Molecular Classification Of Multiple Myeloma Research Articles

Recent Advances in The Definition of the Molecular Alterations Occurring in Multiple Myeloma.

Multiple myeloma (MM) is a disorder of the monoclonal plasma cells and is the second most common hematologic malignancy. MM initiation and progression are dependent upon complex genomic abnormalities. The current pathogenic model of MM includes two types of primary events, represented by chromosome translocations or chromosome number alterations resulting in hyperdiploidy. These primary molecular events are observed both in MM and in monoclonal gammopathy, its premalignant precursor. Subsequent genetic events allow the progression of monoclonal gammopathy to MM and, together with primary events, contribute to the genetic complexity and heterogeneity of MM. Newer therapies have considerably improved patient outcomes; however, MM remains an incurable disease and most patients experience multiple relapses. The dramatic progresses achieved in the analysis of the heterogeneous molecular features of different MM patients allowed a comprehensive molecular classification of MM and the definition of an individualized prognostic model to predict an individual MM patient's response to different therapeutic options. Despite these progresses, prognostic models fail to identify a significant proportion of patients destined to early relapse. Treatment strategies are increasingly. Based on disease biology, trials are enriched for high-risk MMs, whose careful definition and categorization requires DNA sequencing studies.

Fluorescence in situ hybridization in molecular classification of multiple myeloma: Prediction of response to triplet standard combination therapy

Fluorescence in situ hybridization in molecular classification of multiple myeloma: Prediction of response to triplet standard combination therapy

RFWD2 induces cellular proliferation and selective proteasome inhibitor resistance by mediating P27 ubiquitination in multiple myeloma

RFWD2 induces cellular proliferation and selective proteasome inhibitor resistance by mediating P27 ubiquitination in multiple myeloma

Targeting Replicative Stress to Treat Hematological Disorders

▪Background: Combinations of chromosomal translocations, copy-number variations, somatic mutations, and clonal heterogeneity that characterize hematological cancers make every patient unique from a genetic point of view. This variety creates a true challenge for tailored therapy. We have previously described that myeloma (MM) cells present signs of ongoing DNA damage, and activate an ATM/ABL1-dependent DNA damage response (DDR) without overt apoptosis. Here we further characterize the mechanisms of DNA damage and replicative stress in MM, and we extend this knowledge to other hematological malignancies to evaluate a novel and possibly shared approach to synthetic lethality (1).Results: We studied a panel of MM cell lines together with acute myeloid, lymphoid leukemia and lymphoma cell lines. Several cell lines have demonstrable ongoing DNA damage, activate ATR and CHK1 and also present with signs of replicative stress, such as 53BP1, RPA and RAD51 foci. We next evaluated a gene expression signature specific for increased chromosomal instability and DNA damage in a cohort of MM patients, comparing them with normal plasma cells. Specifically, we identified a subset of patients, representing around 20 percent of individuals with MM that show this signature and also present with an unfavorable prognosis due to a more aggressive disease. Interestingly, in a multivariate analysis, this signature was independent from other poor prognostic criteria, including proliferation and the presence of MMSET/FGFR3 or MAF translocations (2), hence representing a potential novel prognostic signature (3). Gene-set enrichment studies are ongoing in other hematological disorders. However, preliminary data show that subsets of patients with other hematological malignancies also present with intense over expression of genes belonging to the instability signature when compared to normal B cells, in a very similar fashion to that seen with MM. An intact ATR/CHK1 pathway is crucial for the survival of tumor cells in vivo, especially in the presence of activated oncogenes. For instance, Em-myc transgenic mice develop B-cell lymphomas with intense replicative stress that can be blocked by crossing the Em-myc transgenic mice with a hypomorphic Atr mouse strain (Atr-Seckel; Atr S/S). We therefore decided to exploit the concept of replicative stress overload, impeding the capacity of the cells for repairing the excess of damaged DNA. We then silenced ATR using shRNAs, the upstream protein involved in the control of stalled replication origins, in two MM cell lines (H929 and OPM-2) and in the Jurkat cell line. Critically, inhibition of ATR caused a reduction in cellular growth and induction of apoptosis. A similar phenotype was observed using VE-821, a specific ATR inhibitor. Finally, a broad panel of MM cells and leukemia cell lines was used to confirm these growth inhibitory effects.Conclusion: Replicative stress is present in multiple groups of patients with aggressive types of MM or leukemia. Strategies which couple pre-existing high rates of DNA damage with reduced DNA repair can specifically cause apoptosis of malignant cells and encouragingly spare normal ones, thus providing a strong rationale for potential clinical benefit to those cohorts of patients with otherwise very unfavorable outcomes.(1) Cottini F, Hideshima T, Xu C, Sattler M, Dori M, Agnelli L, et al. Rescue of Hippo coactivator YAP1 triggers DNA damage-induced apoptosis in hematological cancers. Nature medicine. 2014;20(6):599-606.(2) Zhan F, Huang Y, Colla S, Stewart JP, Hanamura I, Gupta S, et al. The molecular classification of multiple myeloma. Blood. 2006;108(6):2020-8.(3) Cottini F, Teru Hideshima, Rikio Suzuki, Yu-Tzu Tai, et al. Synthetic lethal approaches exploiting DNA damage in aggressive myeloma. Cancer Discovery ahead of print. DisclosuresRichardson:Novartis: Membership on an entity's Board of Directors or advisory committees; Millennium Takeda: Membership on an entity's Board of Directors or advisory committees; Jazz Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Gentium S.p.A.: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene Corporation: Membership on an entity's Board of Directors or advisory committees. Anderson:Celgene Corporation: Consultancy; Oncocorp: Equity Ownership; acetylon pharmaceuticals: Equity Ownership; Gilead: Consultancy; BMS: Consultancy; Millennium: Consultancy.

