Synthetic Lethal Networks for Precision Oncology: Promises and Pitfalls

doi:10.1016/j.jmb.2018.06.026

Journal of Molecular Biology

Volume 430, Issue 18, Part A, 14 September 2018, Pages 2900-2912

https://doi.org/10.1016/j.jmb.2018.06.026 Get rights and content

Highlights

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Synthetic lethal interactions have the potential to greatly expand the scope of precision oncology.
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New technologies allow for high-throughput screens to identify new synthetic lethal interactions.
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Consideration of cellular context will be critical when designing synthetic lethal cancer therapies.

Abstract

Synthetic lethal interactions, in which the simultaneous loss of function of two genes produces a lethal phenotype, are being explored as a means to therapeutically exploit cancer-specific vulnerabilities and expand the scope of precision oncology. Currently, three Food and Drug Administration-approved drugs work by targeting the synthetic lethal interaction between BRCA1/2 and PARP. This review examines additional efforts to discover networks of synthetic lethal interactions and discusses both challenges and opportunities regarding the translation of new synthetic lethal interactions into the clinic.

Graphical Abstract

Section snippets

Introduction: the emerging “synthetic lethal” approach to cancer therapy

Alterations to the tumor genome can be broadly classified into gain-of-function mutations in growth-enhancing genes (oncogenes) and loss-of-function mutations in growth-inhibitory tumor suppressor genes (TSG), as well as so called “passenger” mutations which can arise randomly as a result of impaired DNA repair but do not contribute to oncogenesis. Targeting oncogenes with either specific chemical inhibitors or therapeutic antibodies has proven to be highly effective for cancer therapy [1].

How to define and measure synthetic lethal interactions

Genetic interactions are generally measured in terms of cell growth or viability, although it should be noted that it is possible to derive interaction measurements from other more complex phenotypes [30]. Terminology to describe genetic interaction dates back to the early 1900s and has evolved over time as is described in prior reviews [5], [31]. In the context of the synthetic lethal approach to cancer therapy, the most commonly used terminology for genetic interaction comes from the

General mechanisms of synthetic lethal interactions

Quantitative genetic interactions scores can be used to construct pathway connections between genes, as negative and positive interactions are associated with different pathway relationships. However, there are several different pathway relationships that will give rise to synthetic lethal (negative) interactions, as is illustrated in Fig. 2. If two genes in unrelated pathways are both knocked out, the result is a neutral interaction (interaction score very near zero as there is no difference

How to discover therapeutically relevant synthetic lethal interactions

Given the clinical success of PARP inhibitors, there is a growing effort to identify more synthetic lethal interactions relevant to cancer therapy. Current efforts to map synthetic–lethal interactions can be separated into four basic categories. First are statistical approaches that leverage large populations of tumor genomes. These analyses are based on the assumption that if genes A and B are synthetic lethal, tumors with simultaneous loss-of-function of both A and B should have reduced

Challenges to the implementation of the synthetic lethal approach in cancer therapy

Although high-throughput screening efforts to identify more synthetic lethal interactions remain an important component in furthering the synthetic lethal approach to cancer therapy, it should be noted that the number of literature reported synthetic lethal interactions is now in the thousands [111]. Although the majority of those interactions have not been extensively validated, that number is a sharp contrast to the number of synthetic lethal interactions that can currently be exploited in

Cancer as a network-based disease

Just as ignoring context specificity will lead to treatment of non-responsive tumors, limiting the concept of synthetic lethal interaction to just a single gene pair, such as BRCA1 and PARP1, will exclude many responsive tumors. It has been widely discussed that cancer is a disease that arises because of the action of hallmark cancer pathways [120], [121]. Although any particular mutation or mutated gene may be a rare event when viewed independently, the key hypothesis of the hallmark pathway

