Kinome-wide CRISPR-Cas9 screen to identify kinases involved in Taxol resistance in breast cancer

Breast cancer is the most frequently diagnosed cancer in women, accounting for over 25% of cancer diagnoses and 13% of cancer-related deaths in Canadian women. There are many types of therapies for treatment or management of breast cancer, with chemotherapy being one of the most widely used. Taxol (paclitaxel) is one of the most extensively used chemotherapeutic agents for treating cancers of the breast and numerous other sites. Taxol stabilizes microtubules during mitosis, causing the cell cycle to arrest until eventually the cell undergoes apoptosis. Although Taxol has had significant benefits in many patients, response rates range from only 25-69%, and over half of Taxol-treated patients eventually acquire resistance to the drug. Drug resistance remains one of the greatest barriers to effective cancer treatment, yet little has been discerned regarding resistance to Taxol, despite its widespread clinical use. Kinases are known to be heavily involved in cancer development and progression, and several kinases have been linked to resistance of Taxol and other chemotherapeutic agents. However, a systematic screen for kinases regulating Taxol resistance is lacking. Thus, in this study, a set of kinome-wide screens was conducted to interrogate the involvement of kinases in the Taxol response. Positive-selection and negative-selection CRISPR-Cas9 screens were conducted, whereby a pooled library of 5070 sgRNAs targeted 507 kinase-encoding genes in MCF-7 breast cancer cells that were Taxol-sensitive (WT) or Taxol-resistant (TxR) which were then treated with Taxol. Next generation sequencing (NGS) was performed on cells that survived Taxol treatment, allowing identification and quantitation of sgRNAs. STK38, Blk, FASTK and Nek3 stand out as potentially critical kinases for Taxol-induced apoptosis to occur. Furthermore, kinases CDKL1 and FRK may have a role in Taxol resistance. Further validation of these candidate kinases will provide novel pre-clinical data about potential predictive biomarkers or therapeutic targets for breast cancer patients in the future.