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Syn-Lethality: An Integrative Knowledge Base of Synthetic Lethality towards Discovery of Selective Anticancer Therapies



BioMed Research International
Volume 2014 (2014), Article ID 196034, 7 pages
Research Article

Syn-Lethality: An Integrative Knowledge Base of Synthetic Lethality towards Discovery of Selective Anticancer Therapies

1Bioinformatics Research Centre (BIRC), School of Computer Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798
2Institute for Infocomm Research (I2R), 1 Fusionopolis Way, Singapore 138632
3Genome Institute of Singapore (GIS), Biopolis, Singapore 138672

Received 17 November 2013; Accepted 11 March 2014; Published 22 April 2014

Academic Editor: FangXiang Wu

Copyright © 2014 Xue-juan Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Synthetic lethality (SL) is a novel strategy for anticancer therapies, whereby mutations of two genes will kill a cell but mutation of a single gene will not. Therefore, a cancer-specific mutation combined with a drug-induced mutation, if they have SL interactions, will selectively kill cancer cells. While numerous SL interactions have been identified in yeast, only a few have been known in human. There is a pressing need to systematically discover and understand SL interactions specific to human cancer. In this paper, we present Syn-Lethality, the first integrative knowledge base of SL that is dedicated to human cancer. It integrates experimentally discovered and verified human SL gene pairs into a network, associated with annotations of gene function, pathway, and molecular mechanisms. It also includes yeast SL genes from high-throughput screenings which are mapped to orthologous human genes. Such an integrative knowledge base, organized as a relational database with user interface for searching and network visualization, will greatly expedite the discovery of novel anticancer drug targets based on synthetic lethality interactions. The database can be downloaded as a stand-alone Java application.

1. Introduction

Finding effective anticancer therapies is a major goal of biomedical research. As a devastating human disease, cancer kills millions of people each year. In 2008, the World Health Organization (WHO) predicted that, if new anticancer treatments are not discovered, there will be 26.4 million cancer patients around the world and 17 million cancer deaths by 2030 [1]. The currently prevalent anticancer treatments, chemotherapies, have several limitations, including the drug resistance and the side-effects of toxicity [2]. Although targeted therapies are being developed, the lack of selectivity (i.e., killing both tumour and healthy cells) remains a major issue for current anticancer therapeutics.

Recently, synthetic lethality (SL) has emerged as a novel anticancer strategy that is promising to be highly selective. A pair of genes is defined to have synthetic lethal interactions if the mutation to either gene will not kill the cell but the mutations to both genes will lead to cell death [2] (Figure 1). Compared with healthy cells, cancer cells contain many genetic mutations. Hence, an SL partner of a cancer-specific mutation will be potentially a selective anticancer drug target. A drug that induces a mutation to the SL partner gene will kill cancer cells but spare normal cells, due to the SL interaction with the cancer-specific mutation that is not present in healthy cells.

Figure 1: The concept of synthetic lethality. (a) If just one of the SL pair genes is mutated, then the cell is alive. A/B wild type, a/b-mutated genes; (b) mutation/inhibition of one gene or both genes of a SL gene pair leads to different cell fates [2].

However, the discovery and clinical applications of SL-based anticancer therapies need to overcome several technical obstacles. Most known SL cases are discovered in yeast, and so far only a few SL gene pairs are known in human. A prevalent technique to discover SL genes is high-throughput screening based on chemical or RNAi libraries [3]. Due to genetic heterogeneity of cancer cells, the SL identified from one screening might not be repeatable in another platform or cancer subtypes. Importantly, the screening-based discovery can hardly yield any mechanistic insight into SL interactions. The interpretation of SL candidates is crucial for reliable application of SL-based therapies. To address these issues, systems biology approaches that can uncover the molecular mechanisms of SL in cancer cells would be needed.

The technique of SL was originated from yeast genetics [4]. Due to its rapid generation time, simple culture, and easy-to-handle genetic manipulation, S. cerevisiae has been extensively used to study SL [5]. Computational methods have also been developed to predict and analyze yeast SL [6]. In contrast, there is a dearth of resources (e.g., data, knowledge, or bioinformatics tools) available about SL in human cancer. Recently, some methods have been developed to infer human SL from yeast SL, considering that the genome integrity and cell-cycle related genes from yeast are highly conserved with human and closely related with cancer disease [7]. Massive screening of yeast SL interaction can provide valuable information for SL inference of human cancer. For example, Conde-Pueyo et al. applied the yeast-to-human inference method to obtain potential cancer-related SL target and identified SL partners of cancer-related genes that are drug target [8]. It is highly desirable to integrate data of human cancer SL pairs to reduce the follow-up experimental research in the manageable size.

In this paper, we present an integrative knowledge base dedicated to SL in human cancer, called Syn-Lethality.From literature, we collected SL gene pairs that have been experimentally discovered and verified and integrated them into a network (Figure 2), where each node is a gene and each edge represents an SL interaction. We call such a network as SL network. Moreover, we associated the SL network with related gene annotations and pathway information, to facilitate mechanistic understanding of SL. In addition to human specific SL, we also collected yeast SL, which were mapped to human genes through orthologous correspondence. The information collected as such has been organized into a relational database with user friendly interface. When users input cancer genes (e.g., TP53), Syn-Lethality will search for SL partners of the query genes and display related annotations (e.g., pathways, gene functions, and hyperlinks to the related literature). The SL network we constructed serves as a roadmap for the whole knowledge base.

Figure 2: SL network of human cancer constructing based on SL literatures. Each node in the network denotes a gene/protein and each edge represents an SL interaction (the arrow direction leads from mutation gene to target gene).

To our best knowledge, Syn-Lethality is the first database dedicated to human synthetic lethality. There are few genome wide screenings for SL interactions with human cancer genes, and they are focused on a few well-known oncogenes (e.g., TP53 and KARS). The large-scale screening for human cancer cells is limited by high-cost, false positives, and difficulty to interpret mechanisms, and the information is scattered in the literature. An integrative approach is indispensable for a systematic and mechanistic understanding of human SL. Syn-Lethality database is one of the first attempts to integrate knowledge and data about SL in human cancer. We have also integrated data from yeast and will do so in the future from other model organisms. We believe that it would be a valuable resource and framework that would facilitate novel discovery of potential selective anticancer therapy based on synthetic lethality.

2. Data Integration

2.1. Data Collection and the Literature Search

The primary aim of our Syn-Lethality database is to collect and maintain a high quality set of SL gene pairs, which serves as a comprehensive, fully classified, and accurately annotated knowledge base for SL-related research. The database also provides extensive cross-references and querying interfaces. The SL pairs in Syn-Lethality database are collected by two alternative methods and we will next introduce them in more detail.

The first method for collecting SL pairs is the literature search. We examined the Web of Knowledge and NCBI PubMed databases with the keywords like “synthetic lethality” and then screened with the keyword “human cancer/tumour” from the abstracts. As such, we collected more than one hundred scientific publications. From these articles, we manually extracted more than one hundred SL gene pairs, which have been verified by experiments for cancer treatment. Although the number of SL pairs collected by the literature search is limited, they are highly trustworthy and thus they lay the foundation for our Syn-Lethality database.

The second source of potential SL pairs is the knowledge transfer from the model organism of yeast to human by comparative genomics analysis. Currently, there are quite a few number of SL pairs in yeast which are experimentally detected by various screening techniques. Meanwhile, some human cancer genes (e.g., related with cell cycle, DNA repair) are observed to be highly evolutionarily conserved with yeast cancer genes for inferring human SL pairs of genes based on human-yeast conservation. Therefore, it is possible to infer some SL pairs in human cancers from yeast. We predict a human gene pair to be an SL pair in human cancer based on the following two constraints. First, this human gene pair has a conserved SL interaction in yeast. Second, one of these two genes is a cancer gene. For example, two yeast genes and form an SL relationship while two human genes and are orthologs of and , respectively. If or is a gene that is observed to be mutated in a certain type of cancer, (, ) is then a predicted SL pair in the human cancer. In this paper, all the yeast SL interactions are downloaded from BioGrid [9] (Table 1). However, we noticed that some of these yeast SL pairs from BioGrid involve essential genes. By the definition of SL (i.e., mutation of one gene should not kill the cell, but mutation of both genes kills the cell), both genes in a SL pair should be nonessential. Therefore, with the list of essential genes downloaded from Gerstein Lab at Yale University ( and Saccharomyces Genome Deletion Project ( we collected 6,613 SL pairs without any essential genes. In addition, 507 human cancer genes are downloaded from COSMIC: Cancer Gene Census via the link Finally, we inferred 1,114 SL pairs related with human cancers that are predicted from yeast.

Table 1: Representative entries for human cancer Syn-Lethality database.

Based on the above in silico analysis, the Syn-Lethality database contains 113 SL pairs from NCBI PubMed abstracts and 1,114 SL pairs from the model organism of yeast (Table 3). We also provide additional information about the genes/proteins involved in these SL pairs as shown in Table 1, for example, Entrez gene IDs, full gene name, symbols, gene type (oncogene or tumour suppressor gene), cancer type, pathway information, and some remarks on the molecular mechanisms.

2.2. Pathway/Mechanism Analysis of SL Pairs Directly from the Literature

From the list of SL gene pairs, it is interesting to note that a large fraction of SL pairs are involved in fundamental processes of cell fates, cell cycle, and DNA damage response. We first take the KRAS oncogene as an example. Genome-wide RNAi screen was conducted to identify SL interaction partners of KRAS [10]. We observed that the SL interaction partners of KRAS are involved in the mitotic progression, including the subunits of the anaphase-promoting complex/cyclosome (APC/C) complex (ANAPC1, ANAPC4, CDC16, and CDC27), cyclin A2 (CCNA2), kinesin-like protein 2C (KIF2C), KNL-1 (CASC5), hMis18a and hMis18b (C21ORF45 and OIP5), borealin (CDCA8), and SMC4 and polo-like kinase 1 (PLK1). The inhibition of the above genes will lead to the death of cells in which the KRAS has been mutated [10]. TP53 is another example. It is a major downstream effector of DNA-damage kinase pathways. In response to DNA damage, a normal cell will activate a complex signaling network to arrest cell-cycle progression and facilitate the DNA repair. In contrast, TP53-deficient tumor cells rely on other G2/M checkpoint regulators such as checkpoint kinase 1 (CHK1) to arrest cell-cycle progression. Recently, the SL interactions between TP53 (TP53 is mutated) and ATR/Chk1, WEE1, ATM/Chk2, and MK2 targets have been investigated [11]. As an example, myelocytomatosis viral oncogene homolog, MYC, is a multifunctional, nuclear phosphoprotein that plays a role in cell cycle progression, apoptosis, and cellular transformation, as a transcription factor. Overexpression of MYC sensitizes fibroblasts to agonists of the TNF-related apoptosis-inducing ligand (TRAIL) death receptor DR5. It was shown that MYC mediates increased DR5 expression and signaling as a result of enhanced procaspase-8 autocatalytic activities [12].

As reported by [3, 13], the authors proposed the following four types of mechanisms for SL interactions in human cancers from the perspectives of protein complexes and pathways. First, two complexes may be synthetic lethal when they have an essential function in common and they are uniquely redundant. Second, two units within an essential protein complexes may form SL relationship. Third, two components in a linear essential pathway may be SL partners, because the mutation of each component decreases the flow through the pathway but the pathway still has signal flow, whereas the mutation of both will destroy the pathway. Forth, two components in two parallel essential pathways may be backups of each other for the lethality. Generally, the SL pairs can be interpreted as due to the above four mechanisms. For example, EGFR and BRCA1 are SL pairs because they are in the same essential protein complex [14]. In this paper, we will focus on the analysis of SL pairs from the perspective of signalling pathways and provide three SL examples, in which two partners are from two parallel pathways.

