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Abstract Background Multiple myeloma (MM) is characterized by a strong dependence of the tumor cells on their microenvironment, which produces growth factors supporting survival and proliferation of myeloma cells (MMC). In the past few years, many myeloma growth factors (MGF) have been described in the literature. However, their relative importance and the nature of the cells producing MGF remain unidentified for many of them. Methods We have analysed the expression of 51 MGF and 36 MGF receptors (MGFR) using Affymetrix microarrays throughout normal plasma cell differentiation, in MMC and in cells from the bone marrow (BM) microenvironment (CD14, CD3, polymorphonuclear neutrophils, stromal cells and osteoclasts). Results 4/51 MGF and 9/36 MGF-receptors genes were significantly overexpressed in plasmablasts (PPC) and BM plasma cell (BMPC) compared to B cells whereas 11 MGF and 11 MGFR genes were overexpressed in BMPC compared to PPC. 3 MGF genes (AREG, NRG3, Wnt5A) and none of the receptors were significantly overexpressed in MMC versus BMPC. Furthermore, 3/51 MGF genes were overexpressed in MMC compared to the the BM microenvironment whereas 22/51 MGF genes were overexpressed in one environment subpopulation compared to MMC. Conclusions Two major messages arise from this analysis 1) The majority of MGF genes is expressed by the bone marrow environment. 2) Several MGF and their receptors are overexpressed throughout normal plasma cell differentiation. This study provides an extensive and comparative analysis of MGF expression in plasma cell differentiation and in MM and gives new insights in the understanding of intercellular communication signals in MM.

Abstract Although the risk of a herpes B virus (Cercopithecine herpes virus 1) infection is low, the clinical course of the infectious disease is generally unfavourable. A high safety standard can be achieved if people with professional contact to primates apply proper organisational, technical and personal safety precautions. The risk can be considerably reduced if animal keepers, laboratory assistants and scientists receive adequate information about the pathology of herpes B virus and are well trained in the necessary procedures and the precautions. For this reason, comprehensive and regular training, information and instruction must be provided to all primate workers and to laboratory workers who come into contact with potentially infectious material. After potential contamination, the risk for the affected worker must be assessed immediately and post-exposure chemoprophylaxis performed if necessary. This necessitates internal risk assessment. An interdisciplinary group of experts has developed an action plan for Germany.

Abstract Background The huge amount of data generated by DNA chips is a powerful basis to classify various pathologies. However, constant evolution of microarray technology makes it difficult to mix data from different chip types for class prediction of limited sample populations. Affymetrix® technology provides both a quantitative fluorescence signal and a decision (detection call: absent or present) based on signed-rank algorithms applied to several hybridization repeats of each gene, with a per-chip normalization. We developed a new prediction method for class belonging based on the detection call only from recent Affymetrix chip type. Biological data were obtained by hybridization on U133A, U133B and U133Plus 2.0 microarrays of purified normal B cells and cells from three independent groups of multiple myeloma (MM) patients. Results After a call-based data reduction step to filter out non class-discriminative probe sets, the gene list obtained was reduced to a predictor with correction for multiple testing by iterative deletion of probe sets that sequentially improve inter-class comparisons and their significance. The error rate of the method was determined using leave-one-out and 5-fold cross-validation. It was successfully applied to (i) determine a sex predictor with the normal donor group classifying gender with no error in all patient groups except for male MM samples with a Y chromosome deletion, (ii) predict the immunoglobulin light and heavy chains expressed by the malignant myeloma clones of the validation group and (iii) predict sex, light and heavy chain nature for every new patient. Finally, this method was shown powerful when compared to the popular classification method Prediction Analysis of Microarray (PAM). Conclusion This normalization-free method is routinely used for quality control and correction of collection errors in patient reports to clinicians. It can be easily extended to multiple class prediction suitable with clinical groups, and looks particularly promising through international cooperative projects like the "Microarray Quality Control project of US FDA" MAQC as a predictive classifier for diagnostic, prognostic and response to treatment. Finally, it can be used as a powerful tool to mine published data generated on Affymetrix systems and more generally classify samples with binary feature values.

