Journal of Molecular Biology
ReviewSynthetic Lethal Networks for Precision Oncology: Promises and Pitfalls
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.
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Clinically oriented prediction of patient response to targeted and immunotherapies from the tumor transcriptome
2023, MedCitation 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
Re-defining synthetic lethality by phenotypic profiling for precision oncology
2021, Cell Chemical BiologyCitation 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.
Targeting SMYD3 to Sensitize Homologous Recombination-Proficient Tumors to PARP-Mediated Synthetic Lethality
2020, iScienceCitation 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.
Highly Combinatorial Genetic Interaction Analysis Reveals a Multi-Drug Transporter Influence Network
2020, Cell SystemsCitation 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).
Genetic interaction networks in cancer cells
2019, Current Opinion in Genetics and DevelopmentCitation 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].
Theory and Application of Network Biology Toward Precision Medicine
2018, Journal of Molecular Biology