First, TANK binding kinase (TBK1) was identified as a synthetic lethal gene of KRAS [15]. TBK1 is a noncanonical inhibitor of B protein (IB) that is known to regulate nuclear factor B (NFB) signalling. TBK1 activates NF-kB antiapoptotic signals involving c-Rel and BCL-XL (also known as BCL2L1) that were essential for survival. These indicate that TBK1 and NF-kB signalling pathways are essential in KRAS mutant tumours. Second, the inhibition of both EGFR and Notch signalling pathway is dramatically more effective for suppressing tumor growth than blocking EGFR or Notch signalling pathway alone. Normally the activated form of Notch1 restores AKT activity and enables cells to overcome cell death after dual-pathway blockade [16]. Here, the combined EGFR and Notch inhibition decreases significantly the AKT activation and thus suppresses tumor growth more effectively. Third, EGFR, a protooncogene, belongs to a family of four transmembrane receptor tyrosine kinases that mediate the growth, differentiation, and survival of cells. It is often overexpressed in aggressive triple negative breast cancers (TNBCs) and is also associated with other aggressive disease phenotypes. Nowsheens group recently reported that a contextual synthetic lethality can be achieved both in vitro and in vivo with combined EGFR and PARP inhibition with lapatinib and ABT-888, respectively [14]. The mechanism involves a transient deficit of DNA double strand break repair induced by lapatinib and a subsequent activation of the intrinsic pathway of apoptosis. Our Syn-Lethality database contains SL pairs of genes that likely belong to one of the above four mechanisms. The gene function and pathway information in our database will facilitate in silico interpretation of mechanisms.

3. Database Interface

3.1. Usage of SL Database

Our synthetic lethality database contains SL gene pairs in organised form and provides interface to perform query in the database. Our preliminary database is available in the downloadable form from This software is a Java executable file and requires the installation of Java. The required version 10 of Java (free) and it can be installed from Once the Java is installed on local machine, just double clicking on the Java executable file will launch the database interface. Since the database is available in the single setup file, the database can be used simultaneously by many end users for performing the query (Figure 4). The database includes information such as synthetic lethal gene pairs, type of lethality, type of gene alteration, and target genes for synthetic lethality.

Searching in our database can be divided in the following categories.(a)Simple Search. The user is required to provide abbreviations for gene names. For example, for epidermal growth factor receptor we just need to write EGFR and for cyclin-dependent kinase we just need to write CDK in the search field. This helps the user in search for the SL gene pair information without typing long gene names.(b)Batch Search. User can directly copy and paste names of various genes (separated by space) in each field. Figure 3 shows an example of using KRAS as input to query its related SL pairs. This helps find information simultaneously for various synthetic lethal gene pairs.(c)Smart Search. Users have flexibility of searching SL gene pairs based on the Boolean logical operators by selecting logical AND and OR operators from the drop down menu. This helps in analyzing various combinations of SL gene pairs.(d)Genetic Alteration Search. The interface of our database provides user flexibility to screen the SL pairs based on various types of the gene alteration which refer to the gene mutated in cancer. The gene alteration types captured in our database includes overexpression, mutation, activation, inactivation, and deficiency.

Figure 3: An example of KRAS related SL pairs (the alteration types refer to the cancer mutated gene).
Figure 4: SL query interface.

As of now, it is possible to retrieve complete SL gene pair information based on information such as gene names (MYC, EGFR, CDK, and so forth) (Table 2) and types of genetic alterations (overexpression, mutation, activation, and so forth). The relevant research papers for the SL gene pair are provided via web hyperlinks in database search results.

Table 2: List of annotation database links in Syn-Lethality database.
Table 3: Total statistics for human cancer Syn-Lethality database.
3.2. Synthetic Lethality Network

To provide more clear understanding of SL gene pairs, we constructed the network for available SL gene pairs (Figure 2). The diagram depicts the synthetic lethal genes and the target genes. For example, the SL pair information for MYC oncogenic gene is depicted as shown in Figure 5.

Figure 5: Subnetwork of our SL network for human cancer.

4. Conclusion and Future Perspectives

Syn-Lethality is the first comprehensive database constructed through integrating experimentally validated SL pairs of human cancer with the inferred SL pairs from yeast according to the orthologous relation between human and yeast. It is the first attempt to apply the experimentally verified SL pairs to construct a SL network. In the SL network, each node represents a gene/protein and each edge denotes the SL interactions which can be easily linked to the annotation information including gene/protein alteration type, screening method, pathway, mechanism, and the related literature. It is a valuable resource for better understanding SL mechanism in human cancer and developing useful information for anticancer medicine.

Considering that our current database only includes the predicted SL pairs from yeast, it is desirable to collect and predict more SL pairs from other model organisms, such as Caenorhabditis elegans, Zebrafish, and mouse. With the progress of SL experimental screening technology, it is believed that more SL interactions are expected to be identified. We will continue to collect and curate SL pairs of genes. Additionally, using our SL database, we plan to develop data mining algorithms to quickly extract SL information and mechanistic insights. Moreover, by incorporating the signalling pathways associated with the SL pairs of genes, we will construct a comprehensive and global SL network about human cancer.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Authors’ Contribution

Xue-juan Li and Shital K. Mishra contributed equally to this paper.


This research was supported by NTU Start-up Grant (COE-SUG/RSS-1FEB11-1/8), Singapore Ministry of Education (MOE) AcRF Tier 1 Grant RG32/11 (M4010977), and ARC 39/13 (MOE2013-T2-1-079).


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Predicting Cancer-Specific Vulnerability via Data-Driven Detection of Synthetic Lethality




Volume 158, Issue 5, 28 August 2014, Pages 1199–1209


Predicting Cancer-Specific Vulnerability via Data-Driven Detection of Synthetic Lethality

Under an Elsevier user license
Referred to by
  Open Archive


Genome-scale data-driven identification of synthetic lethality in cancer

Synthetic lethality networks successfully predict cancer gene essentiality

Synthetic lethality networks predict 15 year survival in breast cancer patients

Synthetic dosage lethality networks predict drug response in cancer


Synthetic lethality occurs when the inhibition of two genes is lethal while the inhibition of each single gene is not. It can be harnessed to selectively treat cancer by identifying inactive genes in a given cancer and targeting their synthetic lethal (SL) partners. We present a data-driven computational pipeline for the genome-wide identification of SL interactions in cancer by analyzing large volumes of cancer genomic data. First, we show that the approach successfully captures known SL partners of tumor suppressors and oncogenes. We then validate SL predictions obtained for the tumor suppressor VHL. Next, we construct a genome-wide network of SL interactions in cancer and demonstrate its value in predicting gene essentiality and clinical prognosis. Finally, we identify synthetic lethality arising from gene overactivation and use it to predict drug efficacy. These results form a computational basis for exploiting synthetic lethality to uncover cancer-specific susceptibilities.

Graphical Abstract


Synthetic lethality occurs when the perturbation of two nonessential genes is lethal (Hartwell et al., 1997). This phenomenon offers a unique opportunity to develop selective anticancer drugs that will target a gene whose synthetic lethal (SL) partner is inactive only in the cancer cells (Ashworth et al., 2011 and Hartwell et al., 1997). Toward the realization of this potential, screening technologies have been developed to detect SL interactions in model organisms (Byrne et al., 2007, Costanzo et al., 2010 and Typas et al., 2008) and in human cell lines (Bassik et al., 2013, Brough et al., 2011 and Laufer et al., 2013). However, currently their scope is not sufficiently broad to encompass the large volume of genetic interactions that need to be surveyed across different cancer types. New bioinformatics approaches are hence called for to guide and complement the experimental search for SL interactions in cancer.

Previous computational approaches developed to systematically study genetic interactions have mainly focused on yeast, where there are genome-wide maps of experimentally determined SL interactions (Chipman and Singh, 2009, Kelley and Ideker, 2005, Szappanos et al., 2011 and Wong et al., 2004). In cancer, synthetic lethality has been computationally inferred by mapping SL interactions in yeast to their human orthologs (Conde-Pueyo et al., 2009) and by utilizing metabolic models and evolutionary characteristics of metabolic genes (Folger et al., 2011, Frezza et al., 2011 and Lu et al., 2013). Here, we analyze the rapidly accumulating cancer genomic data to identify candidate SL interactions via the data mining synthetic lethality identification pipeline (DAISY). We show that genome-wide cancer SL networks can be used to successfully predict gene essentiality, drug response, and clinical prognosis.



DAISY is an approach for statistically inferring SL interactions from cancer genomic data of both cell lines and clinical samples. DAISY applies three statistical inference procedures, each tailored to specific cancer data sets.

The first inference strategy, termed genomic survival of the fittest (SoF), is based on the observation that cancer cells that have lost two SL-paired genes do not survive, they are strongly selected against. Accordingly, as cells harboring SL coinactivation are eliminated from the cell population, SL interactions can be identified by analyzing somatic copy number alterations (SCNA) and somatic mutation data and detecting events of gene coinactivation that occur significantly less than expected. In fact, very similar concepts are already extensively used to analyze the outcomes of small hairpin RNA (shRNA) screens in cell lines, in which essential genes and SL gene pairs are detected by identifying the shRNA probes that have been rapidly eliminated from the cell population (Cheung et al., 2011 and Marcotte et al., 2012). More recently, a related concept was implemented to identify synthetic lethality in glioblastoma (Szczurek et al., 2013).

The second inference strategy, shRNA-based functional examination, is based on the notion that the knock down of a synthetically lethal gene is lethal to cancer cells where its SL partner is inactive. Accordingly, the SL pairs of a given gene can be detected by searching for genes whose underexpression and low copy number induce its essentiality. This can be conducted via an integrative analysis of the results obtained in shRNA essentiality screens and their accompanying SCNA and transcriptomic profiles.

The third procedure, pairwise gene coexpression, is based on the notion that SL pairs tend to participate in closely related biological processes and hence are likely to be coexpressed (Costanzo et al., 2010 and Kelley and Ideker, 2005). We show that this trend indeed holds in known SLs that have been experimentally detected in cancer (Figure 2).

Given SCNA, somatic mutation, shRNA, and gene expression data of thousands of cancer samples, DAISY traverses over all gene pairs (∼534 million) and examines for every pair if it fulfills each one of the three criteria described above. Gene pairs that fulfill all three criteria in a statistically significant manner are predicted to be SL pairs. Here, we applied DAISY to analyze nine different genome-wide cancer data sets (Barretina et al., 2012, Beroukhim et al., 2010, Cheung et al., 2011, Garnett et al., 2012, Luo et al., 2008,Marcotte et al., 2012 and Cancer Genome Atlas Research Network et al., 2013) (Table S1 available online).

We expanded DAISY to also detect synthetic dosage lethality (Sajesh et al., 2013). While two genes form an SL pair if the inactivation of one gene renders the other essential, two genes form a synthetic dosage lethal (SDL) pair if the overactivity of one of them renders the other gene essential. Importantly, SDL interactions can permit the eradication of cancer cells with overactive oncogenes that are difficult to target directly (such as KRAS), by targeting the SDL partners of such oncogenes. DAISY detects SDL interactions via three inference procedures that are analogous to those outlined above for detecting SL interactions ( Figure 1; Experimental Procedures). More specifically, DAISY defines two genes, A and B, as an SDL pair if their expression is correlated and if the overactivity (amplification and overexpression) of gene A induces the essentiality of gene B. Induced essentiality is detected in two ways: first, according to shRNA screens, by examining if gene B becomes essential when gene A is overactive; second, according to SCNA data, by examining if gene B has a higher SCNA level when gene A is overactive.

The DAISY WorkflowThe three different inference procedures described in the main ...
Figure 1.

The DAISY Workflow

The three different inference procedures described in the main text are applied in parallel to identify SL and SDL gene pairs. The SL and SDL networks are then assembled from gene-pairs that are identified by all three procedures as SLs or SDLs, respectively (the intersection colored in blue).

See also Figure S1 and Table S1.