A Multiple Myeloma Classification System That Associates Normal Bone Marrow B-Cell Subset Phenotypes with Disease Stage and Prognosis

IntroductionToday’s diagnostic tests for multiple myeloma reflect the criteria of the updated WHO classification based on biological, morphological and clinical heterogeneity. It has been the concept behind the present study to extend our current biological classification and provide a new tool to generate insight into the stage of clonal differentiation and oncogenesis.The goal of the present study was to generate B-cell subset associated gene signatures (BAGS) from the BM hierarchy used to assign individual phenotypic cell of origin (pCOO) subtypes in patients and validate its pathogenetic impact by prognostic evaluation.MethodsBone marrows (BM, n=7) were harvested from sternum during cardiac surgery. B-cell subsets were phenotyped by Euroflow standard for multiparametric flow cytometry and FACS-sorted for microarray analysis on the Human Exon 1.0 ST Arrays platform. This combination allowed us to generate five BAGS for PreBI, PreBII, immature (Im), naïve (N), memory (M) B-cells, and plasma cells (PC) of normal BM. The BAGS classifier was based on all median-centred probe sets from the data set by regularized multinomial regression with 6 discrete outcomes corresponding to each B-cell subset and the elastic net penalty using the algorithm implemented in the R-package glmnet24. Each clinical data set was probe-set-wise adjusted to have a zero median and the same variance as in the BM data set. The associated cell of origin subset for each patient in each data set was predicted by the BAGS classifier by assigning the class with the highest predicted probability score above 0.45 and otherwise UC. All statistical analyses were done with R version 3.0.2. Survival analysis was performed by the Kaplan-Meier method and log-rank tests. Clinical data sets were from University of Arkansas for Medical Sciences UAMS (n=559), the Dana Farber Cancer Institute DFCI (n=170) both available on the GEO website.ResultsFirst, we verified the quality of the sampled B-cell subsets based on the expression patterns of differentiation marker genes. Next, we constructed the BAGS-classifier provided by 38-68 gene probe sets (n), capable of assigning samples to each of the six subtypes of PreBI (n=38), PreBII (n=82), Im (n=71), N (n=68), M (n=52) and PC (n=43).Second, we assigned individual myeloma cases in the 2 patient cohorts including a total of 729 myeloma patients. The resultant assignments generated patient BAGS subtypes with diagnostic frequencies of 0,5-0,6 % preBI, 7-8 % preBII, 27-30 % Im, 8-9 % N, 36-37 % M, 1-5 % PC and 15-16 % UC subtypes. The frequencies were not different between the cohorts.Third, the BAGS subtypes was associated significantly with overall survival (P = 8.6 x 10-5) for high dose melphalan and autologous stem cell transplanted UAMS patients1, as illustrated in Figure 1. The most significant impact was observed within the PreB-II (light blue) and M (acid green) subtypes conferred with significant inferior prognosis compared to the Im (amethyst), N (apple green) and PC (blue) subtypes. The PreB-II and M subtypes in the UAMS cohort were correlated to the ISS stage I-III with 33%, 49% and 69% of the cases, respectively.Fourth, we compared the BAGS subtypes and the TC classification2 with no observed correlations (results not shown).ConclusionsOur data support a new COO defined BAGS classification based on the normal BM B-cell subset phenotypes with impact on staging and prognosis in multiple myeloma and a new diagnostic platform, which may result in more effective disease management.References 1) Zhan F, Huang Y, Colla S et al. The molecular classification of multiple myeloma. Blood. 2006 Sep 15;108(6):2020-8. 2) Bergsagel PL, Kuehl WM, Zhan F, Sawyer J, Barlogie B, Shaughnessy J Jr. Cyclin D dysregulation: an early and unifying pathogenic event in multiple myeloma. Blood. 2005 Jul 1;106(1):296-303. [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

