About the Project
Every individual has an underlying genetic susceptibility for cancer development. Across all cancer types, the full complement of events driving tumour development still defies identification and, in many cases, no genetic drivers are found. Working within the internationally renowned Liggins Institute, the successful applicant will use machine learning to predict, from thousands of cancer genomes, the non-coding genomic elements driving the 38 most common cancer types. Detecting such alterations is fundamentally and technically challenging (combinatorically enormous number of ways that a genome can be altered), but our successfully established computational/bioinformatic approaches provide functional interpretation of the impact of genetic variation, genome structure, and gene expression to identify personalised risk factors. Applying our approaches across cancer types will identify new drivers of cancer susceptibility, initiation, progression, and clinical response. Our goal is to take the predictors to the clinic, translating the results into changes in clinical practice, diagnostic reports, and public policy that improve cancer treatment.
This project will be supervised by Associate Professor Justin M. O’Sullivan and Dr William Schierding (Liggins Institute, the University of Auckland, New Zealand), and collaborate with Professor Cristin Print (Genetics into Medicine) and Dr Jo Perry (Liggins Institute) as part of a very productive and supportive international research team.
1. Identify potential non-coding mutations that can act as drivers influencing cancer susceptibility, initiation, progression, and clinical response
2. Extend previously developed computational approaches (machine learning) to predict, from thousands of cancer genomes, which non-coding genomic elements drive the 38 most common cancer types
3. From those driver mutations, provide a functional interpretation of the impact of genetic variation, genome structure, and gene expression to identify personalised risk factors across cancer types
4. Translate the new molecular understanding into risk prediction (diagnostic reports) that can be used in clinical practice across several NZ-based cancer studies
What we are looking for in a successful applicant
Applicants should have a demonstrated background in genetics, bioinformatics, computational biology, or related subjects. Applicants must fulfil all conditions for admission to the doctoral programme at the University of Auckland. These can be found here: https://www.auckland.ac.nz/en/study/applications-and-admissions/entry-requirements/postgraduate-entry-requirements/doctoral-entry-requirements.html
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