Evaluating DAISY Based on Experimentally Detected SL Interactions in Cancer

We first examined DAISY based on SL interactions that have been experimentally tested in cancer. We applied DAISY to identify the SL partners of PARP1, the tumor suppressors VHL and MSH2, and the SDL partners of the oncogene KRAS. The predictions were performed for over 7,276 gene pairs that have been experimentally tested in six large scale screens ( Bommi-Reddy et al., 2008, Lord et al., 2008, Luo et al., 2009, Martin et al., 2009, Steckel et al., 2012 and Turner et al., 2008). For every gene pair, DAISY returns four p values that denote the significance of the SL or SDL interaction between the two genes according to each one of the three inference strategies described in the previous section and according to all three approaches together (Figure 1;Experimental Procedures). We utilized these p values to examine the predictions along an increasing p value threshold and generate receiver operating characteristic (ROC) curves (Extended Experimental Procedures).

The DAISY predictor obtains an overall AUC of 0.779, which shows the concordance between the predicted and observed SL and SDL pairs (empirical p value <1 × 10−4;Figure 2A). To assess which of the inference strategies enables DAISY to correctly predict synthetic lethality, we repeated the predictions when using the p values obtained based on only one inference strategy at a time (Figure 2A). An AUC of 0.683 was obtained by predicting SL interactions based only on the SoF approach. These results are further improved by requiring that the gene pairs will also be coexpressed, reaching to an AUC of 0.770. As shRNA-based functional examination is not predictive on its own (an AUC of 0.477), we modified DAISY to consider the shRNA criterion as a soft constraint (Experimental Procedures). Despite the nonpredictability of the shRNA-based functional examination approach in this task, shRNA data are important for the generation of predictive SDL-networks (Supplemental Information; Figure S6). Importantly, DAISY captures well-established and clinically important SL interactions, including the prominent SL interaction between PARP1 and BRCA1/BRCA2 and the synthetic lethality between MSH2 and DHFR ( Figures 2B–2G).

DAISY-Inferred SL and SDL Interactions Match Experimentally Detected ...
Figure 2.

DAISY-Inferred SL and SDL Interactions Match Experimentally Detected Interactions in Cancer

(A) The ROC curves obtained when predicting SL and SDL interactions by applying each of the three inference strategies separately (red, light blue, and purple), the SoF and coexpression approaches together (green), and all three inference strategies together (DAISY, blue). The black line denotes the random ROC-curve.

(B–G) The SCNA and expression patterns of experimentally well-established SL pairs: (B and C) PARP1-BRCA1, (D and E) PARP1-BRCA2, and (F and G) MSH2-DHFR, respectively. For each one of these SL pairs the SCNA levels of one gene are significantly higher when its partner is deleted than when its partner is retained, as expected (one-sided Wilcoxon rank sum test) ( Barretina et al., 2012 and Beroukhim et al., 2010) (B, D, and F), and their expression is significantly correlated (Garnett et al., 2012) (C, E, and G). The error bars in (B), (D), and (F) represent the confidence interval of the SCNA levels across the samples.

Experimentally Examining the DAISY-Predicted SL Partners of the Tumor Suppressor VHL

We next turned to experimentally test SL predictions of the tumor suppressor VHL that is frequently mutated in cancer, especially in clear cell renal carcinomas ( Bommi-Reddy et al., 2008). We applied DAISY to predict the SL partners of VHL and identify among these genes those that are essential in renal carcinoma cells (RCC4) exclusively due to the loss of VHL, resulting in a set of 44 genes ( Extended Experimental Procedures).

We performed a small interfering RNA (siRNA) screen to examine if the predicted genes are preferentially essential in VHL−/− renal carcinoma cells compared with isogenic cells in which pVHL function was restored (Extended Experimental Procedures). Overall, compared to the VHL-restored cells, the VHL-deficient cells are significantly more sensitive to the knockdown of the predicted VHL-SL partners (paired t test p value of 8.25 × 10−4) (Figure 3A, Table S2). Reassuringly, compared to the VHL-restored cells, the VHL-deficient cells are not significantly more sensitive to the knockdown of a control set of 30 randomly selected genes (paired t test p value of 0.255). Compared to another screen that searched for the SL partners of VHL among 88 kinases ( Bommi-Reddy et al., 2008), our screen detected 3.83 times more SL genes (Bernoulli p value of 4.76 × 10−9;Extended Experimental Procedures).

Examining DAISY Predictions of VHL-SLs(A) The differential inhibition score of ...
Figure 3.

Examining DAISY Predictions of VHL-SLs

(A) The differential inhibition score of the predicted SL partners of VHL, ordered in ascending order. Highly selective genes (inversely selective) are those with differential inhibition scores >10 (<−10). Selective genes are those with differential inhibition scores ≥4.8 (the score of the positive control gene MYT1, identified in Bommi-Reddy et al., 2008). Dashed horizontal lines denote the threshold values.

(B) The mean percentage of growth inhibition of VHL-deficient (VHL−/−) and VHL-restored (VHL+) cells at the mideffective concentration of each drug. On top are the one-sided t test p values denoting if the inhibition of the VHL-deficient cells is significantly higher than the inhibition of the VHL-restored cells. The error bars represent the SE across the three replicates of the experiment.

See also Table S2.

We then measured the response of the renal cells to nine drugs whose targets were predicted by DAISY to be selectively essential in the VHL-deficient renal cells. Of note, these drugs are not currently administered to treat cancer, but are Food and Drug Administration (FDA)-approved to treat other clinical conditions, such as hypertension and depression. We managed to identify effects on cell growth for six out of the nine drugs. As predicted, the VHL-deficient cells were significantly more sensitive to each one of these six drugs (higher percentage of inhibition at mideffective concentration) (Figure 3B; Table S2). Reassuringly, this specificity was not observed with the negative control drug Staurosporine, indicating that the selective effect is not due to a general susceptibility of the VHL-deficient cells.

Applying DAISY to Construct Genome-wide Networks of SL and SDL Interactions in Cancer

We applied DAISY to identify all gene pairs that are likely to be synthetically lethal in cancer, resulting in an SL network of 2,077 genes and 2,816 SL interactions (Figure 4), and an SDL network of 3,158 genes and 3,635 SDL interactions (Table S3). As each of the nine data sets examined were analyzed separately to identify SL (SDL) pairs, we tested the mutual overlap between the resulting SL (SDL) sets and found it to be significantly higher than expected (Figure S1).

The SL NetworkEach node represents a gene, and each edge represents an inferred ...
Figure 4.

The SL Network

Each node represents a gene, and each edge represents an inferred SL interaction. Genes that are included in one of the six major clusters of the network are colored according to the main biological process that their cluster is enriched for. Node size is proportional to the number of SL pairs a gene has.

See also Figure S2 and Tables S3 and S4.

Properties of the SL and SDL Networks, Related to Figure 1(A and B) The ...
Figure S1.

Properties of the SL and SDL Networks, Related to Figure 1

(A and B) The hypergeometric p values denoting the significance of the overlap between (A) the Synthetic Lethal (SL)-interactions, and (B) the Synthetic Dosage Lethal (SDL)-interactions inferred from different data sets. NS denotes Not Significant. Zero entries denote p values that are smaller than machine precision.

(C) The SDL-network: Each node represents a gene, and each edge represents an SDL-interaction. Genes that are included in one of the major clusters of the network are colored according to the main biological process their cluster is enriched for. The size of the nodes is proportional to the number of SDL-partners the gene has.

(D) The degree distribution of the SL and SDL networks. The distributions are plotted on a log-log scale, with the power law fitting to them. The R2 denotes the fit between the degree distribution and the power-law.

Both networks display scale-free-like characteristics and are enriched with known cancer-associated genes and biological functions (Figures S1 and S2; Table S4). The genes included in the networks are significantly overexpressed both in normal tissues and especially in cancers (Wilcoxon rank sum p values <6.29 × 10−8). Interestingly, the network genes are significantly associated with cancer proliferation and less associated with normal proliferation (Waldman et al., 2013). They are highly enriched with human orthologs of mouse essential genes (hypergeometric p values <1 × 10−30) and are evolutionary conserved (Wilcoxon rank sum p values <2.99 × 10−17). Moreover, each one of these properties is further emphasized in genes that have a higher degree in the SL or SDL networks (Supplemental Information; Figure S2).

Characterization of SL and SDL Network Genes, Related to Figure 4(A) The ...
Figure S2.

Characterization of SL and SDL Network Genes, Related to Figure 4

(A) The fold-enrichment of various gene groups with orthologs of mouse essential genes. The gene groups include: all network genes (light gray), network genes with a degree above the median (light blue), above the 75th percentile (pink), and above the 90thpercentile (gray) of the network degrees. All the results are statistically significant (p value < 1e-30).

(B–I) Depict various biological characteristics of various groups of genes: genes which are not included in the network (blue), network genes with a low degree (below the median degree in the network, light gray), network genes with a degree above the median (light blue), above the 75th percentile (pink), and above the 90th percentile (gray) of the network degrees. (B-C) The dN/dS ratios computed when comparing human genes to (B) mouse or to (C) rhesus macaque genes. (D) The degree in the PPI network. (E) cPI and (F) nPI values. (G) Gene expression in 30 normal adult tissues. (H) The number of tissues in which the different genes are expressed (expression breadth). (I) The expression in cancer clinical samples.

(J) The hypergeometric p values (on a -log10 scale) denoting the significance of the enrichment of the SL and SDL networks with different types of cancer-associated genes.

The SL and SDL pairs are highly enriched with genes that interact in the protein-protein interaction (PPI) network (hypergeometric p values <4.02 × 10−7). Testifying to their importance, genes included in the SL or SDL networks have a higher degree in the PPI network compared to other genes, especially if their degree in the SL or SDL network is high (Wilcoxon rank sum p values <5.79 × 10−22; Figure S2D). Examining the genomic location of the SL and SDL pairs we find that while SL pairs tend to reside on different chromosomes, or at a large distance from each other on the same chromosome, the SDL gene pairs show the opposite behavior. The latter trend is observed also when identifying SDL interactions without considering the SoF approach. Discarding SDL gene pairs that reside close to each other depreciates the predictive signal of the network (Supplemental Information; Figure S3).

The Genomic Distance between SL and SDL Pairs, Related to the Experimental ...
Figure S3.

The Genomic Distance between SL and SDL Pairs, Related to the Experimental Procedures

(A and B) Each region along the circle represents one of the 24 chromosomes, each edge denotes (A) an SL or (B) an SDL interaction.

(C and D) The significance of the unsupervised drug response predictions obtained for different drugs according to the different SDL-networks described in the text, when testing the predictions according to (C) the CGP or (D) the CTRP data set.

As a direct experimental validation of the predicted SL and SDL interactions is yet impossible on a genome scale, we tested the interactions by examining their utility in three fundamental prediction assignments, the prediction of gene essentiality, clinical prognosis, and drug efficacy. In all tasks, the networks are utilized to generate cancer-specific predictions given a genomic characterization of a specific cancer cell line or clinical sample.

SL-Based Prediction of Gene Essentiality in Cancer Cell Lines

Predicting gene essentiality based on the SL network is cell-line-specific. Indeed, examining the results of shRNA screens, the majority of genes are essential in very few cancer cell lines (Supplemental Information; Figure S4A). As we examined the predictions based on the results obtained in shRNA gene knockdown screens, we constructed an SL network without any shRNA data to avoid potential circularity. Based on this SL network and the genomic profiles of the cell lines, we predicted a gene as essential in a given cell line if one or more of its SL partners is inactive in that cell line (seeSupplemental Information for further details, analyses, and results).

Applying the SL Network to Predict Genes Essentiality in Cancer, Related to ...
Figure S4.

Applying the SL Network to Predict Genes Essentiality in Cancer, Related to Figure 5

(A–C) (A) The distribution of gene essentiality across different cancer cell lines. The number of genes (y axis) as a function of the number of cell lines in which they were found to be essential according to the Marcotte et al. (2012) or Achilles (Cheung et al., 2011) screens. The Spearman correlation (B) coefficients and (C) p values obtained when comparing between the fraction of inactive genes in the cell lines and the prediction-p values obtained for these cell lines. The prediction-p value denotes the enrichment of genes predicted to be essential in a given cell line with genes that were found to be essential in that cell line according to the Marcotte et al. (2012) (blue) or Achilles (Cheung et al., 2011) (gray) screen, under various Deletioncutoff definitions (theSLessentialitycuttoff is set to 1).

