Low‐risk identification in multiple myeloma using a new 14‐gene model

Abstract

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 (MMSI14) 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 MMSI14 with an independent data set of other 214 newly diagnosed MM patients and also compared our model with the 70-gene, the 8-subgroup, IFM15, and HMCLs7 models. Survival analysis showed that a low MMSI14 signature was associated with longer survival. Applying MMSI14 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 MMSI14. Comparing previously published models to the same data sets the MMSI14 model retained the best prognostic value. We have developed a new gene model (MMSI14) for defining low-risk, newly diagnosed MM. The multivariate comparative analysis confirmed that MMSI14 is the best available model to predict clinical outcome in MM patients.

Systematic evaluation of medium-throughput mRNA abundance platforms

“…For example, the limited length of 6 microarray probes (25-100 bp) can result in nonspecific binding of transcripts (Zhang et al 2002), particularly in disease states. Instead, many groups have sought to develop multigene biomarkers (sometimes called ''signatures'') composed of tens to hundreds of genes that could be assayed using technologies with superior performance on FFPE material, reduced cost, and increased performance (Chen et al 2012;Kratz et al 2012;Lenehan et al 2012).…”

Prediction of Clinical Outcome for All Stages and Multiple Cell Types of Non-small Cell Lung Cancer in Five Countries Using Lung Cancer Prognostic Index

“…Siggenes was used to identify the differentially expressed genes as previously described ( Chen et al, 2012 ). Since multiple two-group comparisons may introduce some errors, we further compared the three groups simultaneously, and then found the gene expression differences that were common to all comparisons ( Fig.…”

Experimental treatment of multiple myeloma in the era of precision medicine

“…The 70-gene prognostic signature, developed by the University of Arkansas for Medical Sciences (UAMS) and commercialized under the name 'MyPRS ® ' (Myeloma Prognostic Risk Score; Signal Genetics Inc., Carlsbad, CA, USA) is a standardized method of MM prognostication used to identify high-risk disease [24] and contribute to guide therapeutic interventions [25,26]. The IFM15 (Intergroupe Francophone du Myélome) and the MM Survival Index14 developed from gene expression profiling (GEP) data [27], respectively, identified 15 genes strongly associated with survival and 14 genes associated with low-risk disease. All these innovative tools are of help to redefine prognostic stratification and might be useful for prediction of molecular targeted agents activity.…”