Background: Multiple myeloma (MM) is an incurable disease characterized by clonal plasma cell (PC) proliferation within the bone marrow (BM). Next-generation flow cytometry has become the reference tool to follow minimal residual disease (MRD). We developed a new simpler and cheaper flow cytometry method to analyze bone marrow samples in patients with MM., Methods: To identify and characterize abnormal PCs, we designed a simple panel composed of anti-CD38, antikappa, and antilambda light chain antibodies, combined with two antibody pools with the same fluorophore (anti-CD19 and anti-CD27 for the negative pool and anti-CD56, anti-CD117, and anti-CD200 antibodies for the positive pool). We also developed dedicated software for the automated identification of malignant PCs and MRD assessment. We then compared PC identification with our simple antibody panel and with the larger antibody panel routinely used at Montpellier University Hospital Center in 52 patients with MM (M-CHU cohort)., Results: Results for total PC detection (r 2  = 0.9965; P < 0.001; n = 52) and malignant PC detection (r 2  = 0.9486; P < 0.001; n = 38) obtained with the two panels were significantly correlated. Moreover, comparison of the results obtained by automated detection with our software and by manual gating analysis in 80 BM samples (38 from the M-CHU cohort and 42 patients from another MM cohort) showed strong correlation for both total and malignant PC selection (respectively, r 2  = 0.936; P < 0.001 and r 2  = 0.9505; P < 0.001)., Conclusions: Our simple and automated strategy for MRD assessment in MM could help increasing reproducibility and productivity without compromising sensitivity and specificity, while decreasing the test cost. © 2017 International Clinical Cytometry Society., (© 2017 International Clinical Cytometry Society.)

Background: Multiple myeloma (MM) is the second most common hematologic malignancy. Aberrant epigenetic modifications have been reported in MM and could be promising therapeutic targets. As response rates are overall limited but deep responses occur, it is important to identify those patients who could indeed benefit from epigenetic-targeted therapy., Methods: Since HDACi and DNMTi combination have potential therapeutic value in MM, we aimed to build a GEP-based score that could be useful to design future epigenetic-targeted combination trials. In addition, we investigated the changes in GEP upon HDACi/DNMTi treatment., Results: We report a new gene expression-based score to predict MM cell sensitivity to the combination of DNMTi/HDACi. A high Combo score in MM patients identified a group with a worse overall survival but a higher sensitivity of their MM cells to DNMTi/HDACi therapy compared to a low Combo score. In addition, treatment with DNMTi/HDACi downregulated IRF4 and MYC expression and appeared to induce a mature BMPC plasma cell gene expression profile in myeloma cell lines., Conclusion: In conclusion, we developed a score for the prediction of primary MM cell sensitivity to DNMTi/HDACi and found that this combination could be beneficial in high-risk patients by targeting proliferation and inducing maturation.

Brain inflammation is one of the hallmarks of Alzheimer disease (AD) and a current trend is that inflammatory mediators, particularly cytokines and chemokines, may represent valuable biomarkers for early screening and diagnosis of the disease. Various studies have reported differences in serum level of cytokines, chemokines, and growth factors in patients with mild cognitive impairment or AD. However, data were often inconsistent and the exact function of inflammation in neurodegeneration is still a matter of debate. In the present work, we measured the expression of 120 biomarkers (corresponding to cytokines, chemokines, growth factors, and related signaling proteins) in the serum of 49 patients with the following diagnosis distribution: 15 controls, 14 AD, and 20 MCI. In addition, we performed the same analysis in the cerebrospinal fluid (CSF) of 20 of these patients (10 AD and 10 controls). Among the biomarkers tested, none showed significant changes in the serum, but 13 were significantly modified in the CSF of AD patients. Interestingly, all of these biomarkers were implicated in neurogenesis or neural stem cells migration and differentiation. In the second part of the study, 10 of these putative biomarkers (plus 4 additional) were quantified using quantitative multiplex ELISA methods in the CSF and the serum of an enlarged cohort composed of 31 AD and 24 control patients. Our results confirm the potential diagnosis interest of previously published blood biomarkers, and proposes new ones (such as IL-8 and TNFR-I). Further studies will be needed to validate these biomarkers which could be used alone, combined, or in association with the classical amyloid and tau biomarkers.

