1 subtype Ana Rio-Machin 1 , Bibiana Ferreira 1 , Travis Henry 2 , Gonzalo Gómez-López 3 , Xabier Agirre 4 , Sara Alvarez 1 , Sandra Rodriguez-Perales 1 , Felipe Prosper 4 , M José Calasanz 5 , Joaquín Martínez 6 , Rafael Fonseca 2 , and Juan C Cigudosa 1 1 Molecular Cytogenetics Group, Centro Nacional Investigaciones Oncologicas (CNIO), Calle Melchor Fernández Almagro 3, 28029, Madrid, Spain ; Centro de Investigaciones de Enfermedades Raras (CIBERER), Madrid, Spain. 2 Division of Hematology–Oncology, Mayo Clinic, 13400 E. Shea Blvd. Scottsdale, AZ 85259, Arizona, USA. 3 Bioinformatics Unit, Centro Nacional Investigaciones Oncologicas (CNIO), Calle Melchor Fernández Almagro 3, 28029, Madrid, Spain. 4 Foundation for Applied Medical Research, Division of Cancer and Area of Cell Therapy and Hematology Service, Clínica Universitaria, Universidad de Navarra, Avenida de Pío XII 36, 31008, Pamplona, Spain. 5 Department of Genetics, University of Navarra, Campus Universitario, 31080, Pamplona, Spain. 6 Hematology Service, Hospital Universitario 12 de Octubre, Avda. de Córdoba s/n, 28041, Madrid, Spain. Corresponding author: Juan C. Cigudosa Molecular Cytogenetics Group Human Cancer Genetics Program Centro Nacional de Investigaciones Oncológicas (CNIO)
Downregulation of specific miRNAs in hyperdiploid multiple myeloma mimics the oncogenic effect of IgH translocations occurring in the non-hyperdiploid subtype - PowerPoint PPT Presentation
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Downregulation of specific miRNAs in hyperdiploid multiple myeloma mimics the oncogenic effect of IgH translocations occurring in the non-
hyperdiploid subtype
Ana Rio-Machin 1, Bibiana Ferreira 1, Travis Henry 2, Gonzalo Gómez-López 3, Xabier Agirre 4, Sara Alvarez 1,
Sandra Rodriguez-Perales 1, Felipe Prosper 4, M José Calasanz 5, Joaquín Martínez 6, Rafael Fonseca 2, and Juan C
Cigudosa 1
1 Molecular Cytogenetics Group, Centro Nacional Investigaciones Oncologicas (CNIO), Calle Melchor Fernández Almagro 3, 28029, Madrid,
Spain ; Centro de Investigaciones de Enfermedades Raras (CIBERER), Madrid, Spain.2 Division of Hematology–Oncology, Mayo Clinic, 13400 E. Shea Blvd. Scottsdale, AZ 85259, Arizona, USA.3 Bioinformatics Unit, Centro Nacional Investigaciones Oncologicas (CNIO), Calle Melchor Fernández Almagro 3, 28029, Madrid, Spain.4 Foundation for Applied Medical Research, Division of Cancer and Area of Cell Therapy and Hematology Service, Clínica Universitaria,
Universidad de Navarra, Avenida de Pío XII 36, 31008, Pamplona, Spain.5 Department of Genetics, University of Navarra, Campus Universitario, 31080, Pamplona, Spain.6 Hematology Service, Hospital Universitario 12 de Octubre, Avda. de Córdoba s/n, 28041, Madrid, Spain.
Corresponding author: Juan C. Cigudosa
Molecular Cytogenetics GroupHuman Cancer Genetics ProgramCentro Nacional de Investigaciones Oncológicas (CNIO)C/Melchor Fernández Almagro, 328029 Madrid, SpainPhone #: 34 912246900Fax #: 34912246923Mail: [email protected]
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SUPPLEMENTARY INFORMATION SUMMARY:
TABLES:
Supplementary Table 1: Description of the samples: their clinical origin and use in this work.
Supplementary Table 2: Cytogenetic and clinical characteristics of the evaluated patients in the miRNAs microarray.
Supplementary Table 3: Sequence of the primers used for the bisulphite sequencing.
Supplementary Table 4: Locations of the miRNAs consensus binding sites in the 3’UTR regions of the selected target genes.
