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Low‐risk identification in multiple myeloma using a new 14‐gene model
Author(s) -
Chen Tiehua,
Berno Tamara,
Zangari Maurizio
Publication year - 2012
Publication title -
european journal of haematology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.904
H-Index - 84
eISSN - 1600-0609
pISSN - 0902-4441
DOI - 10.1111/j.1600-0609.2012.01792.x
Subject(s) - multiple myeloma , medicine , monoclonal gammopathy of undetermined significance , proportional hazards model , multivariate analysis , oncology , survival analysis , logistic regression , bioconductor , gene , immunology , biology , monoclonal , genetics , monoclonal antibody , antibody
Identifying the best gene expression pattern associated with low‐risk disease in patients with newly diagnosed multiple myeloma ( MM ) is important to direct clinical treatments. The MM Survival Index14 ( MMSI 14) was developed from GEP data sets of 22 normal plasma cells ( NPC ), 5 MM cell lines ( MMCL ), 44 monoclonal gammopathy of undetermined significance ( MGUS ), and 351 newly diagnosed MM patients. R /bioconductor and siggenes package were used to obtain heatmap, boxplot and histogram whose results were then analyzed by Kaplan–Meier analysis. Fourteen genes associated with low‐risk disease in MM were identified. We validated the disease prognostic power of MMSI 14 with an independent data set of other 214 newly diagnosed MM patients and also compared our model with the 70‐gene, the 8‐subgroup, IFM 15, and HMCL s7 models. Survival analysis showed that a low MMSI 14 signature was associated with longer survival. Applying MMSI 14 to independent data sets, we were able to classify 39% of patients as low‐risk, with a survival probability of more than 90% at 60 months. Multiple clinical parameters confirmed significant correlation between low‐ and high‐risk subgroups defined by MMSI 14. Comparing previously published models to the same data sets the MMSI 14 model retained the best prognostic value. We have developed a new gene model ( MMSI 14) for defining low‐risk, newly diagnosed MM . The multivariate comparative analysis confirmed that MMSI 14 is the best available model to predict clinical outcome in MM patients.