Tumour cells may harbour many hundreds of alterations in their genomes, including amplifications, deletions, point mutations, loss of heterozygosity and epigenetic changes1,2,3. The most intensively studied genetic changes have been shown to interact during the complex process of tumour formation and progression4. For example, oncogenes such as MYC and RAS have long been recognized to cooperate in the processes of transformation and immortalization5. With the growing lists of genetic mutations generated by genome sequencing projects, it remains a significant challenge to determine which of these genetic alterations interact and whether those interactions are clinically relevant. Furthermore, it is unknown whether there are a few key commonly mutated factors such as RAS and P53 that interact with large numbers of other factors (as hubs in oncogenic networks) or whether genetic interactions (GIs) are more widespread and common among even less frequently mutated genes.

The observation of an unexpected (for example, non-additive or non-multiplicative) phenotypic outcome when combining two genetic alleles is referred to as a GI or epistasis. Pairwise GIs for thousands of genes have been systematically mapped in yeast by using a large collection of deletion strains6,7,8,9,10. In higher organisms, combinatorial RNA interference (RNAi) methods have been used in cell culture to generate GI maps for genes involved in kinase signalling, chromatin regulation and ricin susceptibility11,12,13,14. Such analyses of large-scale GI maps have successfully identified functionally connected pathways and genetic networks, allowed functional prediction for uncharacterized genes and revealed changes in the interaction landscape upon stimulus11,12,13,14.

Therefore, a comprehensive mapping of epistasis among cancer genes could be tremendously useful for understanding the genetic network that underlies tumorigenesis, and for identifying new tumour suppressors or oncogenes, thus facilitating design of cancer therapies. For example, screens for ‘synthetic lethal’ partners of the RAS oncogene have uncovered genes that could be used as drug targets to specifically eliminate cancer cells with RAS mutation15,16. Studies using PARP1 inhibitors have also demonstrated the potential for utilizing ‘synthetic lethality’ in treating BRCA-deficient patients17.

In this study, using combinatorial RNAi in human breast epithelial cells, we produce a quantitative GI map for 67 genes that are frequently altered in breast cancer as well as in many other cancer types. We find hundreds of previously unreported GIs among these genes. Our analysis of the GI map in the context of gene expression and genome-wide transcriptional factor binding data also reveals potential mechanisms of expression regulation in tumorigenesis. Most strikingly, GIs are enriched in gene pairs that synergistically impact patient survival when mutated, indicating that the GI maps produced in cell lines can have biological relevance in tumours.


Combinatorial RNAi for co-altered breast cancer genes

To study GIs among cancer genes, we began by using diverse genomic data in The Cancer Genome Atlas (TCGA)18 to identify frequently altered genes in breast cancer. By combining somatic mutations, gene expression and copy number variation data from 849 invasive breast cancer samples, we identified 67 genes that are involved in these frequent alterations. These genes span a range of biological functions, including chromatin regulators, transcription factors, signal transduction molecules and metabolic enzymes (Supplementary Data 1).

To build a GI map from these 67 genes, we performed a combinatorial RNAi screen in MCF10A cells by using 66 of the 67 genes as template genes and 29 of the 67 genes as query genes (Fig. 1a,d and Supplementary Data 1). Among the 1,508 gene pairs we assayed, 248 gene pairs corresponded to significantly co-occurring genetic alterations in primary breast tumours (Supplementary Fig. 1). We chose the MCF10A breast epithelial cell line19,20 because these cells are diploid and lack alterations in most cancer genes.

Figure 1: GI mapping of 67 breast cancer genes.
figure 1

(a) A schematic representation of the combinatorial RNAi screen. Heat maps of GI scores for three phenotypes are shown. (b) Overlap of significant GIs within the three phenotypes. (c) Unsupervised clustering of the Pearson correlations of GI profiles across all three phenotypes between the 66 genes (left), and clusters of genes in cell cycle checkpoint (right; module I) and cytoskeleton regulation (right; module II). (d) The design map showing the 67 genes (nodes with yellow labels) and 1,508 interactions (grey lines) assayed in the RNAi screen. Twenty-nine genes (at the centre) were each assayed for interaction with all the other 66 genes, and the other 38 genes (in the outer circle) each for interaction with the 29 genes at the centre. (e) The inferred GI map of 847 significant interactions (FDR: 0.05) across 66 genes in all three phenotypes. One of the 67 genes did not show significant interaction with any other gene, and is therefore absent from the map. A gene with fewer significant interactions than the assayed interactions tends to move away from the centre in comparison with the design graph in d.

