Erik H van Beers1, Martin H van Vliet2, Rowan Kuiper2, Leonie de Best2, Kenneth C Anderson3, Ajai Chari4, Sundar Jagannath5, Andrzej Jakubowiak6, Shaji K Kumar7, Joan B Levy8, Daniel Auclair8, Sagar Lonial9, Donna Reece10, Paul Richardson11, David S Siegel12, A Keith Stewart13, Suzanne Trudel14, Ravi Vij15, Todd M Zimmerman6, Rafael Fonseca16. 1. SkylineDx, Rotterdam, The Netherlands. Electronic address: e.vanbeers@skylinedx.com. 2. SkylineDx, Rotterdam, The Netherlands. 3. Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA. 4. Mount Sinai School of Medicine, New York, NY. 5. Mount Sinai Medical Center, New York, NY. 6. University of Chicago Medical Center, Chicago, IL. 7. Division of Hematology, Mayo Clinic, Rochester, MN. 8. Multiple Myeloma Research Consortium, Norwalk, CT. 9. Winship Cancer Institute, Emory University, Atlanta, GA. 10. Medical Oncology and Hematology, Princess Margaret Hospital, Toronto, ON, Canada. 11. Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA. 12. JTCC, Hackensack University Medical Center, Hackensack, NJ. 13. Research, Mayo Clinic, Scottsdale, AZ. 14. Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada. 15. Department of Medicine, Division of Oncology, Washington University School of Medicine, Saint Louis, MO. 16. Division of Hematology and Oncology, Mayo Clinic, Scottsdale, AZ.
Abstract
BACKGROUND: 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. PATIENTS AND METHODS: 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. RESULTS: The classifiers identified between 9%-21% of patients as high risk, with hazard ratios (HRs) between 1.9 and 8.2. CONCLUSION: 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.
BACKGROUND: High risk and low risk multiple myelomapatients 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. PATIENTS AND METHODS: 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 MMpatients. RESULTS: The classifiers identified between 9%-21% of patients as high risk, with hazard ratios (HRs) between 1.9 and 8.2. CONCLUSION: 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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