Modelling risk factors in paediatric T-ALL

About the Project

Paediatric T-cell acute lymphoblastic leukemia (ALL) is an aggressive form of childhood cancer which has survival rates which are significantly inferior to B-cell ALL. Despite intensive chemotherapy the relapse rate is ~20% and survival after relapse is very poor. Even though the genomic landscape of T-ALL has been comprehensively described, few robust and validated risk factors have been translated into the clinic. The only risk factor widely used to stratify patients is treatment response and there is no consensus regarding optimal methodology, timepoints and thresholds. We hypothesis that integrating genetic, response and clinical features is the optimal way to develop a robust and clinically relevant risk model for paediatric T-ALL. There is an urgent need to personalise therapy—maximising cure while minimising harm.

In this PhD the successful candidate will have access to a unique dataset based on three consecutive UK clinical trial cohorts and compiled over a 20-year period. The datasets include demographic, clinical, cytogenetic, treatment, response, and outcome data on more than 1000 patients. In addition, we have more than 800 tumour samples available for further genomic characterisation.

The aim of this project is to develop and validate a risk stratification tool that can be used to assign patients to optimal treatment pathways to improve their chance of a cure. The project is multi-disciplinary and involves both data science and laboratory methods; including the development of a bespoke next-generation sequencing assay to detect gene mutations and determine gene methylation in one experiment. The project combines methodological innovation with real clinical impact. Models developed during the PhD will be designed with interpretability and translational potential in mind, ensuring relevance to clinical decision-making.

Training and Research Environment

This studentship offers comprehensive interdisciplinary training at the interface of genomics, bioinformatics and artificial intelligence/ machine learning. You will gain expertise in cancer genomics, bioinformatics, survival analysis, machine learning methods and reproducible and ethical data science.

You will be embedded within a collaborative research environment, working closely with computational scientists, genomic researchers and clinicians. Access to high-performance computing infrastructure and real-world clinical datasets will enable rigorous and impactful research.

Who Should Apply?

We welcome applicants with backgrounds in either biomedical science or data science with an enthusiasm for interdisciplinary research and improving outcomes for children with cancer. This project offers the opportunity to work at the forefront of cancer genomics and precision/stratified medicine.

Funding

Students who have, or are expecting to attain, at least an upper second-class honours degree (or equivalent) in a relevant subject, are invited to apply. Funding is available for Home (UK) students to cover tuition fees, a tax-free stipend at the UKRI rate (indicative amount in year 1 in 2026-27, £21,805) and research costs, for four years. Applicants normally required to cover International fees will have to cover the difference between the Home and the International tuition fee rates. There is no additional funding available to cover NHS Immigration Health Surcharge (IHS) costs, visa costs, flights etc.

Funding for this studentship is awarded on a competitive basis and is not guaranteed; availability will depend on the outcome of the selection process and subject to final approval by the University.

HOW TO APPLY

Please complete the following application form – Google Form

Applicants can only apply for 1 project; any additional applications will not be accepted.

Applicants should send the following documents to FMSstudentships@newcastle.ac.uk:

• a CV (including contact details of at least two academic (or other relevant) referees).
• a Cover letter – stating your project choice, as well as including additional information you feel is pertinent to your application.
• copies of your relevant undergraduate degree transcripts and certificates.
• a copy of your IELTS or TOEFL English language certificate (where required)
• a copy of your passport (photo page).

A GUIDE TO THE FORMAT REQUIRED FOR THE APPLICATION DOCUMENTS IS AVAILABLE

Please submit your documents in the following format only:

• each document should be submitted as a separate attachment and should be named as follows: candidate surname, candidate name – document type. For example: Jones, Jamie – CV; Jones, Jamie – cover letter.
• Please submit .pdf documents where possible for your CV, cover letter, transcripts and certificates. Do not submit photos of certificates.
• Do not combine documents into one pdf. You may zip separate documents into a zip file to send via email if required.
• When emailing your application, please use the email subject header: FMS PhD Application 2026

Applications not meeting these criteria may be rejected.

Informal enquiries may be made to the lead supervisor of the project you are interested in.

The deadline for all applications is 12 noon BST (UK time) on Wednesday 20th May 2026.