(D and E) The fraction of cell lines for which the iHSLN (pink and light blue) and the DAISY-derived SL-network (gray and blue) successfully differentiated between essential and nonessential genes (y axis) across 10 different SLessentialitycutoff values (x axis). Results are shown when considering (D) all genes or (E) only network genes as the background random model, and when testing the predictions based on the Marcotte et al. (2012) (pink and gray), or Achilles (Cheung et al., 2011) (light and dark blue) screen.

(F and G) The overlap between the gene essentiality of BT549 according to different experimental screens and according to the SL-based predictor. (F) The ratio between the observed and expected overlap and (G) the significance of the overlap.

(H–K) The ROC curves obtained when predicting gene essentiality in BT549 based on different predictors, and examining the predictions based on various sources: (H-I) The siRNA screen conducted under (H) normoxia or (I) hypoxia, (J) the shRNA screen (Marcotte et al., 2012), or (K) all three screens (Essconfident).

Overall, we predicted gene essentiality in 129 different cancer cell lines and examined the predictions based on the results of two large-scale gene essentiality screens (Cheung et al., 2011 and Marcotte et al., 2012). Per cell line the predicted essential genes are significantly enriched with genes that were found experimentally to be essential in the pertaining cell line (empirical p value < 2.52 × 10−4; Supplemental Information; Figure 5A; Table S5). Furthermore, the higher the number of predicted inactive SL partners a gene has the more essential it is according to the experimental data (Figures 5B and 5C). Of note, the SL network succeeds more in predicting gene essentiality in cell lines with a higher number of gene deletions (Supplemental Information; Figures S4B and S4C; Table S5). Indeed, in such cell lines it is more likely that gene essentiality arises due to synthetic lethality. Finally, we predicted gene essentiality based on gene pairs that are human orthologs of yeast SLs (Conde-Pueyo et al., 2009). This, however, leads to markedly inferior performance, testifying to the value of the DAISY-inferred SLs (Supplemental Information; Figures S4D and S4E; Table S5).

Predicting Cell-Specific Gene Essentiality Based on the SL Network(A) The ...
Figure 5.

Predicting Cell-Specific Gene Essentiality Based on the SL Network

(A) The fraction of cell lines for which the unsupervised SL network predictor successfully differentiated between essential and nonessential genes (y axis). This is plotted for ten different SLessentialitycutoff values (x axis), denoting the minimal number of inactive SL partners a predicted essential gene has (Extended Experimental Procedures). Results are shown when considering only SL network genes (pink and light blue) or all genes (gray and blue) as the background random model and testing the predictions based on theMarcotte et al. (2012) (pink and gray) or Achilles (Cheung et al., 2011) (blue and light blue) screen. All the results are statistically significant (empirical p value <3.40 × 10−3).

(B and C) The experimental essentiality scores (median and confidence interval) of genes across different cancer cell lines as a function of the number of SL partners they have lost, according to (B) the Marcotte and (C) the Achilles screens (lower experimental gene essentiality scores denote higher essentiality).

(D and E) The ROC curves obtained when predicting gene essentiality across the (D) Marcotte and (E) Achilles cancer cell lines via the supervised SL-based predictors.

(F) The ROC curves obtained by predicting gene essentiality in BT549 via the supervised SL-based predictor and testing the predictions with the genes that were found essential in all screens (blue), in the Marcotte shRNA screen (green), or in the siRNA screen we conducted under normoxia (red) and under hypoxia (yellow). The predictor was trained based on the gene essentiality reported in Marcotte et al. (2012), excluding the BT549 cell line data that was used exclusively for testing.

See also Figure S4 and Tables S5 and S6.

We improved the unsupervised SL-based gene essentiality predictions described above by considering additional features that describe the state of a specific gene in a given cell line according to the SL network (e.g., the average SCNA level of its SL partners). Using these features, we trained neural network models on gene essentiality data (Extended Experimental Procedures). The performances of these supervised prediction models on unseen test sets resulted in ROC curves with AUCs of 0.755 and 0.854 for the Marcotte et al. (2012) and Achilles (Cheung et al., 2011) data, respectively (Figures 5D and 5E). For comparison, we considered the nine cell lines that were tested in both screens and utilized the shRNA scores obtained in one screen to predict gene essentiality according to the other screen (Extended Experimental Procedures). Using the Achilles screen to predict gene essentiality as reported in the Marcotte screen, or vice versa, results in inferior prediction performance, with AUCs of 0.663 and 0.706, respectively.

To further examine the SL-based gene essentiality predictions, we conducted a whole genome siRNA screen in the breast cancer cell line BT549 under normoxia and hypoxia (Extended Experimental Procedures; Table S6). We defined a refined set of essential genes, composed of genes that are essential in BT549 according to our siRNA screen under both conditions and according to the shRNA screen of Marcotte et al. (2012). Indeed, the performance of the SL-based predictor (that was not trained on gene essentiality data of BT549) is further improved when tested on this refined set of essential genes, obtaining an AUC of 0.951 (Figures 5F and S4F–S4K; Supplemental Information).

Counderexpression of SL Pairs Is a Marker of Better Prognosis in Breast Cancer

To examine the SL network in a clinical setting, we analyzed gene expression and 15 year survival data in a cohort of 1,586 breast cancer patients (Curtis et al., 2012). We postulated that counderexpression of two SL-paired genes would increase tumor vulnerability and result in better prognosis. To test this hypothesis, we classified the patients according to each SL pair into two groups: patients whose tumors counderexpressed the two SL-paired genes (SL group) and patients whose tumors expressed at least one of these genes (SL+ group). For each SL pair, we computed a signed Kaplan-Meier (KM) score (Extended Experimental Procedures). The higher the signed KM score is, the better the prognosis of the SL group is compared to the SL+group. Indeed, the signed KM score of the SL pairs is significantly higher than those of randomly selected gene pairs (one-sided Wilcoxon rank sum p value of 3.09 × 10−59). To examine if this result arises from the mere essentiality of genes in the SL network rather than the interaction between them, we repeated the analysis with randomly selected gene pairs involving genes from the SL network that are not connected by SL interactions. Reassuringly, the SL pairs have significantly higher signed KM scores also compared to these random SL network gene pairs (one-sided Wilcoxon rank sum p value of 2.00 × 10−9). Highly significant KM plots were obtained based on 271 SL pairs (log rank and Cox regression p values <0.05, following multiple hypotheses testing correction) (Figure 6A; Table S7).

Predicting Clinical Prognosis Based on the SL NetworkIn parenthesis next to the ...
Figure 6.

Predicting Clinical Prognosis Based on the SL Network

In parenthesis next to the name of each group are the number of patients and the number and percentage of deaths in that group.

(A) The KM plot obtained when dividing the breast cancer samples according to the expression of POLA2 and KIF14 (the most predictive SL pair in terms of breast cancer survival). The red and purple arrows point to the estimated effect of KIF14underexpression, in the context of POLA2 expression and underexpression, respectively.

(B) KM plots depicting the survival of samples that counderexpressed a high number of SL pairs (global SL score above the 90th percentile, in blue) and of samples that counderexpressed a low number of SL pairs (global SL score below the 10th percentile, in red).

(C) The KM plots depict the survival of breast cancer patients uniformly divided into four groups according to their global SL score. As predicted, higher global SL scores are characterized with better 15 year survival.

See also Figure S5 and Table S7.

Next, we classified the patients according to all the SL pairs in the network together. For each sample, we computed a global SL score that denotes the number of SL pairs it counderexpressed. As predicted, samples that counderexpressed a high number of SL pairs had a significantly better prognosis compared to those that counderexpressed a low number of SL pairs (log rank p value of 1.482 × 10−7; Figures 6B and 6C). Again, we examined if this result is due to the mere essentiality of the SL network genes rather than due to the specific SL interactions; we repeated this analysis using 10,000 topology preserving randomized networks consisting of the breast cancer essential genes (Marcotte et al., 2012) (Extended Experimental Procedures). Reassuringly, none of these random networks managed to predict patient survival as significantly as the SL network.

Because breast cancer is a highly heterogeneous disease, we examined whether higher global SL scores are associated with improved prognosis in specific and more homogenous groups of patients—all consisting of the same subtype, grade, or genomic instability level (Bilal et al., 2013). This is indeed the case for all groups except one—grade 1 patients. The global SL scores provide the most significant separation in the grade 2 normal-like subtype and moderate genomic instability groups (log rank p values of 8.64 × 10−5, 1.01 × 10−3, and 1.25 × 10−4, respectively). As expected, the global SL score is significantly negatively correlated with the tumor grade and genomic instability level (Spearman correlation coefficients of −0.407 and −0.267, p values of 2.58 × 10−62and 2.43 × 10−27, respectively) and highly associated with the tumor subtype (ANOVA p value of 4.25 × 10−102; Figure S5). Normal-like tumors have the highest global SL scores, while basal tumors have the lowest scores (Figure S5E). Notably, the prognostic value of the global SL score is significant even when accounting for the tumor grade, subtype, or genomic instability level (Cox p values of 7.18 × 10−4, 3.12 × 10−7, and 4.37 × 10−8, respectively). Lastly, the prognostic value of the global SL scores is superior to that obtained by using genomic instability levels (Figures S5I and S5J).

SL Counderexpression as a Marker for Breast Cancer Prognosis, Related to ...
Figure S5.

SL Counderexpression as a Marker for Breast Cancer Prognosis, Related to Figure 6

(A–H) The distribution of the global SL-scores in relation to various clinical characteristics. The Cox p values on top denote the prognostic significance of the global SL score when accounting for the corresponding clinical characteristic. In (A,C, and F) the patients are uniformly divided into 10 groups, such that groups 1 and 10 have the lowest and highest values, respectively.

(I and J) The prognostic value of the global SL-score in comparison to the genomic instability index. A KM-plot depicting the survival of four groups of breast cancer patients, uniformly divided according to their (I) global SL-score or (J) genomic instability index into four groups. In parenthesis next to name of each group are the number of patients, and the number and percentage of deaths in that group.

SDL-Based Drug Response Prediction Is Improved by Including shRNA Data in the ...
Figure S6.

SDL-Based Drug Response Prediction Is Improved by Including shRNA Data in the Network Construction Process, Related to Figure 7

The fraction of drugs whose efficacy is significantly predicted (Wilcoxon rank sum p value < 0.05) by the SDL-network (gray and blue) or by an alternative SDL-network that was constructed without accounting for shRNA data (pink and light gray). The results are shown when testing the predictions based on the CGP (gray and pink) or CTRP (blue and light gray) data.

Harnessing SDL Interactions to Predict Drug Efficacy

We utilized the SDL network to predict the response of various cancer cell lines to anticancer drugs. As these drugs mainly target oncogenes, we used the SDL network to predict their efficacy rather than the SL network, whose performance is indeed inferior in this task (Supplemental Information). Based on the SDL network and the genomic profiles of the cancer cell lines, we predicted for each drug which cell lines are sensitive and which are resistant to its administration (Extended Experimental Procedures). More specifically, if one of the drug targets had more than one overexpressed SDL partner in a given cell line, the cell line was predicted to be sensitive to the drug administration (Supplemental Information).

To test this approach, we utilized two data sets of drug efficacies that were measured in a panel of cancer cell lines: (1) the Cancer Genome Project (CGP) data (Garnett et al., 2012), and (2) the Cancer Therapeutics Response Portal (CTRP) data (Basu et al., 2013). The SDL network enabled to predict the response of 593 cancer cell lines to 23 drugs and of 241 cancer cell lines to 33 additional drugs when utilizing the CGP and CTRP data sets to test the predictions, respectively. Overall, drugs are significantly more effective in the predicted sensitive cell lines than in the predicted resistant cell lines (empirical p values <5.34 × 10−4; Figures 7A and 7B; Table S8). Considering only the predictions that were obtained for drugs with a sufficiently high number of SDL interactions increases the fraction of drugs that are significantly predicted (Figure 7C). As predicted, the efficacies of drugs increase with the number of overexpressed SDL partners that their targets have in a given cell line (Figure 7D). Exceptions to this trend may be explained by noting that drug efficacy is determined only partially by the essentiality of the drug targets, and additional factors, like the drug membrane permeability, may affect drug efficacies. For comparison, we predicted drug response by applying two other well established approaches: (1) based on the mutation and copy-number status of the drug target(s), and (2) based on the genomic instability index of the cancer cells. The SDL network generates significant predictions for more than twice as many drugs compared to these competing predictors (Supplemental Information).