Supplementary Table 7. Predicted Target Genes that are overexpressed in h-MM vs nh-MM (FDR<0.001 ) using expression
dataset provided by Agnelli et al. 2007 (miRanda (miRBase v12.0) and TargetScan v 5.1)
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Supplementary Figure 3: Luciferase assay. The empty reporter plasmid (empty pGL3 luciferase vector) or the luciferase constructs
containing, respectively, a wild-type and a mutated 3’UTR regions of the selected target genes (TACC3, CCND1, FGFR3 and MAFB)
were co-transfected into Hela cells with the miRNA vectors (pMSCV-425, pMSCV-24, pMSCV-152 and scramble miRNA) and
together with Renilla vector for normalization. Luciferase activity was determined 48 h after reporter plasmid transfection in all
cases. The reduction in luciferase activity induced by the three miRNAs expression was observed in each case, allowing us to
demonstrate that MAFB1, CCND1 and FGFR3 are real targets of hsa-miR-152, hsa-miR-425 and hsa-miR-24, respectively. . Data are
presented as mean from four separate experiments with n = 3 for each experiment. Error bars represent Standard error of the
mean (SEM)
Supplementary Figure 3:
LUCIFERASE ASSAY
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13
MAFB
48h 72h
U266 +hsa-miR-152
α- Tubulin
24hU266 SCR
43kDa
55 kDa
FGFR3
48h 72h
U266 +hsa-miR-24
GAPDH
24hU266 SCR
38 kDa
135 kDa
48h 72h
U266 +hsa-miR-425
24hU266 SCR
α- Tubulin
TACC3
55 kDa
140 kDa Cyclin D1
48h 72h
U266 +hsa-miR-425
α- Tubulin
24hU266 SCR
38 kDa
55 kDa
Supplementary Figure 4: Complete Western Blot gel images of Figure 2B. As shown, the signals from Scr transfected cells correspond to the same set of experiments.
Supplementary Figure 4:
Supplementary Figure 5:
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Supplementary Figure 5: Densitometric analysis. The target protein expression was quantified by densitometric analysis
carried out using the ImageJ software on images acquired from the results of Western blotting (Figure 2B). The analysis shows
the percentage of decrease in Tacc3, Cyclin D1 and MafB expression after the overexpression of the corresponding miRNAs
(hsa-miR-425, hsa-miR-425 and hsa-miR-24, respectively) in U266 cells over cells transfected with scramble miRNA vector and
normalized with the GAPDH or α-tubulin protein expression used as loading control. Data are presented as mean from three
separate experiments. Error bars represent Standard error (SE).
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Supplementary Figure 6:
Supplementary Figure 6: OH-2 cell line. Expression levels of the selected miRNAs in the h-MM cell line OH-2 were
assessed by real-time PCR. We were able to show the downregulation of the three miRNAs in the OH-2 cell line, as
occurs in h-MM patients. In both (A) and (B)data are expressed as 2 -∆Ct values obtained by normalization using RNU19 as
endogenous control. Error bars represent SD.
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Supplementary Figure 7:
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Supplementary Figure 7: Densitometric analysis. The target protein expression was quantified by densitometric analysis
carried out using the ImageJ software on images acquired from the results of Western blotting (Figure 3B). The analysis
shows the entity of increase in Tacc3, Cyclin D1, Fgfr3 and MafB expression at 48h or 72h after the inhibition of the
corresponding miRNAs (hsa-miR-425, hsa-miR-24 and hsa-miR-152, respectively) in Hela or 293FT cells. The results were
normalized with the GAPDH protein expression used as loading control and were expressed as percentage of the protein
expression in the cells transfected with miRIDIAN control vector. Data are presented as mean from three separate
experiments. Error bars represent SEM.
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TACC3 hsa-miR-425
CCND1
MAFB
FGFR3
hsa-miR-152
hsa-miR-24
h-MM patients
CD138+ cells
nh-MMpatients
Supplementary Figure 8:
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Supplementary Figure 8: Target genes and miRNAs expression in MM cases . (Extension of Figure 3B and 3C)
Error bars represent SD.
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Supplementary Table 8: Publicly available gene and miRNA expression data on multiple myeloma (MM).
