Synthetic Lethality in Cancer Therapeutics: The Next Generation

Abstract

Synthetic lethality (SL) provides a conceptual framework for tackling targets that are not classically "druggable," including loss-of-function mutations in tumor suppressor genes required for carcinogenesis. Recent technological advances have led to an inflection point in our understanding of genetic interaction networks and ability to identify a wide array of novel SL drug targets. Here, we review concepts and lessons emerging from first-generation trials aimed at testing SL drugs, discuss how the nature of the targeted lesion can influence therapeutic outcomes, and highlight the need to develop clinical biomarkers distinct from those based on the paradigms developed to target activated oncogenes. SIGNIFICANCE: SL offers an approach for the targeting of loss of function of tumor suppressor and DNA repair genes, as well as of amplification and/or overexpression of genes that cannot be targeted directly. A next generation of tumor-specific alterations targetable through SL has emerged from high-throughput CRISPR technology, heralding not only new opportunities for drug development, but also important challenges in the development of optimal predictive biomarkers.

Figure 1.. Synthetic lethality: definition and approaches for the identification of synthetic lethal interactions.

A) Synthetic lethality is defined by cellular or organismal lethality caused by combined alterations of gene pairs that are otherwise individually viable. A commonly employed and therapeutically relevant definition encompasses pharmacologic inhibition of one gene product with genetic inactivation of the other. B) Identification of SL drug targets has been facilitated by high-throughput genetic and chemogenetic screening in human cancer cell lines. The use of isogenic models in forward CRISPR-based screens minimizes potential confounding by co-occurring genetic alterations and facilitates attribution of a cellular phenotype to a specific SL pair. Chemogenetic screens appear to be particularly efficacious in developing new patient-selection hypotheses for compounds with known mechanism-of-action (,–116) or to uncover potential mechanisms of resistance (117,118). C) Overview of the Cancer Dependency Map (DepMaP) a large-scale multi-institution functional genomics project aimed at creating a comprehensive database of potential novel drug targets and biomarkers across cancer types. A recent integrated analysis of CRISPR-based screens from the Cancer Dep Map effort identified >1000 candidate genetic dependencies across 786 cell lines representing 42 cancer types (119). Abbreviations: KO, knocked out; WT, wild-type; RPPA, reverse phase protein array; FDR, false discovery rate; GDSC, Genomics of Drug Sensitivity in Cancer; PRISM, Profiling Relative Inhibition Simultaneously in Mixtures; CTRP, Cancer Therapeutics Response Portal.

Figure 2.. Conceptual framework for optimized prioritization of synthetic lethal therapeutic approaches.

Germane to the successful translation of synthetic lethal interactions into cancer treatments is the consideration of characteristics of the gene altered in the cancer cells including whether its loss is mono- or bi-allelic or biologically sufficient to cause a phenotype in the context of a synthetic lethal interaction, the prevalence of its loss in a given cancer type or across cancers and whether the loss of the gene is essential for tumor development and/or maintenance. The features of the target gene (i.e. gene to be inhibited therapeutically) also need to be considered, included its expression in the cell lineage and/or cancer type of interest and the toxicity impact of its inhibition. The characteristics of the synthetic lethal interaction itself also need to be considered, including the effect size (magnitude of the therapeutic index in preclinical models) and penetrance. Synthetic lethal interactions may be limited to a specific genetic context or tissue type/lineage; assessing the penetrance of the genetic interaction across varying model systems and cell lineages can inform the robustness of the therapeutic window.