DNA microarrays have considerably helped to improve the understanding of biological processes and diseases. Large amounts of publicly available microarray data are accumulating, but are poorly exploited due to a lack of easy-to-use bioinformatics resources. The aim of this study is to build a free and convenient data-mining web site (www.genomicscape.com). GenomicScape allows mining dataset from various microarray platforms, identifying genes differentially expressed between populations, clustering populations, visualizing expression profiles of large sets of genes, and exporting results and figures. We show how easily GenomicScape makes it possible to construct a molecular atlas of the B cell differentiation using publicly available transcriptome data of naïve B cells, centroblasts, centrocytes, memory B cells, preplasmablasts, plasmablasts, early plasma cells and bone marrow plasma cells. Genes overexpressed in each population and the pathways encoded by these genes are provided as well as how the populations cluster together. All the analyses, tables and figures can be easily done and exported using GenomicScape and this B cell to plasma cell atlas is freely available online. Beyond this B cell to plasma cell atlas, the molecular characteristics of any biological process can be easily and freely investigated by uploading the corresponding transcriptome files into GenomicScape.

The inhibitor-of-apoptosis family member survivin has been reported to inhibit apoptosis and regulate mitosis and cytokinesis. In multiple myeloma, survivin has been described to be involved in downstream sequelae of various therapeutic agents. We assessed 1093 samples from previously untreated patients, including two independent cohorts of 392 and 701 patients, respectively. Survivin expression was associated with cell proliferation, adverse prognostic markers, and inferior event-free and overall survival, supporting the evaluation of survivin as a therapeutic target in myeloma. The small molecule suppressant of survivin--YM155--is in clinical development for the treatment of solid tumors. YM155 potently inhibited proliferation and induced apoptosis in primary myeloma cells and cell lines. Gene expression and protein profiling revealed the critical roles of IL6/STAT3-signaling and the unfolded protein response in the efficacy of YM155. Both pathways converged to down regulate anti-apoptotic Mcl-1 in myeloma cells. Conversely, growth inhibition and apoptotic cell death by YM155 was rescued by ectopic expression of Mcl-1 but not survivin, identifying Mcl-1 as the pivotal downstream target of YM155 in multiple myeloma. Mcl-1 expression was likewise associated with adverse prognostic markers, and inferior survival. Our results strongly support the clinical evaluation of YM155 in patients with multiple myeloma.

Objective: Adult primary human hepatocytes (PHHs) support the complete infection cycle of natural HCV from patients' sera. The molecular details underlying sera infectivity towards these cells remain largely unknown. Therefore, we sought to gain a deeper comprehension of these features in the most physiologically relevant culture system., Design: Using kinetic experiments, we defined the optimal conditions to infect PHH and explored the link between cell organisation and permissivity. Based on their infectivity, about 120 sera were classified in three groups. Concentration of 52 analytes was measured in 79 selected sera using multiplexed immunobead-based analyte profiling., Results: PHH permissivity towards HCV infection negatively correlated with cell polarisation and formation of functional bile canaliculi. PHH supported HCV replication for at least 2 weeks with de novo virus production. Depending on their reactivity, sera could be classified in three groups of high, intermediate or low infectivity toward PHH. Infectivity could not be predicted based on the donors' clinical characteristics, viral load or genotype. Interestingly, highly infectious sera displayed a specific cytokine profile with low levels of most of the 52 tested analytes. Among them, 24 cytokines/growth factors could impact hepatocyte biology and infection efficiency., Conclusions: We identified critical factors leading to efficient PHH infection by HCV sera in vitro. Overall, we showed that this cellular model provides a useful tool for studying the mechanism of HCV infection in its natural host cell, selecting highly infectious isolates, and determining the potency of drugs towards various HCV strains., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.)

