Prof Kevin Blyth, University of Glasgow, Prof Colin Semple, University of Edinburgh & Prof Crispin Miller, CRUK Scotland Institute
Background
Mesothelioma is an asbestos-driven cancer, with limited treatment options and a median survival of 12-18 months. Areas of Scotland have the highest global incidence of the disease, reflecting historical utilisation of asbestos in heavy industries. Detailed molecular characterisation of multiple global cohorts has revealed highly prevalent loss of function events in tumour suppressor genes (e.g. BAP1, CDKN2A, NF2) but few protein altering mutations in activating oncogenes. This genomic landscape, which has been defined using late-stage tumour samples, correlates poorly with the rapidly progressive illness experienced by most patients.
This project will apply state-of-the-art computational pipelines to a unique collection of paired pre-tumour and tumour biopsies collected from patients as they transitioned in real-time from a pre-mesothelioma precursor state to early-stage invasive mesothelioma. This rare bioresource has been collected by the CRUK-funded PREDICT-Meso International Accelerator Network, which is led by Blyth (University of Glasgow/CRUKSI) and currently comprises >170 investigators from 98 institutions in 17 countries. The primary vehicle for the assembly of this cohort has been the Meso-ORIGINS study, which has recruited >500 patients since 2022, with the target number of 600 patients expected to be reached by August 2026. PREDICT-Meso resources have been used to generate Whole Exome Sequencing (WES) and bulk RNASeq, which will be the primary datasets used for this project, in addition to multiple other ‘omic layers, detailed clinical information and imaging materials. These datasets have been curated and stored in the bespoke PREDICT-Meso Database, supported by the CRUK Scotland Centre Data Science & Data Management team, with analysis via established variant calling and gene expression pipelines in the Semple Lab, based in the Institute of Genetics in Cancer (University of Edinburgh).
This project will allow deep exploration of exciting preliminary results generated by Semple et al, regarding previously unidentified drivers of mesothelioma evolution. These data generated using uniform, state-of-the-art re-analysis of publicly available mesothelioma datasets, align well with outcomes from other PREDICT-Meso investigators regarding therapeutic targeting of cellular pathways downstream from these drivers. These data include results from high-throughput drug screening, experiments in genetically engineered mouse models and early phase human trials. The outcomes of this project are therefore expected to play an important role in the design of clinical trials testing a new therapeutic strategy for mesothelioma.
PREDICT-Meso is committed to training of the next generation of mesothelioma researchers and currently supports 14 other PhD studentships in related areas providing a unique training environment. This non-clinical PhD project will run in parallel to other projects in aligned disciplines and alongside clinical PhD projects, ensuring comprehensive training in cancer sciences. The project will also be supported by a dedicated project manager via PREDICT-Meso, and the CRUK Scotland Centre Data Science & Data Management team.
Skills/Techniques that will be gained
The student will gain experience in the primary processing of WES data using high performance computing, including sequence QC, alignment and established somatic variant calling pipelines. This will underpin statistical meta-analyses (using R) of mutation co-occurrence and exclusivity at multiple scales, from short nucleotide variants to large structural variants. Variants will be tested for evidence of selection 1, 2 to identify driver variant candidates, and for association with clinical and histopathological variables. Whole genome duplication and aneuploidies will be predicted using established algorithms 3. Primary processing of RNA-seq data will quantify gene expression and establish significantly differentially expressed genes. All variant data and expression data will be integrated and compared to a large, in-house genomic-transcriptomic atlas constructed using uniform processing of WES/WGS/RNA-seq data from previously published mesothelioma studies. Patterns of variation across multiple samples from the same patient will be used to infer dominant evolutionary trajectories during mesothelioma evolution 3.
For further information on the project or informal enquiries, please contact Prof Kevin Blyth, This email address is being protected from spambots. You need JavaScript enabled to view it.
When submitting your application please also upload the completed EDI recruitment form.
To place an application, please visit this site at the University of Glasgow. Please note that due to funding requirements this opportunity is open to UK applicants only.
Duration: 4 years, starting October 2026
Closing Date: Wednesday 6th May 2026
Interview for this position will take place in June 2026
Lab Websites
Prof Kevin Blyth - PREDICT-Meso
References
Ewing A, Meynert A, Silk R, Aitken S, Bendixsen DP, Churchman M, Brown SL, Hamdan A, Mattocks J, Grimes GR, Ballinger T, Hollis RL, S Herrington C, Thomson JP, Sherwood K, Parry T, Esiri-Bloom E, Bartos C, Croy I, Ferguson M, Lennie M, McGoldrick T, McPhail N, Siddiqui N, Glasspool R, Mackean M, Nussey F, McDade B, Ennis D; Scottish Genomes Partnership; McMahon L, Matakidou A, Dougherty B, March R, Carl Barrett J, McNeish IA, Biankin AV, Roxburgh P, Gourley C, Semple CA. Divergent trajectories to structural diversity impact patient survival in high grade serous ovarian cancer. Nat Commun 2025 16: 5586.
ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium. 2020. Pan-cancer analysis of whole genomes. Nature 578: 82-93.
DP Bendixsen, F Semple, A Ironside, MI Özkaraca, N Wilson, A Meynert, A Ewing, CA Semple, O OIkonomidou. Frequent Whole-Genome Duplication Events Drive the Genomic Evolution of Triple-Negative Breast Cancer During Neoadjuvant Chemotherapy. bioRxiv 2025.11.17.688693
J-B Assié, C Meiller, E Stern, J Lasvergnas, M Arnould, L Pan, F Montagne, R Sequeiros, C Al Zreibi, E Del Nery, A Genovesio, S Lantuejoul, F Le Pimpec-Barthes, J Zucman-Rossi, M-C Jaurand, C Blanquart, O Wald, D Jean. Pharmacogenomic Characterization of a Large Cohort of Patient-Derived Cell Lines Identifies Therapeutic Strategies for Pleural Mesothelioma.Cancer Research 2026