Research Interest

Personalised Medicine of Myeloma

Myeloma is a cancer of plasma cells, and its clinical features include lytic bone disease, kidney failure and bone marrow failure. It is usually preceded by a non-malignant condition called MGUS, with 1% of MGUS patients progressing to myeloma per year. Myeloma itself can progress to plasma cell leukaemia, whereby the malignant plasma cells acquire genetic changes that allow them to leave the protective environment of the bone marrow and enter the circulation. These different stages of disease provide a framework for some of our genetic research.
The treatment of myeloma has improved greatly over the past decade, but despite these advances therapy is non-curative and the median overall survival is around 5 years. Within the general myeloma population there are subsets of patients who have a significantly worse prognosis than this. There is, therefore, a clinical need to identify biomarkers able to identify these patients, and to characterise the genetic background of their tumours in order to better understand what governs the poor prognosis and to allow us to improve clinical outcomes in the future.

Although when looked at down the microscope, myeloma is a homogeneous disease when examined at a molecular level it is clearly heterogeneous.  We hypothesise that it is this variation, at a genetic level, that is responsible for the differences in clinical outcome and that by understanding and detecting this variation, we will be able to predict clinical outcomes.

We have used FISH based technologies to define specific genetic sub-sets of the disease, which have prognostic importance. We have taken this approach further by the use of gene expression and methylation profiling to characterise the transition of MGUS to myeloma to plasma cell leukaemia, with the aim of defining the genes driving these important clinical transitions that can be targetted therapeutically.  We have shown distinct expression profiles at each disease stage, as well as distinct profiles associated with specific molecular sub-groups. We are using this data to develop global classification strategies for myeloma, and evaluating the impact of these on disease response and survival.

In order to develop a model for the personalised medicine strategy in myeloma, we have focussed on a specific subgroup of patients with a poor prognosis that is characterised by a chromosomal translocation, the t(4;14).  The translocation deregulates two oncogenes FGFr3 and MMSET, both of which have pathogenic importance in myeloma.  As such, these genes can be targetted with new small molecule therapies, which may change the outcome of patients.  Thus, we have initiated programmes of work to study their role in myeloma, detect their presence and also to understand how they can be targeted therapeutically.  

In addition to this highly focussed approach looking at the t(4;14), we are also looking more broadly at the genetic basis of myeloma using next generation sequencing approaches able to characterise the whole genome.  We aim to define genes that are mutated and contained within the regions of a recurrent chromosomal copy number change.  Importantly, this approach may identify genes, which are clinically relevant, mutated in patient samples and, as such, can be specifically targeted.

One aim of our approach is to understand treatment resistance and how to overcome it.  In order to understand how this develops, we are applying sequencing approaches to study the mutational basis of myeloma and its contribution to disease progression and clinical outcome.  In particular, focussing on the clinical disease model, whereby normal plasma cells transform through MGUS to myeloma and finally to plasma cell leukaemia.  

Epigenetic changes, such as histone acetylation and DNA methylation, can mediate tumour progression and treatment resistance.  There are a range of therapies able to modulate the epigenetic status of a cancer cell and may, therefore, be useful clinically.  Thus, we have initiated work to study the epigenetic changes, giving rise to myeloma and push cells through the N-MGUS-MM-plasma cell transition.

In order to develop therapies able to overcome resistance, we have initiated work using a strategy using libraries of novel drugs and of RNAi’s, both targeted and genome wide to define novel targets and drug combination that can overcome resistance.  

A further important factor contributing to both disease response and the development of side-effects are inherited genetic factors. In this respect, we have looked at the role of inherited variation in cytokine and DNA repair genes, which have shown interesting associations in terms of disease response of myeloma and outcome. We are now moving from studies utilising candidate genes to a more global approach with genetic variants distributed across the whole genome, the so called ‘GWAS’ approach.  Using this strategy, we are looking at the factors giving rise to myeloma as well as studying their impact on survival and side effect endpoints.

The linking of clinical trial data, treatment, outcome data and molecular genetic characterisation information, is a central requirement of our approach to translational science.  As a vehicle for us to carry out our genetic studies, we require access to well characterised patient material.  In order to achieve this, we have set up and run a series of large multi-centre trials looking at the treatment of myeloma.  This ‘MRC myeloma’ series of trials has encompassed a range of clinical questions over the last 3 decades.  More recently, we have focussed on the collection of biological material from these studies.  The material and data from these studies is substantial and in the latest study, Myeloma XI, we are collecting this material prospectively.  

Team Members
Brian Walker, Staff Scientist
David Gonzalez de Castro, Senior Clinical Scientist
David Johnson, Higher Scientific Officer
Christopher Wardell, Computational Biologist
Rosemary Fryer, Post Doctoral Training Fellow
Fabio Mirabella:  Post Doctoral Training Fellow
Ping Wu, Scientific Officer
Lauren Aronson, Scientific Officer
Sanna Hulkki, Scientific Officer
Dil Begum, Scientific Officer
Daniel Itzhak, PhD Student
Kevin Boyd, Clinical Research Fellow
Martin Kaiser, Visiting Research Fellow
Diane Forzani, PA to Professor Morgan

Data collection and analysis team members
Ms Lee Conneely, Senior Trial Coordinator
Ms Lorna Smith, Data Manager
Mr Sam Morgan, Data Manager
Mrs Gemma Findon, Research Nurse
Ms Emily Blackmore, Research Nurse