Full Time PhD Studentship- Prostate Cancer Multi-omics: Risk Stratification and Outcome Prediction

PhD Studentship

Title: Prostate Cancer Multi-omics: Risk Stratification and Outcome Prediction

Grade: PhD Studentship

Department: Research Department of Oncology

Location: UCL Cancer Institute, Paul O'Gorman Building, London

Reports to

The primary supervisor will be Professor Gerhardt Attard. The student will be co-supervised by Dr Stefano Lise, Principal Bioinformatics Scientist in the Attard Laboratory.

Context

The UCL Cancer Institute (UCL CI) is the hub for cancer research at University College London, one of the world's leading universities. Located in the heart of London, the Institute hosts around 300 scientists conducting basic and translational research across a broad range of cancer disciplines. As part of the Faculty of Medical Sciences, the Institute maintains strong links with UCL's partner hospitals, providing a unique environment where discoveries in cancer biology can be translated rapidly into improvements in patient care.

The Treatment Resistance Team, led by Professor Gerhardt Attard, investigates the molecular mechanisms underlying prostate cancer progression and treatment response through the analysis of large-scale clinical and molecular datasets. The group has pioneered the integration of genomic, transcriptomic and epigenomic profiling of tumour tissue and liquid biopsies from patients enrolled in prospective clinical trials, leading to the discovery of biomarkers that improve patient stratification and inform clinical decision-making. Recent work from the laboratory includes the identification of transcriptomic biomarkers predictive of treatment response (Grist et al., Cell, 2025), plasma biomarkers for treatment monitoring (Leone et al., JAMA Oncology, 2025; Brighi et al., Clinical Cancer Research, 2025), and integrated ctDNA and PSA monitoring in metastatic prostate cancer (Jayaram et al., Nature Cancer, 2026).

The successful applicant will join the ReIMAGINE[Life] programme, a major collaborative initiative funded by Prostate Cancer UK that aims to transform prostate cancer diagnosis and risk stratification through the integration of molecular, imaging and clinical data with long-term patient outcomes. The high variability in prostate cancer behaviour has led to a public health crisis with over-treatment of 1000s of men whilst a small proportion continue to suffer distant spread that can be rapidly lethal. Building on the successful ReIMAGINE[Risk] study, https://www.reimagine-pca.org, the programme provides access to one of the world's most comprehensively characterised MRI-led prostate cancer cohorts, comprising over 1,000 men with extensive imaging, pathology, biospecimens, multi-omics profiling and prospective clinical follow-up. The availability of long-term outcome data through ReIMAGINE[Life] creates an exceptional opportunity to understand how molecular features present at diagnosis influence disease progression and patient outcome.

Main Purpose of the Position

We are seeking a highly motivated and talented scientist who will be registered for and be expected to successfully complete a PhD. The position requires a background in bioinformatics, computational biology, genomics, computer science, physics, mathematics, statistics or another quantitative discipline, together with a strong interest in biomedical research.

The overarching aim of this PhD project is to determine how molecular characteristics present at diagnosis shape the long-term clinical trajectory of prostate cancer and to develop integrated predictive models that improve disease risk stratification. The student will analyse and integrate multi-omics datasets generated within the ReIMAGINE programme, including genomic, transcriptomic, epigenomic and proteomic data, together with MRI, pathological and clinical information.

The successful candidate will join the project at a unique stage, as long-term clinical outcome data from ReIMAGINE[Life] mature during the course of the four-year PhD. This will provide a rare opportunity to perform comprehensive analyses linking molecular features measured at diagnosis with subsequent disease progression, metastasis and patient survival, and to refine these analyses as longer-term follow-up becomes available.

Using state-of-the-art computational approaches, the student will identify molecular signatures associated with clinically significant disease and develop predictive models of disease progression, metastasis and survival. The project will contribute to the development of integrated molecular models that complement current clinicopathological assessment and support more accurate and personalised approaches to prostate cancer diagnosis and risk stratification.

The student will work within a highly multidisciplinary environment alongside clinicians, computational biologists, radiologists, pathologists and statisticians in the Attard Laboratory and across the ReIMAGINE consortium. Comprehensive training will be provided in cancer genomics, multi-omics analysis, statistical genomics, machine learning, integrative analysis of molecular and clinical data, scientific programming (Python and R), and translational cancer research.

