Risk and Response-Adapted Treatment in Multiple Myeloma

Myeloma therapeutic strategies have been adapted to patients' age and comorbidities for a long time. However, although cytogenetics and clinical presentations (plasmablastic cytology; extramedullary disease) are major prognostic factors, until recently, all patients received the same treatment whatever their initial risk. No strong evidence allows us to use a personalized treatment according to one cytogenetic abnormality in newly diagnosed myeloma. Retrospective studies showed a benefit of a double autologous transplant in high-risk cytogenetics according to the International Myeloma Working Group definition (t(4;14), t(14;16) or del(17p)). Moreover, this definition has to be updated since other independent abnormalities, namely gain 1q, del(1p32), and trisomies 5 or 21, as well as TP53 mutations, are also prognostic. Another very strong predictive tool is the response to treatment assessed by the evaluation of minimal residual disease (MRD). We are convinced that the time has come to use it to adapt the strategy to a dynamic risk. Many trials are ongoing to answer many questions: when and how should we adapt the therapy, its intensity and duration. Nevertheless, we also have to take into account the clinical outcome for one patient, especially adverse events affecting his or her quality of life and his or her preferences for continuous/fixed duration treatment

Four-color flow cytometry bypasses limitations of IG/TCR polymerase chain reaction for minimal residual disease detection in certain subsets of children with acute lymphoblastic leukemia.

International audienceBACKGROUND AND OBJECTIVES: Competitive immunoglobulin/T-cell receptor polymerase-chain reaction (PCR) analysis with fluorescent detection is a rapid, cheap and reproducible method for quantifying minimal residual disease (MRD), which is well adapted to the recognition of high-risk childhood acute lymphoblastic leukemia (ALL). We aimed at defining whether flow cytometry (FC) techniques can bypass limitations of PCR for MRD determination. DESIGN AND METHODS: We analyzed 140 remission samples from 91 patients using both competitive PCR amplification of antigen-receptor genes and four-color FC identification of leukemia immunophenotype. These methods were chosen with the aim of detecting at least 0.1% blasts. RESULTS: MRD was measured using both PCR and FC methods in 123 samples and the two methods provided concordant results in 119 of them (97%). Moreover, three out of the four discordant results appeared minor since MRD was detectable by both methods, but at different levels. In 12 of 13 samples from nine patients, mainly infants with early CD10- and/or t(4;11) B-cell ALL and children with immature T-cell ALL, MRD could be determined using FC whereas PCR failed. Conversely, FC methods were unfeasible due to inappropriate leukemia immunophenotype in three additional children (including two with T-cell ALL) for whom PCR successfully provided MRD results. INTERPRETATION AND CONCLUSIONS: The MRD results provided by FC techniques were highly concordant with those of competitive PCR. Moreover, the applicability of FC appeared higher in certain ALL subsets, although the appropriateness of this technique in terms of outcome prediction remains to be demonstrated

The shaping and functional consequences of the dosage effect landscape in multiple myeloma

Background: Multiple myeloma (MM) is a malignant proliferation of plasma B cells. Based on recurrent aneuploidy such as copy number alterations (CNAs), myeloma is divided into two subtypes with different CNA patterns and patient survival outcomes. How aneuploidy events arise, and whether they contribute to cancer cell evolution are actively studied. The large amount of transcriptomic changes resultant of CNAs (dosage effect) pose big challenges for identifying functional consequences of CNAs in myeloma in terms of specific driver genes and pathways. In this study, we hypothesize that gene-wise dosage effect varies as a result from complex regulatory networks that translate the impact of CNAs to gene expression, and studying this variation can provide insights into functional effects of CNAs. Results: We propose gene-wise dosage effect score and genome-wide karyotype plot as tools to measure and visualize concordant copy number and expression changes across cancer samples. We find that dosage effect in myeloma is widespread yet variable, and it is correlated with gene expression level and CNA frequencies in different chromosomes. Our analysis suggests that despite the enrichment of differentially expressed genes between hyperdiploid MM and non-hyperdiploid MM in the trisomy chromosomes, the chromosomal proportion of dosage sensitive genes is higher in the non-trisomy chromosomes. Dosage-sensitive genes are enriched by genes with protein translation and localization functions, and dosage resistant genes are enriched by apoptosis genes. These results point to future studies on differential dosage sensitivity and resistance of pro- and anti-proliferation pathways and their variation across patients as therapeutic targets and prognosis markers. Conclusions: Our findings support the hypothesis that recurrent CNAs in myeloma are selected by their functional consequences. The novel dosage effect score defined in this work will facilitate integration of copy number and expression data for identifying driver genes in cancer genomics studies. The accompanying R code is available at http://www.canevolve.org/dosageEffect/