Gene expression profiling for molecular classification of multiple myeloma in newly diagnosed patients

AbstractTo identify molecularly defined subgroups in multiple myeloma, gene expression profiling was performed on purified CD138+ plasma cells of 320 newly diagnosed myeloma patients included in the Dutch-Belgian/German HOVON-65/GMMG-HD4 trial. Hierarchical clustering identified 10 subgroups; 6 corresponded to clusters described in the University of Arkansas for Medical Science (UAMS) classification, CD-1 (n = 13, 4.1%), CD-2 (n = 34, 1.6%), MF (n = 32, 1.0%), MS (n = 33, 1.3%), proliferation-associated genes (n = 15, 4.7%), and hyperdiploid (n = 77, 24.1%). Moreover, the UAMS low percentage of bone disease cluster was identified as a subcluster of the MF cluster (n = 15, 4.7%). One subgroup (n = 39, 12.2%) showed a myeloid signature. Three novel subgroups were defined, including a subgroup of 37 patients (11.6%) characterized by high expression of genes involved in the nuclear factor kappa light-chain-enhancer of activated B cells pathway, which include TNFAIP3 and CD40. Another subgroup of 22 patients (6.9%) was characterized by distinct overexpression of cancer testis antigens without overexpression of proliferation genes. The third novel cluster of 9 patients (2.8%) showed up-regulation of protein tyrosine phosphatases PRL-3 and PTPRZ1 as well as SOCS3. To conclude, in addition to 7 clusters described in the UAMS classification, we identified 3 novel subsets of multiple myeloma that may represent unique diagnostic entities.

International Myeloma Working Group molecular classification of multiple myeloma: spotlight review

Myeloma is a malignant proliferation of monoclonal plasma cells. Although morphologically similar, several subtypes of the disease have been identified at the genetic and molecular level. These genetic subtypes are associated with unique clinicopathological features and dissimilar outcome. At the top hierarchical level, myeloma can be divided into hyperdiploid and non-hyperdiploid subtypes. The latter is mainly composed of cases harboring IgH translocations, generally associated with more aggressive clinical features and shorter survival. The three main IgH translocations in myeloma are the t(11;14)(q13;q32), t(4;14)(p16;q32) and t(14;16)(q32;q23). Trisomies and a more indolent form of the disease characterize hyperdiploid myeloma. A number of genetic progression factors have been identified including deletions of chromosomes 13 and 17 and abnormalities of chromosome 1 (1p deletion and 1q amplification). Other key drivers of cell survival and proliferation have also been identified such as nuclear factor- B-activating mutations and other deregulation factors for the cyclin-dependent pathways regulators. Further understanding of the biological subtypes of the disease has come from the application of novel techniques such as gene expression profiling and array-based comparative genomic hybridization. The combination of data arising from these studies and that previously elucidated through other mechanisms allows for most myeloma cases to be classified under one of several genetic subtypes. This paper proposes a framework for the classification of myeloma subtypes and provides recommendations for genetic testing. This group proposes that genetic testing needs to be incorporated into daily clinical practice and also as an essential component of all ongoing and future clinical trials.