To minimize the influence of short interfering RNA (siRNA) off-target effects on GI detection, for each gene we tested multiple siRNAs and selected two independent siRNAs that yielded quantitatively similar phenotypes for the screen (Supplementary Fig. 2; see Methods section). To validate the on-target knockdown efficiency of the selected siRNAs, we measured target transcripts levels by quantitative reverse transcription–polymerase chain reaction (qRT–PCR) for 48 selected siRNAs targeting 24 of the 67 genes (Supplementary Fig. 3 and Supplementary Data 2). The siRNAs exhibited various degrees of knockdown efficiency, with the percentage reduction of targeted transcript levels ranging from 18% to ~100% with a median of 69% and an interquartile range of 45–85%. In addition, 28 of the 48 siRNAs were able to reduce the targeted transcript levels by >60%, including 10 that reduced the transcripts levels by >90%. These results on siRNA efficiency are consistent with previous reports13. For six genes where 40% silencing was observed at 24 h, an enhanced reduction in mRNA levels was observed at 72 h, the time point when the phenotypic assays were performed (Supplementary Fig. 3b and Supplementary Data 2). Ten of the 24 genes have two siRNAs that are different in knockdown efficiencies at 24 h (Student’s t-test, P0.05; Supplementary Data 2). Although the siRNA knockdown efficiencies varied and sometimes resulted in partial knockdown, in our assay they generated consistent phenotypes in single knockdowns, as well as reproducible and statistically significant quantitative phenotypic changes in double knockdowns. Overall each gene pair was assayed by four different combinations of siRNA pairs (6,032 siRNA pairs in total; Fig. 1a). Two to three replicates were performed for each siRNA pair, yielding a total of 18,623 measurements.

We used automated fluorescence microscopy to record cell morphology in images and extracted 10 quantitative features from each image. The three most reproducible and non-redundant features were used for further analysis: the number of cells per image, the mean nucleus size and the mean cell size (Supplementary Fig. 4). Although the features we measured in our screen do not necessarily correspond to pathological phenotypes, they are complex traits influenced by many cancer-relevant pathways. For example, the number of cells is a reflection of the cell proliferation capacity that tends to change significantly in tumours. Furthermore, the quantitative nature of these features allows us to detect subtle deviations from the expected effects of each pair of genes. The multitude and quantitative nature of the phenotypes thus increase the likelihood of ascertaining whether the two genes can interact at all, regardless of the type of alteration (amplifications, deletions or point mutations) seen in patients. Our RNAi assay can still detect a loss-of-function interaction, even when the pair of gene alterations includes a gain in copy number or an activating mutation in primary tumours.

To calculate the GI scores, we adopted the regression framework, comparing the observed and predicted phenotypes after double knockdowns in a single linear model, similar to the π score method11,12 (see Methods section). However, since the three phenotypes in our assay are correlated (Supplementary Fig. 5), we developed a seemingly unrelated regression (SUR) model21, which computes the GI scores of a gene pair for each phenotype while accounting for the correlation among the phenotypes. Our model also accounts for batch effects (see Methods section). The statistical significance of a GI score, which is the coefficient of the interaction term in the SUR model, is determined by a t-test following the standard approach for assessing the significance of a coefficient in a linear model. We further adjusted the P values of the GI scores in order to control the false discovery rate (FDR; see Methods section).

Topology of the inferred GI map

In total, 847 significant gene pair interactions were identified at an FDR of 0.05 (Fig. 1b and Supplementary Data 3, heat maps in Fig. 1a and Supplementary Figs 6–8), demonstrating widespread epistasis among cancer genes. Out of the 67 genes assayed, 66 have at least one significant interaction; PTPN13 being the only gene for which our data do not support interaction with any other gene (Supplementary Data 3). This result is robust even with an FDR of 0.01 (579 GIs identified across 65 genes).

The inferred GI map has a high median connectivity of 62% for each gene (accounting for different numbers of probed interactions for each gene, Fig. 1e; Supplementary Data 5 and Supplementary Figs 9 and 10), and exhibits small-worldness22 (S=1.5, higher than 1.1, which is the small-worldness of all the probed interactions; see Methods section).

GI profiles identify functional clusters of genes

We compiled the interaction profiles for each gene with respect to all three phenotypes and calculated the pairwise correlation matrix of interaction profiles (Fig. 1c). As expected, if our approach is identifying biologically relevant interactions12,13, unsupervised clustering of genes based on the correlation of their GI profiles revealed genes with previously described functional connections. For example, four genes (CCND2, BRCA2, NDRG1 and FHIT) shown in subcluster I (Fig. 1c) are involved in DNA damage checkpoint pathways for cell cycle progression23,24,25,26. Subcluster II (Fig. 1c) highlights genes (MEN1, CCT2, FBN1 and TSC1) with roles in the regulation of focal adhesion and cell migration27,28,29,30,31. Overall, this clustering reveals that there are distinct subsets of correlated genes, and that at least some of these correlated gene sets represent functional relationships.

