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Integration of miRNAs and Pathways in Cancer Research

This presentation explores the joint ranking of microRNAs and pathways for understanding cancer. It covers statistical and biological significance, data structures, target prediction algorithms, gene set tests, miRNA-mRNA integration methods, and evaluation techniques. The application to melanoma data and findings on prognosis and BRAF mutations are discussed. Acknowledgements to research collaborators are also included.

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Integration of miRNAs and Pathways in Cancer Research

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  1. The joint ranking of micro-RNAs and pathways Ellis Patrick, Michael Buckley, Samuel Mueller, Dave M. Lin and Jean Yang

  2. www.ellispatrick.com/presentationswww.ellispatrick.com/r-packageswww.ellispatrick.com/presentationswww.ellispatrick.com/r-packages

  3. What am I interested in? Specific questions might give more specific answers Statistical significance Biological significance

  4. What is a microRNA (miRNA)?

  5. Can we... Identify groups of genes (mRNA) that are being regulated by a microRNAin response to some stimulus? gene 1 gene 2 mir 1 gene1 gene 1 gene 3 gene2 gene 7 mir2 gene 6 mir 2 gene3 gene 8

  6. Data Structure ~1000 microRNA Number of samples mRNA-Seq Data ~20000 mRNA ~20000 mRNA TargetMatrix Number of samples miRNA-Seq Data ~1000 microRNA

  7. External data : target prediction algorithms • Several computational microRNA-target prediction algorithms have been developed e.g. TargetScan, PicTar, microCosm (based on miRanda), and TargetMiner • Large variations in results obtained using different algorithms • Most widely used approach combines the results from multiple target prediction algorithms microCosm Number of Targets per miRNA TargetScan Number of Targets per miRNA

  8. Vector of p-values DE test ~1000 microRNA Number of samples Vector of p-values Vector of p-values mRNA-Seq Data TargetMatrix Gene set test (GST) DE test ~20000 mRNA ~20000 mRNA ~1000 microRNA Number of samples ~1000 microRNA miRNA-Seq Data

  9. Problems • Target information often not specific. • Perform another battery of gene set tests to identify enriched biological pathways. • Three p-value cut-offs: • microRNA DE, • Gene set test on target genes and • Gene set test of pathways within target genes.

  10. We would like to… Identify groups of genes that are being regulated by a miRNA and share some common biological function. gene 7 gene 1 gene 2 gene 6 gene 3 gene 5 mir 1 gene 4

  11. Mir-pathways # microRNA # pathways # genes # genes TargetMatrix KeggMatrix Mir- pathways # microRNA # genes # pathways

  12. P-value Combination • Fisher’s Method • Stouffer’s Method • maxP • Pearson’s Method

  13. genes KEGG Pathways Database pathways GP PP mRNA data genes genes Target matrix (TargetScan) miRNAs Perform gene set tests Correlation Or Association GP PP miRNA data miRNAs miRNA DE Mir- pathways

  14. pMimIntegration of pathways, miRNA and mRNA pathways Integrative scores miRNAs

  15. Evaluation Methods: 1. cMimDE - Classic microRNA and mRNA integration based on DE. Tests whether a miRNA is DE and its target genes are DE in the opposite direction. 2. pMimDE - Pathway, microRNA and mRNA integration using DE. 3. pMimCor - Pathway, microRNA and mRNA integration using correlation.

  16. (A) Evaluation via literature search • For each miRNA (eg. mir-150) and a key word of interest (melanoma) • Search PubMed for mir-150 melanoma* • Call mir-150 associated with melanoma if we see more than one search hit. • Treating this as truth, use this information to generate ROC plots.

  17. (A) Evaluation via literature search

  18. [B] Randomisation: Evaluating thesignal in our data The average number of DE mir-pathways

  19. An application: Melanoma • Melanoma data set from MIA. • Predict prognosis. • Investigate effects of BRAF mutations.

  20. pMimCor results for down-regulatedmiRNAs in patients with BRAF mutations The cancer hallmark (Hanahan and Weinberg, 2011) were a major theme for most of the pathways

  21. miR-197 and Metabolic pathways

  22. Melanoma conclusions • The miRNA expression phenotype of poor prognosis tumours was dominated by anti-proliferative signals that may indicate the tumours are becoming more invasive. • These findings suggested a network of miRNAs that appeared to be reacting to tumour progression, not driving it. • The DE miRNA analysis identified a few miRNAs with prognosis potential. • A number of different miRNAs – mRNA pairs were identified using “cool” approaches. • pMimidentified miRNAs-pathways related to cancer; links are not as obvious in the “cool” analysis.

  23. pMim summary -- Jointly ranks miRNAs and pathways. -- Appears to identify more meaningful miRNAs. -- Handle small sample size. -- Available on www.ellispatrick.com/r-packages

  24. Acknowledgements • Melanoma program at MIA/WMI/RPA • Graham Mann (Usyd) • GuliettaPupo • VarshaTembe • Sara-Jane Schramm • Mitch Stark (UQ) • John Thompson • Lauren Haydu • RichardScolyer (RPA) • James Wilmott (RPA) Proteomics research unit • Ben Crossett • SwetlanaMactier • Richard Christopherson • School of Mathematics and Statistics (Usyd) • Jean Yang • Samuel Mueller • John Ormerod • KaushalaJayawardana • Dario Strbenac • Rebecca Barter • ShilaGhanazfar • Others • Michael Buckley (CSIRO) • David Lin (Cornell University) • VivekJayaswal (Biocon Bristol-Myers Squibb R&D)

  25. Thankyou

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