Gene Expression Profiles for Molecular Classification of Multiple Myeloma in Newly Diagnosed Patients Included in a Prospective, Multicenter, Phase 3 Trial.

Multiple Myeloma (MM) is characterized by the presence of a monoclonal protein, immunodeficiency, anemia, renal failure and bone lesions. New agents like Bortezomib (Bor) and Thalidomide (Thal) have shown efficacy in 40% of patients with relapsed/refractory MM, and up to 75% in first-line treatment. Classical prognostic classifications such as serum b2-microglobulin, albumin and chromosomal aberrations, have insufficient predictive power in estimating long-term outcome with these targeted agents. We have performed gene expression profiling (GEP) in newly diagnosed MM patients who were included in a large multicenter, prospective phase III trial (HOVON65) comparing Bor with standard induction prior to high dose therapy (HDT). The aim of this profiling was to gain new insights in the pathogenesis of MM and to design a prognostic index for treatment based on molecular profiling. In 258 patients, CD138 magnetic cell selected (MACS) myeloma plasma cells (PC) obtained at diagnosis with PC purity > 80% were used for RNA extraction and applied to the Affymetrix GeneChip U133 plus 2.0 arrays. Data from the gene expression arrays were pre-processed using GCRMA (Bioconductor). Unsupervised gene clustering (Omniviz) using a correlation visualisation matrix could determine the clusters based on the top variably expressed genes, and differential gene expression within these clusters could be determined (BRB-array tools, p<0.001, FDR<1%). Enriched genes and pathways were determined using Ingenuity systems®. Based on GEP, the translocations t(4;14), t(14;16), t(14;20) or t(11;14) could be shown to drive the clusters using 1.25% of the most variable genes (675 genes). Increasing the variable genes within the analysis (2.5%, 5%, 10% or 20%) identifies 10 different clusters of myeloma, with the translocations being distributed differentially. This could indicate that other factors, besides the chromosomal aberrations have an important role in biologically driving the clustering of myeloma GEP. Using 10% of most variable genes, 4 out of 10 clusters were predominantly driven by genes involved in the translocations; for example: up-regulation of MMSET/FGFR3 (cluster 1), MAF downstream targets (cluster 9), CCND1/MS4A1/VPREB3 (cluster 5) and INHBE (cluster 10). Three of the larger clusters show an expression of genes involved in protein biosynthesis and/or interferon signalling; one of which showed expression of XRCC4, FRZB and HGF (cluster 4), one showing expression of IFIT27, IFIT3 and TRAIL (cluster 6) and the third showed expression of EGFR and CHES1 (cluster 8). Another cluster was identified with a high NF-κB index (cluster 3), in which NF-κB transcriptional activity is calculated from the mean expression level of four probe sets corresponding to CD74, IL2RG, and TNFAIP3 (2×) as described by Keats et al. 2 out of 10 clusters, each containing 6 samples, were shown to be non-descriptive (clusters 2 and 7). When combining GEP with clinical symptoms at presentation, 2 clusters (4 & 6) were identified which had a high frequency of trisomies, IgG isotype and a significantly higher incidence of bone lesions. In addition, clusters 1, 8, 9 and 10 presented more frequently with renal failure, thrombocytopenia and anemia, and clusters 5, 9 and 10 showed less bone involvement. These data indicate that GEP may identify clusters of patients, who have a distinct clinical expression of organ impairment (ROTI). In conclusion, we describe molecular subgroups of MM showing distinct gene expression signatures in combination with specific chromosomal aberrations and clinical characteristics. Awaiting the clinical analysis of the trial, the response and survival data of the molecular subgroups will be included in the analysis.

Validation of a Novel Tissue Array Based Classification of Multiple Myeloma (MM).

