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 studiesLupus PatientNormal 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