Novel Combination of ISS and EMC92 Gene Classifier Leads to Better Risk Stratification in Multiple Myeloma

Patients with multiple myeloma have variable survival rates and require reliable prognostic and predictive scoring systems. Currently, the available clinical and biologic risk markers are used independently; but in a recent study published in Blood, investigators found that the combination of the International Staging System (ISS) and biologic markers identified from fluorescence in situ hybridization (FISH)-based cytogenetics and gene-expression profiling (GEP) led to novel risk classifications.

“Adequate prognostication of disease outcome is important in order to make treatment choices and to allocate high-risk patients to alternative treatment options,” Rowan Kuiper, BSc, from the Department of Hematology at Erasmus MC Cancer Institute in Rotterdam, Netherlands, and co-authors explained.

In the study, Dr. Kuiper and colleagues systematically evaluated all published risk markers used in multiple myeloma, then compared different combinations of FISH-, ISS-, and GEP-based prognostic systems, to find novel risk classifications in a discovery/validation setting.

The researchers used data from the following clinical studies:

  • HOVON-65/GMMG-HD4 (newly diagnosed patients)
  • MRC-IX—intensive and nonintensive (newly diagnosed patients)
  • UAMS-TT2 (newly diagnosed patients)
  • UAMS-TT3 (newly diagnosed patients)
  • IFM-G (newly diagnosed patients)
  • APEX (relapsed patients)

Data on overall survival (OS) or progression-free survival (PFS) and at least one prognostic marker were available for all patients. A total of 20 risk markers were evaluated in 4,750 patients.

For OS, the following markers were found to be significant:

  • the FISH markers t(4;14), del17p, gain1q, and del13q
  • all GEP classifiers (EMC92, UAMS17, UAMS70, UAMS80, MRCIX6, IFM15, HM19, GPI50)
  • ISS levels 1, 2, and 3

The data were split into discovery and validation sets. The discovery set was used to find meaningful combinations of markers, in addition to the most optimal way to split patients into risk subgroups using these combinations. The validation set was performed to confirm their prognostic strength. Lastly, all new combinations and existing markers were ranked, with a low ranking score indicating a higher-performing risk marker.

The HRs for GEP–identified abnormalities were consistently higher than for risk factors identified via FISH or ISS, suggesting better risk separation for GEP–identified abnormalities compared with FISH markers.

In terms of survival:

  • GEP–identified abnormalities typically performed better in predicting OS than PFS, with HRs for OS ranging from 2.0 (IFM15) to 3.3 (UAMS70), and HRs for PFS ranging from 1.8 (IFM15) to 2.3 (EMC92).
  • FISH markers also were predictive of OS, with HRs between 1.7 (del13q) and 2.3 (del17p). FISH markers that included gain9, t(11;14), t(14;16), and t(14;20) were not significantly predictive of survival.
  • ISS was a “valuable and highly significant prognostic marker,” with HRs ranging from 1.6 (ISS stage 2) to 2.3 (ISS stage 3) for OS and HRs ranging from 1.4 (ISS stage 2) to 1.7 (ISS stage 3) for PFS.

Overall, the authors observed that ISS is a valuable partner to GEP classifiers and FISH markers, and, in general, compound markers tended to score better than single markers.

“The number of patients positive for specific markers was remarkably stable between cohorts, irrespective of the type of marker,” Dr. Kuiper and colleagues noted. “This adds strength to the belief that these markers, and thus decisions based on them, can be reliably replicated.”

When ranked with all combination possibilities, EMC92-ISS (a novel prognostic tool that combines the EMC92 gene classifier with ISS level) was the strongest predictor of OS, with a median rank score (RS) of 0.05, resulting in a four-group risk classification (38%, 24%, 22%, and 17% for the lowest- to highest-risk group, respectively). The median survival was 24 months for the high-risk group, 47 months for the intermediate-high-risk group, 61 months for the intermediate-low-risk group, and it was not reached after 96 months of follow-up for the low-risk group.

“The clinical applicability of stratification into four risk groups will be increasingly relevant in the era of novel treatment modalities being available,” Dr. Kuiper and colleagues noted. “Increased accuracy of prognosis can improve patient counseling, and risk-stratification may lead to adaptation of treatment according to risk status.”

“Based on the current study, we concluded that the combination of EMC92 with ISS is a strong disease-based prognosticator for survival in MM,” the authors stated. “This risk classification is a good candidate to stratify patients for treatment options in a clinical trial.”

However, the authors noted some limitations of the current study, including the fact that treatment protocols varied among the clinical trials analyzed. Because five out of six of the trials analyzed involved newly-diagnosed patients, further research is needed to determine if these risk-stratification systems are applicable to relapsed patients or at other times during the disease course.


Kuiper R, van Duin M, van Vliet MH, et al. Prediction of high- and low-risk multiple myeloma based on gene expression and the International Staging System. Blood. 2015 September 1. [Epub ahead of print]