Download A lethal combination for cancer cells: Synthetic lethality screenings

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available at www.sciencedirect.com
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Review
A lethal combination for cancer cells: Synthetic lethality
screenings for drug discovery
Elisa Ferrari a, Chiara Lucca a, Marco Foiani
a
b
a,b,*
Fondazione IFOM (Istituto FIRC di Oncologia Molecolare), IFOM-IEO Campus, via Adamello 16, 20139 Milan, Italy
DSBB-Università degli Studi di Milano, Milan, Italy
A R T I C L E I N F O
A B S T R A C T
Article history:
In recent years, cancer drug discovery has faced the challenging task of integrating the
Received 2 July 2010
huge amount of information coming from the genomic studies with the need of developing
Accepted 21 July 2010
highly selective target-based strategies within the context of tumour cells that experience
Available online 17 August 2010
massive genome instability.
Keywords:
and has contributed to efficiently transfer certain approaches typical of basic science to
Synthetic lethality screens
drug discover projects. An example comes from the synthetic lethal approaches, very pow-
Cancer
erful procedures that employ the rational used by geneticists working on model organisms.
Saccharomyces cerevisiae
Applying the synthetic lethality (SL) screenings to anticancer therapy allows exploiting the
Drug discovery
typical features of tumour cells, such as genome instability, without changing them, as
The combination between genetic and genomic technologies has been extremely useful
opposed to the conventional anticancer strategies that aim at counteracting the oncogenic
signalling pathways.
Recent and very encouraging clinical studies clearly show that certain promising anticancer compounds work through a synthetic lethal mechanism by targeting pathways that
are specifically essential for the viability of cancer cells but not of normal cells.
Herein we describe the rationale of the synthetic lethality approaches and the potential
applications for anticancer therapy.
Ó 2010 Elsevier Ltd. All rights reserved.
1.
Introduction
The majority of chemotherapeutic drugs were identified for
their ability to kill rapidly growing cells. Consequently, most
of these drugs hit not only cancer cells but also normal dividing cells like bone marrow haematopoietic precursors, stomach, intestine and hair follicle cells.1 This lack of selectivity
for tumour cells is one of the major causes of chemotherapeutic failure in cancer treatment.
Genetic instability is a hallmark of tumour cells. Cancer
cells genetically differ from normal cells as they have accumulated a large number of mutations including growth-promoting
mutations. In fact, the genetic and epigenetic alterations that
characterise cancer cells can be instrumental for developing
more selective pharmacological approaches. As Paul Workman
said, ‘What do cancer cells have that normal cells don’t?. . .They have
mutations, and you can take advantage of those’.2 Cancer genetic
instability may indeed provide the key to tumour vulnerability.
* Corresponding author at: Fondazione IFOM (Istituto FIRC di Oncologia Molecolare), IFOM-IEO Campus, via Adamello 16, 20139 Milan,
Italy.
E-mail address: [email protected] (M. Foiani).
0959-8049/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved.
doi:10.1016/j.ejca.2010.07.031
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Recent years have witnessed a change in the drug discovery strategies in the cancer field. Thanks to the genomic and
post genomic technologies, the integration between basic and
translational research is becoming extremely productive. An
example comes from the synthetic lethal approach, a very
promising drug discovery procedure that employs the rational
used by geneticists working on model organisms. While the
conventional anticancer strategies aim at counteracting the
oncogenic signalling pathways, the synthetic lethal approach
seeks to exploit the typical features of tumour cells without
changing them.
Synthetic lethality (SL) is a genetic phenomenon originally
observed in Drosophila melanogaster by Bridges in 1922, while
the term was coined by Dobzhansky in 1946, to describe complementary lethal systems in wild-type population of Drosophila pseudoobscura.3,4 It refers to cases in which the
combination of two non-lethal mutations yields to lethality;
the less severe situation, in which the final phenotype corresponds to reduced fitness, is defined as synthetic sickness
(SS).