Background: Gene expression profiling molecular classification of MM was proven to be an independent predictor of survival post autologous stem cell transplant (ASCT); however it had limited clinical applicability due to its complex methodology and high costs. We have previously reported the results of a protein-array based classification of MM in an initial testing cohort and concluded that positive immunoperoxidase staining for FGFR3, Cyclin B2 or Integrin beta7 correlates with a shortened survival post ASCT (Bahlis et al. Blood 2007: 110:449a). We now report on the results of this TMA classification in a larger and independent validation cohort.Methods: Immunoperoxidase staining for Cyclins B1, B2, D1, D2 and D3, FGFR3, PAX5 and Integrin beta 7 were previously validated in our initial testing cohort (n=52). Further analysis of our initial testing cohort identified 3 risk groups: positive expression of FGFR3 or Integrin beta 7 defined as “High risk”, positive Cyclin B2 (in the absence of FGFR3 or Integrin beta 7) as “Intermediate risk” and the lack of expression of any of these biomarkers defined as “Low risk”. In order to confirm the predictive value of our proposed protein-array classification, these immunohistochemical (IHC) stains were performed on the bone marrow biopsies of a larger and independent validation cohort of 79 newly diagnosed MM patients uniformly treated with a dexamethasone based regimen followed by ASCT. The clinical parameters, response criteria and survival outcomes (TTP and OS) of this validation cohort were defined according to the international uniform response criteria. For IHC analysis two pathologists who were blinded with regards to the clinical outcome of these patients scored the cases independently as positive or negative. Discordance in their scoring was seen in 20/79 (25.7%) with a consensus scoring reassigned to all of these cases. The Kaplan-Meier method was used to estimate OS and TTP. Multivariate analysis was performed using the Cox regression method. [Display omitted] Results: 79 patients were included in this validation cohort, the median age was 54.4 yrs (27.9–71), 23.7% had ISS stage III, median beta 2-microglobulin was 3.29 mg/L (1.16–37.5). Del13q and t(4;14) were detected by FISH in 35.6% and 13.6% of patients, respectively. Post ASCT, 68% achieved a CR or VGPR with an overall median TTP and OS of 2.29 years (CI 1.84–2.73) and 5.74 years (CI 4.98–6.51) respectively. Expression of FGFR3 was detected in 7.6% of the patients, cyclin B2 in 58.2% and integrin-beta7 in 17.7%. In univariate analysis expression of FGFR3 was associated with a significantly shorter TTP (P=0.011) but not OS (P=0.114). Similarly integrin-beta7 predicted for a shorter TTP (P=0.008) but not OS (P=0.570). Cyclin B2 also predicted for worse TTP (P=0.047) but not OS (P=0.098), whereas the expression of cyclins D1, D2, D3 and PAX5 did not affect survival. Based on our testing cohort definition of risk groups, 18/79 (22.8%) were considered as “High risk” with significantly shorter TTP 0.93 years (CI 0.74–1.12) compared to 2.29 years (CI 1.88–2.69) and 3.35 years (CI 2.51–4.19) for the “Intermediate” (34/79; 43%) and “Low” (27/79; 34.2%) risk groups respectively (P=0.002). The 5-years estimates for OS was 57.1% for the High-risk group compared to 66.3% and 71.6% for the Intermediate and Low risk group respectively (P=0.258). Multivariate analysis was performed using ISS, del13q and the TMA risk group classification as variables. The TMA classification and del 13q were the only independent predictors of TTP with the high-risk group having 3.4 fold greater risk of relapse (P=0.001).Conclusion: We have validated our protein array based classification of Multiple Myeloma and confirmed its survival predictive value post ASCT. MM patients with the High-risk signature should be spared the toxicity of ASCT and considered instead for other frontline novel therapeutic agents.

Molecular biology of myeloma

Multiple myeloma (MM) is a B-cell malignancy characterised by the accumulation of clonal plasma cells (PC) in the bone marrow (BM). The molecular bases for this incurable disease have been widely investigated in the last years, and the development of modern genomic technologies has contributed to the understanding of the pathogenesis of MM. The molecular mechanisms that explain the cellular origin of myeloma cells, the cytogenetic abnormalities and their clinical implications, and the biological information provided by gene expression profiling analysis are reviewed in this paper. In addition, a molecular classification of MM in seven groups based on the relationship between gene expression profiling, chromosomal translocations and prognostic outcome is also presented. And finally, the recent hypothesis of a potential unifying event in the pathogenesis of MM, supported by cyclin D deregulation in virtually all MM tumours, will be summarised.