The majority of GIs exhibit epistatic masking effects

The magnitude of the interaction effect size may have functional implications, especially when compared with the effect sizes of single knockdowns. We classified epistasis into three broad classes: epistatic masking, synthetic suppression and synthetic enhancement (Fig. 2 and Methods section). Surprisingly, over half of significantly interacting gene pairs exhibited masking effects (epistasis as first described by Bateson and Mendel32), where the effects of double knockdown are indistinguishable from the effects of one of the two single knockdowns (Fig. 2). Further examination of these pairs may generate useful information for personalized cancer therapy. For example, if inhibiting gene A masks the phenotype of gene B, gene A might be an alternate drug target for treating cancers caused by gene B activation. Indeed, largely because we focused the study on known cancer genes, ~20% of gene pairs we selected included a potential drug target.

Figure 2: Definition of the three types of GIs.
figure 2

(a) Illustration of the definition of three types of GIs for genes with single-knockdown effect of same direction. (b) Illustration of definition of three types of GIs for genes with single-knockdown effects of different directions. ΔA and ΔB represent single-knockdown effects of genes A and B, respectively. ΔA+ΔB represents expected effects of double knockdown of genes A and B. Shaded bars represent the observed double-knockdown effect. (c). Counts of the three types of interactions for each phenotype.

Epistatic masking is not an artefact due to differences in transcript abundance or knockdown efficiencies. We could identify which gene is masked if the two single-gene effects are different and the double-knockdown effect is equal to one of them. In 188 of the 403 epistatic masking gene pairs identified for the cell number, the masked gene in these pairs had lower transcript abundance (measured by FPKM (fragments per kilobase per million mapped reads) in RNA-sequencing (RNA-seq) data), which gives an insignificant P value of 0.18 by a χ2-test. In addition, in 40 of the 88 epistatic masking gene pairs, for which we have qRT–PCR knockdown efficiency data, the masked gene had a lower remaining transcript level (measured as the percentage of untreated), which also gives an insignificant P value of 0.66 by a χ2-test.

Most inferred GIs are novel

Using the STRING 9.0 database, we identified significant enrichment of gene pairs previously shown to be involved in the same pathways or complexes (Fisher’s exact test P=0.05; Supplementary Fig. 11a). These interactions included direct protein–protein interactions (for example, BRCA2 and MCPH1; Fig. 3a)33, interactions between genes in kinase signalling pathways (for example, MAP2K4 and MAP3K1; Fig. 3b)34, interactions between genes encoding factors that compete for substrate binding (for example, CREBBP and MEN1; Fig. 3c)35 and interactions that are likely explained by transcriptional regulatory relationships (for example, BIRC3 and NFKB2; Fig. 3d)36.

Figure 3: Combinatorial RNAi screening reveals both known and novel molecular interactions.
figure 3

(a) MCPH1 recruits BRCA1 to DNA damage sites through physical protein–protein interaction. (b) MAP3K1 activates MAP2K4 by phosphorylation. (c) MEN1, a histone methyltransferase complex member, interferes with binding of CREBBP, a histone acetyltransferase, at target gene promoters. (d) NFKB2 regulates BIRC3 expression through transcriptional activity. In ad, shaded bars indicate observed single- or double-knockdown effect, and error bars represent s.e.’s. (e) Interactions may exist between the DNA damage response pathway and MAPK pathway, possibly at the JNK activation step. The edges are GIs identified from the RNAi screen. (f) Bar plot of the count in each category of GIs.

However, the majority of GIs are not annotated by protein interaction databases, and yet extensive cross talk between pathways was revealed. We define cross talk as the interactions between one or more components of two pathways defined in the literature. For example, as shown in Fig. 3e, we found 12 GIs between the DNA damage repair pathway and the mitogen-activated protein kinase (MAPK) pathway. In this case, MAP2K4 is upstream of the Jun N-terminal kinase (JNK) pathway, which is also activated by DNA damage through JNK phosphatase37,38, perhaps explaining the observed cross talk. In total, 45 of the inter-pathway GIs are supported by published results (Fig. 3f and Supplementary Data 3). We classify another 78 GIs as potential inter-pathway interactions, because the two genes in each pair each interact either with another gene that is known to have a role in pathway cross talk or with other genes that have inter-pathway interactions (Supplementary Data 3). An overview of the cross talk between different biological processes (Supplementary Data 4) is plotted in Supplementary Fig. 11b.

Integrative network analysis reveals transcriptional modules

We also identified several potential transcriptional regulatory modules by overlaying the GI network with a co-expression network built from TCGA RNA-seq data (see Methods section). One such module is centred on the transcription factor GATA3 (Fig. 4a). GATA3 is an important transcriptional regulator in both normal mammary gland development and breast cancer39,40,41, and low expression levels of GATA3 are associated with a poor prognosis42. Three genes, PTCH1, PPARA and NFIB, exhibit epistatic interactions with GATA3 and also display negatively correlated expression levels with GATA3 (Fig. 4a). According to the published ENCODE data43, GATA3 binds to gene regions near NFIB, PTCH1 and PPARA in T47-D cells, a human ductal breast epithelial tumour cell line (Fig. 4b–d). These peaks of GATA3 binding also overlap with DNaseI hypersensitivity clusters, indicating that GATA3 may actively regulate the expression of these three genes in breast cancer cells.