The two synthetically lethal mutations have an addictive
negative impact on a function required for the cell viability.
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This effect can be derived from the loss-of-function of two
genes that act in parallel redundant pathways, or belong to
the same essential pathway or act in two distant pathways
that are needed to react to a specific cellular perturbation
(Fig. 1).
2.
The synthetic lethal approach for cancer
therapy
In 1997, Hartwell and colleagues pioneered the idea of applying genetic approaches to drug discovery through the concept
of synthetic lethality.5 They extended the use of the term beyond classical genetics to all the cases in which the combination of a mutation and a drug causes cellular death, whereas
the presence of the mutation alone or the drug alone is viable.
The rationale behind this approach is that the effect of a drug
that targets a specific gene product resembles the phenotype
caused by a mutation in the gene encoding the same protein.
The authors emphasised two fundamental advantages of
relying on genetic screens for drug discovery: first, a gene
mutation represents an ideal model for designing a new drug
that can mimic the loss-of-function of a specific protein by
Fig. 1 – Different mechanisms leading to synthetic lethality. The possible SL gene pairs are reported on the right side of each
panel. Synthetic lethality can arise form the absence of two genes acting in redundant parallel pathways (a1) or distant
pathways (a2). Alternatively, it can originate from the lack of two subunits of an essential protein complex (b1) or two proteins
of the same essential pathway (b2).
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inhibiting or poisoning it; second, genetic screens are unbiased, without precluding any unexpected possibility.
The application of the synthetic lethality rationale offers
new possibilities for cancer research as shown in Fig. 2. A cancer-related mutated gene can sensitise the tumour cells to a
drug that specifically inhibits its synthetic lethal partner. In
addition to this, the same drug should not affect normal cells,
thus allowing higher therapeutic selectivity. Moreover, in
principle, this approach is applicable to any type of cancer
mutation, not only loss-of-function mutations in tumour suppressors but also gain-of-function mutations leading to oncogene expression.
In retrospect, the mechanism of action of many clinical
compounds is based on synthetic lethality as documented
by an increasing number of cases in the literature. For example, the rapamycin derivative CCI-779 exhibits enhanced
activity against tumours with mutations in PTEN compared
to tumours with normal PTEN.6 PTEN is a tumour suppressor
gene encoding a phosphatase that regulates the PI3-K/AKT
pathway, which plays a central role in growth and anti-apoptotic mechanisms. The target of CCI-779 is the protein kinase
mTOR that acts downstream of the PI3-K/AKT pathway,
which is, in turn, up-regulated in PTEN null cancers. These
observations explain the higher responsiveness to mTOR
inhibition of PTEN-deficient cells compared to PTEN-proficient cells, even if CCI-779 inhibits the mTOR pathway in both
cells types.
Another example, which is becoming a paradigm for SL
applications, is the genetic interaction between BRCA1 or
BRCA2 and PARP1.7,8
PARP is involved in the repair of DNA single strand breaks:
it binds to the break region and, through autopoly(ADP-rybosil)ation, attracts proteins involved in the repair process. Conversely, the products of BRCA1 and BRCA2 genes are
implicated in homology-directed DNA double-strand break
repair, so both pathways participate in the repair of DNA lesions. Therefore, the inhibition of PARP1 in BRCA1- or
BRCA2-defective cells results in the accumulation of DNA lesions that cannot be repaired and causes cell lethality.
The validation of synthetic lethal interactions in human
cells may also provide a mechanistic rationale for clinical
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trials studies. Methotrexate, for example, is currently under
phase II evaluation for its efficacy in the treatment of
advanced colorectal cancer with defects in the DNA mismatch repair genes.9 Methotrexate is able to induce oxidative DNA lesions and it has been shown to be highly
selective for MSH2-defective cells.10 Differently from MSH2
wild-type cells, in MSH2 mutant cells these DNA lesions
are not repaired and so the methotrexate treatment should
be lethal. Therefore, the subtype of colorectal cancer characterised by a deficiency in mismatch repair may represent a
selective hit of methotrexate.
