Biologically-informed machine learning to identify clinically actionable subgroups in bladder cancer

About the Project

Lead supervisor:Dr Andrew Mason

Co-supervisors:Dr Simon Baker

The student will be registered with the Department of Biology

Project Overview

Muscle-invasive bladder cancer is a diverse disease where machine learning methods have so far failed to deliver clinically actionable subgroups. Most approaches have ignored biological knowledge, instead focusing on highly variable genes which often point towards hard to target changes in differentiation status, or the variable presence of infiltrating cells. Our work understanding gene regulation in healthy urothelium gives us a fresh perspective. We have generated RNAseq profiles of healthy tissues and used these to focus on tissue-specific biology in bladder cancer. We recently identified a novel subgroup with NRF2 overactivity (https://www.biorxiv.org/content/10.1101/2025.06.03.657659v1), presenting a new treatment option for advanced bladder cancer, and informing pan-cancer patient selection for NRF2 inhibition therapy. We identified this new, clinically-tractable subgroup by returning biological knowledge to the centre of this biological question. We use data-driven techniques, but make tissue-specific hypotheses in order to derive and manipulate gene expression networks to identify subgroups with an identifiable oncogenic mechanism which could be treated in the clinic. We are now looking for an enthusiastic, self-motivated and ambitious PhD student to develop this work and find new targets for clinical translation.

The candidate should have a strong biological background, ideally in cancer or cell biology, and have coding experience. We are looking for a student to develop a broad skillset covering both these disciplines to ensure biological insight is at the heart of any bioinformatic discovery.

Key Objectives

1. Derive gene co-expression networks and integrate biological knowledge to select informative genes for stratification of cancer samples
2. Determine strategies for multi-omic data integration (mutations, CNAs, splice variants, miRNA etc.) within networks to improve subgroup identification
3. Develop suitable model systems to validate exciting clinically-relevant targets in vitro
4. Create novel hypothesis-driven datasets to feed in to your bioinformatic pipelines

Techniques and Skills Training

Bioinformatic training will include: advanced Python/R, linux command line, open access high performance computing (https://www.york.ac.uk/it-services/tools/viking/), network construction, unsupervised machine learning. Laboratory training will include: human tissue processing, cancer and primary cell culture, nucleic acid extraction and QC, western blotting, immunohistochemistry and immunofluorescence.

The Supervisory Team

Dr Andrew Mason and Dr Simon Baker are the co-leads of the urothelial cancer strand within the Jack Birch Unit for Molecular Carcinogenesis (https://www.york.ac.uk/biology/research/jack-birch-unit/) at The University of York, collaborating closely to integrate modern bioinformatic and genomic techniques in human urothelial cell systems and patient samples. Simon (https://www.linkedin.com/in/simon-baker-640b1614/) has over 20 years of experience developing, refining and manipulating urothelial models of disease, and is focused on uncovering (and targeting) the likely viral origins of urothelial carcinoma. Andrew (https://www.linkedin.com/in/andrew-mason-7a383263/) is a translational, biology-focused bioinformatician who uses sequencing data to better inform personalised treatment strategies in diverse cancers, primarily focused on bladder cancer. Andrew is dedicated to training the next generation of biomedical informaticians, with roles in the Northern Bioinformatics User Group (https://northernbug.github.io/), the 100,000 Genomes Project (https://www.genomicsengland.co.uk/initiatives/100000-genomes-project), and Elixir-UK (https://elixiruknode.org/). Together, Andrew and Simon are looking to train, support and mentor a PhD student to occupy this exciting multidisciplinary project, working both in the laboratory and at the command line to have maximum impact on patient outcomes.

The University of York is committed to recruiting future scientists regardless of age, ethnicity, gender, gender identity, disability, sexual orientation or career pathway to date. We understand that commitment and excellence can be shown in many ways and we have built our recruitment process to reflect this. We welcome applicants from all backgrounds, particularly those underrepresented in science, who have curiosity, creativity and a drive to learn new skills.

The Department of Biology holds an Athena SWAN Gold Award. We are committed to supporting equality and diversity and strive to provide a positive working environment for all staff and students.

Entry Requirements: Students with, or expecting to gain, at least an upper second class honours degree, or equivalent, are invited to apply. The interdisciplinary nature of this programme means that we welcome applications from students with any biological, chemical, and/or physical science backgrounds.

Programme: PhD in Biomedical Science (3 year)

Start Date: 21 September 2026