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

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

Immunological case-control studies Lupus Patient Normal Donor

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 correction gives p >> 0.05 – Statistical aggregation e.g. PCA Often difficult to give biological interpretation

HOW DOES AN IMMUNE RESPONSE ACTUALLY WORK?

Cancer microenvironment network

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

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)

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

Components Contexts

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

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

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

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)

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)

STARTING POINT

T cell social network analysis

Initial networks in Protégé (courtesy of Anna Maria)

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