These examples clearly pinpoint the potential offered by
the synthetic lethal approach when applied to drug discovery.
The synthetic lethality strategy can be relevant in cancer research in different ways:
(I) It can contribute to the identification of novel pharmacological targets. Most of cancer therapies aim at inhibiting hyperactive oncoproteins, but often these targets
are not ‘druggable’. Moreover, only 10% of the most
common cancer genes are oncogenes, while the vast
majority is tumour suppressor genes. Synthetic lethality interactions may provide a source of cancer-selective drug targets: a cancer gene that is frequently
inactivated in tumours embodies the first SL partner
while any other gene that once mutated exhibits SL
interactions with the original cancer gene represents
the second SL partner and, therefore, a potential drug
target.
(II) Synthetic lethal screens may contribute to the identification of novel biomarkers by unmasking those genes
that are absolutely required for cell viability following
treatment with a specific drug. This approach allows
the identification of not only those genetic profiles that
sensitise the cells to the drug of interest but also those
mutations that cause resistance to the pharmacological
compound.
(III) Another application comes from the integration of the
synthetic lethal genetic profiles with the chemicalgenetic profiles.11 A loss-of-function mutation in a gene
that encodes the target of an inhibitory drug mimics
Fig. 2 – Synthetic lethality in chemotherapy. Differently from healthy cells, cancer cells are characterised by mutations; in
the figure, yfg2 represents the cancer mutation (YFG: your favourite gene). If YFG1 and YFG2 represent a SL-pair, a drug that
inhibits YFG1 can selectively damage the tumour cells, without affecting the normal cells.
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the effect of the compound. Therefore, the comparison
between the chemical-genetic profiles and genetic synthetic lethal interactions panels may help to identify
those pathways that are altered by the drug treatment.
In the following paragraph, we will review the available
technologies for large-scale identification of gene–gene and
gene–compound SL interactions (GGSL and GCSL screens,
respectively12; Fig. 3).
3.
High-throughput synthetic lethal screens
Synthetic lethal studies have been carried out in many different model organisms including D. melanogaster and Caernorhabditis elegans by employing RNA-interference (RNAi)
approaches13, but the concept of synthetic lethality has been
mainly applied in the budding yeast Saccharomyces cerevisiae.
S. cerevisiae represents a powerful tool for studying basic
cellular functions of eukaryotic cells, including those processes controlling genome integrity. Thanks to its genetic
amenability and versatility, it has received enormous attention and is a widely used model organism for studying a variety of eukaryotic cellular processes including those that have
relevance for human health. The budding yeast represents a
key experimental option for determining the function of a
conserved gene of interest through the phenotypic analysis
of the corresponding mutants. Gene ablation can be easily
performed, in haploid or diploid cells, using a polymerase
chain reaction (PCR)-based strategy. The Saccharomyces Genome Deletion Consortium has created a complete knockout
collection of all the annotated yeast genes, the yeast knockout
library (YKO).14 Each open reading frame (ORF) was replaced
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by a genetic marker and uniquely tagged with two 20-nucleotide TAGs (molecular barcodes).15 Four different YKOs were
generated: Mata, Mat haploids and homozygous diploids for
non-essential genes, heterozygous diploids for both essential
and non-essential genes. The libraries have been widely used
for high-throughput synthetic lethality analysis: in 2001, Tong
and colleagues set up a systematic approach called synthetic
genetic array (SGA).16 Using robotic stations, haploid double
mutants are generated by mating and meiotic recombination:
the query strain (in question) carrying the mutation of interest is crossed to the array of deletion mutants to produce heterozygous diploids that can be easily induced to sporulate,
thus generating the haploid combinations. The final selection
steps are aimed at identifying those double mutants exhibiting synthetic lethal or synthetic sick phenotypes.