Figure 4: Transcriptional modules were identified by overlaying the GI network with co-expression networks.
figure 4

Solid blue lines represent GIs identified from the RNAi screen; dashed red lines represent correlated expression levels (Pearson correlation coefficients are indicated) based on TCGA RNA-seq data of breast cancer patient samples. (a) GATA3 module. (bd) ENCODE data support the direct binding of GATA3 in the gene regions of NFIB, PPARA and PTCH1. Black bars represent chromatin immunoprecipitation-sequencing (ChIP-seq) raw signal of GATA3 binding in T47-D cells. Grey bars represent DNaseI hypersensitivity clusters in 125 cell types from ENCODE. (e) A module composed of FLI1, JAK2 and CCND2. (f) Kaplan–Meier curves of survival probabilities in patients with and without alterations in the FLI1 module. Each tick mark indicates a patient with censored survival time. Lower expression of at least one of the three genes in FLI1 module is associated with shorter average survival time in breast cancer patients (P=0.03; two-sided log rank test).

Another transcriptional module consists of FLI1, JAK2 and CCND2 (Fig. 4e). The expression levels of the three genes are all positively correlated (red dashed lines in Fig. 4e). In addition, JAK2 and FLI1 have correlated interaction profiles (Fig. 1c and Supplementary Fig. 6). We found that knockdown of FLI1 in MCF10A cells reduced the expression levels of JAK2 (Supplementary Fig. 12). It has also been reported that FLI1 modulates CCND2 expression44. While FLI1 expression levels have been associated with the clinical outcome in acute myeloid leukaemia45, to our knowledge, this module has not been previously associated with breast cancer patient outcome. To test this association, we examined TCGA breast cancer patient survival data and found that low expression or mutation in one or more members of the FLI1-JAK2-CCND2 module is associated with reduced overall survival time in all patients (Fig. 4f) as well as in estrogen receptor-positive patients (Supplementary Fig. 13). These results indicate that this module may have a previously unappreciated role in breast cancer.

GIs are associated with patient survival

To more broadly investigate the role of GIs in cancer progression, we performed patient survival analysis to assess the potential impact of co-alterations in gene pairs. We used TCGA genomic data to define the alteration of a gene in a given sample, similar to the approach we took to selecting significantly co-altered gene pairs (see Methods section). Each gene has one of the three states: ‘gain’ if the gene in the sample is amplified and expressed at a high level relative to the average level of the gene in samples that are diploid for the gene, or if the gene has previously identified gain-of-function point mutations; ‘loss’ if the gene is deleted and expressed at a low level, or if the gene has (putative) loss-of-function mutations; or ‘no alteration’ (see Methods section). For each gene pair, we examined each of the four co-alteration scenarios, namely, ‘gain–gain’, ‘loss–loss’, ‘gain–loss’ and ‘loss–gain’.

By comparing the survival time of patients with both genes altered to survival time of patients with only one gene altered, we found 51 gene pairs, spanning 43 genes, whose co-alterations were associated with worse outcome for breast cancer patients (Fig. 5a and Supplementary Data 6). Interestingly, GIs are significantly enriched in those gene pairs (P=0.023, Fisher’s exact test), indicating that GIs indeed have effects on patient survival. For example, in the case of SS18L1 gain and MAP2K4 loss (Fig. 5b), patients with both alterations have shorter survival time than those with only one alteration (Fig. 5c). This property is independent of the ER status since the association was also discovered in ER-positive patients (Fig. 5d). In addition, this property is independent of TP53 status, which is located in the same chromosome region as MAP2K4 (Supplementary Fig. 14). For the overall association between GIs and patient survival, the instability of tumour genomes was not a confounding factor (P>0.1 for both survival time and survival status; see Methods section). These data indicate that information on GIs can be useful for risk assessment and outcome prediction.

Figure 5: Co-alterations of GI gene pairs are significantly associated with clinical outcome in breast cancer patients.
figure 5

(a). GIs are enriched within gene pairs that impact overall survival when co-altered in breast patient samples. One-sided Fisher’s exact test was used. (b). Frequent co-occurrence of MA2K4 loss and SS18L1 gain in 822 breast cancer patients. (c) Kaplan–Meier curves of overall survival in patients with MAP2K4 loss, SS18L1 gain or both alterations (P=0.00098; two-sided log rank test). (d) Kaplan–Meier curves of overall survival in ER-positive patients with MAP2K4 loss, SS18L1 gain or both changes (P=0.0074; two-sided log rank test).


Although GIs are thought to be pervasive in cancer, quantitative large-scale mapping of GIs have not been performed among cancer genes. In this study, we systematically profiled GIs among 67 cancer-related genes in human cells by using combinatorial RNAi and found over 800 significant GIs across 66 genes. Our results strongly indicate that epistatic effects of somatic mutations are widespread, revealing a highly connected and complicated genetic architecture associated with breast cancer.