DNA repair is critical to resolve extrinsic or intrinsic DNA damage to ensure regulated gene transcription and DNA replication. These pathways control repair of double strand breaks, interstrand crosslinks, and nucleotide lesions occurring on single strands. Distinct DNA repair pathways are highly inter-linked for the fast and optimal DNA repair. A deregulation of DNA repair pathways may maintain and promote genetic instability and drug resistance to genotoxic agents in tumor cells by specific mechanisms that tolerate or rapidly bypass lesions to drive proliferation and abrogate cell death. Multiple Myeloma (MM) is a plasma cell disorder characterized by genetic instability and poor outcome for some patients, in which the compendium of DNA repair pathways has as yet not been assessed for a disease-specific prognostic relevance. We design a DNA repair risk score based on the expression of genes coding for proteins involved in DNA repair in MM cells. From a consensus list of 84 DNA repair genes, 17 had a bad prognostic value and 5 a good prognostic value for both event-free and overall survival of previously-untreated MM patients. The prognostic information provided by these 22 prognostic genes was summed within a global DNA repair score (DRScore) to take into account the tight linkage of repair pathways. DRscore was strongly predictive for both patients' event free and overall survivals. Also, DRscore has the potential to identify MM patients whose tumor cells are dependent on specific DNA repair pathways to design treatments that induce synthetic lethality by exploiting addiction to deregulated DNA repair pathways.

Every day, cells are faced with thousands of DNA lesions, which have to be repaired to preserve cell survival and function. DNA repair is more or less accurate and could result in genomic instability and cancer. We review here the current knowledge of the links between molecular features, treatment, and DNA repair in multiple myeloma (MM), a disease characterized by the accumulation of malignant plasma cells producing a monoclonal immunoglobulin. Genetic instability and abnormalities are two hallmarks of MM cells and aberrant DNA repair pathways are involved in disease onset, primary translocations in MM cells, and MM progression. Two major drugs currently used to treat MM, the alkylating agent Melphalan and the proteasome inhibitor Bortezomib act directly on DNA repair pathways, which are involved in response to treatment and resistance. A better knowledge of DNA repair pathways in MM could help to target them, thus improving disease treatment.

Diffuse gliomas are incurable brain tumors divided in 3 WHO grades (II; III; IV) based on histological criteria. Grade II/III gliomas are clinically very heterogeneous and their prognosis somewhat unpredictable, preventing definition of appropriate treatment. On a cohort of 65 grade II/III glioma patients, a QPCR-based approach allowed selection of a biologically relevant gene list from which a gene signature significantly correlated to overall survival was extracted. This signature clustered the training cohort into two classes of low and high risk of progression and death, and similarly clustered two external independent test cohorts of 104 and 73 grade II/III patients. A 22-gene class predictor of the training clusters optimally distinguished poor from good prognosis patients (median survival of 13-20 months versus over 6 years) in the validation cohorts. This classification was stronger at predicting outcome than the WHO grade II/III classification (P≤2.8E-10 versus 0.018). When compared to other prognosis factors (histological subtype and genetic abnormalities) in a multivariate analysis, the 22-gene predictor remained significantly associated with overall survival. Early prediction of high risk patients (3% of WHO grade II), and low risk patients (29% of WHO grade III) in clinical routine will allow the development of more appropriate follow-up and treatments.

Although functionally competent cytotoxic, T cells are frequently observed in malignant diseases, they possess little ability to react against tumor cells. This phenomenon is particularly apparent in multiple myeloma. We here demonstrate that cytotoxic T cells reacted against myeloma antigens when presented by autologous dendritic cells, but not by myeloma cells. We further show by gene expression profiling and flow cytometry that, similar to many other malignant tumors, freshly isolated myeloma cells expressed several carcinoembryonic antigen-related cell adhesion molecules (CEACAMs) at varying proportions. Binding and crosslinking of CEACAM-6 by cytotoxic T cells inhibited their activation and resulted in T-cell unresponsiveness. Blocking of CEACAM-6 on the surface of myeloma cells by specific monoclonal antibodies or CEACAM-6 gene knock down by short interfering RNA restored T-cell reactivity against malignant plasma cells. These findings suggest that CEACAM-6 plays an important role in the regulation of CD8+ T-cell responses against multiple myeloma; therefore, therapeutic targeting of CEACAM-6 may be a promising strategy to improve myeloma immunotherapy.