In 2003, Ooi and colleagues developed an alternative technique termed synthetic lethality analysis by microarray
(SLAM) for the investigation of global synthetic lethality interactions.17 This method exploits the transformation of the
YKO library with the query mutation to create double mutants. The strains are grown in pools and the genomic DNA
isolated from the transformants is amplified by PCR using
the molecular barcodes. The TAGs are flanked by universal
priming sites, which allow the amplification of all the strains
in the same PCR. By hybridising the amplified molecular barcodes to DNA microarray and by evaluating the hybridisation
intensities, it is possible to estimate the growth rate of the different strains. A third technology called genetic interaction
mapping (GIM) has been described. It is very sensitive as it allows the identification of subtle synthetic and epistatic interactions.18 This method combines SGA and SLAM approaches:
it relies on mating yeast cells to obtain double mutants, which
Fig. 3 – Gene–gene and gene–compound SL interactions (GGSL and GCSL). The figure represents one of the different
technologies available for large-scale analysis of GGSL and GCSL: generation of S. cerevisiae double mutants through synthetic
genetic array (SGA) (a), analysis of drug-sensitivity of the Yeast Knock Out library (b). The concept of synthetic lethality is
extended to cancer research: a drug can mimic the absence of a protein generating a gene–compound SL interaction. See the
text for details.
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are grown in pools; the fitness of the query population relative
to a reference population is assessed by quantifying microarray hybridisation signals.
A systematic mapping of synthetic lethal interactions has
recently been performed in S. cerevisiae by SGA analysis.19
Over 1700 query genes have been screened and a total of
nearly 5.4 million gene pairs analysed allowing the identification of 170,000 interactions. This represents the first example of a global genome scale interaction map: it comprises
75% of (all) yeast genes and embodies a huge source of
information.
All those genes, whose mutation or deletion enhances or
reduces the activity of a chemical compound, represent the
chemical-genomic profile and have an enormous importance
for drug discovery. The chemical-genetic profile can be assimilated to a synthetic lethal profile. Numerous studies employed yeast YKO libraries to analyse the differential
response of the mutant collections to drug treatments.
These studies have relevant implications: (i) the chemicalgenetic profile identifies all those genes whose function is
crucial for the efficacy of the drug treatment and some of
these genes may represent useful biomarkers. (ii) The chemical-genetic profiles may change according to the concentration of the drug, thus providing key information for
designing the treatment protocol. (iii) Comparing the chemical-genetic profile of a compound of interest with the ones
of other known drugs may help to unmask potential synergistic interactions between drugs. (iv) Integrating the chemicalgenetic interaction profiles with the genetic interaction data
provided by the SGA studies may help in elucidating the
mechanism of action of the drug.
The GCSL screens also include screens aimed at testing
the effect of a collection of chemicals on a mutant of interest. This type of GCSL screens represents nowadays a very
powerful drug discovery tool. A large variety of small-molecule libraries are now available, ranging from collections of
Drug Administration-approved drugs or compounds with
known activities to collections of novel and uncharacterised
chemicals.
In 2002, Dunstan and colleagues screened a library of more
than 85,000 compounds on yeast strains deficient in DNA
double-strand break repair (rad50 and rad52 mutants) and
were able to identify 126 compounds that showed higher toxicity towards these mutants.20
The results obtained from the small-molecule librarybased screens can then be integrated with the genetic interaction information derived from the SL screens by clustering the
profiles.
Recently, an increasing number of studies are facing the problem of investigating GGSL and GCSL interactions in metazoans.
The major setback to these strategies in higher organisms
is the mode of delivery of the small interference RNA (siRNA)
molecule.
In C. elegans, RNA-interference (RNAi) can be easily induced by feeding, injection, soaking and in vivo-delivery of
double-stranded RNA (dsRNA21); RNA-interference can be
combined with a query strain characterised by a loss-of-function mutation to obtain a panel of genetic interactions.