Our GI mapping strategy is similar to those developed in flies and mammalian systems11,12,13, using combinatorial RNAi and high-content imaging. In all of these studies, epistasis is commonly defined as deviation from the expected phenotype of combining two alleles. However, there are several choices for quantitative definitions of GI scores46,47,48,49. For example, additive models examine the difference from the sum of the two single-gene phenotypes, whereas multiplicative models examine the fold change over the product of the two single-gene phenotypes (see Methods section). Here, we adopted a regression framework, following the definition in classical quantitative genetics50 and similar to previous approaches11,12. In this case, we used SUR to provide a unified framework for both additive and multiplicative models. This unified SUR framework is particularly relevant for our analysis as we considered three related but non-redundant features. It enables us to used multiplicative models and additive models for different features based on the scales and distributions of the raw data, while accounting for any correlation among the phenotypes and for confounding factors as covariates (for example, the batches in our model).

Our results systematically specify, and for the first time quantify, the genetic architecture that underlie cancer gene interactions. It is very likely that our GI mapping results will generalize to other cancer types, since the genes we studied are mutated in many different types of tumours and are involved in known cancer-related pathways. The high connectivity for our gene set is most likely owing to the fact that cancer genes often have pleiotropic effects, and pleiotropy is characteristic for hub genes in networks8,9. Therefore, the genes we selected based on tumour mutation data are more likely to be hubs in the overall genetic network and mediate cross talk between multiple pathways. Consistent with our results, in a recent study of cancer driver genes, genes significantly altered in cancer patients are found to have a high connectivity in the BioGRID human protein–protein interaction network51, as well as more likely to be in protein complexes than a typical protein in the CORUM database of experimentally validated human protein complexes. Interestingly, haploinsufficiency appears to be common in cancer51, which is consistent with our observation that a partial knockdown of 40–50% by siRNAs was sufficient to generate reproducible phenotypic changes in cells.

The extensive interactions among cancer genes could be a critical component for explaining nonlinear cumulative effects of mutations in cancer. Our results strongly indicate that epistatic effects of somatic mutations are likely critical for breast cancer development and progression. By combining GI information with publically available genomic and survival data for breast cancer patients, we found that the identified GIs are dominantly present in gene pairs whose co-alteration is associated with reduced survival time of breast cancer patients. Current models of cancer progression usually do not account for GIs. Therefore, more comprehensive quantitative mapping of GIs among cancer genes will facilitate the modelling of complex effects that result from multiple mutations. Finally, our results indicate that patients with certain combinations of mutations have poorer prognosis as a result of epistasis, suggesting that therapeutic interventions counteracting these epistatic interactions may hold promise.


TCGA data acquisition

TCGA breast cancer data was downloaded through cBio Cancer Genomics Portal52 ( on 27th December, 2012. These data include copy number variation (GISTIC score), somatic mutation, mRNA expression (RNA-seq z score) and survival data.

Detection of significantly co-altered gene pairs

To identify genetic aberrations that co-occur non-randomly, we used data sets generated by TCGA18. By combining somatic mutations, gene expression and copy number variation data from 849 invasive breast cancer samples, we generated a gene-by-sample matrix. In this matrix, each gene-sample pair has one of the three states: (i) no alteration, denoted by 0; (ii) gain, denoted by 1, which is defined by either a higher copy number (GISTIC score >0) with high gene expression (z score >0.5), or gain-of-function mutations (from manually curated literature); or (iii) loss, denoted by −1, which is defined either as a lower copy number (GISTIC score 0) and low gene expression (z score −0.5), or loss-of-function mutations. For a pair of genes A and B, co-occurrence of alterations corresponds to the number of tumour samples containing both genes being altered (this number is denoted C11; Table 1). Within the tumour samples, these counts each follow a binomial distribution, with the ‘success’ probability computed under the null hypothesis that the two genes are altered independently (Table 1). We conducted three one-sided binomial tests for each gene pair, corresponding to the three possible scenarios: both genes being ‘gain’, both being ‘loss’, and one being ‘loss’ and the other ‘gain’). Binomial tests are exact tests, thus alleviating the problems with small counts. We further computed the Q values for the binomial P values by using the R function Q value from package Q value.

Table 1 Observed count and expected frequency of the mutation pattern of two genes.

Human tissue culture

Human MCF10A cells were obtained from American Type Culture Collection (number: CRL-10317, LOT: 7635052). The cells were maintained in DMEM/F12 medium containing 5% horse serum and supplemented with epidermal growth factor (EGF) (20 ng ml−1), hydrocortisone (0.5 ng ml−1), cholera toxin (100 ng ml−1), penicillin (100 U ml−1) and streptomycin (100 μg ml−1). Cells were cultured at 37 °C and in 5% CO2.