Treatment of Newly Diagnosed Multiple Myeloma Based on Mayo Stratification of Myeloma and Risk-Adapted Therapy (mSMART): Consensus Statement

Treatment of Newly Diagnosed Multiple Myeloma Based on Mayo Stratification of Myeloma and Risk-Adapted Therapy (mSMART): Consensus Statement

The molecular classification of multiple myeloma

AbstractTo 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.

P21. A molecular classification of multiple myeloma (MM) based on gene expression profiling and fluorescence in situ hybridisation as independent prognostic factor for event free survival (EFS)

P21. A molecular classification of multiple myeloma (MM) based on gene expression profiling and fluorescence in situ hybridisation as independent prognostic factor for event free survival (EFS)

Molecular Classification of Multiple Myeloma (MM) Based on Gene Expression Profiling (GEP) and Fluorescence In Situ Hybridisation (FISH) Is an Independent Predictor for Event Free Survival (EFS).

AIM was to test the new molecular classification of MM based on changes in global gene expression attributable to cytogenetic aberrations detected by interphase FISH (iFISH) in order to (i) predict EFS in a group of 100 MM-patients treated by high dose chemotherapy, and (ii) to investigate whether the classification represents an independent prognostic factor.PATIENTS AND METHODS. Bone marrow aspirates from MM-patients and normal donors were CD138-purified by magnetic activated cell sorting. RNA was in-vitro transcribed and hybridised to Affymetrix HG U133 A+B GeneChip (TG) and HG U133 2.0 plus array (VG). CCND1, CCND2 and FGFR3 expression was verified by real time RT-PCR and western blotting. iFISH was performed on purified MM-cells using probes for chromosomes 11q23, 13q14, 17p13 and the IgH-translocations t(4;14)(p16;q32.3) and t(11;14)(q13;q32.3). Expression data were normalised (Bioconductor package gcrma), and nearest shrunken centroids (NSC) applied to calculate and cross validate a predictor on a training group (TG) of 40 patients in whom a comprehensive iFISH panel combined with data on CCND overexpression were available. The ExpressMiner tool of the HUSAR bioinformatics laboratory was used to analyze genes differentially expressed between GEP-defined groups. A log-rank test and a Cox proportional hazard model were used to test the influence of prognostic parameters in combination with the predicted groups.RESULTS. Four groups were distinguished: (1.1) CCND1 (11q13) overexpression and trisomy 11q13, (1.2) CCND1 overexpression and translocations involving 11q13, i.e. t(11;14), (2.1) CCND2 overexpression without 11q13+, t(11;14), t(4;14), (2.2) CCND2 overexpression with t(4;14) and FGFR3 upregulation. A predictor of 6 genes correctly classified all 40 patients of the TG (estimated cross validated error rate 0%). An independent validation group (VG) of 65 patients was used. Distribution of clinical parameters (i.e. beta2M, Durie Salmon stages, ISS) was not significantly different between the 4 groups. The groups defined by the predictor have a significantly different EFS after autologous stem cell transplantation according to the GMMG-HD3 protocol (n=100; median: 26 /not reached /22 /6 months in groups 1.1 /1.2 /2.1 /2.2, respectively). A model testing the combination of the predicted group and B2M (above or below 3.5 mg/dl) showed a significant (p<0.006, log-rank-test) correlation with EFS. The distribution of del(13q14) (n=118) was (1.1) 34.0%, (1.2) 60.8%, (2.1) 46.4% and (2.2) 94%. The presence of a del(13q14) either in a subclone (<60% of analyzed nuclei) or major clone had no significant influence on EFS.CONCLUSION. Gene expression and iFISH allow a molecular classification of MM which can be predicted by GEP. Groups in the classification have distinct gene expression patterns as well as statistically significant different EFS. GEP-defined groups and B2M represent independent prognostic parameters.

Molecular Classification of Multiple Myeloma: A Distinct Transcriptional Profile Characterizes Patients Expressing CCND1 and Negative for 14q32 Translocations.

