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Patients with Multiple Myeloma (MM) are now classified according to the revised International Staging System (R-ISS), which combines information from FISH analysis, LDH measurements and ISS, to identify patients as either high-, standard- or low- risk MM. In addition, most patients with the genetic abnormalities (4;14), t(14;16)), (del(17p), gain(1q)) and gene mutations will be classified as high-risk. These high-risk patients are known to progress very differently to patients classified as low-risk. Therefore, finding markers to accurately stratify patients according to predicted prognosis is paramount in designing optimal treatment programs.
A previously published study used gene expression profiles (GEP) to search for risk classifiers for MM and identified a combination of 92 genes, both directly or indirectly related to the disease, for which the activity was found to be a strong prognostic indicator for MM. This test has been developed commercially and is now called SKY92. It has been extensively validated using a number of patient datasets, showing strong prognostic significance for both progression free survival (PFS) and overall survival (OS). It was further validated using an independent multicenter dataset from the Multiple Myeloma Genomics Initiative (MMGI). In this study, conducted by Dr. Erik H. van Beers along with a large team of researchers based in The Netherlands, and published in Clinical Lymphoma Myeloma and Leukemia, six risk-assessment platforms were assessed. The combination of SKY92/ISS was also further validated for risk stratification, and proved to be a highly effective prognostic indicator for MM using this dataset.
The use of SKY92 was further validated using a dataset previously not tested, and its use in combination with ISS for risk stratification showed strong prognostic significance in identifying both high- and low- risk MM patients. Whilst several other risk classifiers exist, the ability of SKY92/ISS to accurately identify low-risk, as well as high-risk patients, makes it an attractive option. These early classifications will help steer patient-tailored treatment options from the outset, which is the ultimate goal in MM therapy. However, the universal use of such an assay will be dictated primarily by the cost and ready availability to all.
High risk and low risk multiple myeloma patients follow a very different clinical course as reflected in their PFS and OS. To be clinically useful, methodologies used to identify high and low risk disease must be validated in representative independent clinical data and available so that patients can be managed appropriately. A recent analysis has indicated that SKY92 combined with the International Staging System (ISS) identifies patients with different risk disease with high sensitivity.
Here we computed the performance of eight gene expression based classifiers SKY92, UAMS70, UAMS80, IFM15, Proliferation Index, Centrosome Index, Cancer Testis Antigen and HM19 as well as the combination of SKY92/ISS in an independent cohort of 91 newly diagnosed MM patients.
The classifiers identified between 9%-21% of patients as high risk, with hazard ratios (HRs) between 1.9 and 8.2.
Among the eight signatures, SKY92 identified the largest proportion of patients (21%) also with the highest HR (8.2). Our analysis also validated the combination SKY92/ISS for identification of three classes; low risk (42%), intermediate risk (37%) and high risk (21%). Between low risk and high risk classes the HR is >10.
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