Motivation: Despite huge prognostic promises, gene expression-based survival assessment is rarely used in clinical routine. Main reasons include difficulties in performing and reporting analyses and restriction in most methods to one high-risk group with the vast majority of patients being unassessed. The present study aims at limiting these difficulties by (i) mathematically defining the number of risk groups without any a priori assumption; (ii) computing the risk of an independent cohort by considering each patient as a new patient incorporated to the validation cohort and (iii) providing an open-access Web site to freely compute risk for every new patient., Results: Using the gene expression profiles of 551 patients with multiple myeloma, 602 with breast-cancer and 460 with glioma, we developed a model combining running log-rank tests under controlled chi-square conditions and multiple testing corrections to build a risk score and a classification algorithm using simultaneous global and between-group log-rank chi-square maximization. For each cancer entity, we provide a statistically significant three-group risk prediction model, which is corroborated with publicly available validation cohorts., Conclusion: In constraining between-group significances, the risk score compares favorably with previous risk classifications., Availability: Risk assessment is freely available on the Web at https://gliserv.montp.inserm.fr/PrognoWeb/ for personal or test data files. Web site implementation in Perl, R and Apache.

High throughput DNA microarray has made it possible to outline genes whose expression in malignant plasma cells is associated with short overall survival of patients with Multiple Myeloma (MM). A further step is to elucidate the mechanisms encoded by these genes yielding to drug resistance and/or patients' short survival. We focus here on the biological role of the DEP (for Disheveled, EGL-10, Pleckstrin) domain contained protein 1A (DEPDC1A), a poorly known protein encoded by DEPDC1A gene, whose high expression in malignant plasma cells is associated with short survival of patients. Using conditional lentiviral vector delivery of DEPDC1A shRNA, we report that DEPDC1A knockdown delayed the growth of human myeloma cell lines (HMCLs), with a block in G2 phase of the cell cycle, p53 phosphorylation and stabilization, and p21(Cip1) accumulation. DEPDC1A knockdown also resulted in increased expression of mature plasma cell markers, including CXCR4, IL6-R and CD38. Thus DEPDC1A could contribute to the plasmablast features of MMCs found in some patients with adverse prognosis, blocking the differentiation of malignant plasma cells and promoting cell cycle.

Multiple myeloma is a plasma cell cancer with poor survival, characterized by the clonal expansion of multiple myeloma cells (MMC), primarily in the bone marrow. Novel compounds are currently tested in this disease, but partial or minor patients' responses are observed for most compounds used as a single agent. The design of predictors for drug efficacy could be most useful to better understand basic mechanisms targeted by these drugs and design clinical trials. In the current study, we report the building of a DNA methylation score (DM score) predicting the efficacy of decitabine, an inhibitor of DNA methyltransferase (DNMT), targeting methylation-regulated gene expression. DM score was built by identifying 47 genes regulated by decitabine in human myeloma cell lines and the expression of which in primary MMCs of previously untreated patients is predictive for overall survival. A high DM score predicts patients' poor survival, and, of major interest, high sensitivity of primary MMCs or human myeloma cell lines to decitabine in vitro. Thus, DM score could be useful to design novel treatments with DMNT inhibitor in multiple myeloma and has highlighted 47 genes, the gene products of which could be important for multiple myeloma disease development.

Annexin A2 (ANXA2) promotes myeloma cell growth, reduces apoptosis in myeloma cell lines, and increases osteoclast formation. ANXA2 has been described in small cohorts of samples as expressed by myeloma cells and cells of the BM microenvironment. To investigate its clinical role, we assessed 1148 samples including independent cohorts of 332 and 701 CD138-purified myeloma cell samples from previously untreated patients together with clinical prognostic factors, chromosomal aberrations, and gene expression-based high-risk scores, along with expression of ANXA2 in whole BM samples, stromal cells, osteoblasts, osteoclasts, and BM sera. ANXA2 is expressed in all normal and malignant plasma cell samples. Higher ANXA2 expression in myeloma cells is associated with significantly inferior event-free and overall survival independently of conventional prognostic factors and is associated with gene expression-determined high risk and high proliferation. Within the BM, all cell populations, including osteoblasts, osteoclasts, and stromal cells, express ANXA2. ANXA2 expression is increased significantly in myelomatous versus normal BM serum. ANXA2 exemplifies an interesting class of targetable bone-remodeling factors expressed by normal and malignant plasma cells and the BM microenvironment that have a significant impact on survival of myeloma patients.