ReferenceDatabase accession number
Patients data needed
miRNA expression
profile
Gene expression
profile (GEP)Chng et al, Molecular dissection of hyperdiploid multiple myeloma by gene expression profiling. (Cancer Res, 2007) GEP: GSE6477 YES NO YES
Agnelli et al, Upregulation of translational machinery and distinct genetic subgroups characterise hyperdiploidy in multiple myeloma (Br J Haematol. 2007)
GEP: GSE6401 YES NO YES
Agnelli et al, A SNP microarray and FISH-based procedure to detect allelic imbalances in multiple myeloma: An integrated genomics approach reveals a wide gene dosage effect (Genes Chromosomes Cancer. 2009)
GEP: GSE13591 Only TC classification NO YES
Lionetti et al, Identification of microRNA expression patterns and definition of a microRNA/mRNA regulatory network in distinct molecular groups of multiple myeloma (Blood. 2009)
miRNAs: GSE17498GEP: GSM341951 NO YES YES
Zhoua et al, High-risk myeloma is associated with global elevation of miRNAs and overexpression of EIF2C2/AGO2 (PNAS. 2009)
miRNAS and GEP: GSE17306 NO YES YES
Gutierréz et al, Deregulation of microRNA expression in the different genetic subtypes of multiple myeloma and correlation with gene expression profiling (Leukemia. 2010)
miRNAS and GEP: GSE16558 NO YES YES
Agnelli et al, Molecular classification of multiple myeloma: a distinct transcriptional profile characterizes patients expressing CCND1 and negative for 14q32 translocations. (J Clin Oncol. 2005)
GEP: GSE2912 NO NO YES
Mattioli et al, Gene expression profiling of plasma cell dyscrasias reveals molecular patterns associated with distinct IGH translocations in multiple myeloma (Oncogene. 2005)
GEP: GSE2113 NO NO YES
Carrasco et al, High-resolution genomic profiles define distinct clinico-pathogenetic subgroups of multiple myeloma patients. (Cancer Cell. 2006)
profile (GEP)Shaughnessy et al, A validated gene expression model of high-risk multiple myeloma is defined by deregulated expression of genes mapping to chromosome 1 (Blood. 2007)
GEP: GSE2658 NO NO YES
Jenner et al, Gene mapping and expression analysis of 16q loss of heterozygosity identifiesWWOX and CYLD as being important in determining clinical outcome in multiple myeloma (Blood. 2007)
GEP: GSE4452 NO NO YES
Decaux et al, Prediction of Survival in Multiple Myeloma Based on Gene Expression Profiles Reveals Cell Cycle and Chromosomal Instability Signatures in High-Risk Patients and Hyperdiploid Signatures in Low-Risk Patients: A Study of the Intergroupe Francophone du Myélome (J Clin Oncol. 2008)
GEP: GSE70390 NO NO YES
Hose et al, Inhibition of aurora kinases for tailored risk-adapted treatment of multiple myeloma (Blood. 2009)
GEP: E-GEOD-2658
andE-MTAB-81
NO NO YES
Broyl et al, Gene expression profiling for molecular classification of multiple myeloma in newly diagnosed patients(Blood. 2010)
GEP: GSE19784 NO NO YES
Driscoll et al, The sumoylation pathway is dysregulated in multiple myeloma and is associated with adverse patient outcome (Blood. 2010)
GEP: GSE5900 NO NO YES
Pichiorri et al, MicroRNAs regulate critical genes associated with multiple myeloma pathogenesis (PNAS. 2008)
miRNAs: E-TABM-508 NO YES NO
Roccaro et al, MicroRNAs 15a and 16 regulate tumor proliferation in multiple myeloma (Blood. 2009) NO NO YES NO
Jianxiang et al, MicroRNA expression in multiple myeloma is associated with genetic subtype, isotype and survival (Biology Direct 2011)
profile (GEP)Moreaux et al, CD200 is a new prognostic factor in multiple myeloma. (Blood. 2006) NO NO NO YES
Condomines et al, Cancer/Testis Genes in Multiple Myeloma: Expression Patterns and Prognosis Value Determined by Microarray Analysis (J Immunol. 2007)
NO NO NO YES
Jourdan et al, Gene expression of anti- and pro-apoptotic proteins in malignant and normal plasma cells (Br J Haematol. 2009)
NO NO NO YES
Zhang et al, Overexpression of microRNA-29b induces apoptosis of multiple myeloma cells through down regulating Mcl-1 (Biochem Biophys Res Commun. 2011)