data) (Forbes et al., 2015, Garnett et al., 2012, Iorio et al., 2016, Yang et al., 2013). An oncogene is considered amplified if the entire coding sequence has 8 or more copies while a tumor suppressor is considered deleted if any part of the coding sequence has a copy number of 0 as per Garnett et al (Garnett et al., 2012). For the majority of driver genes we integrate the two sources together. For all tumor suppressors we consider a functional alteration to be either a deletion (derived from copy number profiles) or a presumed loss-of-function mutation (as identified in the exome data). For most oncogenes we consider a functional alteration to be either an amplification or a recurrent mutation/indel. For a small number of oncogenes (ERBB2, MYC, MYCN) we consider only amplifications as functional events, while for another group (KRAS, BRAF, NRAS, HRAS) we only consider recurrent mutations/indels.

Loss of Function Screens

Four large-scale RNAi datasets and one CRISPR dataset are currently included in CancerGD (Campbell et al., 2016, Cowley et al., 2014, Marcotte et al., 2012, Marcotte et al., 2016, Wang et al., 2017). These include a kinome focussed siRNA screen covering a panel of 117 cell lines from diverse histologies (Campbell et al., 2016), a genome-scale shRNA screen focussed on 77 breast tumor cell lines (Marcotte et al., 2016), a genome-scale shRNA screen focussed on 72 breast, ovarian and pancreatic cell lines(Marcotte et al., 2012), a large-scale shRNA screen covering 216 cell lines from diverse histologies (Cowley et al., 2014), and a genome-scale CRISPR screen covering 14 AML cell lines(Wang et al., 2017). Cowley et al (Cowley et al., 2014) is largely a superset of a previous screen from the same lab (Cheung et al., 2011) and hence the two resources are not included separately. Similarly the kinome siRNA screen from Cambell et al (Campbell et al., 2016) contains the majority of the breast tumor cell lines screened in a previous breast cancer kinome siRNA screen from the same lab (Brough et al., 2011b) and hence they are not included separately. The breast cell lines in (Marcotte et al., 2016) are a superset of those included in (Marcotte et al., 2012) and consequently we do not store breast specific dependencies from (Marcotte et al., 2012).

Cell Line Naming

Internally we follow the naming convention established by the Cancer Cell Line Encyclopedia (Barretina et al., 2012). The CCLE naming convention is the cell line name (containing only numbers and upper case letters) followed by an underscore, followed by the tissue/primary site in upper case. The cell line names are taken from (Iorio et al., 2016), converted to uppercase and punctuation removed. Where possible we use the same tissue types as the CCLE, in a small number of cases where a tissue was absent from the CCLE (e.g. CERVIX) we have created a new tissue type. Having the tissue type in the cell line name facilitates filtering the boxplots (e.g. to show the gene inhibition sensitivities for cell lines from a specific tissue) in the browser without having to perform additional database queries. Furthermore two of the published loss-of-function screens already follow this naming convention (Campbell et al., 2016, Cowley et al., 2014) while a third features only breast cell lines and was trivially converted (Marcotte et al., 2016). In instances where the same cell line is featured in two datasets but there is a naming disagreement (e.g. H1299_LUNG in Campbell et al (Campbell et al., 2016) is NCIH1299_LUNG in our genotype set) we manually rename the screen dataset to match the genotype data.

Gene Identification

CancerGD provides links to multiple external sources that use a variety of different gene identifiers. Consequently for each gene in the database we store multiple identifiers (Entrez Gene ID, Ensembl Gene identifiers, HUGO Gene Names, Ensembl Protein IDs). We also store synonyms for each gene to facilitate easy gene look up (e.g. ERBB2 can be identified by searching for HER2). These synonyms are obtained from the HGNC resource (Gray et al., 2015).

Drug Target Annotations

Drug-gene relationships are obtained from the Drug-Gene Interaction Database (DGIdb), which integrates drug-gene relationships from multiple sources (Wagner et al., 2016). Only inhibitor relationships are retrieved, as we are interested in drugs that inhibit the products of specific genes, rather than drugs whose efficacy is associated with the mutation of specific genes. Results from DGIdb sourced from MyCancerGenome and MyCancerGenomeClinicalTrial are excluded for the same reason.

Functional Interactions

Functional interactions are obtained from STRING. We store all interactions that are medium confidence (STRING score > 0.4) or higher. Cut-offs to identify interactions as ‘Medium’, ‘High’ and ‘Highest’ confidence are those defined by STRING. For displaying the functional interactions between the dependencies associated with each driver gene we use the STRING API (Szklarczyk et al., 2015).

Implementation

CancerGD is implemented in Python using the Django framework and follows a model/view/controller architecture. JQuery is used for Javascript processing in the browser interface. MySQL is used by default for data storage but SQLite can be used for development / testing purposes with minimal documented changes. The application is currently hosted on the PythonAnywhere system, a generic Python web services host, suggesting that the application is portable.

