Functional modules predict cancer-relevant genetic interactions in mammalian cells

Area of Science:

• Genetics
• Cancer Biology
• Synthetic Lethality

Background:

• Systematic mapping of genetic interactions in human cells is challenging due to inefficient reagents and a large search space.
• Understanding gene function and identifying synthetic lethals are crucial for cancer research.

Purpose of the Study:

• To develop a scalable strategy for systematic genetic interaction mapping in human cells.
• To identify novel cancer-relevant synthetic lethal interactions within specific gene modules.

Main Methods:

• Leveraged yeast genetic network principles to identify human gene modules enriched for interactions.
• Employed a Cas12a-based In4mer combinatorial knockout platform for pairwise interaction screening.
• Screened interactions across eight diverse cancer cell lines, focusing on receptor tyrosine kinase and DNA damage response modules.

Main Results:

• Identified hundreds of previously unreported synthetic lethal interactions.
• Discovered a dense network of synthetic lethals within the protein glycosylation machinery.
• Confirmed that 2D cell culture interactions translate to more physiologically relevant models.
• Demonstrated up to 16-fold enrichment of interaction density in targeted modules.

Conclusions:

• The developed platform provides a scalable strategy for systematic genetic interaction mapping in human cells.
• The findings highlight the potential of targeting protein glycosylation for cancer therapy.
• This approach accelerates the discovery of gene function and cancer-relevant synthetic lethals.