The deregulation of CCND1, CCND2 and CCND3 genes represents a common event in multiple myeloma (MM), being at least one of them deregulated in almost all MM tumors. A recently proposed TC classification1 grouped MM patients into five classes on the basis of their cyclins D expression profiles and the presence of the main translocations involving the immunoglobulin heavy-chain (IGH) locus at 14q32. The aim of our study was to identify the putative transcriptional fingerprints associated with the deregulation of the different D-type cyclins and the presence of IGH translocations. The cyclin D expression levels obtained by high-density oligonucleotide microarray analysis of purified plasma cells from 50 MM cases were used to stratify the samples into the five TC classes, along with the molecular characteristics. The cyclin D expression data were validated by means of real-time quantitative PCR analysis; fluorescence in-situ hybridization was used to investigate the cyclin D loci arrangements, and to detect the main IGH translocations and the chromosome 13q deletion. A multi-class classification analysis was performed on the gene expression data and used to identify the transcriptional fingerprints of the 5 TC groups. 112 probe sets were selected as characterizing the TC1, TC2, TC4 and TC5 groups, whereas the TC3 samples showed heterogeneous phenotypes and no marker genes. In particular, TC1, TC4 and TC5 groups were characterized by the molecular signatures associated with the primary IGH translocations target genes. The TC2 group, showing significantly extra copies of the CCND1 locus (P=5.9×10−3) and neither IGH translocations nor the chromosome 13q deletion (P=1.7×10−3), was characterized by the overexpression of 30 genes, mainly involved in protein biosynthesis at translational level. Among the most specifically modulated transcripts within the group we identified a novel gene containing a BTB/POZ domain, typical of many zinc finger transcription factors and associated with transcriptional repression activity. A meta-analysis performed on two publicly available MM datasets, containing almost 250 cases, validated the identified gene expression signatures with a global classification rate (indicating the correct prediction of the TC class for the independent set) of 86% and 90%, respectively. Our data contribute to the understanding of the molecular and biological features of distinct MM subtypes; the identification of a distinctive gene expression pattern in TC2 patients may improve risk stratification and indicate novel therapeutic targets.

Novel treatments of multiple myeloma

Over the last decade significant advances in the treatment of multiple myeloma (MM) have been achieved. Most prospective randomized trials indicate that high dose therapy with autologous stem cell transplantation is associated with improved eventfree and overall survival. Thalidomide, an oral immunomodulatory and anti-angiogenetic agent is associated with objective responses in 30% of patients with advanced and refractory MM. The addition of dexamethasone to thalidomide is associated with a 50% response rate in refractory patients and with a 70% response rate in previously untreated patients. This oral combination is particularly useful in newly diagnosed patients with features of advanced disease who are candidates for stem cell collection. Bortezomib is a potent and selective inhibitor of the 26S proteasome. Bortrezomib has shown activity in 30% of patients with refractory/relapsed MM including patients who have failed thalidomide. The combination of bortezomib with thalidomide and dexamethasone in previously untreated patients has been associated with a response rate exceeding 80%. More recently the Imid lenalidomide combined with dexamethasone has been associated with significantly higher response rates and longer event-free survival than dexamethasone alone. Lenalidomide and dexamethasone is also very active in previously untreated patients. The development of genomics and proteomics provide the basis for a novel molecular classification of MM, can identify unique targets for combination therapy in individual patients and provide the framework for clinical protocols to enhance cytotoxicity and to avoid the emergence of drug resistance.

Molecular Classification of Multiple Myeloma: A Distinct Transcriptional Profile Characterizes Patients Expressing CCND1 and Negative for 14q32 Translocations