Gene expression-based scores used to predict risk in cancer frequently include genes coding for DNA replication, repair or recombination. Using two independent cohorts of 206 and 345 previously-untreated patients with Multiple Myeloma (MM), we identified 50 cell cycle-unrelated genes overexpressed in multiple myeloma cells (MMCs) compared to normal human proliferating plasmablasts and non-proliferating bone marrow plasma cells and which have prognostic value for overall survival. Thirty-seven of these 50 myeloma genes (74%) were enriched in genes overexpressed in one of 3 normal human stem cell populations--pluripotent (18), hematopoietic (10) or mesenchymal stem cells (9)--and only three genes were enriched in one of 5 populations of differentiated cells (memory B lymphocytes, T lymphocytes, polymorphonuclear cells, monocytes, osteoclasts). These 37 genes shared by MMCs and adult or pluripotent stem cells were used to build a stem cell score ((SC)score), which proved to be strongly prognostic in the 2 independent cohorts of patients compared to other gene expression-based risk scores or usual clinical scores using multivariate Cox analysis. This finding highlights cell cycle-unrelated prognostic genes shared by myeloma cells and normal stem cells, whose products might be important for normal and malignant stem cell biology.

Multiple myeloma patients' survival under treatment varies from a few months to more than 15 years. Clinical prognostic factors, especially beta2-microglobulin (B2M) and the international staging system (ISS), allow risk assessment to a certain extent, but do not identify patients at very high risk. As malignant plasma cells are characterized by a variety of chromosomal aberrations and changes in gene expression, a molecular characterization ofCD138-purified myeloma cells by interphase fluorescence in situ hybridization (iFISH) and gene expression profiling (GEP) can be used for improved risk assessment, iFISH allows a risk stratification with presence of a translocation t(4;14) and/or deletion of 17p13 being the best documented adverse prognostic factors. A deletion of 13q14 is no longer considered to define adverse risk. Patients harbouring a t(4;14) seems to benefit from a bortezomib- or lenalidomide containing regimen, whereas patients with deletion 17p13 seem only to benefit from a high dose therapy approach using long term bortezomib (in induction and maintenance) and autologous tandem-transplantation as used in the GMMG-HD4 trial, or the total therapy 3 concept. Gene expression profiling allows the assessment of high risk scores (IFM, UAMS), remaining prognostic despite treatment with novel agents, and prognostic surrogates of biological factors (e.g. proliferation) and (prognostic) target gene expression (e.g. Aurora-kinase A). Thus, assessment of B2M and ISS-stage, iFISH, and GEP is considered extended routine diagnostics in therapy requiring multiple myeloma patients for risk assessment and, even now, to a certain extent selection of treatment.

Purpose: Multiple myeloma is an incurable malignant plasma cell disease characterized by survival ranging from several months to more than 15 years. Assessment of risk and underlying molecular heterogeneity can be excellently done by gene expression profiling (GEP), but its way into clinical routine is hampered by the lack of an appropriate reporting tool and the integration with other prognostic factors into a single "meta" risk stratification., Experimental Design: The GEP-report (GEP-R) was built as an open-source software developed in R for gene expression reporting in clinical practice using Affymetrix microarrays. GEP-R processes new samples by applying a documentation-by-value strategy to the raw data to be able to assign thresholds and grouping algorithms defined on a reference cohort of 262 patients with multiple myeloma. Furthermore, we integrated expression-based and conventional prognostic factors within one risk stratification (HM-metascore)., Results: The GEP-R comprises (i) quality control, (ii) sample identity control, (iii) biologic classification, (iv) risk stratification, and (v) assessment of target genes. The resulting HM-metascore is defined as the sum over the weighted factors gene expression-based risk-assessment (UAMS-, IFM-score), proliferation, International Staging System (ISS) stage, t(4;14), and expression of prognostic target genes (AURKA, IGF1R) for which clinical grade inhibitors exist. The HM-score delineates three significantly different groups of 13.1%, 72.1%, and 14.7% of patients with a 6-year survival rate of 89.3%, 60.6%, and 18.6%, respectively., Conclusion: GEP reporting allows prospective assessment of risk and target gene expression and integration of current prognostic factors in clinical routine, being customizable about novel parameters or other cancer entities., (©2011 AACR.)