Liquid biopsy for early detection and disease monitoring of aggressive T-cell lymphomas

About the Project

Background

T-cell lymphomas are rare but highly aggressive blood cancers. Despite advances in cancer research, outcomes for patients with these diseases have seen little improvement in the past 30 years. New treatment strategies are urgently needed but to realise precision medicine strategies, we first need precision diagnostics and tools that can track disease risk and treatment response in real time.

Circulating tumour DNA (ctDNA), tiny fragments of DNA shed from cancer cells into the bloodstream, offers a powerful, non-invasive way to detect and monitor cancer. ctDNA analysis, “liquid biopsy”, is already transforming how many cancers are diagnosed and managed. However, for T-cell lymphomas, there is currently very little robust evidence to support its clinical use.

Project description

This PhD project aims to change that. Using a large collection of plasma samples from the UK T-cell Lymphoma Biobank, along with ongoing prospective collections, the student will develop and validate a multi-modal ctDNA workflow tailored to T-cell lymphomas. The goal is to enable non-invasive molecular profiling at diagnosis and to track molecular response during treatment.

The project is structured into three main work packages:

1. Non-invasive molecular stratification using Illumina sequencing - Evaluate a targeted panel of gene mutations and genome-wide copy number changes to define molecular subtypes at diagnosis.
2. Enhanced ctDNA detection beyond mutations using Nanopore sequencing - Develop and optimise native sequencing approaches to capture fragmentomic and methylation-based features unique to T-cell lymphoma.
3. Sensitive ctDNA detection for dynamic molecular response assessment - Assess the performance of Illumina and Nanopore methods for detecting minimal molecular disease and tracking treatment response.

Impact

This work will help bring ctDNA testing closer to routine clinical use for T-cell lymphoma patients. By improving early detection and enabling real-time monitoring of treatment response, it has the potential to guide more personalised and effective therapy decisions—ultimately improving patient outcomes.

Training

Collectively, this project will equip the student with a broad set of translational, molecular, and computational tools relevant to cancer genomics.

Working at the interface of clinical and research settings, they will collaborate with clinicians, laboratory scientists, and bioinformaticians to develop and validate diagnostic assays for real-world application.

There will also be opportunities to collaborate with Leicester’s Research Centre for Artificial Intelligence, Data Analytics and Modelling, applying machine learning methods to enhance ctDNA detection using large-scale public cancer datasets.