The dChip survival analysis module for microarray data

International audienceBACKGROUND: Genome-wide expression signatures are emerging as potential marker for overall survival and disease recurrence risk as evidenced by recent commercialization of gene expression based biomarkers in breast cancer. Similar predictions have recently been carried out using genome-wide copy number alterations and microRNAs. Existing software packages for microarray data analysis provide functions to define expression-based survival gene signatures. However, there is no software that can perform survival analysis using SNP array data or draw survival curves interactively for expression-based sample clusters. RESULTS: We have developed the survival analysis module in the dChip software that performs survival analysis across the genome for gene expression and copy number microarray data. Built on the current dChip software's microarray analysis functions such as chromosome display and clustering, the new survival functions include interactive exploring of Kaplan-Meier (K-M) plots using expression or copy number data, computing survival p-values from the log-rank test and Cox models, and using permutation to identify significant chromosome regions associated with survival. CONCLUSIONS: The dChip survival module provides user-friendly way to perform survival analysis and visualize the results in the context of genes and cytobands. It requires no coding expertise and only minimal learning curve for thousands of existing dChip users. The implementation in Visual C++ also enables fast computation. The software and demonstration data are freely available at http://dchip-surv.chenglilab.org

Classify Hyperdiploidy Status of Multiple Myeloma Patients Using Gene Expression Profiles

Multiple myeloma (MM) is a cancer of antibody-making plasma cells. It frequently harbors alterations in DNA and chromosome copy numbers, and can be divided into two major subtypes, hyperdiploid (HMM) and non-hyperdiploid multiple myeloma (NHMM). The two subtypes have different survival prognosis, possibly due to different but converging paths to oncogenesis. Existing methods for identifying the two subtypes are fluorescence in situ hybridization (FISH) and copy number microarrays, with increased cost and sample requirements. We hypothesize that chromosome alterations have their imprint in gene expression through dosage effect. Using five MM expression datasets that have HMM status measured by FISH and copy number microarrays, we have developed and validated a K-nearest-neighbor method to classify MM into HMM and NHMM based on gene expression profiles. Classification accuracy for test datasets ranges from 0.83 to 0.88. This classification will enable researchers to study differences and commonalities of the two MM subtypes in disease biology and prognosis using expression datasets without need for additional subtype measurements. Our study also supports the advantages of using cancer specific characteristics in feature design and pooling multiple rounds of classification results to improve accuracy. We provide R source code and processed datasets at www.ChengLiLab.org/software

Detection of t(11;14) using interphase molecular cytogenetics in mantle cell lymphoma and atypical chronic lymphocytic leukemia

The chromosomal translocation t(11;14)(q13;q32) fuses the IGH and CCND1 genes and leads to cyclin D1 overexpression. This genetic abnormality is the hallmark of mantle cell lymphoma (MCL), but is also found in some cases of atypical chronic lymphocytic leukemia (CLL), characterized by a poor outcome. For an unequivocal assessment of this specific chromosomal rearrangement on interphase cells, we developed a set of probes for fluorescence in situ hybridization (FISH). Northern blotting was performed for analysis of the cyclin D1 expression in 18 patients. Thirty-eight patients, with either a typical MCL leukemic phase (17 patients) or atypical CLL with an MCL-type immunophenotype, i.e., CD19+, CD5+, CD23(-/low), CD79b/sIgM(D)++, and FMC7+ (21 patients), were analyzed by dual-color interphase FISH. We selected an IGH-specific BAC probe (covering the JH and first constant regions) and a commercially available CCND1 probe. An IGH-CCND1 fusion was detected in 28 of the 38 patients (17 typical MCL and 11 cases with CLL). Cyclin D1 was not overexpressed in two patients with typical MCL and an IGH- CCND1 fusion. In view of the poor prognosis associated with MCL and t(11;14)- positive CLL, we conclude that this set of probes is a valuable and reliable tool for a rapid diagnosis of these entities

Analysis of a compartmental model of endogenous immunoglobulin G metabolism with application to multiple myeloma

