PhD Computing Science: Algorithmic analysis of clonal evolution and therapeutic resistance in pancreatic cancers

Pancreatic cancer is the 4th leading cause of death in western societies, and predicted to be the second within a decade. Genomic sequencing of ~520 pancreatic cancer cases, by the Glasgow pancreatic cancer team, has provide the foundation to understand tumour biology and identify improved therapeutic options for pancreatic cancer, in which there has been minimal improvement in outcomes for 40 years.

These data suggest that many patients develop resistance with therapy either by selecting for resistant clones within the tumour, or through the rapid acquisition of secondary mutations. Understanding cancer evolution and the resulting subclonal architecture of cells within a tumour is thus essential to developing the next generation of therapeutics. This proposal aims to develop machine learning algorithms that reveal patterns of cancer evolution at multiple –omics levels (genomics, transcriptomics, and metabolomics) and link these with therapy to identify mechanisms of resistance and inform combinatorial therapeutic strategies.

The project will be based on a solid foundation of algorithmic and experimental advances in genomics [1], transcriptomics [2] and metabolomics [3] by the team members. The candidate will have the opportunity to develop highly sophisticated probabilistic models and test them on large scale patient data, including data from ongoing clinical trials as part of the Precision-Panc project. Reference: [1]. Yuan, K., et al. (2015) BitPhylogeny: a probabilistic framework for reconstructing intra-tumor phylogenies. Genome Biology, 16(1), p. 36. [2]. Bailey, P. et al. (2016) Genomic analyses identify molecular subtypes of pancreatic cancer. Nature, 531(7592), pp. 47-52. [3]. van Der Hooft et al (2016) Topic modeling for untargeted substructure exploration in metabolomics. Proceedings of the National Academy of Sciences of the United States of America, 113(48), pp. 13738-13743

Project Team - The successful candidate will be jointly supervised by Drs Ke Yuan and Simon Rogers at the School of Computing Science, Prof. Andrew Biankin, Drs Peter Bailey and 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 (International Cancer Genome Consortium), UK (Precision-Panc) and Scottish consortiums (Scottish Genome Partnership).

Person Specification - Applicants should demonstrate the following:

- Academic qualifications: Undergraduate Degree - 2:1/1; Master’s Degree (Desired) - Pass /Merit /Distinction
- 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 or R.