The SDL Network Predicts the Efficacy of Anticancer Drugs in Cancer Cell Lines(A ...
Figure 7.

The SDL Network Predicts the Efficacy of Anticancer Drugs in Cancer Cell Lines

(A and B) The prediction signal obtained when predicting the response of cancer cell lines to different drugs and testing the predictions based on (A) the CGP data and (B) the CTRP data. Drugs that are significantly predicted via the unsupervised SDL-based predictor are colored in red.

(C) The fraction of significantly predicted drugs when considering only drugs whose targets have an above-threshold number of SDL interactions (unsupervised).

(D) The IC50 (left) and area-under-dose-curve (right) of drugs decrease in cell lines where their target(s) have an increasing number of overexpressed SDL partners (lower values denote higher efficacy).

(E) A subnetwork of the SDL network that enables to significantly predict the sensitivity to ten anticancer drugs. Each node denotes a gene: a drug target (purple) or an SDL partner of a drug target (pink). Each edge represents an SDL interaction. Self-loops denote that the gene is an SDL-partner of itself (see Supplemental Information for further discussion and results concerning such SDL interactions). The edge color denotes the predictive power of the SDL interaction (blue, and red denote a p value lower than 1 × 10−3, and 0.05, respectively). The significance of the prediction based on the entire set of SDL interactions is written next to the name of the drug, in parenthesis; predictions that were tested based on the CGP and CTRP data are written in black and blue, respectively.

(F–I) The drug efficacy predictions obtained by the supervised SDL-based predictors. (F) The predicted versus experimental IC50 log values of 41 drugs measured across 414 cancer cell lines (CGP data). (G) The predicted versus experimental area-under-dose-curve of 50 drugs measured across 241 cancer cell lines (CTRP data). (H–I) For each cancer cell line we computed the Spearman correlation between the measured and predicted efficacies of different drugs in it. The histograms show the distribution of these correlation coefficients across the different cancer cell lines in (H) the CGP and (I) the CTRP data. The blue lines mark the median correlation coefficient.

See also Figure S6 and Table S8.

Focusing on the drugs that were most accurately predicted by using the SDL-network, we found that each one of the SDL interactions involving the targets of these drugs enables, on its own, to accurately predict the response to the pertaining drug (Figure 7E;Extended Experimental Procedures). Among these interactions is the predicted SDL interaction between EGFR and IGFBP3, whose overexpression should accordingly induce sensitivity to drugs targeting EGFR. Reassuringly, it has been shown that IGFBP3is underexpressed in Gefitinib-resistant cells, and the addition of recombinant IGFBP3restored the ability of Gefitinib to inhibit cell growth ( Guix et al., 2008). Another interesting example is the predicted SDL interaction between PARP1 and MDC1. The latter contains two BRCA1 C-terminal motifs and also regulates BRCA1 localization and phosphorylation in DNA damage checkpoint control ( Lou et al., 2003). Indeed,BRCA1/BRCA2 are known to be synthetically lethal with PARP1 ( Lord et al., 2008).

In a manner analogous to that described earlier for predicting gene essentiality, we utilized the SDL network to build supervised neural network predictors of drug efficacies in cancer cell lines (Extended Experimental Procedures). Using only 53 features, we predicted drug efficacies with Spearman correlation coefficients of 0.721 and 0.547 and p values <1 × 10−350 for the CGP and CTRP data, respectively (Figures 7F–7I). We further examined our SDL-based predictors by analyzing results of a large pharmacological screen carried out recently by the same team as CTRP. In this study, the efficacies of ∼500 compounds were measured across >850 cancer cell lines (P.A.C., personal communication). One hundred and twenty six of the tested compounds have at least one target in the SDL network, enabling to predict the response to their administration. Based the SDL network and the genomic profiles of these cell lines (Barretina et al., 2012), we predicted the efficacies of these drugs by using the unsupervised and supervised predictors (trained on the CTRP data). The SDL-based predictors obtained significant predictions (p value < 0.05) of drug efficacy for 83 (65.87%) and 70 (55.6%) drugs, when applying the unsupervised or supervised approach, respectively.


DAISY is a genome scale, data-driven, approach for the identification of cancer SL and SDL interactions. As shown, DAISY successfully captures the results obtained in key large scale experimental studies exploring SLs in cancer, identifies valid SL interactions, and enables to predict gene essentiality, drug efficacy, and clinical prognosis in cancer.

DAISY presents a complementary effort to genetic and chemical screens, narrowing down the number of gene pairs that need to be examined experimentally to detect SL and SDL interactions in cancer. Based on the ROC curve presented in Figure 2A, an experimental screen for discovering SL interactions could be designed to check the SL pairs predicted by DAISY such that 5%, 25%, 50%, or 70% of all the SL interactions that are out there will be detected by examining only 0.25%, 4%, 14%, or 24% of all possible gene pairs, respectively. Hence, testing only the most confident predictions will enable to find up to 20 times more SL pairs than expected by random. Likewise, by applying DAISY to design an siRNA screen for detecting the SL interactions of VHL we identified almost four times as many SL interactions compared to a screen that was designed by applying biological reasoning. In light of these results DAISY could facilitate a more rapid and rational discovery of SL interactions in cancer by guiding focused experimental screens.

Nonetheless, DAISY has several limitations one needs to account for. First, it is restricted to the identification of SL interactions in cancer, as it is based on unique cancer-specific data that captures the genomic instability of cancer cells (e.g., SCNA). As such DAISY cannot be tested by applying it to identify SL interactions in model microorganisms as yeast. Second, DAISY identifies SL interactions based on large scale genomic data and shRNA screens, which are at times noisy and inaccurate (Bhinder et al., 2014). Third, as DAISY is based on the identification of gene inactivation, additional mechanisms of gene inactivation, such as epigenetic and posttranscriptional regulation, should be accounted for in the future. Fourth, the genomic location of genes may result in false-negative and false-positive predictions of SL and SDL interactions, respectively (see Supplemental Information for further analysis). Last, the ability of the SL network to accurately predict gene essentiality in vivo remains to be determined.

We have shown that SL and SDL interactions have a marked cumulative effect (Figures 5B, 5C, and 7D). Thus, a gene can form a useful drug target due to the (possibly partial) inactivation or overactivation of several of its SL or SDL partners, respectively. SL-based treatment can therefore be especially effective in targeting genetically unstable tumors that harbor many gene deletions and amplifications. Furthermore, a drug may be able to kill a broad array of genomically heterogeneous cells, each sensitive to the drug due to the inactivity (overactivity) of a different subset of the SL (SDL) partners of the drug targets. Targeting a gene with many inactive SL and/or overactive SDL partners may hence counteract the development of treatment resistance, especially if the SL/SDL partners reside on different chromosomes or in distant genomic locations. Moreover, SL-based treatment can induce the reactivation of a tumor suppressor or the inactivation of an oncogene by targeting its SL or SDL pair, respectively.

Four main translational challenges could potentially be tackled by utilizing SL and SDL networks: (1) ranking existing treatments for a given patient based on the genomic characteristics of the tumor, as initially shown here in cell lines; (2) repurposing approved drugs that are currently used to treat other diseases to treat cancer, as shown here for treating a VHL-deficient cancer; (3) systematically identifying new drug targets; and (4) predicting cancer prognosis, as shown here for breast cancer. Taken together, SL and SDL network-based analysis combined with personalized genomics can provide an important future tool for assessing response to treatment and for developing more selective and effective personalized therapeutics.

Experimental Procedures

Description of DAISY

DAISY identifies candidate SL and SDL interactions by applying three separate statistical inference procedures. Each procedure has its own input and outputs a set of candidate SL or SDL pairs. Gene pairs that are identified as candidate SL or SDL pairs by all three procedures are identified by DAISY as SL or SDL pairs, respectively. The three inference procedures are described below (comments in parenthesis refer to changes made to identify SDL pairs):


The genomic SoF procedure analyzes a set of input data sets denoted as SoFdata sets. Each data set includes SCNA profiles of cancer samples and optionally their mRNA and somatic mutation profiles. For every pair of genes, denoted as A and B, and every data set S in SoFdata sets, a Wilcoxon rank sum test is conducted to examine if gene B has a significantly higher SCNA level in samples in which gene A is inactive (overactive) than in the rest of the samples. The output consists of gene pairs that, according to at least one of the data sets in SoF data sets, pass the test described above in a statistically significant manner (a Wilcoxon rank sum p value <0.05 following Bonferroni correction for multiple hypotheses testing).


The shRNA-based functional examination procedure analyzes a set of data sets denoted as shRNAdata sets. Each data set includes the results obtained in a gene essentiality (shRNA) screen together with the SCNA and gene expression profiles of the cancer cell lines examined in that screen. For every pair of genes, denoted as A and B, and every data sets S in shRNAdata sets, a Wilcoxon rank sum test is conducted to examine if gene B has significantly lower shRNA scores in samples in which gene A is inactive (overactive) than in the rest of the samples (the lower the shRNA score is, the more essential the gene is). The output consists of gene pairs that, according to at least one of the data sets in shRNAdata sets, pass the test described above in a statistically significant manner (a Wilcoxon rank sum p value <0.05).


The pairwise gene coexpression procedure is given a set of transcriptomic data sets of cancer samples and returns gene pair whose expression, in at least one of the data sets, is significantly positively correlated (a Spearman correlation coefficient ≥Rmin and a p value < 0.05 following Bonferroni correction for multiple hypotheses testing).

The candidate SL or SDL pairs that are identified in the first and third procedures are obtained with highly stringent statistical cutoffs, a p value <0.05 following Bonferroni correction. The data obtained in shRNA screens has a low statistical power and is hence utilized (in the second procedure) only to further refine the already highly statistically significant SL and SDL sets identified in the first and third procedures.

The first and second procedures are based on the detection of gene inactivation and overactivation in the samples analyzed. A gene is defined as inactive in a sample if it is underexpressed and its SCNA is below −0.3 or if it is mutated with a deleterious mutation. The latter refers to nonsense and frame-shift mutations. Likewise, a gene is defined as overactive in a sample if it is overexpressed and its SCNA is above 0.3. Of note, the SCNA parameters (−0.3 and 0.3) used here are more stringent cutoffs compared to those used in the literature to define gene amplification and deletion (Beroukhim et al., 2010). A gene is defined as underexpressed in a given sample if its expression level is below the 10th percentile of its expression levels across all samples in the data set, and similarly, as overexpressed if its expression level is above its 90th percentile. In the third procedure we set Rmin to 0.5.

To find the candidate pairs and construct the SL and SDL networks, we applied DAISY with the data sets listed in Table S1 and traversed over all ∼535 million gene pairings. To do so efficiently, DAISY was implemented and run on the HTcondor architecture, which enables parallel computing (Thain et al., 2005).

Network Availability and Visualization

Interactive maps of the networks are accessible through∼livnatje/ and can be explored using the freely available Cytoscape software (Cline et al., 2007). The maps include different gene properties and annotations, as well as alternative views that dissect the network hubs or genes with specific characteristics. We clustered the SL and SDL networks by applying the Girvan-Newman fast greedy algorithm as implemented by the GLay Cytoscape plug-in (Morris et al., 2011 and Su et al., 2010) and performed gene annotation enrichment analysis for every network and every network cluster via DAVID (Huang et al., 2009).