RNA-seq of MCF10A cells

A quantity of 2 μg of RNA was extracted from MCF10A cells by using the AllPrep RNA mini kit (Qiagen). The RNA integrity number of the sample was over 7.5 (Bioanalyzer, Agilent). A single-end library was prepared following the Illumina TruSeq RNA sample preparation protocol. The library was indexed with one of the Illumina barcodes and sequenced in 1/4 lane on an Illumina Hiseq 2000. Single-end reads (37,386,273 in total) of length 44 bp were generated. Sequence files were parsed and low-quality reads (average Phred-scaled quality score 15) were removed by using custom perl scripts. The remaining reads were then mapped to hg18 by using TopHat and output in BAM format. Unmapped reads were removed from further analysis. Cufflinks was used to estimate transcript abundance using RefSeq gene models and output as FPKM. Genes (12,332) with FPKM >1 were considered as expressed in MCF10A cells.


Transfection was performed in 384-well plates. Each well contains 0.25 pmol of both siRNAs in 5 μl Opti-MEM (Life Technologies) and 0.03 μl RNAiMAX (Life Technologies) diluted in 5 μl Opti-MEM. For assaying single siRNA knockdown phenotypes, negative control siRNAs were combined with the targeting siRNAs. Five-hundred cells were seeded in 40 μl MCF10A growth medium with no antibiotics by using a Multidrop Nano reagent dispenser (Thermo Scientific). Cells were incubated at 37 °C and in 5% CO2 for 72 h before fixation and staining.

Cell staining

All steps of staining and imaging were performed at room temperature. Cells were fixed in 5% paraformaldehyde (Electron Microscopy Sciences) in PBS for 20 min., followed by permeabilization with 0.2% Triton X-100 in PBS. Cells were then washed with 0.05% Triton X-100 and blocked in 3% BSA PBS for 1 h. Cells were incubated in anti-alpha-tubulin (Clone B-5-1-2, Sigma, 1:1,000) for 2 h followed by three washes with PBS. Secondary antibody staining was performed with Alexa 488 goat anti-mouse immunoglobulin G (Invitrogen, 1:500), Hoechst 33342 (Invitrogen, 13 ng ml−1) and rhodamine phalloidin (Invitrogen, 1:50) for 45 min, followed by three PBS washes. The plates were then stored in PBS at 4 °C before imaging.

Automated image acquisition

Images were acquired on a Beckman ImageXpress Micro by using a × 10 objective lens in a 12-bit tiff format. Images from four different sites were collected for each well of a 384-well plate. Each site typically contains 200–600 cells. Channels for Hoechst 33342 (DNA), Alexa 488 (tubulin) and rhodamine were imaged. The total number of images acquired was 58,212.

Image analysis

The image analysis pipeline was built upon the EBImage package53 in R. Images of 12-bit tiff were first converted to 16-bit tiff and then to JPEG format for display and storage. The illumination non-uniformity across the field of view was corrected by using background subtraction before analysis. Artefacts were removed from all channels by a thresholding method. Connected regions that are larger than 4,000 pixels in the nucleus channel were removed from subsequent processing in all three channels.

Nuclei and cell segmentations were performed following the protocols described in Fuchs et al.54 The images were first normalized for better segmentation while the raw images were kept for raw intensity calculation. Nuclei were segmented by adaptive thresholding of the DNA channel with the threshold set to 0.01. Objects that were too dark (average intensity 0.01), too large (>2,000 pixels) or too small (75 pixels) were treated as artefacts and removed. Cell masks were calculated by using the summation of the actin, tubulin and DNA channel signals. Cell boundaries were then separated by location of the nucleus by using the Voronoi segmentation algorithm through the ‘propagate’ function incorporated in EBImage54. Cells that were too large (>145,000 pixels), too small (150 pixels), too dark (average intensity 0.1) or too close to the border (edge/peripheral length >0.3) were removed as artefacts. Each cell was characterized by eight morphological features for further analysis: cell number, cell size, nucleus size, cell eccentricity, nucleus eccentricity, nucleus fluorescence intensity, actin intensity and tubulin intensity.

siRNA selection

366 Silencer Select siRNA targeting 122 genes were obtained from Ambion Applied Biosystems (Life Technologies). To identify high-quality siRNAs, we screened three siRNAs for each of the 122 genes and selected the two siRNAs with the most consistent phenotype. Each siRNA was arrayed twice in 384-well plates and cells were transfected by using RNAiMAX (Life Technologies) following the manufacturer’s instructions on reverse transfection. After staining, images were obtained and ten cell features were extracted. Euclidean distances between two feature vectors were calculated. Two siRNAs with distances not larger than the mean distance between technical replicates were selected for 67 genes. The sequences of the 134 siRNAs used in combinatorial screen are listed in Supplementary Data 1.

