GENE EXPRESSION AND microrna PROFILING IN LYMPHOMAS. AN INTEGRATIVE GENOMICS ANALYSIS

Transcription

1 GENE EXPRESSION AND microrna PROFILING IN LYMPHOMAS. AN INTEGRATIVE GENOMICS ANALYSIS Dra. Margarita Sánchez-Beato Lymphoma Group Molecular Pathology Programme CNIO Agilent Microarray and Sequencing Roadshow, Barcelona, 2010

2 Gene expression arrays in the clinic Tumor cells RNA Complex Microarray Gene expression profile Tumor classification DIAGNOSIS TREATMENT PREDICTION OF DISEASE OUTCOME

3 Gene expression arrays in the clinic Identification of new molecular markers Analysis of lymphoma (or other tumors) mechanisms? Improve the diagnosis: New markers? New entities Prognostic markers: Risk stratification of patients? Therapeutic response predictor Drug development Identifying new therapeutic targets: new genes discovery as potential drugable targets finding new signalling or metabolic pathways for new treatments Target validation? Mechanism of action of new/old drugs

4 Identification of genes involved in metastasis in melanoma Multiples pathways are involved in tumor molecular profiling Non-metastasic MM Metastasic MM CELL CYCLE AND APOPTOSIS EPITHELIAL MESENCHYMAL TRANSITION IMMUNE MODULATION METABOLISM NUCLEIC ACID BINDING _ TRANSCRIPTION FACTOR PROTEIN SYNTHESIS AND DEGRADATION SIGNAL TRANSDUCTION Alonso S. et al. Cancer Res 2007 CELL CYCLE AND APOPTOSIS EPITHELIAL MESENCHYMAL TRANSITION -EMT IMMUNE MODULATION METABOLISM NUCLEIC ACID BINDING-TRANSCRIPTION FACTOR PROTEIN SYNTHESIS AND DEGRADATION SIGNAL TRANSDUCTION

5 Identification of genes involved in metastasis in melanoma IHC validation using TMAs N-CADHERIN OSTEONECTIN (SPARC) OSTEOPONTIN

6 Identification of genes involved in metastasis in melanoma Clinical Validation Marker Criteria N d HR IC p-value Total N-Cadherin_mb Osteonectin Osteopontin Glypican PKC alpha Kaplan-Meier surv iv al estimates, by ncad_mb analysis time Kaplan-Meier surv iv al estimates, by sparc_ analysis time Kaplan-Meier surv iv al estimates, by ospont_ analysis time ncad_mb = 0 ncad_mb = 1 sparc_3 = 0 sparc_3 = 1 ospont_3 = 0 ospont_3 = 1

7 B CLL: Identification of predictor genes p<0.001

8 Treatment of cell lines with Vorinotast SAHA 5uM DMSO 5uM Overnight Treatment 0,1,2,4,8,12,24 hours Cell cycle Apoptosis Harvesting Wozniak MB. et al. Haematologica Gene Expression Profiling

9 Mechanism of action, changes in gene expression after Vorinostat treatment Short Time-Series Expression Miner (STEM) CDK2AP1 ZAP SUMO3 AVEN IRAK1 TP53I3 102 CDKL5 STAT1 HIST1H3AB H1FX CYP2R1

10 Integrative Genomic Analysis Di Lisio et al. Leukaemia 2010

11 micrornas mirnas are short 19- to nt RNAs processed from much longer primary transcripts: pri-mirnas mirnas bind to mrnas at their 3 UTRs via partial complementarity with their seed sequences and seedless complementary sequences. mrna translation and/or stability are impaired, resulting in a reduction in protein expression levels

12 micrornas & cancer The human genome contains more than 1000 mirnas mirnas are negative gene regulators that control a wide range of biological functions such as cellular proliferation, differentiation and apoptosis About half of the annotated human mirnas map within fragile regions of chromosomes, which are areas of the genome that are associated with various human cancers Expression profiling of mirnas has been shown to be a more accurate method of classifying cancer subtypes than using the expression profiles The differential expression of certain mirnas in various tumours might become a powerful tool to aid in the diagnosis and treatment of cancer Gene therapies that use mirnas might be an effective approach to blocking tumour progression

13 micrornas & cancer mirnas can function as tumour suppressors and oncogenes, and they are therefore referred to as oncomirs. Factors that are required for the biogenesis of mirnas have also been associated with various cancers and might themselves function as tumour suppressors and oncogenes

14 Why are mirnas relevant in cancer/lymphoma research? A single mirna can regulate a whole set of different mrnas. Gene expression regulation by mirnas can be combinatorial since multiple mirnas can bind the same mrna, modulating its translation. mirnas may constitute valid biomarkers, and potentially explaining increased (tonic) signaling by BCR

15 Why mirnas in lymphoma research? 1. Better lymphoma pathogenesis understanding 2. Identification of molecular markers for lymphoma progression and therapy response 3. Improvement in B cell lymphoma classification Integrative genomic analysis Are there any gene expression signature abnormalities correlated to mir signature? Are there any connection between pathways alterations and mir signature? Could abnormal DNA CNV be responsible of any alteration in mirep?