Author Contributions

E.R. supervised the research. E.R. and L.J.A. conceived and designed the computational approach, analyzed the data, and wrote the paper. L.J.A. performed the statistical and machine learning analyses. E.G. designed and supervised the siRNA screens performed in his lab by N.P., L.M., D.J., and E.S., P.A.C., and B.S.-L. provided and analyzed pharmacological screening data. L.J.A. and Y.Y.W. performed the clinical survival analysis. Y.Y.W. performed the evolutionary and PPI network analysis. A.W. preprocessed the SCNA data. T.G. and E.G. provided insights regarding the biological aspects of the work. T.G. and Y.Y.W assisted in writing the paper.


We thank A. Wagner, D. Horn, D. Steinberg, E. Halperin, I. Meilijson, L. Wolf, M. Kupiec, M. Oberhardt, and R. Sharan for their help and comments. We thank E. MacKenzie for technical support. L.J.A. and A.W. are partially funded by the Edmond J. Safra bioinformatics center and the Israeli Center of Research Excellence program (I-CORE, Gene Regulation in Complex Human Disease Center No 41/11). L.J.A. was also funded by the Dan David foundation and by the Adams Fellowship Program of the Israel Academy of Sciences and Humanities. Y.Y.W. was supported in part by Eshkol fellowship (the Israeli Ministry of Science and Technology). E.R.’s research in cancer is supported by grants from the Israeli Science Foundation (ISF) and Israeli Cancer Research Fund (ICRF). E.R. and T.G. are supported by the I-CORE program.

Supplemental Information


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Corresponding author
Corresponding author

The Concept of Synthetic Lethality in the Context of Anticancer Therapy






Nature Reviews Cancer 5, 689-698 (September 2005) |doi:10.1038/nrc1691

The Concept of Synthetic Lethality in the Context of Anticancer Therapy

William G. Kaelin, Jr1  About the author


Two genes are synthetic lethal if mutation of either alone is compatible with viability but mutation of both leads to death. So, targeting a gene that is synthetic lethal to a cancer-relevant mutation should kill only cancer cells and spare normal cells. Synthetic lethality therefore provides a conceptual framework for the development of cancer-specific cytotoxic agents. This paradigm has not been exploited in the past because there were no robust methods for systematically identifying synthetic lethal genes. This is changing as a result of the increased availability of chemical and genetic tools for perturbing gene function in somatic cells.

The bottleneck to the development of safe and effective anticancer drugs does not lie in an inability to identify chemicals that will kill cancer cells. In fact, thousands of compounds have been identified over the past 50 years that will accomplish this feat. Instead, the bottle-neck lies in our inability to identify chemicals that will kill cancer cells at concentrations that do not harm patients. Most of the chemotherapeutic agents used today have remarkably low THERAPEUTIC INDICES and narrowTHERAPEUTIC WINDOWS. The therapeutic window is influenced by a number of factors, including the shape of the curve that relates the intended biological effect of the drug to changes in the activity of its intended target (‘on-target’), and the propensity of the drug to affect unintended targets (‘off-targets’) at higher doses. Off-target effects can cause toxicity and, in some cases, antagonize on-target biological effects. Most anticancer drugs in use today were discovered based on their ability to kill rapidly dividing cancer cells in vitro. Predictably, when administered to patients, many of these drugs also injure rapidly dividing normal cells, such as bone-marrow haematopoietic precursors and gastrointestinal mucosal epithelial cells. In addition, many of these drugs are toxic to normal cells that are not rapidly dividing. Examples include doxorubicin (toxic to the heart), bleomycin (toxic to the lung) and cytarabine (toxic to the cerebellum). These other forms of organ damage become particularly important (dose-limiting) in settings in which toxicity to rapidly dividing cells can be partially ameliorated through supportive-care measures (such as bone-marrow transplantation). For these reasons, it is imperative that anticancer drugs be developed that can kill cancer cells at clinically achievable concentrations, with therapeutic indices that are higher than those of classic cytotoxic agents.

Therapeutic index

Many factors influence the therapeutic index of a drug. Some relate to the quality of the drug itself — for example, its ability to distinguish between intended and unintended targets. Others relate to the nature of its target — for example, its distribution, its normal function(s), and the degree to which those functions must be altered to achieve the desired effect. Most antibacterial agents are remarkably safe because their targets are present in the organisms they are designed to kill but not in normal host cells. However, many other relatively ‘safe’ drugs — such as anti-hypertensives, anti-anxiety drugs and cholesterol-lowering agents — inhibit normal cellular proteins. These drugs are clinically useful because their effects are titratable (through changes in dose and schedule), and quantitative changes in the activities of their targets lead to the desired changes in host physiology.

Two paths can be envisioned to arrive at an anticancer drug that would selectively kill cancer cells. The first, which is modelled on the development of anti-infectious agents, would be to identify drug targets that are essential for the viability of cancer cells but are not present in normal cells (the so-called ‘target-driven therapeutic index’)1, 2 (Fig. 1). The fusion proteins generated by cancer-associated chromosomal translocations might, at first glance, seem to be ideal in this regard. However, this presumes that drugs can be developed that will discriminate between a particular protein (or functional subdomain) in its normal context and in its pathogenic, fused state. This might be difficult. For example, it is fallacious to argue that the efficacy and safety of imatinib mesylate (Glivec) for the treatment of chronic myelogenous leukaemia (CML) stems from the fact that its target, breakpoint cluster region (BCR)–Abelson murine leukaemia viral oncogene homologue (ABL), is unique to CML cells because imatinib mesylate inhibits the kinase activities of both BCR–ABL and ABL (in addition to several other cellular kinases)3. So, the relatively high therapeutic index of imatinib mesylate cannot be explained by the restriction of its target(s) to CML cells (see below for potential alternative explanations). Similarly, it might be difficult to develop drugs that directly inhibit oncoproteins that result from point mutations without affecting their normal counterparts.

Figure 1 | Framework for developing anticancer drugs with a high therapeutic index.

Figure 1 : Framework for developing anticancer drugs with a high therapeutic index. Unfortunately we are unable to provide accessible alternative text for this. If you require assistance to access this image, or to obtain a text description, please contact npg@nature.comAn anticancer drug might have a high therapeutic index because its target is uniquely present in cancer cells (a), or because the requirement for its target is quantitatively or qualitatively different in cancer cells than in normal cells (b and c). This differential requirement might be because of intrinsic differences in the cells (b), such as genetic (red) and epigenetic (blue) differences, or extrinsic differences in the cells (c), such as loss of survival signals provided by normal cell–cell and cell–matrix interactions. Modified with permission from Ref. 2 © (2002) Elsevier Science.

A second way to achieve enhanced cancer-cell selectivity, however, would be to identify situations where the requirement for a particular target was enhanced in the context of a cancer cell compared with normal cells (the so-called ‘context-driven therapeutic index’)1, 2 (Fig. 1). The requirement for a particular target might be increased because of changes that are intrinsic to the cancer cell (for example, through epigenetic or genetic changes), extrinsic to the cancer cell (for example, as a result of microenvironmental changes leading to altered cell–matrix and cell–cell interactions), or both.

All of the anticancer drugs in use today affect targets that are shared between normal cells and cancer cells, including enzymes involved in fundamental processes such as DNA replication. The fact that their therapeutic indices, however small, exceed unity, coupled with the observation that they can, in certain settings, induce striking remissions and occasionally cures (for example, cisplatinum-based regimens for testicular cancer), indicates that contextual differences between normal cells and cancer cells are therapeutically exploitable. So, can our growing knowledge of cancer genetics, coupled with a more sophisticated understanding of gene–gene interactions, be used to identify drug targets that have enhanced therapeutic indices by virtue of such contextual differences? Studies of gene–gene interactions in model organisms have provided a conceptual framework for this task.

Synthetic lethality

Two genes (‘A‘ and ‘B‘) are said to be ‘synthetic lethal’ if mutation of either gene alone is compatible with viability but simultaneous mutation of both genes causes death4, 5, 6, 7, 8, 9 (Fig. 2). This concept can be extended to situations in which simultaneous mutation of two genes impairs cellular fitness more than mutation of either gene alone (‘synthetic sick’). In either of these two situations, A buffers the effect of changes in B and vice-versa, but this buffering is lost when both A and B are mutated at the same time4, 6, 10. Synthetic lethal interactions have most commonly been described for loss-of-function alleles, but can also involve gain-of-function alleles. For example, gene B might become essential for survival when a particular gene A is overexpressed (known as synthetic dosage lethality)11, 12, 13. Approximately 20% of genes in the budding yeastSaccharomyces cerevisiae are individually essential, but genetic screens in this organism suggest that synthetic lethal interactions are common among the remaining 80% (perhaps on the order of 10 interactions per gene)10, 14, 15.

Figure 2 | Gene–gene interactions: synthetic lethal and suppressive interactions for two genes.

Figure 2 : Gene|[ndash]|gene interactions: synthetic lethal and suppressive interactions for two genes. Unfortunately we are unable to provide accessible alternative text for this. If you require assistance to access this image, or to obtain a text description, please contact npg@nature.comTwo genes (‘A‘ and ‘B‘) are said to be ‘synthetic lethal’ if mutation of either gene alone is compatible with viability but simultaneous mutation of both genes causes death. B is an extragenic suppressor of A if mutation of Bsuppresses the phenotype observed whenA is mutated. A lowercase letter denotes a mutant.

Loss-of-function alleles having a synthetic lethal (or synthetic sick) relationship can often, but not always, be easily rationalized based on the functions of their protein products. They might, for example, be uniquely redundant with respect to an essential function (as occurs in some PARALOGUES), be two subunits of an essential multiprotein complex, be two interconnected components in an essential linear pathway (with each mutation decreasing the flux through the pathway), or participate in parallel pathways that are together essential for survival (for example, a crucial metabolic pathway and an alternative or salvage pathway). The concept of synthetic lethality can be further extended to embrace the situation where mutation of A is lethal only in combination with mutations that affect several non-essential genes B, C, D and so on2, 6.

It has been suggested that the concept of synthetic lethality could be used to choose anticancer drug targets1, 7, 16. In particular, the protein products of genes that are synthetic lethal to known cancer-causing mutations, if amenable to pharmacological attack (for example, if they encode an enzyme), should theoretically represent excellent targets for anticancer therapy. This approach simultaneously tackles two vexing problems in cancer pharmacology. The first relates to the fact that many cancer-associated mutations, like most drugs, induce a loss of function1, 2. Therefore, it is not immediately obvious how to pharmacologically approach cancer cells in which, for example, a particular tumour-suppressor protein is crippled (or worse yet, absent). Targeting a protein that is synthetic lethal to such a lost or crippled protein provides an elegant solution to this problem. The second problem relates to whether it is possible to achieve selectivity by inhibiting proteins that are also important for cellular homestasis. If A and B are synthetic lethal (or synthetic sick), then inhibitors of B should selectively kill (or inhibit) cancer cells with mutant A. In the ideal situation, complete neutralization of B, genetically or pharmacologically, would have no effect on normal cells, and even partial inhibition of B in cancer cells would cause death (because of mutant A; Fig. 3, left panel). However, Binhibitors might display a significant therapeutic index even when these ideal conditions are not met. This would require that the Amutation shifts or alters the fitness dose–response curve of the Binhibitor such that keeping B activity below a certain threshold selectively impairs cells with mutant A (Fig. 3, middle and right panels).

Figure 3 | Theoretical fitness curves for wild-type andA-/- cells in response to a drug that inhibits the B gene product.

Figure 3 : Theoretical fitness curves for wild-type and A|[minus]|/|[minus]| cells in response to a drug that inhibits the B gene product. Unfortunately we are unable to provide accessible alternative text for this. If you require assistance to access this image, or to obtain a text description, please contact npg@nature.comA reading of 0% fitness denotes death, whereas 100% fitness denotes the wild-type state (for simplicity, fitness >100% is not considered in these examples). In the middle panel, a therapeutic window is created by a shift in the fitness curve when gene A is absent. In the left and right panels the therapeutic window is created by changes in the shapes of the fitness curves when gene A is absent.