The future of the synthetic lethal approach to cancer therapy

Given the growing basic research investment in the identification, validation, and mechanistic characterization of cancer-relevant synthetic lethal interactions, one might expect that the number of approved drugs that work in a synthetic lethal fashion will continue to grow. As technological advances continue to expand capabilities for genetic interaction mapping in human cancer cells, more will be learned about the context specificity of these interactions. One context that will be

Conclusions

Because loss-of-function mutations and gene deletions are common events in cancer, targeting these via synthetic lethal interactions has great promise to extend precision oncology to tumors without dominant oncogenic drivers. However, although the clinical success of PARP inhibitors has validated the synthetic lethal approach to cancer therapy, currently the impact of synthetic lethality is limited by the small number of synthetic lethal interactions that are understood mechanistically. New

Acknowledgments

This work was generously supported by grants from the US National Institutes of Health (R01 ES014811 and U54 CA209891 to T. Ideker, L30 CA171000 to J.P. Shen) and a Career Development Grant from the Tower Cancer Research Foundation to J.P. Shen.

References (139)

F.L. Muller et al.
Collateral lethality: a new therapeutic strategy in oncology
Trends Cancer
(2015)
R. Srivas et al.
A network of conserved synthetic lethal interactions for exploration of precision cancer therapy
Mol. Cell
(2016)
S.M.B. Nijman
Synthetic lethality: general principles, utility and detection using genetic screens in human cells
FEBS Lett.
(2011)
J. Ledermann et al.
Olaparib maintenance therapy in patients with platinum-sensitive relapsed serous ovarian cancer: a preplanned retrospective analysis of outcomes by BRCA status in a randomised phase 2 trial
Lancet Oncol.
(2014)
J.A. Ledermann et al.
Overall survival in patients with platinum-sensitive recurrent serous ovarian cancer receiving olaparib maintenance monotherapy: an updated analysis from a randomised, placebo-controlled, double-blind, phase 2 trial
Lancet Oncol.
(2016)
E.M. Swisher et al.
Rucaparib in relapsed, platinum-sensitive high-grade ovarian carcinoma (ARIEL2 Part 1): an international, multicentre, open-label, phase 2 trial
Lancet Oncol.
(2017)
S.R. Collins et al.
Quantitative genetic interaction mapping using the E-MAP approach
Methods Enzymol.
(2010)
M. Schuldiner et al.
Exploration of the function and organization of the yeast early secretory pathway through an epistatic miniarray profile
Cell
(2005)
J. Luo et al.
A genome-wide RNAi screen identifies multiple synthetic lethal interactions with the Ras oncogene
Cell
(2009)
L. Jerby-Arnon et al.
Predicting cancer-specific vulnerability via data-driven detection of synthetic lethality
Cell
(2014)

R. Fishel et al.

The human mutator gene homolog MSH2 and its association with hereditary nonpolyposis colon cancer

Cell

(1993)

X. Pan et al.

A robust toolkit for functional profiling of the yeast genome

Mol. Cell

(2004)

A.H.Y. Tong et al.

16 High-throughput strain construction and systematic synthetic lethal screening in Saccharomyces cerevisiae

A. Frost et al.

Functional repurposing revealed by comparing S. pombe and S. cerevisiae genetic interactions

Cell

(2012)

E.J. Haagensen et al.

Pre-clinical use of isogenic cell lines and tumours in vitro and in vivo for predictive biomarker discovery; impact of KRAS and PI3KCA mutation status on MEK inhibitor activity is model dependent

Eur. J. Cancer

(2016)

C.L. Sawyers

Finding and drugging the vulnerabilities of RAS-dependent cancers

Cell

(2009)

L.A. Gilbert et al.

Genome-scale CRISPR-mediated control of gene repression and activation

Cell

(2014)

T. Hart et al.

High-resolution CRISPR screens reveal fitness genes and genotype-specific cancer liabilities

Cell

(2015)

C. Sawyers

Targeted cancer therapy

Nature

(2004)

C. Le Tourneau et al.

Treatment algorithms based on tumor molecular profiling: the essence of precision medicine trials

J. Natl. Cancer Inst.

(2016)

L.G. Morris et al.