The deregulation of CCND1, CCND2 and CCND3 genes represents a common event in multiple myeloma (MM). A recently proposed classification grouped MM patients into five classes on the basis of their cyclin D expression profiles and the presence of the main translocations involving the immunoglobulin heavy chain locus (IGH) at 14q32. In this study, we provide a molecular characterization of the identified translocations/cyclins (TC) groups. The gene expression profiles of purified plasma cells from 50 MM cases were used to stratify the samples into the five TC classes and identify their transcriptional fingerprints. The cyclin D expression data were validated by means of real-time quantitative polymerase chain reaction analysis; fluorescence in situ hybridization was used to investigate the cyclin D loci arrangements, and to detect the main IGH translocations and the chromosome 13q deletion. Class-prediction analysis identified 112 probe sets as characterizing the TC1, TC2, TC4 and TC5 groups, whereas the TC3 samples showed heterogeneous phenotypes and no marker genes. The TC2 group, which showed extra copies of the CCND1 locus and no IGH translocations or the chromosome 13q deletion, was characterized by the overexpression of genes involved in protein biosynthesis at the translational level. A meta-analysis of published data sets validated the identified gene expression signatures. Our data contribute to the understanding of the molecular and biologic features of distinct MM subtypes. The identification of a distinctive gene expression pattern in TC2 patients may improve risk stratification and indicate novel therapeutic targets.

A New Molecular Classification of Multiple Myeloma Using Gene Expression Profiling and Fluorescence In Situ Hybridisation as Predictor for Event Free Survival.

AIM was to establish a new molecular classification of Multiple Myeloma (MM) based on changes in global gene expression attributable to cytogenetic aberrations detected by interphase FISH (iFISH) in order to (i) predict event free survival (EFS) and (ii) investigate differentially expressed genes as basis for a group specific and risk adapted therapy.PATIENTS AND METHODS. Bone marrow aspirates of 105 newly diagnosed MM-patients (65 trial (TG) / 40 independent validation group (VG)) and 7 normal donors (ND) were CD138-purified by magnetic activated cell sorting. RNA was in-vitro transcribed and hybridised to Affymetrix HG U133 A+B GeneChip (TG) and HG U133 2.0 plus arrays (VG). CCND1 and CCND2 expression was verified by real time RT-PCR. iFISH was performed on purified MM-cells using probes for chromosomes 11q23, 11q13, 13q14, 17p13 and the IgH-translocations t(4;14) and t(11;14). Expression data were normalised (Bioconductor package gcrma) and nearest shrunken centroids (NSC) applied to calculate and cross validate a predictor on 40 patients of the TG with a comprehensive iFISH panel available combined with CCND overexpression. Differentially expressed genes were identified using empirical Bayes statistics for pairwise comparison.RESULTS. Overexpression of a D-type cyclin (D1 or D2) was found in 61/65 patients with MM compared to ND. CCND3 overexpression only appeared concomitantly with CCND2 overexpression. Four groups could be distinguished: (1.1) CCND1 (11q13) overexpression and trisomy 11q13, (1.2) CCND1 overexpression and translocations involving 11q13 i.e. t(11;14), (2.1) CCND2 overexpression without 11q13+, t(11;14), t(4;14), (2.2) CCND2 overexpression with t(4;14) and FGFR3 upregulation. A predictor of 6 to 566 genes correctly classifies all 40 patients of the TG (estimated cross validated error rate 0%). An independent VG of 40 patients was used. Genes with highest scores in NSC are: (1.1) CCND1, ribosomal proteins (e.g. RPL 28, 29), GPX1, CCRL2, (1.2) CCND1, TGIF, and NCAM (non-overexpression), (2.1) CCND2, (2.2) FGFR3, WHSC1, CCND2, IRTA2, SELL, and MAGED4. Distribution of clinical parameters (i.e. β2M, Durie Salmon stages, ISS) was not significantly different between the groups. The distribution of del(13)(q14q14) was (1.1) 31.5%, (1.2) 37.5%, (2.1) 37.5% and (2.2) 100%. (p<0.01). I.e. HGF, DKK1, VCAM, CD163 are differentially expressed between all 4 groups and ND (adjusted p<0.001). The groups defined by the predictor show a significantly different EFS after autologous stem cell transplantation according to the GMMG-HD3 protocol (median: (1.1) 18 / (1.2) not reached (no event) / (2.1) 22 / (2.2) 6 months; log-rank-test: p=0.004).CONCLUSION. CCND1 or CCND2 overexpression is nearly ubiquitous in MM and attributable to defined cytogenetic aberrations. Gene expression and iFISH allow a molecular classification of MM which can be predicted by gene expression profiling alone. Groups in the classification show a distinctive pattern in gene expression as well as a different EFS interpretable as risk stratification and indicator of therapeutic targets.