Informal enquiries about this post (including arrangements to visit UCL Cancer Institute) are welcome and may be directed by email to Prof Gert Attard (g.attard@ucl.ac.uk) and/or Dr Stefano Lise (s.lise@ucl.ac.uk)

Duties and responsibilities

• Contribute to all phases of the project, from study design and data analysis through to publication and presentation of research findings.
• Perform quality control, processing and analysis of large-scale genomic, transcriptomic, epigenomic and proteomic datasets generated within the ReIMAGINE programme.
• Develop reproducible computational workflows for the integration and analysis of multimodal molecular, imaging and clinical datasets.
• Investigate associations between molecular characteristics measured at diagnosis and long-term clinical outcomes, including disease progression, metastasis and survival.
• Develop and evaluate predictive models to improve prostate cancer diagnosis and risk stratification.
• Work closely with clinicians, pathologists, radiologists, bioinformaticians and statisticians within the Attard Laboratory and the wider ReIMAGINE consortium to interpret findings and translate results into clinically relevant insights.
• Maintain high standards of research data management, code documentation and reproducible computational research.
• Keep abreast of relevant literature and methodology developments relevant to the field.
• Present research findings at laboratory meetings, national and international conferences, and contribute to the preparation of manuscripts for publication in peer-reviewed journals.
• Participate actively in the scientific activities of the Attard Laboratory and the wider ReIMAGINE consortium, contributing scientific ideas and fostering a collaborative research environment.
• Contribute to the supervision of undergraduate or Master's student projects where appropriate
• Undertake relevant training and professional development opportunities offered by UCL and the ReIMAGINE consortium.
• Comply with UCL policies relating to research integrity, information governance, health and safety, equality, diversity and inclusion.

Safety

The appointee will comply with the safety and quality requirements of the institution in which they are located.

Management

The appointee will be expected to engage in the management processes of the institution as far as it relates to the post.

Terms and Conditions of Employment

This is a full-time, fully funded 4-year PhD studentship. The studentship provides a non-taxable annual stipend, increasing over the four years of the award as follows:

• Year 1: £23,805
• Year 2: £24,522
• Year 3: £25,260
• Year 4: £26,021

The studentship will also cover tuition fees at the UK Home fee rate for the full four years of the PhD.

The post holder will actively follow UCL policies including

• Equal Opportunities Policy
• Policy and Guidelines for Conflicts of Interest
• Policy for Investigating and Resolving Allegations of Financial Irregularity
• Personal Relationships at Work: Code of Conduct
• Public Interest Disclosure
• Policy on the Investigation of Research Misconduct
• Financial Regulations
• Policy on Data Protection
• Code of Conduct for Undertaking Business on Behalf of UCL

Please see the Human Resources website http://www.ucl.ac.uk/hr/ for information on employment policies.

The Code of Conduct for Undertaking Business on Behalf of UCL can be found at: https://www.ucl.ac.uk/research-innovation-services/compliance-and-assurance/research-integrity

Person Specification

Criteria: Essential or Desirable Assessment method (Application/Interview)

Qualifications, experience and knowledge

First-class Honours degree (or equivalent) in Bioinformatics, Computational Biology, Computer Science, Physics, Mathematics, Statistics, Engineering or a related quantitative discipline: Essential Application Experience programming in Python and/or R: Essential Application/Interview Strong interest in molecular biology, cancer genomics and translational biomedical research: Essential Interview Experience with transcriptomic, genomic, epigenomic or other multi-omics datasets: Desirable Application/Interview Experience analysing large biological or biomedical datasets: Desirable Application/Interview

Skills and abilities

Strong quantitative and analytical skills: Essential Interview Ability to analyse, interpret and visualise complex datasets: Essential Interview Ability to write clear, well-structured and reproducible scientific code: Essential Interview Excellent written and verbal communication skills: Essential Interview Excellent organisational skills, attention to detail and problem-solving ability: Essential Interview Ability to work effectively both independently and within a multidisciplinary team: Essential Interview Familiarity with Linux/UNIX environments and high-performance computing.: Desirable

Personal attributes

Strong interest in computational biology, cancer genomics and translational research: Essential Interview Highly motivated, enthusiastic and committed to scientific research: Essential Interview Demonstrated ability to think critically and solve complex scientific problems: Essential Interview Willingness to learn new computational and analytical methods: Essential Interview Ability to manage multiple tasks and work effectively to agreed deadlines: Essential