Therapeutic targeting of tumor suppressor genes

Cancer

(2015)

L.C. Brody

Treating cancer by targeting a weakness

N. Engl. J. Med.

(2005)

C. Boone et al.

Exploring genetic interactions and networks with yeast

Nat. Rev. Genet.

(2007)

F.L. Muller et al.

Passenger deletions generate therapeutic vulnerabilities in cancer

Nature

(2012)

W. Pao et al.

Chipping away at the lung cancer genome

Nat. Med.

(2012)

T. Dobzhansky

Genetics of natural populations; recombination and variability in populations of Drosophila pseudoobscura

Genetics

(1946)

J. Ledermann et al.

Olaparib maintenance therapy in platinum-sensitive relapsed ovarian cancer

N. Engl. J. Med.

(2012)

G. Kim et al.

FDA approval summary: olaparib monotherapy in patients with deleterious germline BRCA-mutated advanced ovarian cancer treated with three or more lines of chemotherapy

Clin. Cancer Res.

(2015)

B. Kaufman et al.

Olaparib monotherapy in patients with advanced cancer and a germline BRCA1/2 mutation

J. Clin. Oncol.

(2015)

H. Farmer et al.

Targeting the DNA repair defect in BRCA mutant cells as a therapeutic strategy

Nature

(2005)

H.E. Bryant et al.

Specific killing of BRCA2-deficient tumours with inhibitors of poly(ADP-ribose) polymerase

Nature

(2005)

P.C. Fong et al.

Inhibition of poly(ADP-ribose) polymerase in tumors from BRCA mutation carriers

N. Engl. J. Med.

(2009)

C.J. Lord et al.

Synthetic lethality and cancer therapy: lessons learned from the development of PARP inhibitors

Annu. Rev. Med.

(2015)

C.J. Lord et al.

PARP inhibitors: synthetic lethality in the clinic

Science

(2017)

J. Murai et al.

Trapping of PARP1 and PARP2 by Clinical PARP Inhibitors

Cancer Res.

(2012)

S.J. Pettitt et al.

A genetic screen using the PiggyBac transposon in haploid cells identifies Parp1 as a mediator of olaparib toxicity

PLoS ONE

(2013)

M. Sisay et al.

PARP inhibitors as potential therapeutic agents for various cancers: focus on niraparib and its first global approval for maintenance therapy of gynecologic cancers

Gynecol. Oncol. Res. Pract.

(2017)

D.A. Chan et al.

Harnessing synthetic lethal interactions in anticancer drug discovery

Nat. Rev. Drug Discov.

(2011)

J.P. Shen et al.

Combinatorial CRISPR–Cas9 screens for de novo mapping of genetic interactions

Nat. Methods

(2017)

N.J. O'Neil et al.

Synthetic lethality and cancer

Nat. Rev. Genet.

(2017)

S.M.B. Nijman et al.

Potential of the synthetic lethality principle

Science

(2013)

M.C. Jonikas et al.

Comprehensive characterization of genes required for protein folding in the endoplasmic reticulum

Science (New York, N.Y.)

(2009)

P.C. Phillips

The language of gene interaction

Genetics

(1998)

M. Costanzo et al.

A global genetic interaction network maps a wiring diagram of cellular function

Science

(2016)

R. Mani et al.

Defining genetic interaction

Proc. Natl. Acad. Sci.

(2008)

A.H. Tong et al.

Systematic genetic analysis with ordered arrays of yeast deletion mutants

Science

(2001)

B.L. Drees et al.

Derivation of genetic interaction networks from quantitative phenotype data

Genome Biol.

(2005)

M. Boettcher et al.

Dual gene activation and knockout screen reveals directional dependencies in genetic networks

Nat. Biotechnol.

(2018)

A. Roguev et al.

Quantitative genetic-interaction mapping in mammalian cells

Nat. Methods

(2013)

R.O. Lindén et al.

