Identifying Novel Gene Targets in RET-Driven Cancers Using Synthetic Dosage Lethality Models

Located on chromosome 10, the RET gene encodes a transmembrane receptor tyrosine kinase that, when activated by a ligand, is essential for cell differentiation, proliferation, and survival. RET specifically supports the development of neuronal tissues and neural crest-derived cell types. However, RET is a proto-oncogene and mutations can lead to aggressive cancers like thyroid and lung tumours, or endocrine syndromes. Treatments for RET-driven cancers remain limited, relying on therapies that use RET-specific inhibitors. However, they show limited success in clinical settings due to adverse side effects and acquired resistance over time. To find alternative treatments, integrating the principles of synthetic dosage lethality (a genetic interaction causing a lethal effect in cancer cells) with machine learning may identify novel gene targets to complement existing therapies. In previous studies, we sought candidate genes that induce cell death when depleted in cells overexpressing oncogenic RET, but not in normal cells. A previously identified candidate, NCAM1, which is involved in neuronal cell migration, has been found to contribute to the differentiation of neural cell types. Using the SH-SY5Y neuroblastoma cell line as a model, multiple techniques and assays were performed to validate the synthetic lethal relationship between NCAM1 and RET. NCAM1 knockdown cell lines were generated using lentiviral transduction, and a live/dead cell assay was used to assess potential cytotoxic effects of gene knockdown. Additionally, a cycloheximide assay was performed to examine RET half-life by tracking its degradation over twelve hours. Over the summer, the project enabled us to reassess gene priority rankings using synthetic dosage lethal logic, characterize phenotypes of NCAM1 knockdown SH-SY5Y cells, optimize various assays under in-vitro conditions, and consolidate our understanding of NCAM1’s relationship to RET. The project thus demonstrates the value of combining bioinformatic and in-lab techniques as a productive approach to finding precise therapeutic targets against RET-driven cancers.