Although the introduction of dsRNA in D. melanogaster is more
complicated than in C. elegans, Wheller and colleagues devel-
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oped living cell microarrays that allow the screening of large
collections of RNAi and, by reducing the expression of two
genes simultaneously, the identification of synthetic lethal
interactions.22
In 2001 it was demonstrated that RNA-interference is also
feasible in mammalian cells and systematic SL studies became possible.23 Several approaches are now available to gain
RNA-interference: short duplex RNAs (called small interference RNA (siRNA)), short hairpin vector encoded RNAs
(shRNA) and endoribonuclease prepared-siRNAs (esiRNA).24
siRNAs and esiRNAs can be used for high-throughput single-well assays, in which each well contains a single siRNA reagent. shRNAs can be used in both single-well or polyclonal
assays, in which a single dish of cells is infected with a pool
of shRNA vectors.25
A recent study identified the genetic interactors of the human oncogene KRAS in cancer cells by targeting 1011 human
genes in eight cancer cell lines through shRNA constructs;
STK33 turned out to be a component of a signalling pathway
essential in the context of mutant KRAS, thus establishing a
potential drug target in cancers bearing KRAS mutations.26
High-throughput chemical SL screens in mammalian cells
have been performed in various ways over the past decade,
but the most recent approaches employed RNA-interference.
The two categories of GCSL screens described above for the
yeast model can be now performed in mammalian cells.
In 2008, following the successful identification of synthetic
lethality between PARP1 and BRCA1 or BRCA2, an RNAi library
made up by genes with known roles in DNA repair was tested
for KU0058948 (PARP inhibitor) sensitivity. This screen identified novel determinants of the PARP inhibition response such
as the transcription coupled DNA repair proteins DDB1 and
XAB2.27 These novel SL partners may contribute to the development of PARP inhibitors-based pharmacological strategies.
High-throughput chemical screens in mammalian cells
have been realised by means of different approaches: one
method is based on the complementation of the mutated
gene of interest through a low-copy number unstable episomal plasmid expressing the wild-type copy of the gene.
The retention of the plasmid is forced in all the cases in which
there is a synthetic lethal interaction between the gene and
the drug.28,29
As illustrated for yeast, another approach consists in the
treatment of the cell line with the genotype of interest with
compound libraries; cells are grown in multi-well plates and
tested for viability. Recently, Ji et al. performed a chemical-genetic screen aimed at the identification of drugs able to selectively target pancreatic cancer cells with gain-of-function the
KRAS mutation. KRAS is an oncogene mutated in more than
90% of pancreatic cancers, playing an essential role in the initiation and progression of these tumours. The authors
screened almost 3200 chemical compounds and they found
one KRAS synthetic lethal inhibitor that may be further
characterised.30
4.
Conclusions
Cancer is a heterogeneous disease and this diversity results in
different and often unpredictable responses to the therapies.
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The concept of synthetic lethality applied to drug discovery is
nowadays receiving increasing interest and a growing number of clinically relevant SL interactions are proving its
efficacy.
The integration of the data obtained from GGSL and GCSL
screens owns powerful applications. Firstly, knowing the
mutations that are responsible for particular types of tumours, the concept of SL permits the identification of drugs
that spare normal tissues while selectively killing cancer cells
characterised by a specific background.
As was pointed out above, gene–gene synthetic lethality
data represent also a source of potential targets to address
the discovery of novel clinically useful compounds.
Once verified, the SL interactions may be exploited before
planning clinical trials. Actually this information allows the
stratification of the patients in subpopulations on the basis
of responsiveness prediction: the knowledge of which genetic
alterations lead to drug-sensitivity/resistance may foretell
which patients will benefit from the treatment and which will
not or even be harmed by it. In the end, this approach may allow to save lives, time and money.
As shown above, new methods to identify genetic and
chemical-genetic SL interactions are emerging in the literature. RNAi collections are now available, covering the whole
mammalian genome and being compatible with highthroughput studies also in mammals.