Immunoglobulin G (IgG) metabolism has received much attention in the literature for two reasons: (i) IgG homeostasis is regulated by the neonatal Fc receptor (FcRn), by a pH-dependent and saturable recycling process, which presents an interesting biological system; (ii) the IgG-FcRn interaction may be exploitable as a means for extending the plasma half-life of therapeutic monoclonal antibodies, which are primarily IgG-based. A less-studied problem is the importance of endogenous IgG metabolism in IgG multiple myeloma. In multiple myeloma, quantification of serum monoclonal immunoglobulin plays an important role in diagnosis, monitoring and response assessment. In order to investigate the dynamics of IgG in this setting, a mathematical model characterizing the metabolism of endogenous IgG in humans is required. A number of authors have proposed a two-compartment nonlinear model of IgG metabolism in which saturable recycling is described using Michaelis-Menten kinetics; however it may be difficult to estimate the model parameters from the limited experimental data that are available. The purpose of this study is to analyse the model alongside the available data from experiments in humans and estimate the model parameters. In order to achieve this aim we linearize the model and use several methods of model and parameter validation: stability analysis, structural identifiability analysis, and sensitivity analysis based on traditional sensitivity functions and generalized sensitivity functions. We find that all model parameters are identifiable, structurally and taking into account parameter correlations, when several types of model output are used for parameter estimation. Based on these analyses we estimate parameter values from the limited available data and compare them with previously published parameter values. Finally we show how the model can be applied in future studies of treatment effectiveness in IgG multiple myeloma with simulations of serum monoclonal IgG responses during treatment

Toll-like receptor 4 selective inhibition in medullar microenvironment alters multiple myeloma cell growth.

peer reviewedBone marrow (BM) mesenchymal stromal cells (MSCs) are abnormal in multiple myeloma (MM) and play a critical role by promoting growth, survival, and drug resistance of MM cells. We observed higher Toll-like receptor 4 (TLR4) gene expression in MM MSCs than in MSCs from healthy donors. At the clinical level, we highlighted that TLR4 expression in MM MSCs evolves in parallel with the disease stage. Thus, we reasoned that the TLR4 axis is pivotal in MM by increasing the protumor activity of MSCs. Challenging primary MSCs with TLR4 agonists increased the expression of CD54 and interleukin-6 (IL-6), 2 factors directly implicated in MM MSC-MM cell crosstalk. Then, we evaluated the therapeutic efficacy of a TLR4 antagonist combined or not with conventional treatment in vitro with MSC-MM cell coculture and in vivo with the Vk*MYC mouse model. Selective inhibition of TLR4 specifically reduced the MM MSC ability to support the growth of MM cells in an IL-6-dependent manner and delayed the development of MM in the Vk*MYC mouse model by altering the early disease phase in vivo. For the first time, we demonstrate that specific targeting of the pathological BM microenvironment via TLR4 signaling could be an innovative approach to alter MM pathology development

A polygenic risk score for multiple myeloma risk prediction

This work was partially supported by intramural funds of the University of Pisa, DKFZ, and University Hospital of Southern Jutland, Denmark, and by a grant of the French National Cancer Institute (INCA). The authors wish to thank Dr. Dominic Edelmann (Division of Biostatistics, DKFZ) for helpful advice about data analysis.There is overwhelming epidemiologic evidence that the risk of multiple myeloma (MM) has a solid genetic background. Genome-wide association studies (GWAS) have identified 23 risk loci that contribute to the genetic susceptibility of MM, but have low individual penetrance. Combining the SNPs in a polygenic risk score (PRS) is a possible approach to improve their usefulness. Using 2361 MM cases and 1415 controls from the International Multiple Myeloma rESEarch (IMMEnSE) consortium, we computed a weighted and an unweighted PRS. We observed associations with MM risk with OR = 3.44, 95% CI 2.53-4.69, p = 3.55 x 10(-15) for the highest vs. lowest quintile of the weighted score, and OR = 3.18, 95% CI 2.1 = 34-4.33, p = 1.62 x 10(-13) for the highest vs. lowest quintile of the unweighted score. We found a convincing association of a PRS generated with 23 SNPs and risk of MM. Our work provides additional validation of previously discovered MM risk variants and of their combination into a PRS, which is a first step towards the use of genetics for risk stratification in the general population.University of Pisa, DKFZUniversity Hospital of Southern Jutland, DenmarkInstitut National du Cancer (INCA) Franc

Heterogeneity of genomic evolution and mutational profiles in multiple myeloma.

Multiple myeloma is an incurable plasma cell malignancy with a complex and incompletely understood molecular pathogenesis. Here we use whole-exome sequencing, copy-number profiling and cytogenetics to analyse 84 myeloma samples. Most cases have a complex subclonal structure and show clusters of subclonal variants, including subclonal driver mutations. Serial sampling reveals diverse patterns of clonal evolution, including linear evolution, differential clonal response and branching evolution. Diverse processes contribute to the mutational repertoire, including kataegis and somatic hypermutation, and their relative contribution changes over time. We find heterogeneity of mutational spectrum across samples, with few recurrent genes. We identify new candidate genes, including truncations of SP140, LTB, ROBO1 and clustered missense mutations in EGR1. The myeloma genome is heterogeneous across the cohort, and exhibits diversity in clonal admixture and in dynamics of evolution, which may impact prognostic stratification, therapeutic approaches and assessment of disease response to treatment