The NIHR Leeds MIC specialises in the development of in vitro diagnostic tests for four key clinical themes, one of which is Oncology. Our Clinical Director and Oncology Theme Lead, Professor Gordon Cook, has an extensive national clinical research portfolio and this case study explores one of his current research projects defining myeloma patients who are at risk of treatment-related toxicity. At its core, this research aims to define the physiological rather than chronological age of a patient, in order to personalise treatment plans for better outcomes.
Professor Gordon Cook explains: “The rationale for this Cancer Research UK funded initiative can be summed up using the old adage ‘one size does not fit all’ in that treatment should not be universally given across all age ranges. The older you get the impact of aging biology comes into play, which limits how we deliver treatment and therefore the outcomes for those patients.”
Myeloma in the UK
Myeloma is a type of bone marrow cancer that effects around 6,000 patients in the UK every year, 43% are over the age of 75 (1). It is therefore regarded as a disease of older people, with its incidence increasing as the population ages. Given the aging profile of the UK population, advancements in myeloma treatments may not achieve their full potential in the older person. The effectiveness of treatments will likely be leveraged for some time to come.
Patient profiling and the response to treatments
Recent developments in treatments used for the management of myeloma has significantly improved outcomes for patients that are younger/fitter, but the impact on older, less fit patients, particularly those over 75 years, has been less marked. Generally accepted reasons for these different outcomes include one or a combination of:
- differences in patient physiology;
- increased treatment-related toxicity (limiting delivery of effective therapy);
- less effective, rigorous anti-myeloma treatment being administered (undertreatment) (2).
It is also considered that the health status of the patient is a factor, given that life expectancy amongst the same age group is more heterogeneous.
Reliably assessing patient physiology and wellness is recognised as the key to developing personalised treatments and therefore improving outcomes for the vulnerable patient group identified (older and less fit).
In 2015, the International Myeloma Working Group (IMWG), created an assessment for defining how likely a patient was to respond well to treatment using categories of ‘fit’, ‘unfit’ and ‘frail’, according to a scoring system. This scoring system, known as the IMWG Frailty Score, is however laborious to implement in clinic and there is a degree of variability when different practitioners apply the scoring system to the same patient (referred to as ‘interobserver variability and bias’).
Professor Gordon Cook, Dr David Cairns and Kara-Louise Royle (Clinical Trials Research Unit, University of Leeds) and Professor Graham Jackson (Department of Haematology, University of Newcastle) in partnership with the UK Myeloma Research Alliance developed a more objective model involving an alternative risk score based on key blood determinants (using results from routine blood investigations performed on myeloma patients) and data extracted from myeloma clinical trials. The purpose was to develop a more clinically practicable to use model that would also eliminate or reduce interobserver variability and bias. The outcome of their work is captured in a profiling assessment model which is named Myeloma Risk Profile (MRP).
Professor Cook explains progress made with the development and validation of the MRP score:
“To date, this model has been tested in a UK and a Danish real-world data set with a third real-world data setting about to be published by the Leeds team. It has been proven that the MRP is a ‘prognostic biomarker’, providing information about patients at greater risk of treatment related toxicity leading to withdrawal from treatment early and poorer outcomes. We now need to test whether this can now be used to direct treatment decisions, which is what we call a ‘predictive biomarker’.
“The testing of both the MRP and the IMWG Frailty Score as predictive biomarkers is the next step in this project and will take place in the Myeloma XIV (FiTNEss) study developed by the UK Myeloma Research Alliance and being run in Leeds. The ultimate aim of this is to help clinicians decide on the best treatment for the older myeloma patient at any given stage of their disease pathway.”
Interim results from the Myeloma XIV (FiTNEss) study [ISRCTN17973108] will be available in September/October 2022.
The development of MRP is an example of a patient-led initiative. The aim is to improve how patients are managed in the clinic and it has benefitted from the experience that Leeds has in the delivery of clinical trials for myeloma.
Professor Cook explains: “Our experience in delivering clinical trials allowed us to analyse data that had already been created for another reason and re-purpose it for this project. We used the data to create hypotheses, prove them, and then advance to the next level, all using existing data. We are maximising outputs by using existing resources. This also applied to the blood biomarkers – we focused on routine bloodwork that would be done in every lab for myeloma patients.”
Personalised treatment plan outcome
Most cancer research looks at understanding cancers genomics. Instead, this research is focussed on characterising the host factors; only when you consider both the tumour and the host factors together can medicine be truly personalised.
Professor Cook concludes: “The ultimate aim of the MRP is to be able to create personalised treatment plans for the frail Myeloma patient that they can better tolerate. If we can start with lower dosage for these patients, and then have the ability to implement proactive dose modifications in a staged way, this will result in them being able to tolerate treatment better and increase the likelihood of a successful outcome.
“This research objective is aligned with the driving principles of NIHR Leeds MIC, particularly the focus on patient-led outcomes and value from the health economics perspective, whilst also benefitting from transferable clinical trial expertise and experience.”
- Cancer Research UK. Myeloma statistics. https://www.cancerresearchuk.org/health-professional/cancer-statistics/statistics-by-cancer-type/myeloma [accessed May 2022];
- Pawlyn C, Cairns D, Kaiser M, Striha A, Jones J, Shah V, et al. The relative importance of factors predicting outcome for myeloma patients at different ages: results from 3894 patients in the Myeloma XI trial. Leukemia. 2020; 34:604–