Despite the enormous progress made in recent years,
obtaining SL data for metazoans is still technically complex
and has some limitations as gene inactivation efficacy can
vary. However, an increasing number of systematic studies
based on RNA-interference are coming out, some with promising results. Therefore, SL studies are possible and are showing nowadays their enormous potentialities also in mammals.
Moreover, Zender and colleagues recently performed an RNAi
screen for shRNAs that promote tumourigenesis in a mouse
cancer model showing the feasibility of in vivo RNAi31; this
new technique paves the way to in vivo mammalian SL
screens.
Is yeast still meaningful in drug discovery despite the progress that took place in mammalian system? S. cerevisiae
shows a high degree of conservation with mammalian cells:
40% of yeast proteins share aminoacid sequence similarity
with a human protein.32 Thanks to conservation, the data obtained in this model system may be used to predict new metazoans genetic interactions.
RNAi is a powerful technique but it possesses some drawbacks: apart from the variation in gene expression abolishment, it is time-consuming and expensive. Moreover,
differently from yeast cells, the high genomic redundancy
that characterised human cells can impede the discovery of
novel genetic interactions.
As described previously, Costanzo et al. revealed the genetic interaction profile for 75% of yeast genes; the integration of
this huge amount of data with the other SL studies available
in the literature can open to new research directions, focusing
on interesting interactions and validating them in higher
eukaryotic systems. This approach allows reducing the number of combinations that have to be analysed in mammalian
systems and, as a consequence, diminishing the costs.
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Hence, the SL interactions found in yeast can help elucidating the mammalian SL interactions; in 2009, McManus and colleagues exemplify the validation of a yeast prediction in
mammalian cells. The 5 0 –3 0 exonuclease Rad27 was shown to
be SL with homologous recombination mutants, members of
the Rad52 epistasis group.33 Differently from RAD54-proficient
cells, RAD54B-deficient human colorectal cancer cells resulted
sensitive to shRNA-mediated silencing of FEN1, confirming the
prediction made in a model organism such as S. cerevisiae.
In summary, the concept of synthetic lethality has huge
potentialities for anticancer drug discovery; its most important features consist in the possibility to selectively affect
cancer cells and to hit ‘undruggable targets’.
Conflict of interest statement
None declared.
Acknowledgements
We wish to thank Linda Cairns and all members of our laboratory for helpful comments. Work in M.F. laboratory is supported by grants from Italian Association for Cancer
Research, from Telethon-Italy, European Community, Regione
Lombardia and Italian Ministry of Health.
R E F E R E N C E S
1. Kaelin Jr WG. The concept of synthetic lethality in the context
of anticancer therapy. Nat Rev Cancer 2005;5(9):689.
2. Garber K. Synthetic lethality: killing cancer with cancer. J Natl
Cancer Inst 2002;94(22):1666.
3. Bridges CB, Brehme KS. The mutants of Drosophila
melanogaster. Carnegie Inst Wash Publ 1944;552:1.
4. Dobzhansky Th. Genetics of natural populations. XIII.
Recombination and variability in populations of Drosophila
pseudoobscura. Genetic 1946;31:269.
5. Hartwell LH et al. Integrating genetic approaches into the
discovery of anticancer drugs. Science 1997;278(5340):1064.
6. Neshat MS et al. Enhanced sensitivity of PTEN-deficient
tumors to inhibition of FRAP/mTOR. Proc Natl Acad Sci USA
2001;98(18):10314.
7. Bryant HE et al. Specific killing of BRCA2-deficient tumours
with inhibitors of poly(ADP-ribose) polymerase. Nature
2005;434(7035):913.
8. Farmer H et al. Targeting the DNA repair defect in BRCA
mutant cells as a therapeutic strategy. Nature
2005;434(7035):917.
9. Hewish M et al. Mismatch repair deficient colorectal cancer in
the era of personalized treatment. Nat Rev Clin Oncol
2010;7(4):197.
