About the Project
Cancer cells must overcome the shortening of telomeres to achieve replicative immortality. While most cancers activate telomerase to maintain telomere length, approximately 10-40% of cancers, particularly mesenchymal and neuroepithelial tumours, rely on the Alternative Lengthening of Telomeres (ALT) pathway (1). ALT allows for telomere maintenance in a telomerase-independent manner, yet remains poorly understood, and there are currently no effective therapies targeting ALT-reliant tumours. This represents a significant challenge, especially as ALT-positive cancers often exhibit aggressive clinical behaviour and resistance to conventional treatments. Recent advances from our laboratory have implicated replication fork restart (RF) mechanisms as key vulnerabilities in the ALT pathway. By targeting these mechanisms, there is potential to exploit a therapeutic weakness in ALT-reliant cancers (2).
This project will leverage an advanced machine learning-based bioinformatics pipeline to identify novel synthetic lethal (SL) targets and biomarkers specific to ALT-positive cancers. Through a combination of computational predictions and experimental validation, the project aims to uncover new therapeutic avenues, with the potential to transform precision treatments for these hard-to-treat cancers.
The student involved in this project will develop expertise in a wide range of cutting-edge techniques, including CRISPR-Cas9 genome editing, super-resolution fluorescence microscopy, single-molecule analysis of DNA replication, bioinformatics, and proteomics. They will also have the opportunity to attend and present their work at national and international conferences, as well as participate in journal clubs and seminar series.
Additionally, collaboration with industry partners in bioinformatics and drug development will give the student access to state-of-the-art technologies, resources, and expertise, enhancing the translational potential of the project. This partnership will help drive the project toward identifying actionable targets that could significantly impact the treatment landscape for ALT-reliant tumours.
Candidates must have, or be on track to receive, a First- or Upper Second- class Honours degree (or a Masters) in in biological sciences or medicine and have experience in bioinformatics, and must have a basic knowledge of genomics, cancer biology/ DNA repair mechanism and proficiency in python and R.
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