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/

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

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

CONTRIBUTION AU CLONAGE DE NOUVEAUX RECEPTEURS APPARTENANT A LA FAMILLE DES RECEPTEURS DES CYTOKINES HEMATOPOIETIQUES. ETUDE DE LA REGULATION DES SIGNAUX DE TRANSDUCTION INDUITS PAR LES CYTOKINES APPARTENANT A LA FAMILLE DE L'INTERLEUKINE-6

LES TRAVAUX REALISES SUR LA CARACTERISATION ET L'EXPRESSION DES RECEPTEURS DES CYTOKINES HEMATOPOIETIQUES ONT PERMIS : (1) D'IDENTIFIER UN NOUVEAU RECEPTEUR HUMAIN APPELE, CRLP-1. CE RECEPTEUR EST EXPRIME DE FACON CONSTITUTIVE DANS DE NOMBREUX TISSUS ET LIGNEES CELLULAIRES HUMAINS ET EXISTE UNIQUEMENT SOUS FORME SOLUBLE. IL POURRAIT CORRESPONDRE A LA SOUS-UNITE D'UN COMPLEXE COMPOSE D'UNE CYTOKINE ET D'UN RECEPTEUR SOLUBLE COMME DANS LE CAS DE L'INTERLEUKINE-12 (IL-12). (2) DE METTRE EN EVIDENCE UN ARNM DE FUSION GENERE PAR UN MECANISME DE COTRANSCRIPTION ET D'EPISSAGE INTERGENIQUE ENTRE LE GENE GALT ET LE GENE DU RECEPTEUR SPECIFIQUE DE L'IL-11. CET ARN BIGENIQUE, EXPRIME DANS DIFFERENTS TISSUS NORMAUX ET LIGNEES CELLULAIRES HUMAINS, PEUT PRODUIRE UNE PROTEINE HYBRIDE DEPOURVUE D'ACTIVITE ENZYMATIQUE ET CORECEPTRICE. CE PHENOMENE POURRAIT ETRE UN MECANISME NATUREL UTILISE PAR LES CELLULES EUCARYOTES POUR GENERER DE NOUVELLES PROTEINES AU COURS DE L'EVOLUTION ET/OU POUR ATTENUER L'ACTIVITE D'UN PROMOTEUR SITUE EN AVAL D'UN GENE ACTIVEMENT TRANSCRIT DONT L'EFFICACITE DE TERMINAISON EST FAIBLE. LES ETUDES REALISEES SUR LA REGULATION DE LA TRANSDUCTION DU SIGNAL INDUIT PAR LES CYTOKINES HEMATOPOIETIQUES ONT PERMIS : (1) L'IDENTIFICATION CHEZ L'HOMME, D'UNE NOUVELLE PROTEINE, LA CIS6, APPARTENANT A LA FAMILLE DES INHIBITEURS SOCS/CIS/SSI. LE GENE CIS6 EST LOCALISE SUR LES CHROMOSOMES 3P22 ET 2P21, IL EST EXPRIME DANS DE NOMBREUX TISSUS, MAIS CONTRAIREMENT A BEAUCOUP D'AUTRES GENES DE CETTE FAMILLE, IL N'EST PAS INDUIT EN REPONSE AUX CYTOKINES HEMATOPOIETIQUES TESTEES. LA MISE EN EVIDENCE DU ROLE MAJEUR DE SOCS-3, UN AUTRE MEMBRE DE LA FAMILLE, (2) DANS LE RETRO-CONTROLE DU SIGNAL D'ARRET DE PROLIFERATION ET D'ENTREE EN APOPTOSE DES CELLULES A375 EN REPONSE A L'OSM. (3) DANS LES PHENOMENES D'INHIBITION CROISEE OBSERVES ENTRE L'IL-3 ET L'IL-11. CES RESULTATS SUGGERENT QUE LES PROTEINES SOCS/CIS/SSI POURRAIENT AVOIR UN ROLE IMPORTANT DANS LE CONTROLE DES SIGNAUX, PARFOIS CONTRADICTOIRES, QUI ASSURENT UN DEVELOPPEMENT COORDONNE DES LIGNAGES IMMUNO-HEMATOPOIETIQUES.PARIS7-Bibliothèque centrale (751132105) / SudocSudocFranceF

Constraints on signaling network logic reveal functional subgraphs on Multiple Myeloma OMIC data

International audienceBackground: The integration of gene expression profiles (GEPs) and large-scale biological networks derived from pathways databases is a subject which is being widely explored. Existing methods are based on network distance measures among significantly measured species. Only a small number of them include the directionality and underlying logic existing in biological networks. In this study we approach the GEP-networks integration problem by considering the network logic, however our approach does not require a prior species selection according to their gene expression level. Results: We start by modeling the biological network representing its underlying logic using Logic Programming. This model points to reachable network discrete states that maximize a notion of harmony between the molecular species active or inactive possible states and the directionality of the pathways reactions according to their activator or inhibitor control role. Only then, we confront these network states with the GEP. From this confrontation independent graph components are derived, each of them related to a fixed and optimal assignment of active or inactive states. These components allow us to decompose a large-scale network into subgraphs and their molecular species state assignments have different degrees of similarity when compared to the same GEP. We apply our method to study the set of possible states derived from a subgraph from the NCI-PID Pathway Interaction Database. This graph links Multiple Myeloma (MM) genes to known receptors for this blood cancer. Conclusion: We discover that the NCI-PID MM graph had 15 independent components, and when confronted to 611 MM GEPs, we find 1 component as being more specific to represent the difference between cancer and healthy profiles