Quantitative maps of genetic interactions in yeast—comparative evaluation and integrative analysis

BMC Syst. Biol.

(2011)

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Citation Excerpt :
In a recent effort to overcome these limitations and develop a uniform systematic approach for stratifying patients to multiple therapies based on the tumor transcriptome, Lee et al.29 have demonstrated that synthetic lethality (SL) and synthetic rescue (SR) interactions can be leveraged to predict treatment response via the transcriptome. An SL interaction between two genes means that the simultaneous inactivation of both genes reduces the viability of the cell while the individual inactivation of each does not.30,31 An SR interaction is one in which the inactivation of one gene reduces cell viability, but an alteration of another gene’s activity, termed the rescuer, restores (rescues) viability.32,33
Precision oncology is gradually advancing into mainstream clinical practice, demonstrating significant survival benefits. However, eligibility and response rates remain limited in many cases, calling for better predictive biomarkers.
We present ENLIGHT, a transcriptomics-based computational approach that identifies clinically relevant genetic interactions and uses them to predict a patient’s response to a variety of therapies in multiple cancer types without training on previous treatment response data. We study ENLIGHT in two translationally oriented scenarios: personalized oncology (PO), aimed at prioritizing treatments for a single patient, and clinical trial design (CTD), selecting the most likely responders in a patient cohort.
Evaluating ENLIGHT’s performance on 21 blinded clinical trial datasets in the PO setting, we show that it can effectively predict a patient’s treatment response across multiple therapies and cancer types. Its prediction accuracy is better than previously published transcriptomics-based signatures and is comparable with that of supervised predictors developed for specific indications and drugs. In combination with the interferon-γ signature, ENLIGHT achieves an odds ratio larger than 4 in predicting response to immune checkpoint therapy. In the CTD scenario, ENLIGHT can potentially enhance clinical trial success for immunotherapies and other monoclonal antibodies by excluding non-responders while overall achieving more than 90% of the response rate attainable under an optimal exclusion strategy.
ENLIGHT demonstrably enhances the ability to predict therapeutic response across multiple cancer types from the bulk tumor transcriptome.
This research was supported in part by the Intramural Research Program, NIH and by the Israeli Innovation Authority.
Re-defining synthetic lethality by phenotypic profiling for precision oncology
2021, Cell Chemical Biology
Citation Excerpt :
In cancer research, these functional and genomic screening methods offer new opportunities for the discovery of therapeutic targets, such as synthetic lethal (SL) partners of cancer-related genotypes or phenotypes. There are excellent reviews on the recent developments made in mapping of SL interactions using high-throughput genetic and functional screens, as well as on the factors that should be considered when developing robust analysis pipelines and when seeking to implement novel SL interactions into the clinical practice (Shen and Ideker, 2018; Tutuncuoglu and Krogan, 2019; Huang et al., 2020; Topatana et al., 2020). Here, we focus instead on the very definition, interpretation, and the usage of the SL concept for the discovery of targeted cancer therapies in the light of new experimental techniques, with the aim to provide maximal output to therapeutically exploit cancer-specific vulnerabilities in precision oncology.
High-throughput functional and genomic screening techniques provide systematic means for phenotypic discovery. Using synthetic lethality (SL) as a paradigm for anticancer drug and target discovery, we describe how these screening technologies may offer new possibilities to identify therapeutically relevant and selective SL interactions by addressing some of the challenges that have made robust discovery of SL candidates difficult. We further introduce an extended concept of SL interaction, in which a simultaneous perturbation of two or more cellular components reduces cell viability more than expected by their individual effects, which we feel is highly befitting for anticancer applications. We also highlight the potential benefits and challenges related to computational quantification of synergistic interactions and cancer selectivity. Finally, we explore how tumoral heterogeneity can be exploited to find phenotype-specific SL interactions for precision oncology using high-throughput functional screening and the exciting opportunities these methods provide for the identification of subclonal SL interactions.
Targeting SMYD3 to Sensitize Homologous Recombination-Proficient Tumors to PARP-Mediated Synthetic Lethality
2020, iScience
Citation Excerpt :