16 Mantle Cell Lymphoma 7 % of B cell lymphomas t(11;14) Deregulation of Apoptosis NF kb DNA damage and repair Cell cycle BCR signaling Classical chemotherapy obtains 20 80% of total or partial clinical responses Frequent relapse Jares P, Colomer D, Campo E.. Nat Rev Cancer Oct;7(10): Review

17 Integrative genomics: mirna GEP CNV in MCL GEP mirna profile CNV analysis

18 mirna expression

19 mirna expression in MCL Heat map of significant mirna MCL samples & control tissues FDR<0.05 & >2-fold change

20 mirna expression in MCL Normalized vs. lymph nodes Values are expressed in log 2 ratio Deregulation of 117 mirnas (FDR<0.05) 85 downregulated 32 upregulated Validation by qpcr 13/19 mirnas confirmed 3/19 mirna not significant 3/19 not amplified

21 Gene expression in MCL Normalized vs. lymph nodes Values are represented in log2 ratio Homogeneous signature and common alterations already described About gene with significant altered expression

22 Data integration Starting from mirnas signature (155) Genes expression signature (3000) Common DNA CNV (10) Using miranda and Targetscan prediction software to get a list of predicted mirna targets Matching data by these criteria Direct correlation between mirna gain or loss and DNA CNV Down regulated mirnas and upregulated genes Up regulated mirnas and down regulated genes

23 Integration of all data: mir & CNV

24 Integration of all data: mir & GEP Down regulated mirnas and upregulated genes Up regulated mirnas and down regulated genes How?

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26 Integration of all data: mir & GEP Linking mirnas and mrnas high throughput experiments: a new statistical approach : Gonzalo Gómez

27 Integration of all data: mir & GEP For each mirna a contingency table relating the mirna and its predicted gene targets was produced using mirbase and TargetScan, taking into account whether these targets were included in a consistent gene expression signature (downregulated targets for upregulated mirnas and vice versa). Spider web of interaction between downregulated mirs and upregulated genes

28 Integration of all data: mir & GEP Downregulated mirnas were tested for their association with upregulated genes, and viceversa. The ranked target list was subjected to gene set enrichment analysis (GSEA). Blimp1 targets: BCR signalling, GC function and proliferation CD40 signalling: CD40, NF-kB, TRAF1, IRF4, CD44 Memory B-cells: IgM, MAPK1, CD44, BCL2 MAPK pathway: BCR+CD40 leads to MAPK activation BCR signalling PI3K induced survival pathways Spider web of interaction between downegulated mirs and upregulated pathways

29 Validation of interaction between mirs and pathways Mino 72h RelA DAPI Merge Mino 96h RelA DAPI Merge Cells Cells NC NC 26a 26a mir-26a and mirna-nc were electroporated and RelA translocation to the nucleus was checked Induced expression of mir-26a abrogated the nuclear translocation of RelA

30 Clinical value Correlation between mir20b expression and overall survival: confirmation group of 54 cases

31 Conclusions MCL patients show a specific mirna signature (82 downregulated and 35 upregulated mirnas) This MCL mirna signature includes potential regulators of NF kb pathway, CD40 pathway and B cell receptor signaling, distinctively deregulated pathways in MCL Functional studies demonstrate that the loss of mir 26a induces p65 NF kb subunit nuclear translocation, thus impairing NF kb pathway activation The expression of mir 20b distinguish a group of low risk patients

32 Lymphoma players Histology and Immunohistochemistry Unit David G. Pisano Bioinformatics Unit Gonzalo Gómez Monoclonal Antibodies Unit Tumor Bank Unit Lymphoma Group Miguel Ángel Piris Nerea Martínez Santiago Montes Socorro Mª Rodríguez Margarita Sánchez-Beato Raquel Villuendas Cristina Isabel Gómez María Pilar Sancho Magdalena B Wozniak Lorena DiLisio Elena Domenech Beatriz Herreros Daniel Martín Esperanza Martín Lina S. Odqvist Beatriz Sánchez María Encarnación Castillo Mª Mar López Helena Pisonero Luis M Rodrigues Mª Elena Rodríguez Pierfrancesco Vargiu