Immune Cell Ontology for Networks (ICON)

1. Immune Cell Ontology for Networks (ICON) Immunology Ontologies and Their Applications in Processing Clinical Data June 11-13, Buffalo, NY

2. Confessions • I am an ontological newbie • Idea for a new ontology of immune networks • Immunologists I’ve talked to like the idea • Biostatisticians I’ve talked to like the idea • So, possibly not entirely stupid • Looking for feedback and advice • Looking for friendly collaborators

3. Immunological case-control studies Lupus Patient Normal Donor

4. Typical case-control study • Data collection • Hundreds of cell subsets from flow cytometry • Dozens of cytokines from Luminex • Other assays (IHC, single cell PCR etc) • Data analysis • Pairwise comparisons • Apply Bonferroni correctiongives p >> 0.05 • Statistical aggregation e.g. PCA • Often difficult to give biological interpretation

5. How does an immune response actually work?

7. Cancer microenvironment network

8. Missing biological knowledge • Immune response does not consist of isolated cells and cytokines acting independently • Networks coordinated by cell-cell communication • Gap – immune network ontology • Applied ontology that draws strength from pre-existing ontologies

9. Is a network ontology feasible? • Analysis of regulatory networks suggest that networks map to dynamical attractors • Typically surprisingly few attractors given potential combinatorial explosion • Examples • Boolean regulatory networks (e.g. Kauffman) • Recurring gene network motifs (e.g. Alon)

10. What’s needed? • Networks consist of cells that communicate via contact- and cytokine-mediated signaling • Components • Cells, cell surface molecules, cytokines • Cell-cell interactions may be specific to particular species, local environments and disease states • Contexts • Species, tissue, disease

11. Components Contexts

12. Tentative construction strategy • Iterate • Build cheap “weak links” graph database by text mining • Edges for cell:cell surface molecule, cell surface molecule:cell surface molecule, cell:cytokine, cytokine:cell surface molecule • Question: Does text mining work for anyone here? • Human review to identify spurious links and add species, disease and tissue contexts • Use “confirmed” and “spurious” links as training, validation and test data sets to improve text mining • Split into networks • Split into discrete subgraphs by cutting “weakest” links based on some method of assigning weights to edges

13. Usage • Queries • Find networks associated with a disease • Find cell subsets, receptors and cytokines associated with a network • Find reagents associated with cell subsets, receptors and cytokines • Find networks most relevant for given cell subsets, receptors and cytokines • Applications • Reference, targeted assay development, better informed fishing expeditions • Basic science – validate novel links or networks

14. App: Web accessible reference • No existing database • Literature review is laborious • Useful public resource

15. App: Targeted assay development • What networks are potentially active in disease X? • Which are the most informative cell subsets and/or cytokines for these networks? • What reagents are available to identify the cell subsets and/or cytokines of interest? (Needs additional reagent database)

16. App: Better fishing expeditions • Sets of cells +/- cytokines in networks • Test for enrichment of networks in treatment groups rather than pairwise-comparisons • Adapt statistical methods developed for enrichment analysis in expression array data (e.g. TANGO or GSEA) • Allows integration of immune biomarkers over multiple panels (e.g. T, B, innate flow panels, Luminex, immunohistochemistry)

17. Starting point

18. T cell social network analysis

19. Initial networks in Protégé (courtesy of Anna Maria)

21. Acknowledgements • Duke Center for Computational Immunology • Tom Kepler • Lindsay Cowell • Anna Maria Masci • Duke Immune Profiling Cores • Kent Weinhold • David Murdoch • Janet Staats • Sarah Sparks