The genetic principle is that the combination of two genetic perturbations is lethal, whereas each of them individually is not, because the function of the targeted genes is compensatory or partially redundant. Thus, the clinical effect of single drugs individually targeting one of the genes is limited, but their impact is greatly potentiated when they are used in combination (Shen and Ideker, 2018). At present, the only synthetic lethality approach approved in the clinics is based on the use of PARP inhibitors (i.e., olaparib, rucaparib, niraparib, and talazoparib) in BRCA1/2-mutated tumors, as breast, ovarian, and pancreatic cancers.
SMYD3 is frequently overexpressed in a wide variety of cancers. Indeed, its inactivation reduces tumor growth in preclinical in vivo animal models. However, extensive characterization in vitro failed to clarify SMYD3 function in cancer cells, although confirming its importance in carcinogenesis. Taking advantage of a SMYD3 mutant variant identified in a high-risk breast cancer family, here we show that SMYD3 phosphorylation by ATM enables the formation of a multiprotein complex including ATM, SMYD3, CHK2, and BRCA2, which is required for the final loading of RAD51 at DNA double-strand break sites and completion of homologous recombination (HR). Remarkably, SMYD3 pharmacological inhibition sensitizes HR-proficient cancer cells to PARP inhibitors, thereby extending the potential of the synthetic lethality approach in human tumors.
Highly Combinatorial Genetic Interaction Analysis Reveals a Multi-Drug Transporter Influence Network
2020, Cell Systems
Citation Excerpt :
Model eukaryotes, including the yeast S. cerevisiae and cultured human cells, have been an important testbed for understanding complex traits. Observing genetic interactions between pairs of genes, e.g., using synthetic genetic array analysis (SGA), has systematically uncovered functional relationships in yeast (Costanzo et al., 2016) and human cells (Horlbeck et al., 2018; Shen and Ideker, 2018), improving our understanding of gene function (Costanzo et al., 2016) and order-of-action in biological pathways (St Onge et al., 2007). Genetic interactions with higher complexity, e.g., three-gene perturbations yielding phenotypes that are unexpected given the corresponding one- and two-gene perturbation phenotypes, can reveal additional important functions (Haber et al., 2013; Kuzmin et al., 2018).
Many traits are complex, depending non-additively on variant combinations. Even in model systems, such as the yeast S. cerevisiae, carrying out the high-order variant-combination testing needed to dissect complex traits remains a daunting challenge. Here, we describe “X-gene” genetic analysis (XGA), a strategy for engineering and profiling highly combinatorial gene perturbations. We demonstrate XGA on yeast ABC transporters by engineering 5,353 strains, each deleted for a random subset of 16 transporters, and profiling each strain’s resistance to 16 compounds. XGA yielded 85,648 genotype-to-resistance observations, revealing high-order genetic interactions for 13 of the 16 transporters studied. Neural networks yielded intuitive functional models and guided exploration of fluconazole resistance, which was influenced non-additively by five genes. Together, our results showed that highly combinatorial genetic perturbation can functionally dissect complex traits, supporting pursuit of analogous strategies in human cells and other model systems.
Genetic interaction networks in cancer cells
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Citation Excerpt :
While the promise is huge, only one SL interaction has been translated into the clinical setting to date: as noted above, breast and ovarian cancer cells carrying mutations in BRCA1 or BRCA2 are highly sensitive to PARP inhibitors [1]. Even though many promising candidates have been identified in tumor cell models [1,7•], most published SL interactions have not withstood pre-clinical evaluation. These failures may result from off-target effects, incomplete loss-of-function and poor reproducibility in RNAi screens [8,9], variable consistency of drug screens [10,11], incomplete penetrance [12•], and context dependency [2].
The genotype-to-phenotype relationship in health and disease is complex and influenced by both an individual’s environment and their unique genome. Personal genetic variants can modulate gene function to generate a phenotype either through a single gene effect or through genetic interactions involving two or more genes. The relevance of genetic interactions to disease phenotypes has been particularly clear in cancer research, where an extreme genetic interaction, synthetic lethality, has been exploited as a therapeutic strategy. The obvious benefits of unmasking genetic background-specific vulnerabilities, coupled with the power of systematic genome editing, have fueled efforts to translate genetic interaction mapping from model organisms to human cells. Here, we review recent developments in genetic interaction mapping, with a focus on CRISPR-based genome editing technologies and cancer.
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Journal of Molecular Biology

