Multiple myeloma (MM) is the second most common hematological malignancy and remains incurable. The marked variation in survival of patients with symptomatic myeloma ranging from few months to more than 15 years can be explained by differences in tumor mass, proliferative activity and, more recently, by cytogenetic and molecular genetic characteristics of the myeloma clone. Oligonucleotide microarray-based gene expression analysis was applied to CD138-enriched plasma cells from newly diagnosed patients with symptomatic or progressive multiple myeloma treated with melphalan-based high-dose therapy. Here we discuss recent progress made in the development of molecular-based diagnostics and prognostics for MM from Myeloma Institute for Research and Therapy of University Arkansas for Medical Sciences, where we treat more patients with myeloma than anywhere else in the world. Seven distinct entities of myeloma were elucidated by genomic profiling. Expression extremes of 70 genes from a high-risk signature profile,30% of which were derived from chromosome 1, were strongly linked to disease-related survival. CKS1B located on chromosome 1q21, responsible for promoting cell cycle progression by inducing the degradation of p27Kip1, represented a strong candidate gene related to rapid patient death and was studied in detail. The data suggest that CKS1B influences myeloma cell growth and survival through SKP2j and P27(Kip1) -dependent and independent mechanisms and that therapeutic strategies aimed at abolishing CKS1B function may hold promise for the treatment of high-risk disease for which effective therapies are currently lacking.
[Show abstract][Hide abstract] ABSTRACT: M icroarrays used for measuring chromosomal aberrations in genomic DNA and for defining gene expression patterns have become almost routine. A microarray consists of an arrayed series of microscopic spots each containing either DNA or protein molecules known as feature reporters. Advances in microarray fabrication and in feature detection systems, such as high-resolution scanners and their associated software, lead to high-throughput screening of the genome or the tran-scriptome of a cell or a group of cells in only few days. Despite the potential of high-density microarrays, several problems about data interpretation are still to be solved. In addition, targeted microarrays are shown to be useful tools for rapid and accurate diagnosis of diseases. The aim of this review was to discuss the impact of microarrays on different application levels from the definition of disease biomarkers to pharmaceutical and clinical diagnostics.
Journal of the Association for Laboratory Automation 10/2010; 15(5):405-13. DOI:10.1016/j.jala.2010.06.011 · 1.50 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: We performed a molecular study aimed at identifying a gene expression profile (GEP) signature predictive of attainment of at least near complete response (CR) to thalidomide-dexamethasone (TD) as induction regimen in preparation for double autologous stem cell transplantation in 112 younger patients with newly diagnosed multiple myeloma. A GEP supervised analysis was performed on a training set of 32 patients, allowing to identify 157 probe sets differentially expressed in patients with CR versus those failing CR to TD. We then generated an eight-gene GEP signature whose performance was subsequently validated in a training set of 80 patients. A correct prediction of response to TD was found in 71 % of the cases analyzed. The eight genes were downregulated in patients who achieved CR to TD. Comparisons between post-autotransplantation outcomes of the 44 non-CR-predicted patients and of the 36 CR-predicted patients showed that this latter subgroup had a statistically significant benefit in terms of higher rate of CR after autotransplant(s) and longer time to progression, event-free survival, and overall survival. These results can be an important first step to identify at diagnosis those patients who will respond more favourably to a particular treatment strategy.
Annals of Hematology 05/2013; 92(9). DOI:10.1007/s00277-013-1757-6 · 2.63 Impact Factor
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