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.

Characterization of ALDH1A1 as a novel tumorigenic target mediating TAZ-induced lung tumorigenesis and stem cell phenotypes

Recent studies suggest that lung cancer stem cells (CSCs) may play major roles in lung cancer development, metastasis and drug resistance. Therefore, identification of lung CSC drivers may provide promising targets for lung cancer. TAZ (transcriptional co-activator with PDZ-binding motif) is a transcriptional co-activator and key downstream effector of the Hippo pathway, which plays critical roles in various biological processes. TAZ has been shown to be overexpressed in non-small cell lung cancer (NSCLC) and involved in tumorigenicity of lung epithelial cells. However, whether TAZ is a driver for lung CSCs and tumor formation in vivo is unknown. In addition, the molecular mechanism underlying TAZ-induced lung tumorigenesis remains to be determined. In this study, we provided evidence that constitutively active TAZ (TAZ-S89A) is a driver for lung tumorigenesis in vivo in mice and formation of lung CSC. Oncogenes upregulated in TAZ-overexpressing cells were identified with further validation. The most dramatically activated gene, Aldh1a1 (Aldehyde dehydrogenase 1 family member a1), a well-established CSC marker, showed that TAZ induces Aldh1a1 transcription by activating its promoter activity through interaction with the transcription factor TEA domain (TEAD) family member. Most significantly, inhibition of ALDH1A1 with its inhibitor A37 or CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) gene knockout in lung cancer cells suppressed lung tumorigenic and CSC phenotypes in vitro, and tumor formation in mice in vivo. In conclusion, this study identified TAZ as a novel inducer of lung CSCs and the first transcriptional activator of the stem cell marker ALDH1A1. Most significantly, we identified ALDH1A1 as a critical meditator of TAZ-induced tumorigenic and CSC phenotypes in lung cancer. Our studies provided preclinical data for targeting of TAZ-TEAD-ALDH1A1 signaling to inhibit CSC-induced lung tumorigenesis and drug resistance in the future.

Petri Net Siphon Analysis and Network Centrality Measures for Identifying Combination Therapies in Signaling Pathways

Aberrant behavior of biological signaling pathways has been implicated in diseases such as cancers. Therapies have been developed to target proteins in these networks in the hope of curing the illness or bringing about remission. However, identifying targets for drug inhibition that exhibit good therapeutic index has proven to be challenging since signaling pathways have a large number of components and many interconnections such as feedback, crosstalk, and divergence. Unfortunately, some characteristics of these pathways such as redundancy, feedback, and drug resistance reduce the efficacy of single drug target therapy and necessitate the employment of more than one drug to target multiple nodes in the system. However, choosing multiple targets with high therapeutic index poses more challenges since the combinatorial search space could be huge. To cope with the complexity of these systems, computational tools such as ordinary differential equations have been used to successfully model some of these pathways. Regrettably, for building these models, experimentally-measured initial concentrations of the components and rates of reactions are needed which are difficult to obtain, and in very large networks, they may not be available at the moment. Fortunately, there exist other modeling tools, though not as powerful as ordinary differential equations, which do not need the rates and initial conditions to model signaling pathways. Petri net and graph theory are among these tools. In this thesis, we introduce a methodology based on Petri net siphon analysis and graph network centrality measures for identifying prospective targets for single and multiple drug therapies. In this methodology, first, potential targets are identified in the Petri net model of a signaling pathway using siphon analysis. Then, the graph-theoretic centrality measures are employed to prioritize the candidate targets. Also, an algorithm is developed to check whether the candidate targets are able to disable the intended outputs in the graph model of the system or not. We implement structural and dynamical models of ErbB1-Ras-MAPK pathways and use them to assess and evaluate this methodology. The identified drug-targets, single and multiple, correspond to clinically relevant drugs. Overall, the results suggest that this methodology, using siphons and centrality measures, shows promise in identifying and ranking drugs. Since this methodology only uses the structural information of the signaling pathways and does not need initial conditions and dynamical rates, it can be utilized in larger networks.