ReviewSynthetic Lethal Networks for Precision Oncology: Promises and Pitfalls

Highlights

Abstract

Graphical Abstract

Section snippets

Introduction: the emerging “synthetic lethal” approach to cancer therapy

How to define and measure synthetic lethal interactions

General mechanisms of synthetic lethal interactions

How to discover therapeutically relevant synthetic lethal interactions

Challenges to the implementation of the synthetic lethal approach in cancer therapy

Cancer as a network-based disease

The future of the synthetic lethal approach to cancer therapy

Conclusions

Acknowledgments

Trends Cancer

Mol. Cell

FEBS Lett.

Lancet Oncol.

Lancet Oncol.

Lancet Oncol.

Methods Enzymol.

Cell

Cell

Cell

Cell

Mol. Cell

Cell

Eur. J. Cancer

Cell

Cell

Cell

Targeted cancer therapy

Nature

Treatment algorithms based on tumor molecular profiling: the essence of precision medicine trials

J. Natl. Cancer Inst.

Therapeutic targeting of tumor suppressor genes

Cancer

Treating cancer by targeting a weakness

N. Engl. J. Med.

Exploring genetic interactions and networks with yeast

Nat. Rev. Genet.

Passenger deletions generate therapeutic vulnerabilities in cancer

Nature

Chipping away at the lung cancer genome

Nat. Med.

Genetics of natural populations; recombination and variability in populations of Drosophila pseudoobscura

Genetics

Olaparib maintenance therapy in platinum-sensitive relapsed ovarian cancer

N. Engl. J. Med.

FDA approval summary: olaparib monotherapy in patients with deleterious germline BRCA-mutated advanced ovarian cancer treated with three or more lines of chemotherapy

Clin. Cancer Res.

Olaparib monotherapy in patients with advanced cancer and a germline BRCA1/2 mutation

J. Clin. Oncol.

Targeting the DNA repair defect in BRCA mutant cells as a therapeutic strategy

Nature

Specific killing of BRCA2-deficient tumours with inhibitors of poly(ADP-ribose) polymerase

Nature

Inhibition of poly(ADP-ribose) polymerase in tumors from BRCA mutation carriers

N. Engl. J. Med.

Synthetic lethality and cancer therapy: lessons learned from the development of PARP inhibitors

Annu. Rev. Med.

PARP inhibitors: synthetic lethality in the clinic

Science

Trapping of PARP1 and PARP2 by Clinical PARP Inhibitors

Cancer Res.

A genetic screen using the PiggyBac transposon in haploid cells identifies Parp1 as a mediator of olaparib toxicity

PLoS ONE

PARP inhibitors as potential therapeutic agents for various cancers: focus on niraparib and its first global approval for maintenance therapy of gynecologic cancers

Gynecol. Oncol. Res. Pract.

Harnessing synthetic lethal interactions in anticancer drug discovery

Nat. Rev. Drug Discov.

Combinatorial CRISPR–Cas9 screens for de novo mapping of genetic interactions

Nat. Methods

Synthetic lethality and cancer

Nat. Rev. Genet.

Potential of the synthetic lethality principle

Science

Comprehensive characterization of genes required for protein folding in the endoplasmic reticulum

Science (New York, N.Y.)

Review
Synthetic Lethal Networks for Precision Oncology: Promises and Pitfalls