PhD Computational Biology: Algorithmic analysis of clonal evolution and therapeutic resistance in pancreatic cancers

Large-scale genomic sequencing studies such as the International Cancer Genome Consortium (ICGC) and the Cancer Genome Atlas (TCGA) are mapping the genomes of multiple cancer types. The Glasgow pancreatic cancer team has contributed ~520 pancreatic cancer genomes [1]. These data form the foundation of ongoing studies to understand tumour and microenvironment biology and identify improved therapeutic options for pancreatic cancer where there has been minimal improvement in outcomes for over 40 years. Importantly, these studies suggest that rather than pancreatic cancers being inherently resistant to therapy, ~30% have initial responses, but rapidly develop resistance with therapy either selecting for resistant clones within the tumour, or through the rapid acquisition of secondary mutations.

Recent algorithmic development [2] has allowed subclonal architecture within tumour to be reconstructed by advanced machine learning models. Applications of this type of algorithms in other cancers have inferred substantial intratumoral heterogeneity with branching “Darwinian” evolution of truncal clones that evolves with the selective pressure of therapy and leads to the rapid emergence of resistance [3]. Algorithmically, how to integrate analysis form different types of genomic and transcriptomic information remains a key challenge, as treating them separately often leads to conflicting results. As a consequence, the overall aim of this proposal is to discover mechanisms of resistance by integrative modelling of subclonal architecture in pancreatic cancer both pre and post treatment.

Project Team

The successful candidate will be jointly supervised by Dr. Ke Yuan at the School of Computing Science, Prof. Andrew Biankin, Dr. Peter Bailey and Dr. David Chang at the Institute of Cancer Sciences.

The candidate will be primarily based in the Inference, Data, and Algorithms (IDA) section at the School of Computing Science where he or she will be benefit from interactions with experts in machine learning and statistical inference. In addition, the School is an integral part of the Scottish Informatics and Computer Science Alliance (SICSA), which organise events in its Data Science section.

The candidate will also be a member of the Translational Research Centre at Institute of Cancer Sciences. This will enable the candidate to interact with leading biologists and clinicians from the greater cancer research community in Glasgow. In addition, the candidate will have the opportunity to collaborate in large international, UK and Scottish consortiums.

Person Specification

This studentship is open to candidates of any nationality – UK, EU or International. Applicants should demonstrate the following:

- Academic qualifications: Undergraduate Degree - 2:1/1; Master’s Degree (Desired)
- Experience: We welcome candidates from computational backgrounds (i.e. machine learning, statistics, and related fields) who are interest in developing methods for biomedical problems. Candidates with experimental backgrounds (i.e. molecular biology, systems biology and related fields) who want to move into computational biology are encouraged to apply as well. Previous experience with analysing sequencing data is a plus.
- Skills: Good programing skills in Python, R.
Application Process

In the first instance prospective applicants should contact Dr Ke Yuan, [Email Address Removed] to discuss your eligibility. Applicants may submit applications up until the application deadline of 12 noon, Friday 13 January 2017.