It could be argued that some (and perhaps most) anticancer drugs in use today are, at least in hindsight, exploiting synthetic lethal, or synthetic sick, interactions. For example, synthetic lethal relationships between DNA-replication genes (such as certain DNA polymerases) and DNA-repair genes (such as mismatch-repair genes) are well documented in model organisms7, 16. It seems likely that the efficacy of the many anticancer drugs that interfere with DNA synthesis is due, at least in some cases, to the presence of tumour-associated mutations that affect DNA repair or the response to DNA damage. Another example of synthetic interactions is provided by certain chemotherapeutic agents and mutations that directly or indirectly compromise the function of the retinoblastoma protein (pRB, encoded by the RB1 gene) tumour suppressor. Inactivation of pRB has been documented in many cancers and leads to an increase in E2F activity, which, in turn, activates various genes involved in S-phase entry17. One of these, topoisomerase II, causes DNA strand breaks and apoptosis when bound to topoisomerase inhibitors such as etoposide. As would be predicted, pRB-pathway mutations sensitize cells to drugs that inhibit topoisomerase II (Refs 18–21). In addition, E2F1, like the oncoprotein MYC, increases the expression of many pro-apoptotic genes, including the p53 paralogue p73, which might sensitize pRB-defective cells to drugs that elicit additional apoptotic signals (such as DNA-damaging agents)22, 23, 24, 25.

Two newer anticancer agents also exploit contextual differences between cancer cells and normal cells. Studies in model organisms suggest that mutations affecting chaperones that are involved in protein folding can unmask the deleterious consequences of various mutations26. Preclinical data indicate that HSP90 (heat-shock protein of 90kDa) inhibitors have anticancer activity, and that certain mutant oncoproteins, such as mutant BRAF and mutant EGFR (epidermal growth factor receptor), have an increased requirement for HSP90 function27,28, 29. One HSP90 inhibitor, 17AAG, has completed phase I testing and is entering phase II studies. The accumulation of mutated and/or misfolded proteins might also alter the requirement of a cell for proteasomal function30. The proteasomal inhibitor bortezomib is well tolerated in humans and was recently approved for the treatment of multiple myeloma31.

Discovery of human synthetic lethal interactions

Our knowledge of the molecular networks that are established in normal cells and cancer cells is too rudimentary to allow reliable predictions of the genes that will be synthetic lethal to a given cancer gene. Nonetheless, a few ideas have been put forward for how synthetic lethal combinations might be achieved, based on first principles. Many oncoproteins, including E2F1 and MYC, represent a double-edged sword for cancer cells because they deliver both pro-mitogenic and pro-apoptotic signals. A counterintuitive approach to treating cancer cells that have hyperactive oncoproteins such as these would be treating them with drugs that enhance their action further, in the hope of crossing an apoptotic threshold. For example, E2F1 is negatively regulated by both pRB and cyclin A32, 33, 34, 35. Loss of the pRB pathway establishes a positive-feedback loop in which E2F1 activates its own promoter36, and blocking the remaining interaction of cyclin A with E2F1 kills transformed cells but not their normal counterparts37, 38, 39. Unfortunately, inhibiting the activity of the cyclin-A partner CDK2 (cyclin-dependent kinase 2) does not have the same effect40, possibly because another catalytic partner can substitute for CDK2 in its absence41, 42. Synthetic lethal interactions might also be predicted based on the loss of particular cell-cycle checkpoints16. For example, S-phase cells, in contrast to G1 cells, can be induced to undergo premature chromosomal condensation under certain conditions, such as treatment with caffeine at doses that inhibit ATR (ataxia telangiectasia and RAD3-related protein)43, 44, 45, 46. Cells that lack p53, which has a role in G1 control, are more susceptible to caffeine than their wild-type counterparts47.

There are now multiple examples of cancers that seem to be dependent on or ‘addicted’ to certain activated oncogenes (gene-replacement experiments suggest that tumour cells can also become addicted to the inactivation of tumour-suppressor genes). Oncogene addiction might underlie the success of the kinase inhibitor imatinib mesylate for CML (in which the oncogene is BCR–ABL) and gastrointestinal stromal tumours (in which the oncogene is KIT)3 and of the EGFR inhibitor gefitinib for EGFR-mutated non-smallcell lung cancer48, 49, 50, 51. Bernard Weinstein, who coined the term ‘oncogene addiction’, initially envisioned that this phenomena was related to the ability of such oncogenes, which can be viewed as nodes in complex molecular networks, to simultaneously deliver proliferative and antiproliferative signals52 (Fig. 4A). As long as the oncogene signal is sustained, the proliferative signal — which might promote mitogenesis, survival, or both — would dominate. However, if the oncogene is acutely silenced, the antiproliferative signal dominates, leading to cessation of growth or cell death (in this scenario it must be invoked that the antiproliferative signal ‘decays’ more slowly than the proliferative signal when the oncogene is inhibited)2.

Figure 4 | Models of oncogene addiction.

Figure 4 : Models of oncogene addiction. Unfortunately we are unable to provide accessible alternative text for this. If you require assistance to access this image, or to obtain a text description, please contact npg@nature.coma | Many oncogenes paradoxically induce pro-mitogenic signals as well as anti-mitogenic (or pro-apoptotic) signals. Growth stimulation results from oncogene activation presumably because the former is dominant to the latter. However, acute inactivation of the oncogene might cause growth cessation or death if the anti-mitogenic/pro-apoptotic signals decay more slowly than the mitogenic signals (for example, because of differences in mRNA and protein half-life). Adapted from Ref. 53. b | Oncogene dependency due to gene–gene interactions. Cancer cells accumulate mutations (arrows) over time that cumulatively lead to a transformed phenotype. Selection favours acquisition of mutations that are neutral or beneficial (adaptive) in the context of the mutations that preceded them. However, some of these changes might be deleterious (red arrow) were it not for the changes that preceded them. If true, correcting early genetic changes (yellow arrow) will unmask these deleterious effects. In this model, cancer cells behave like a molecular ‘house of cards’. c | Activation (indicated by bold arrow) of an oncogenic pathway diminishes selection pressure to maintain collateral signalling pathways. Silencing of these collateral pathways over time, because of genetic or epigenetic changes, leads to oncogene dependency. Adapted from Ref. 57.

Superimposed on the network abnormalities that are induced by activated oncogenes are network abnormalities that are induced by mutations at other loci. The resulting abnormalities in molecular circuitry create additional opportunities for oncogene addiction1, 2, 53, 54, including those that arise as a result of gene–gene interactions, such as synthetic lethality and extragenic suppression. Cancers arise through sequential genetic changes that ultimately convert a normal cell to a fully transformed one. These mutations are under selective pressure to be adaptive or neutral, from the point of view of the cancer, in the context of the mutations that preceded them (Fig. 4B). It seems likely, a priori, that some of the mutations that occur late in the evolution of a cancer cell might only be advantageous, or indeed even tolerated, because of the mutations that preceded them (or put another way, these mutations would be deleterious if not for the mutations that had preceded them). In the extreme case, an early A mutation might be an extragenic suppressor of the lethality that would otherwise be caused by a late B mutation (Fig. 2, right panel). If this is true, correcting the A mutation should cause death because of the acquisition of the B mutation. For example, RB1 inactivation, as described above, leads to increased E2F activity, which can stimulate S-phase entry but can also promote p53-dependent apoptosis55, 56. So, a tumour in which TP53 was already mutated might derive an additional benefit from mutating RB1 but at the price of becoming addicted to p53 loss (in the sense that restoring p53 function would lead to apoptosis).

Similarly, Mills and colleagues have suggested that oncogene addiction might arise because of the loss of collateral signalling pathways. This is due to genomic instability coupled with the loss of selection pressure to maintain the collateral signalling pathways57, a process referred to as ‘genetic streamlining’58(Fig. 4C). Collectively, these ideas suggest that the pathways that are activated early in the course of tumour progression (owing to oncogene activation or tumour-suppressor-gene inactivation) are likely to be excellent therapeutic targets because of synthetic interactions with the mutational changes that followed them. Silencing these pathways should reveal the deleterious consequences of these subsequent changes, whether these changes did or did not contribute to tumour progression. The potential interrelationship between oncogene addiction and synthetic lethality is illustrated by the phosphatase and tensin homologue (PTEN) tumour-suppressor protein, which negatively regulates the phosphatidylinositol 3-kinase (PI3K) pathway, and mTOR (mammalian target of rapamycin). PTEN-/- cells are reported be more sensitive to the antiproliferative effects of mTOR inhibitors than their wild-type counterparts59. This observation indicates that PTEN-/- cells are ‘addicted’ to PI3K–mTOR signalling, and that PTEN and mTOR have a synthetic sick relationship.

Chromosomal deletions in cancer cells lead to the loss of one or both copies of many genes. Frei suggested that cancer-cell vulnerabilities to pharmacological attack might also be gleaned by examining the functions of contiguous genes that are homozygously deleted along with tumour-suppressor genes60. For example, the gene encoding methylthioadenosine phosphorylase (MTAP) — which has a role in a salvage pathway for adenosine biosynthesis — is often co-deleted with the adjacent CDKN2A locus, which encodes the tumour-suppressor proteins INK4A and ARF on 9p21 (Ref. 61). As would be predicted, cells that lack MTAP have increased sensitivity to L-alanosine — a potent inhibitor of de novo AMP synthesis — and to an inhibitor of de novo purine-nucleotide synthesis, 6-methylmercaptopurine riboside (MMPR)62.

Kamb suggested that expression databases be mined for paralogous genes in which one or more members were underexpressed in cancer cells relative to normal cells (for example, as a result of haploinsufficiency or homozygous deletion)58. A drug that inhibited the remaining paralogue(s), but not the differentially expressed paralogue, would, theoretically, be cancer-cell selective. This approach, however, presumes that it is possible to develop drugs that can discriminate between paralogous proteins. Moreover, synthetic lethal screens in yeast indicate that paralogous pairs represent a minority of the potential synthetic lethal combinations in a cell10, 15, 63. Therefore, unbiased chemical and genetic screens are likely to be the most fruitful methods for identifying novel synthetic lethal relationships on which to base new cancer treatments.

Screens for synthetic lethal interactors

The example of topisomerase II inhibitors, as cited above, demonstrates that proteins bound to drugs might have effects that are very different from those predicted by true null mutations, or by techniques such as RNA interference (RNAi) that cause quantitative reductions in protein abundance. For example, a drug might interfere with one function of a multifunctional protein, or cause a protein to act in a dominant-negative or dominant-positive manner. For this reason, screens for synthetic lethality that are carried out using libraries of chemical compounds are likely to be complementary to screens that are carried out using genetic tools (such as RNAi or short interfering RNA; siRNA).

Chemical screens. Hartwell and Friend pioneered the idea of screening for drug-like chemicals that specifically kill yeast deletion mutants with defects in cell-cycle checkpoints or DNA repair16, 64. This paradigm can be extended to human cells. A number of groups have identified chemicals from collections of pure compounds, or that are present in complex mixtures (for example, extracts or broths), that selectively inhibit cells with cancer-relevant genetic alterations using isogenic human cell-line pairs grown in multiwell plates (Fig. 5). Schreiber and co-workers identified marine sponge extracts that preferentially inhibited the proliferation of Trp53-/- mouse embryonic fibroblasts, as determined by BROMODEOXYURIDINE (BRDU) INCORPORATION, relative to wild-type mouse embryonic fibroblasts65. However, the chemical entities responsible for these effects were not identified. Kinzler and co-workers co-cultured KRAS-mutated colon cancer cells (engineered to produce blue fluorescent protein) with a subclone in which the mutant KRAS allele was eliminated by homologous recombination (and engineered to produce yellow fluorescent protein), and monitored differential killing using the ratio of blue/yellow fluorescence66 (Fig. 6A). Several chemical entities, including a novel cytidine nucleoside, were found that selectively killed cells containing mutant KRAS. A fluorescence-based mammalian synthetic lethal assay, which was modelled after earlier yeast assays67, was also developed by Canaani and colleagues68, 69 (Fig. 6B). Leder and co-workers discovered a small molecule called F16, which selectively killsERBB2 (also known as HER2/NEU)-overexpressing mammary epithelial cells, compared with their normal counterparts70, 71. The toxicity of F16 correlates with its selective uptake in, and disruption of, mitochondria of cells that are transformed with ERBB2. Stockwell and co-workers identified a number of compounds that preferentially killed primary human cells that were transformed in vitro with human telomerase reverse transcriptase (TERT), RAS, and oncoproteins that affect pRB, p53 and/or protein phosphatase 2A (PP2A)21. Included among these were clinically useful inhibitors of topoisomerase I and II. In a focused screen of pro-apoptotic agents Quon and colleagues discovered that human cells overexpressing MYC displayed increased sensitivity to the death receptor DR5 agonist tumour-necrosis-factor-related apoptosis-inducing ligand (TRAIL) in vitroand in vivo, and linked this to p53-independent induction of DR5 by MYC72. Recent studies suggest that it is possible to screen pairwise combinations of drugs against ISOGENIC cell lines to uncover novel drug–gene and drug–drug interactions73, 74.

