Synthetic Lethality Emerges as Next-Generation Strategy for Targeting Undruggable Cancer Mutations
This Cancer Discovery review examines how synthetic lethality provides a framework for therapeutically targeting loss-of-function mutations in tumour suppressor and DNA repair genes that are not directly druggable. The authors discuss lessons from first-generation clinical trials including PARP inhibitors, and highlight how high-throughput CRISPR screens are identifying a new wave of synthetic lethal targets. For laboratory professionals, the review underscores the critical need for novel predictive biomarkers distinct from traditional companion diagnostic paradigms.
The original study
Synthetic Lethality in Cancer Therapeutics: The Next Generation.
- Authors
- Setton J, Zinda M, Riaz N, Durocher D, Zimmermann M, Koehler M, et al.
- Journal
- Cancer discovery
- Type
- Journal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't, Review
- PMID
- 33795234
Original 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.