10. Martin SA et al. Methotrexate induces oxidative DNA damage
and is selectively lethal to tumour cells with defects in the
DNA mismatch repair gene MSH2. EMBO Mol Med 2009;1(6–7):
323.
11. Parsons AB et al. Integration of chemical-genetic and genetic
interaction data links bioactive compounds to cellular target
pathways. Nat Biotechnol 2004;22(1):62.
12. Ooi SL et al. Global synthetic-lethality analysis and yeast
functional profiling. Trends Genet 2006;22(1):56.
EUROPEAN JOURNAL OF CANCER
13. Hannon GJ. RNA interference. Nature 2002;418(6894):244.
14. Winzeler EA et al. Functional characterization of the S.
cerevisiae genome by gene deletion and parallel analysis.
Science 1999;285(5429):901.
15. Shoemaker DD et al. Quantitative phenotypic analysis of
yeast deletion mutants using a highly parallel molecular barcoding strategy. Nat Genet 1996;14(4):450.
16. Tong AH et al. Systematic genetic analysis with ordered
arrays of yeast deletion mutants. Science 2001;294(5550):2364.
17. Ooi SL, Shoemaker DD, Boeke JD. DNA helicase gene
interaction network defined using synthetic lethality
analyzed by microarray. Nat Genet 2003;35(3):277.
18. Decourty L et al. Linking functionally related genes by
sensitive and quantitative characterization of genetic
interaction profiles. Proc Natl Acad Sci USA 2008;105(15):5821.
19. Costanzo M et al. The genetic landscape of a cell. Science
2010;327(5964):425.
20. Dunstan HM et al. Cell-based assays for identification of
novel double-strand break-inducing agents. J Natl Cancer Inst
2002;94(2):88.
21. Timmons L. Delivery methods for RNA interference in C.
elegans. Methods Mol Biol 2006;351:119.
22. Wheeler DB et al. RNAi living-cell microarrays for loss-offunction screens in Drosophila melanogaster cells. Nat Methods
2004;1(2):127.
23. Elbashir SM et al. Duplexes of 21-nucleotide RNAs mediate
RNA interference in cultured mammalian cells. Nature
2001;411(6836):494.
24. Brummelkamp TR, Bernards R, Agami R. A system for stable
expression of short interfering RNAs in mammalian cells.
4 6 ( 2 0 1 0 ) 2 8 8 9 –2 8 9 5
25.
26.
27.
28.
29.
30.
31.
32.
33.
2895
Science 2002;296(5567):550;
Buchholz F, Kittler R, Slabicki M, Theis M. Enzymatically
prepared RNAi libraries. Nat Methods 2006;3(9):696.
Mullenders J, Bernards R. Loss-of-function genetic screens as
a tool to improve the diagnosis and treatment of cancer.
Oncogene 2009;28(50):4409.
Scholl C et al. Synthetic lethal interaction between oncogenic
KRAS dependency and STK33 suppression in human cancer
cells. Cell 2009;137(5):821.
Lord CJ et al. A high-throughput RNA interference screen for
DNA repair determinants of PARP inhibitor sensitivity. DNA
Repair (Amst) 2008;7(12):2010.
Simons A et al. Establishment of a chemical synthetic
lethality screen in cultured human cells. Genome Res
2001;11(2):266.
Canaani D. Methodological approaches in application of
synthetic lethality screening towards anticancer therapy. Br J
Cancer 2009;100(8):1213.
Ji Z et al. Chemical genetic screening of KRAS-based
synthetic lethal inhibitors for pancreatic cancer. Front Biosci
2009;14:2904.
Zender L et al. An oncogenomics-based in vivo RNAi screen
identifies tumor suppressors in liver cancer. Cell
2008;135(5):852.
Parsons AB, Geyer R, Hughes TR, Boone C. Yeast genomics and
proteomics in drug discovery and target validation. Prog Cell
Cycle Res 2003;5:159.
Symington LS. Homologous recombination is required for
the viability of rad27 mutants. Nucleic Acids Res 1998;26(24):
5589.