Combinatorial RNAi screen

For the combinatorial RNAi screen, a template-query design was used similar to the one described in Horn et al.11 A quantity of 100 nM each of the 132 template siRNAs stocks were arrayed twice in a 384-well plate (Beckman), and aliquoted to 63 plates. Each plate also contained 16 empty wells, five positive controls (PLK2) and eight negative controls (non-targeted cells), all randomly arrayed on the plate. Each template plate was then combined with one of the 29 query siRNAs or Opti-MEM medium. In this way, each pair of genes was assayed four times through four different combinations of siRNA within one plate. Liquid handling was performed by the automated liquid handlers Evolution Precision Pipetting Platform (PerkinElmer) and Freedom EVO100 (Tecan). Cells were then plated and stained as described above.

Validation of RNAi knockdown using qRT–PCR

siRNA transfections were carried out in the same conditions as in the primary screen. Twenty-four hours later, siRNA-transfected cells were lysed by using the Cells-to-ct Kit (Life Technologies), followed by reverse transcription and real-time PCRs according to the manufacturer’s instructions. PCRs were performed on a Step One Plus (Applied Biosystems). Percent mRNA remaining was calculated by using the Step One software. Expression data were first normalized to a house keeping gene (GAPDH), and expression fold changes were calculated by comparison with cells treated with non-targeting control siRNA. For selected genes, remaining mRNA levels after 48 or 72 h were assayed. The low abundance of templates in our assay may result in high variances between technical replicates for some of the genes. The primers used for qRT–PCRs are listed in Supplementary Data 2.

SUR for interaction analysis

We focused on three phenotypes in the RNAi screen: the cell number, cell size and nucleus size, which are correlated among each other, with the correlation between cell size and nucleus size being stronger. To identify significant GIs for each phenotype while accounting for the correlation among the phenotypes, we adopted a SUR framework21. That is,

where t represents indexes phenotypes, k batches, i and j genes, and r replicates. Gi (or Gj) takes value 1 if the i-th (or j-th) gene is knocked down, and εtkijr denotes the error term, which follows a multivariate normal distribution with mean 0 and covariance matrix Ω. Furthermore, β0 indicates the baseline level, β1k the impact of the k-th batch, β2 and β3 the single-gene effect size, and β4 the interaction effect size (or score). The SUR accounts for dependence among phenotypes through the covariance matrix Ω of the error term εtkijr. Specifically, if Σ is the covariance matrix of 3 by 3 for the phenotypes, the covariance matrix Ω of the error term in the above regression is,

where IN is an N by N matrix with 1 along the diagonal and 0 off the diagonal, N is the total number of observations for each phenotype and represents the Kronecker product.

Since the cell number grows exponentially, we log2-transformed this phenotype in regression. We also log2-transformed the cell size, as the transformed data were closer to a normal distribution (Supplementary Fig. 5). The data on the nucleus size followed roughly a normal distribution (Supplementary Fig. 5), so we did not transform these data. In terms of their implications on the interaction, these strategies mean that we examine a multiplicative model of GIs on the cell number and nucleus size, and an additive mode on the cell size (see the description of the additive and multiplicative models in next section).

We carried out the linear regression for each phenotype by using the R function lm and performed SUR with the function systemfit in the R package systemfit55,56. To assess the statistical significance of the P values for the estimated coefficients of the interactions, we computed the Q values for the P values of the estimated coefficients of the interactions for each phenotype, using the function Q value in the R package Q value.

In addition, we pooled the interaction effect sizes across the three phenotypes that generated the overall GI profile for each gene. We computed the Pearson correlation for all gene pairs and applied hierarchical clustering to the resulting correlation matrix.

Notes on additive and multiplicative model for epistasis

Ignoring the noise in the data and assuming that the single-gene and double-knockdown phenotypes can be estimated perfectly, we can describe epistasis through the models below:

Additive model:

Multiplicative model (raw scale):

Multiplicative model (log transformed):

In the models above, β is the GI score, xij is the observed phenotype of double knockdown, xi and xj each are the phenotype of single knockdowns. Both xi + xj and xixj (log xi+log xj on the log scale) are the expected phenotype from the double knockdown. In the additive model, the GI score β is the (mathematical) difference between observed and the expected phenotypes. In the multiplicative model, β is the ratio between observed and the expected phenotypes.

Both multiplicative and additive models are commonly used when assessing epistasis46,47,48,49. The practical choice of model often depends on the particular phenotype and scale of measurement57. Whereas additive models are suitable for phenotypes that change linearly, multiplicative models are intuitively suitable for growth-related phenotypes, such as yeast fitness or cell growth, because the cells are known to grow nonlinearly.

Network analysis of the inferred GI map

For a graph, g, with n nodes and m edges, the metric of the small-worldness, denoted S, is largely based on the definition proposed in Humphries and Gurney22:

where Cg and Crand are the clustering coefficient of the graph g and that of a random graph with the same number of nodes and edges, and Lg and Lrand are the mean shortest path length of g and that of a random graph. To estimate Crand and Lrand, however, we generated a large number of random graphs with n nodes and m edges under the Erdos–Renyi model58, such that each edge is created with the same probability. We computed C and L for each random graph, and used the average as the estimate for Crand and Lrand. We used functions, transitivity and average.path.length in the R package igraph3 for the computation.