Formatting Screens for CancerGD

To enable easy inclusion of future screens in CancerGD we request that data be provided as a tab-delimited table with each row representing a particular cell line and each column representing reagents targeting a specific gene. Cell line names should preferably follow the Cancer Cell Line Encyclopaedia naming convention described above, but COSMIC IDs are also acceptable. Genes should preferably be identified using ENTREZ IDs but other unique IDs (ENSEMBL Gene IDs) are acceptable. Due to regular changing and updating, gene symbols alone should not be used as unique gene identifiers. We favour SYMBOL_ENTREZID (e.g KRAS_3846) for ease of use but this is not required. In cases where multiple distinct scores are provided for a specific gene, as happens with scores from the ATARIS algorithm, we request that they be identified using distinct suffixes (e.g. KRAS_3846_1, KRAS_3846_2).

Individual entries in the table should be quantitative scores indicating how sensitive a specific cell line is to perturbation of a particular gene. As different scoring procedures are used to quantify the results of screens using different experimental approaches (e.g. ATARIS (Shao et al., 2013) and zGARP (Marcotte et al., 2012) for shRNA screens, Z score for siRNA screens (Campbell et al., 2016)) we do not require the scores to be in any standard format or range. However, we follow the convention in the field and suggest that increasingly negative scores should indicate greater inhibition of cell growth. A sample screen from Campbell et al (Campbell et al., 2016) is provided in the appropriate format here: http://www.cancergd.org/static/gendep/Campbell_cancergd.txt

Quantification and Statistical Analysis

We use R for all statistical analysis. For each driver gene / target gene combination we compare cell lines harbouring a likely functional alteration in the driver gene to cell lines with no alteration in that gene and test if the cell lines with the functional alteration are more sensitive to RNAi reagents that inhibit that gene. This is tested using a one-sided Mann-Whitney U test. A variety of alternative two-sample tests have been used in previous publications, including median permutation tests (Brough et al., 2011b, Campbell et al., 2016) and mutual information based measures (Cowley et al., 2014). The Mann-Whitney U test has a number of advantages for CancerGD – it is rapid to calculate and it does not assume that the scores for each gene are normally distributed. The latter is important as it means the test can be used uniformly on loss-of-function screens from multiple sources that use different scoring schemes. For all screens we use the authors’ provided scoring scheme (zGARP for Marcotte et al (Marcotte et al., 2012, Marcotte et al., 2016), ATARIS phenotype score for Cowley et al (Cowley et al., 2014), robust Z score for Campbell et al (Campbell et al., 2016), and CS score for Wang et al (Wang et al., 2017)). As in (Marcotte et al., 2012) we apply Z score normalization to the zGARP scores from (Marcotte et al., 2012) to enable reasonable comparison of scores across cell lines. In addition to the p-value from the Mann-Whitney U test we calculate a common language effect size (CLES) for each dependency. The CLES is equivalent to the Area under the ROC curve and the Probability of Superiority and indicates the probability that a cell line with an alteration in a particular driver gene is more sensitive to a given RNAi reagent than a cell line without that alteration. In the database we store all nominally significant dependencies (p<0.05) with a CLES ≥ 0.65. In a small number of instances multiple ATARIS scores are presented for a single gene – when storing CGDs we incorporate the ATARIS score with the lower p-value.

Data and Software Availability

Source code for the entire project (R/Python/Javascript/HTML) is publicly available on GitHub (https://github.com/cancergenetics/cancergd). Detailed instructions on how to run the statistical analysis, install the web application and populate the database are also provided in the GitHub repository.

Author Contributions

S.J.B. and C.J.R. wrote code for the database. J.C. contributed R code for statistical analysis. C.J.L. provided guidance on the design of the database and the manuscript. C.J.R. conceived and designed the resource and wrote the manuscript. All authors read and approved the final manuscript.

Acknowledgments

We thank Lars Juhl Jensen for help with STRING integration. We also thank members of the ICR Gene Function Team and Systems Biology Ireland for providing feedback on earlier versions of this resource. We acknowledge NHS funding to the ICR/Royal Marsden Hospital Biomedical Research Center. Work on this project was supported by a grant to C.J.R. from the Irish Health Research Board (KEDS-2015-1636). C.J.L. is supported by Cancer Research UK (grant number C347/A8363) and Breast Cancer Now. C.J.R. is a Sir Henry Wellcome Fellow jointly funded by Science Foundation Ireland, the Health Research Board, and the Wellcome Trust (grant number 103049/Z/13/Z) under the SFI-HRB-Wellcome Trust Biomedical Research Partnership.

Published: July 12, 2017

Footnotes

Supplemental Information includes one figure, one table, and one data file and can be found with this article online at http://dx.doi.org/10.1016/j.cels.2017.06.002.

Supplemental Information

Document S1. Figure S1, Table S1, and Methods S1
mmc1.pdf (1.6MB, pdf)
Document S2. Article plus Supplemental Information
mmc2.pdf (3.4MB, pdf)

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Associated Data

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Supplementary Materials

Document S1. Figure S1, Table S1, and Methods S1
mmc1.pdf (1.6MB, pdf)
Document S2. Article plus Supplemental Information
mmc2.pdf (3.4MB, pdf)

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