Molecular Medicine Israel

CRISPR/Cas9 Reveals Cancer’s Synthetic Lethal Vulnerabilities

The CRISPR/Cas9 gene-editing system has been used to identify more than 120 synthetic-lethal gene interactions in cancer cells. These interactions could guide drug developers to new combination therapies that could selectively kill cancer cells and spare healthy cells.

Synthetic-lethal gene interactions may occur when certain pairs of mutated genes are present. When there is a mutation in either of these genes within a cell, the cell remains viable. But when there are mutations in both genes, the result is cell death. Synthetic-lethal gene interactions are especially important in the context of cancer therapies. If at least one of the genes in the interaction is specific to cancer, then a drug that inhibits the other gene would selectively kill only cancer cells.

The synthetic-lethal concept has been around for years, but it has been underdeveloped because chemical and genetic tools for the perturbation of gene function in somatic cells have been lacking. But this limitation has been addressed by researchers at the University of California, San Diego. These researchers report that they developed a new method to search for synthetic-lethal gene combinations.

The method appeared March 20 in the journal Nature Methods, in an article entitled “Combinatorial CRISPR–Cas9 Screens for De Novo Mapping of Genetic Interactions.”

“We developed a systematic approach to map human genetic networks by combinatorial CRISPR–Cas9 perturbations coupled to robust analysis of growth kinetics,” wrote the article’s authors. “We targeted all pairs of 73 cancer genes with dual guide RNAs in three cell lines, comprising 141,912 tests of interaction.”

In this article, the UC San Diego team described how they used the gene-editing technique CRISPR/Cas9 to simultaneously test for thousands of synthetic-lethal interactions. The researchers designed a CRISPR/Cas9 system with two guide RNAs: (1) one that targets a tumor suppressor gene that is commonly mutated in cancer and (2) one that targets a gene that could also be disrupted by a cancer drug. They deployed this system against 73 genes in three laboratory cell lines—human cervical cancer, lung cancer, and embryonic kidney cells. Then they measured cell growth and death.

“Numerous therapeutically relevant interactions were identified, and these patterns replicated with combinatorial drugs at 75% precision,” the authors noted. “From these results, we anticipate that cellular context will be critical to synthetic-lethal therapies.”

“The ovarian cancer drug olaparib works by synthetic lethality—it inhibits a gene that, when a BRCA gene is also mutated, kills just those cancer cells,” said John Paul Shen, M.D., clinical instructor and postdoctoral fellow at UC San Diego School of Medicine and Moores UCSD Cancer Center. “Many other cancers could likely be treated this way as well, but we don’t yet know which gene mutation combinations will be synthetic-lethal.”

“Identifying underlying genetic interactions in this way can reveal important functional relationships between genes, such as contributions to the same protein complex or pathway,” co-senior author Trey Ideker, Ph.D., professor in the UC San Diego School of Medicine, founder of the UC San Diego Center for Computational Biology and Bioinformatics and co-director of the Cancer Cell Map Initiative. “This in turn can impact both our fundamental understanding of biological systems, as well as therapeutics development.”

Many of the gene interactions the team identified were synthetic-lethal in just one of the three cell lines tested. This means that synthetic-lethal interactions may be different in different types of cancer. The researchers said this will be an important consideration for future drug development.

“Moving forward, we intend to further refine our technology platform and make it more robust,” said co-senior author Prashant Mali, Ph.D., assistant professor in the Jacobs School of Engineering at UC San Diego. “And we are scaling our cancer genetic networks maps so we can systematically identify new combination therapies.”

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