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New model helps predict myeloma patient outcomes

Researchers have developed a new model using patient data (e.g. patient age and disease stage) to help predict the outcome of myeloma patients ineligible for high-dose therapy and stem cell transplant (HDT-SCT).

Research news // 27th February 2019

Researchers from the UK Myeloma Research Alliance (UKMRA) have developed a new model to help predict the outcome of myeloma patients who are not suitable for high-dose therapy and stem cell transplant (HDT-SCT), the UKMRA Risk Profile (MRP).

The model uses features, such as patient age and disease stage, to categorise myeloma patients into three groups based on risk status (the risk of reduced overall survival): high, medium and low risk.

Rebecca Dawson, Biomedical Research Manager at Myeloma UK said:

“We welcome the publication of this new UKMRA Risk Profile to help predict outcomes and inform decision making in transplant ineligible patients. Myeloma is a variable disease where outcomes and response rates differ between individuals, therefore it is important to research which patient and disease characteristics are driving these differences. We look forward to see this model develop patient centred approaches towards tailored treatments.“

The model published in Lancet Haematology was developed through analysis of patient data from two of the largest myeloma clinical trials, Myeloma IX and Myeloma XI.

Researchers carried out a number of statistical analyses to assess which patient and disease features were associated with reduced overall survival in newly diagnosed myeloma patients who were not suitable for HDT-SCT. The analyses showed that disease stage (measured using the International Staging System (ISS), C-reactive protein (CRP) concentration (an inflammation marker), patient fitness (measured using ECOG performance status) and patient age were associated with poor overall survival, and therefore could be used to predict myeloma patient outcomes.

This resulted in the development of a scoring system (based on disease stage, CRP concentration, fitness and age) to categorise patients into groups based on risk status: high, medium or low risk.

The MRP risk status (high, medium and low risk) was found to be predictive of progression free survival (the length of time before myeloma returns or further treatment is required), early mortality (patient death within 60 days of diagnosis) and treatment tolerability (likelihood patients would stop treatment early due to side effects).

Furthermore, the MRP risk status was predictive of patient outcomes irrespective of the treatment used or the genetic risk profile (genetic abnormalities) of the myeloma patient.

This demonstrates that both disease and patient features impact the outcomes of patients not suitable for transplant, indicating that patient age and fitness impact overall survival and treatment tolerability.

It is hoped that the model will support the move towards frailty adapted treatments. The MRP was predictive of treatment tolerability and helped identify patients who are more likely to stop treatment early. Therefore, it could allow pre-planned changes in dosing to enable patients to stay on treatments for longer. This hypothesis will be explored further in the Myeloma XIV (FiTNEss) study.

The new MRP model is based on readily available and routinely collected clinical and laboratory data and so would be quick and easy for clinicians to use in everyday practice. However, the model was developed using data from clinical trials and will need to be further validated in the wider patient population before it can be routinely used in clinical practice. The researchers plan to do this through analysis of the CoMMpass study which studied the “real-world” myeloma patient outcomes in the USA, Canada, Italy and Spain.

Jayne Galinsky, Health Services Research Manager said:

“This is an exciting project which will help us better understand patients and work on improving outcomes for the future. I look forward to seeing how findings generated by the model might shape treatments in the real world in the next few years.“

References:

  1. Cook, G., Royle, K.L., Pawlyn, C., Hockaday, A., Shah, V., Kaiser, M.F., Brown, S.R., Gregory, W.M., Child, J.A., Davies, F.E. and Morgan, G.J., (2019). A clinical prediction model for outcome and therapy delivery in transplant-ineligible patients with myeloma (UK Myeloma Research Alliance Risk Profile): a development and validation study. The Lancet Haematology.
  2. Palumbo, A., Avet-Loiseau, H., Oliva, S., Lokhorst, H.M., Goldschmidt, H., Rosinol, L., Richardson, P., Caltagirone, S., Lahuerta, J.J., Facon, T. and Bringhen, S., (2015). Revised international staging system for multiple myeloma: a report from International Myeloma Working Group. Journal of clinical oncology33(26),2863.
  3. Cancer Research UK, (2013). A trial comparing treatments for myeloma (MRC Myeloma IX). Accessed at: https://www.cancerresearchuk.org/about-cancer/find-a-clinical-trial/a-trial-comparing-treatments-for-myeloma
  4. Cancer Research UK, (2018). A trial looking at lenalidomide, bortezomib and vorinostat for myeloma (Myeloma XI – non intensive group). Accessed at: https://www.cancerresearchuk.org/about-cancer/find-a-clinical-trial/a-trial-looking-lenalidomide-bortezomib-myeloma-non-intensive-treatment-group-myeloma-XI
  5. US National Library of Medicine, (2018). Study Record: Myeloma XIV: Frailty-adjusted Therapy in Transplant Non-Eligible Patients With Newly Diagnosed Multiple Myeloma (FiTNEss). Accessed at: https://clinicaltrials.gov/ct2/show/NCT03720041
  6. Multiple Myeloma Research Foundation, (2019). MMRF CoMMpass Study. Accessed at: https://themmrf.org/we-are-curing-multiple-myeloma/mmrf-commpass-study/