We used this method to compute the small-worldness for the ‘prior’ graph of all 67 genes and 1,508 edges assayed in the RNAi screen, and for the GI map of 66 genes and 847 edges (significant GIs at FDR of 0.05). The small-worldness is 1.1 for the ‘prior’ graph and 1.5 for the GI map, suggesting that the GI map has a strong level of small-worldness, not an artefact of the experimental design.

To compute the connectivity of each gene in the GI map, we first computed the unadjusted connectivity, which is the number of edges (or signification GIs) a gene has, and then divided it by the number of interactions probed (66 or 29 in our experimental design) to obtain the proportion of significant GIs as the adjusted connectivity.

We computed the betweenness59 under the canonical definition (using function betweenness in the R package igraph59), rescaled it for the nodes with 29 ‘prior’ edges and normalized for all nodes to obtain a value between 0 and 1. Specifically, there exists a log-linear relationship between the canonical betweenness and adjusted connectivity in each node group. For nodes with 29 ‘prior’ edges, log (betweenness)=−3.5+5.2 × (adj connectivity). For nodes with 66 ‘prior’ edges, log (betweenness)=−0.7+6.3 × (adj connectivity). Note that the slope is roughly the same, whereas the intercept differs by 2.8. We therefore multiplied the canonical betweenness for nodes with 29 ‘prior’ edges by e2.8, such that the values are comparable to that of the other node group. We then divided all the betweenness values by the maximum (Supplementary Data 4).

Modes of interaction

We compared the effect size of a double knockdown (ΔAB) with that of single knockdowns (ΔA or ΔB) and the expected effect size of the double knockdown (ΔA+ΔB), and defined three broad types of epistasis: (i) epistatic masking, when ΔAB=ΔA or ΔAB=ΔB. In other words, the effect of one gene is completely masked (or dominated) by the other gene; (ii) synthetic suppression, when ΔAB(ΔA+ΔB); (iii) synthetic enhancement, when ΔAB>(ΔA+ΔB). To infer these types of epistasis from the RNAi data, we used the estimated effect sizes of single and double knockdowns from the linear regression, and allowed deviation of 1 s.e. in the comparisons in order to account for the estimation uncertainty. For example, if the estimated effect size of the double knockdown is within 1 s.e. of the estimated effect size of the single knockdown of gene A, then we categorize genes A and B as in epistatic masking.

Co-expression network from TCGA data

We calculated the pairwise Pearson correlation of gene expression z scores of the 67 genes in samples where both genes had a neutral copy number.

Survival analysis

Gene alterations used in the survival analysis here are the same as defined in ‘Detection of significantly co-altered gene pairs’ section. For a pair of genes, we examined the following three questions: (i) Are single-gene alterations (alteration of either gene) and double gene alterations associated with different survival prospects? (ii) Does this association hold after accounting for commonly mutated genes in cancers? (iii) Does this association hold after accounting for tumour subtypes? We further examined whether genome instability is a confounding factor in the above analysis.

At the core of the analysis to address these questions is survival analysis, which estimates the survival curves (that is, the survival probability as a function of the survival time) for groups with different alteration patterns (such as single alteration versus double alteration), and compares the statistical significance between the estimated survival curves. A log rank test based on a χ2-distribution was used for the comparison. We used functions survfit and survdiff in the R package survival for this analysis. Similar to the detection of significant interactions, we computed the Q values by using the R function Q value from package Q value. Q values 0.05 were considered significant. In each of the three analyses, we further considered three cases of alteration patterns: gain–gain, gain–loss and loss–loss, and required that there are at least five cases with double alteration for a gene pair in any of the three cases.

To address question (ii), we extracted patient samples with TP53 deletion, and performed survival analysis in these samples.

To address question (iii), we extracted breast cancer patients diagnosed as ER-positive or ER-negative and compared single and double alterations in each of the two cases.

To examine the impact of genomic instability, we measured genomic instability in two ways: one is the metric defined by TCGA, which is the fraction of the genome being altered and is based on copy number changes, and the other is our own metric, which is the total number of alterations in the genome. We performed beta regression60 for the fraction, and quasi Poisson61,62 regression for our own metric, using the overall survival months and survival status as covariates. We used the R function glm for parameter estimation and P value calculation.

Data availability

The analyses in this paper are implemented in the R package cancerGI, which will be available on CRAN (

Additional information

How to cite this article: Wang, X. et al. Widespread genetic epistasis among cancer genes. Nat. Commun. 5:4828 doi: 10.1038/ncomms5828 (2014).

Accession codes: RNA-seq data has been deposited in the Gene Expression Omnibus under the accession code GSE58834.