To better define the molecular basis of multiple myeloma (MM), we performed unsupervised hierarchic clustering of mRNA expression profiles in CD138-enriched plasma cells from 414 newly diagnosed patients who went on to receive high-dose therapy and tandem stem cell transplants. Seven disease subtypes were validated that were strongly influenced by known genetic lesions, such as c-MAF-and MAFB-, CCND1-and CCND3-, and MMSET-activating translocations and hyperdiploidy. Indicative of the deregulation of common pathways by gene orthologs, common gene signatures were observed in cases with c-MAF and MAFB activation and CCND1 and CCND3 activation, the latter consisting of 2 subgroups, one characterized by expression of the early B-cell markers CD20 and PAX5. A low incidence of focal bone disease distinguished one and increased expression of proliferation-associated genes of another novel subgroup. Comprising varying fractions of each of the other 6 subgroups, the proliferation subgroup dominated at relapse, suggesting that this signature is linked to disease progression. Proliferation and MMSET-spike groups were characterized by significant overexpression of genes mapping to chromosome 1q, and both exhibited a poor prognosis relative to the other groups. A subset of cases with a predominating myeloid gene expression signature, excluded from the profiling analyses, had more favorable baseline characteristics and superior prognosis to those lacking this signature. IntroductionMultiple myeloma (MM) is a malignancy of antibody-secreting, terminally differentiated B cells that home to and expand in the bone marrow, with symptoms related to anemia, immunosuppression, bone destruction, and renal failure. 1,2 Bone lesions developing adjacent to plasma cell foci result from the activation of osteoclasts and inactivation of osteoblasts. [3][4][5] Many of the pathogenetic mechanisms of this clinically heterogeneous malignancy have been unraveled by application of molecular genetics. [6][7][8] While sharing most of the genetic lesions seen in MM, 9-11 monoclonal gammopathy of undetermined significance (MGUS) rarely progresses to overt MM. 12 The universal activation of 1 of the 3 cyclin D genes is consistent with this being an initiating event in MM. 13 Nonhyperdiploid MM, present in 40%, is characterized by transcriptional activation of CCND1, CCND3, MAF, MAFB, or FGFR3/MMSET genes (resulting from translocations involving the immunoglobulin heavy chain locus). 8 While hyperdiploidy and CCND1 activation confer a favorable prognosis, MAF, MAFB, or FGFR3/MMSET activation and deletion of chromosomes 13 and 17 are associated with poor prognosis. [14][15][16][17][18][19][20][21][22][23][24][25] Although high-dose therapy has markedly improved MM prognosis, 26-28 individual patients' survival remains variable 29,30 and cannot be accurately predicted with current prognostic models. 31,32 In lymphoma and leukemia, microarray profiling has helped establish clinically relevant disease subclassifications. [33][34][35][36][37][38][39][40][41...

To molecularly define high-risk disease, we performed microarray analysis on tumor cells from 532 newly diagnosed patients with multiple myeloma (MM) treated on 2 separate protocols. Using log-rank tests of expression quartiles, 70 genes, 30% mapping to chromosome 1 (P < .001), were linked to early disease-related death. Importantly, most up-regulated genes mapped to chromosome 1q, and downregulated genes mapped to chromosome 1p. The ratio of mean expression levels of up-regulated to down-regulated genes defined a high-risk score present in 13% of patients with shorter durations of complete remission, event-free survival, and overall survival (training set: hazard ratio [HR], 5.16; P < .001; test cohort: HR, 4.75; P < .001). The high-risk score also was an independent predictor of outcome endpoints in multivariate analysis (P < .001) that included the International Staging System and high-risk translocations. In a comparison of paired baseline and relapse samples, the high-risk score frequency rose to 76% at relapse and predicted short postrelapse survival (P < .05). Multivariate discriminant analysis revealed that a 17-gene subset could predict outcome as well as the 70-gene model. Our data suggest that altered transcriptional regulation of genes mapping to chromosome 1 may contribute to disease progression, and that expression profiling can be used to identify high-risk disease and guide therapeutic interventions. (Blood.

The incidence of venous thromboembolism (VTE) is more than 1 per thousand annually in the general population and increases further in cancer patients. The risk of VTE is higher in multiple myeloma (MM) patients who receive thalidomide or lenalidomide, especially in combination with dexamethasone or chemotherapy. Various VTE prophylaxis strategies, such as low-molecular-weight heparin (LMWH), warfarin or aspirin, have been investigated in small, uncontrolled clinical studies. This manuscript summarizes the available evidence and recommends a prophylaxis strategy according to a risk-assessment model. Individual risk factors for thrombosis associated with thalidomide/lenalidomide-based therapy include age, history of VTE, central venous catheter, comorbidities (infections, diabetes, cardiac disease), immobilization, surgery and inherited thrombophilia. Myeloma-related risk factors include diagnosis and hyperviscosity. VTE is very high in patients who receive high-dose dexamethasone, doxorubicin or multiagent chemotherapy in combination with thalidomide or lenalidomide, but not with bortezomib. The panel recommends aspirin for patients with < or = 1 risk factor for VTE. LMWH (equivalent to enoxaparin 40 mg per day) is recommended for those with two or more individual/myeloma-related risk factors. LMWH is also recommended for all patients receiving concurrent high-dose dexamethasone or doxorubicin. Full-dose warfarin targeting a therapeutic INR of 2-3 is an alternative to LMWH, although there are limited data in the literature with this strategy. In the absence of clear data from randomized studies as a foundation for recommendations, many of the following proposed strategies are the results of common sense or derive from the extrapolation of data from many studies not specifically designed to answer these questions. Further investigation is needed to define the best VTE prophylaxis.

When incorporated into high-dose therapy for myeloma, thalidomide increased the frequency of complete responses and extended event-free survival at the expense of added adverse effects without improving overall survival. (ClinicalTrials.gov number, NCT00083551.).

Bone marrow plasma cells (PCs) from 74 patients with newly diagnosed multiple myeloma (MM), 5 with monoclonal gammopathy of undetermined significance (MGUS), and 31 healthy volunteers (normal PCs) were purified by CD138 ؉ selection. Gene expression of purified PCs and 7 MM cell lines were profiled using highdensity oligonucleotide microarrays interrogating about 6800 genes. On hierarchical clustering analysis, normal and MM PCs were differentiated and 4 distinct subgroups of MM (MM1, MM2, MM3, and MM4) were identified. The expression pattern of MM1 was similar to normal PCs and MGUS, whereas MM4 was similar to MM cell lines. Clinical parameters linked to poor prognosis, abnormal karyotype (P ‫؍‬ .002) and high serum ␤ 2 -microglobulin levels (P ‫؍‬ .0005), were most prevalent in MM4. Also, genes involved in DNA metabolism and cell cycle control were overexpressed in a comparison of MM1 and MM4. In addition, using 2 and Wilcoxon rank sum tests, 120 novel candidate disease genes were identified that discriminate normal and malignant PCs (P < .0001); many are involved in adhesion, apoptosis, cell cycle, drug resistance, growth arrest, oncogenesis, signaling, and transcription. A total of 156 genes, including FGFR3 and CCND1, exhibited highly elevated ("spiked") expression in at least 4 of the 74 MM cases (range, 4-25 spikes). Elevated expression of these 2 genes was caused by the translocation t(4;14)(p16;q32) or t(11;14)(q13;q32). Thus, novel candidate MM disease genes have been identified using gene expression profiling and this profiling has led to the development of a gene-based classification system for MM. (Blood. 2002;99: 1745-1757