Aggressive lymphomas, including diffuse large B cell lymphoma and Burkitt’s lymphoma, are common and serious cancers affecting white blood cells. Survival on current treatment ranges from 40-80% of patients, presenting significant opportunities for improvement. It is increasingly recognised that tumours in this group are heterogeneous in the underlying pathological mechanisms that drive the initiation and development of the cancer cells, and that different molecular mechanisms relate to differences in prognosis and response to treatment.  Current treatments are dominated by untargeted chemotherapy, but there are an increasing number of new drugs which target specific molecular mechanisms operating in individual tumours. The use of these drugs is known as precision medicine.

This project is about using large data sets of molecular information on lymphomas to identify the pathogenic mechanisms that are operating in the cancer cells and to direct targeted specific drugs to them. The data sets include DNA sequences (genetic information) and measurement of gene expression (which genes are used by the cancer cells).  To achieve this we need to analyse the data using statistical and machine learning methods (see Figure 1), and develop biomarkers that allow us to identify the right drugs for the individual patient.  This requires very large data sets containing molecular information alongside information on treatment and progression from many past patients. The project depends on information from local NHS hospitals and also from a number of national clinical trials.

The project is presently in its early stages, with one of the first trials of targeted therapy just about to conclude.



Figure 1. Using machine learning to distinguish different lymphoma variants using patterns of gene expression.


Research Team

Prof. David R. Westhead, Dr. Reuben Tooze (University of Leeds)

In collaboration with

St. James University Hospital (Haematological Malignancy Diagnostic Service) (Dr. Cathy Burton)

University of Southampton (Prof. Peter Johnson)

Queen Mary College, University of London (Prof. Jude Fitzgibbon)

University of Oxford (Dr. Anna Schuh)

University of Cambridge (Dr. Ming Du)