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A personalised medicine tool for multiple myeloma treatment

Treatment decision for multiple myeloma is currently based on trial and error, causing significant side effects. European researchers have developed a diagnostic tool that identifies the best treatment option for each individual patient.

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Multiple myeloma is caused by the abnormal proliferation of plasma cells which produce antibodies as part of the organism’s natural defence. Although treatment options have expanded over the years, it remains incurable, and patients often receive multiple treatments before switching to an effective alternative. The availability of more than 20 treatment options complicates treatment decision making even more, while tumour heterogeneity and patient intrinsic characteristics further diversify the clinical outcomes of available medications.

Gene expression profiles predictive of therapy outcome

The key objective of the EU-funded MMpredict project was to develop a tool capable of predicting the most effective treatment strategy for individual patients with multiple myeloma. Previously, partners had identified a 92-gene signature which the MMpredict partner SkylineDx developed into the microarray based diagnostic test ‘MMprofiler™’. Through gene expression profiling, this commercially available test can help patient management by distinguishing high-risk from standard-risk disease, facilitating patient stratification and survival prediction. “MMpredict wished to expand the clinical value and possibilities of MMprofiler™ to include the prediction of treatment effectiveness in individual patients,” explains project coordinator Pieter Sonneveld. Through extensive collaborations with clinical partners, MMpredict successfully collected and tested biobanked material from more than 830 patients with multiple myeloma. An additional 272 patients with next generation sequencing information were included in the analysis. Combining the genetic information, treatment data and associated survival metrics allowed scientists to discover predictive biomarkers using bioinformatics. “Recruiting such a large number of patients with full microarray and clinical data enabled us to study predictive biomarkers across different nationalities, studies and patient backgrounds,” emphasises Erik Valent, senior scientist at project partner, SkylineDx, and responsible for the daily management of the project. By studying all these patients and their associated clinical and transcriptomic data, researchers were able to identify specific subgroups of patients that seem to respond better to certain drugs than others. By combining gene expression profiling with already established diagnostic procedures, they improved the clinical prediction accuracy of the MMprofiler™ and were able to make treatment-specific recommendations.

Improving performance of MMprofiler™

It is vitally important to make good choices in the treatment of patients to avoid unnecessary side effects associated with most cancer medications. MMpredict worked on the development of a personalised treatment approach, which according to Sonneveld, is “the future of cancer medicine.” Although MMprofiler™ warrants further validation as a personalised medicine tool, partners highlight the importance of investigating and learning more about the biology of the disease. Combined with the identification of the key genes and pathways implicated in disease pathogenesis, it can help improve treatment decision making on an individual patient level. Project partners are hopeful that MMprofiler™ will help clinicians select the most appropriate therapy from a large spectrum of currently approved treatment options. Considering that multiple myeloma presents with approximately 40 000 new cases in Europe every year, accurate treatment choice will not only improve clinical outcome but also cut down on associated healthcare costs. This is paramount for the widespread adoption of MMprofiler™ as a precision therapy tool for the tailored treatment of multiple myeloma.

Keywords

MMpredict, MMprofiler™, multiple myeloma, treatment decision, biomarker, personalised medicine

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