Unmasking epigenetic redundancies in cancer
Final Report Abstract
The aim of this project was to generate a platform to identify and characterize redundancies in cancer models. I was able to generate constructs that use staphylococcus pyogenes Cas9 to knockout two genes simultaneously. The vector system includes two small guide RNA (sgRNA) tracer sequences that are diversified in order to achieve high knockout efficiency and retain cloning and sequencing capabilities. I could show that both tracer work almost equally efficient. Following the successful generation of mentioned vector, we generated pooled libraries to target over 5,000 paralog gene pairs using over 120,000 sgRNA combinations. We aimed to identify redundancies across multiple cancer models and screened 28 cancer cell lines, identifying that over 7% of all tested gene pairs showed significant epistatic relationships including previously described and novel combinations like: GSK3A/B, HDAC1/2, NONO/PSPC1, SSH1/2, ZFP36L1/2. Some of the strongest interactions showed essentiality in lineage dependent and independent manner. Attractive for potential therapy are those that show lineage dependence, especially those contain druggable functional domains. We are aiming to further validate the most attractive genetic interactions and to investigate the underling mechanisms.