Figure 5 | Synthetic lethal screening with chemical or interfering RNA libraries.

Figure 5 : Synthetic lethal screening with chemical or interfering RNA libraries. Unfortunately we are unable to provide accessible alternative text for this. If you require assistance to access this image, or to obtain a text description, please contact npg@nature.comIsogenic cell-line pairs that do or do not harbour a cancer-relevant mutation (in the case illustrated, the cell-line pair differs only with respect to a particular tumour-suppressor gene (TSG)) are grown in multiwell plates to which different chemical or genetic (short interfering RNAs, short hairpin RNAs or other interfering RNAs) perturbants are added. In time, such assays might be carried out using microarrays spotted with chemicals or siRNA species104, 105. A ‘hit’ is a perturbant that is cytostatic or cytotoxic to the cell with the cancer-relevant mutation (arrow). It should be noted that the interpretation of such assays needs to consider potentially confounding effects, such as differences in proliferation rate and cell-cycle distribution.

Figure 6 | Fluorescence-based mammalian synthetic lethal assay.

Figure 6 : Fluorescence-based mammalian synthetic lethal assay. Unfortunately we are unable to provide accessible alternative text for this. If you require assistance to access this image, or to obtain a text description, please contact npg@nature.coma | The Kinzler method66. Isogenic cell-line pairs that do/do not harbour a cancer-relevant mutation are engineered to produce blue fluorescent protein (BFP) and yellow fluorescent protein (YFP), respectively, and are co-cultured in multiwell plates to which different chemicals are added. Selective killing of blue cells is indicative of a synthetic lethal interaction (yellow well). b | The Canaani method68, 69. Cells lacking a tumour-suppressor gene (TSG) are engineered to stably produce a green fluorescent protein (GFP) with an emission wavelength of ‘1’. These cells are transfected with an unstable episomal plasmid encoding theTSG along with a GFP that has a different emission wavelength (‘2′). Retention of the episomal plasmid after exposure to chemical or genetic perturbants is indicative of a synthetic lethal relationship. WT, wild type.

The use of isogenic cell-line pairs to identify compounds that selectively kill cancer cells as a result of synthetic interactions is a powerful approach for the following reason. It is not uncommon for approx1% of the compounds in a chemical library to inhibit the growth of human cancer cells at the concentrations used in typical high-throughput screens. This translates into thousands of potential anticancer drugs from a screen conducted with 105 to 106 compounds (such as might be found at a large pharmaceutical company or public consortium). Without the use of a filter, such as differential killing in a genotype-specific manner, there are too many ‘hits’ to pursue. In the past, this has led to ‘hits’ being prioritized on the basis of factors such as ease of synthesis, potency, intellectual-property issues and the likelihood of having desirable absorption, distribution, metabolism and excretion (ADME) properties based on accepted criteria such as ‘LIPINSKI’S RULES75, 76. Although they are important, none of these latter considerations address selectivity. Furthermore, these factors can sometimes be addressed by modifying the chemical structure of the initial compound (medicinal chemistry). It would be ironic if chemicals that can selectively kill cancer cells through synthetic lethal interactions were present but missed for this reason during the countless cytotoxic screens that have been conducted since the mid-twentieth century.

A generic problem for cell-based screening of libraries of chemical compounds relates to successful target identification. In some cases, it is possible to use a chemical entity identified in such a screen to capture its protein target by affinity chromatography77,78. For chemicals that induce a phenotype in yeast, mutants that display increased or decreased resistance (fitness) can be sought79, 80. Such mutants often provide clues as to the pathways that are affected by a compound, and therefore its potential target (or targets). A conceptually attractive approach to target identification would be to generate compendia of molecular signatures (for example, gene-expression profiles) for various loss-of-function mutations in a suitable host (for example, yeast or human cells)81. The signature generated by the compound of interest could then be compared in silico to the compendium, with the rationale that the compound signature and target-disruption signature should be near(est) neighbours in an ideal situation. The search for targets of chemicals identified in cell-based synthetic lethal screens should also be expedited by a knowledge of the genes that score as synthetic lethal in genetic screens carried out in model organisms and human cells, as described below.

Genetic screens. In the past, genetic screens for synthetic lethal interactors have been largely relegated to model organisms such as yeast, the fruitfly Drosophila melanogaster and the wormCaenorhabditis elegans that are amenable to forward-genetic approaches. Typically, these approaches have combined random mutagenesis with phenotypic screens, reflecting the retention of the query gene linked to a suitable reporter. Synthetic lethal screens in yeast have been invaluable for elucidating certain principles surrounding synthetic lethal interactions. Unfortunately, many tumour-suppressor genes and oncogenes do not have clear yeast orthologues. Although forward-genetic screens are more cumbersome in fruitflies and worms than in yeast, they offer the advantage that their genomes do contain orthologues of most human cancer genes. In worms the RB1 orthologue, lin-35, has been well studied in the context of vulvar development82. Fay and co-workers reported that a gene encoding a ubiquitin-conjugating enzyme related to human UBCH7 is synthetic lethal to lin-35 (Ref. 83), as is the worm homologue of CDH1 (Ref. 84). Using a fruitfly-based screen in which the fruitfly RB1-like geneRbf1 was conditionally inactivated in the eye, Belvin and co-workers discovered that RBF1 is synthetic lethal to a novel prolyl isomerase85. It is not yet known whether these synthetic lethal interactions will hold true in all cell types, nor whether they will hold true across species.

However, forward-genetic approaches such as these are now giving way to genome-wide reverse-genetic approaches. Successful studies have been carried out in yeast (Box 1) but, for the reasons cited above, metazoan models are usually more appropriate than yeast for synthetic lethal screens for human cancer genes.

RNAi is a powerful method for silencing genes in worms and fruitflies, and collections of interfering RNAs have been created to facilitate high-throughput genome-wide screens in these organisms86, 87, 88, 89 (for an excellent review, see Ref. 90). RNAi can be conveniently achieved in wild-type or mutant worms by growing them on lawns of Escherichia coli carrying a plasmid that produces the interfering RNAs of interest, which are then ingested. Alternatively, interfering RNAs can be delivered to worms by soaking them in a solution that contains the appropriate molecules. An interfering RNA that exacerbated the mutant phenotype without affecting wild-type animals would indicate a synthetic lethal, or synthetic sick, interaction. High-throughput screens have also been conducted to identify interfering RNAs that inhibit the proliferation of fruitfly cells grown in multiwell plates88. Such screens could easily be adapted to carry out synthetic lethal screens. In this scenario, the identification of interfering RNAs that do not affect wild-type fruitfly cells but kill fruitfly cells in which the gene of interest was mutated or silenced would be desired. If required, silencing could be accomplished by simultaneously administering two interfering RNAs (one corresponding to the query gene and one corresponding to the gene of interest).

Many cancer-relevant genes are linked to specific types of cancer despite being ubiquitously expressed and performing functions that are thought to be generic rather than tissue specific. In addition, there are now many examples where different phenotypes have been observed following heterozygous inactivation of a particular tumour-suppressor gene in both mice and humans. These observations indicate that context, with respect to cell-type and species, is important. As a corollary, they indicate that synthetic lethal relationships ultimately need to be discovered or validated in relevant human cells, and that caution needs to be exercised when extrapolating cell-culture results to intact organisms. In the past, the use of RNAi in mammalian cells was problematic because double-stranded RNA elicits an antiviral response on entry into mammalian cells. In 2001, however, Tuschl and co-workers showed that siRNAs can be used to silence genes in mammalian cells without triggering a nonspecific host response91. Soon thereafter several groups showed that the actions of siRNAs in cells can be mimicked with short hairpin RNAs (shRNAs) encoded by plasmid or viral vectors92, 93, 94,95, 96. siRNA libraries and shRNA vector libraries are being created, and proof-of-concept experiments indicate that these libraries can be used to carry out genome-wide phenotypic screens in mammalian cells (including human cells)97, 98, 99,100. In theory, these libraries could be used to carry out synthetic lethal screens using isogenic cell-line pairs, scoring for siRNA (or shRNA) species that specifically kill cells with a cancer-relevant mutation in a one well/one siRNA (or shRNA) species format (Fig. 5). Alternatively, several groups are incorporating DNA ‘bar codes’ (Box 1) into shRNA vectors, modelled after the use of DNA bar codes in yeast and E. coli (or have used the shRNA sequence itself as a bar code)97, 98. If successful, it should be possible to infect isogenic cell-line pairs with pools of vectors encoding different shRNAs, and then identify those shRNAs that cause a fitness defect specifically in those cells that harbour the cancer-relevant mutation under investigation.

Combination therapy

Random mutations that lead to gene inactivation should theoretically decrease the genetic buffering capacity of an individual cancer cell. As outlined above, therapies predicated on synthetic lethal relationships are one way to exploit this. At the same time, random mutations and genome plasticity, viewed at the level of a tumour, markedly increase the likelihood that rare therapy-resistant subclones will emerge. Decades of clinical experience, including recent examples of imatinib mesylate resistance101, 102, as well as tumour models incorporating the use of conditionally expressed oncogenes103, support this view. A 1-cm3 tumour already contains >109 cells. So, the likelihood of clinical success will increase with early diagnosis (to minimize the number of cells in the pool from which resistant cells might arise) and the use of effective drug combinations. The use of drug combinations to minimize chemotherapeutic resistance is a well-established pharmaceutical principle. It is based on the knowledge that the probability of a given cell being simultaneously resistant to a combination of non-cross-resistant drugs varies as the product of the probabilities of becoming resistant to each of the individual components. The choice of which drugs to combine might be based on a knowledge of cancer molecular biology (for example, by simultaneously targeting two or more cancer-relevant mutations), empirical testing (for example, by systematically testing combinations of active agents for additive or synergistic effects) or both.

Implications and future directions

Over the decades, the medical therapy of metastatic cancer has, with a few notable exceptions, been a frustrating and often futile exercise. This has contributed to the view that each mutation within a cancer cell is another plate of armour that serves as a barrier to successful therapy. However, our empirical knowledge of the susceptibility of cancer cells to drugs in humans stems from an armamentarium that was largely discovered and developed using the same paradigm. Moreover, there is every reason to believe that certain genetic changes within cancer cells will create liabilities under the appropriate conditions. There are now tools to systematically search for mutated oncogenes that encode molecules, such as kinases, that can be targeted by drugs, as well as the tools to reveal vulnerabilities created by synthetic lethal interactions. Understanding how the phenotypes created by cancer genotypes (for example, tumour type and resistance to therapy), as well as synthetic lethal relationships, are influenced by contextual differences (for example, cell type and species) remains a formidable task. Nonetheless, we are clearly poised to move away from empirically discovered cytotoxics and towards new agents that are based on a knowledge of cancer genetics and a more sophisticated view of gene–gene interactions.



I would like to thank S. Elledge, A. Reddy, M. Tyers, P. Silver and M. Vidal for their critical reading of this manuscript and/or helpful comments. I apologize to colleagues whose work was not cited due to space limitations or my ignorance. Dedicated to the memory of Nancy P. Kaelin.

Competing interests statement

The author declares no competing financial interests.



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Author affiliations

  1. Howard Hughes Medical Institute, 44 Binney Street, Mayer 457, Boston, Massachusetts 02115, USA.