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Cell centered database for immunology and cancer research feb252016
- 1. 02.25.2016 Maria Shkrob, PhD Project Manager, Elsevier Professional Services m.shkrob@elsevier.com Cell-centered Database for Immunology and Cancer Research
- 2. | 2 Information avalanche 0 1000 2000 3000 4000 5000 6000 7000 201620112006200119961991198619811976197119661961195619511946192419191912 Number of publications covering cancer immunotherapy and immunooncology *Data from PubMed
- 3. Too much information: how to survive Our approach -> Pathway Studio Too much information about cells in particular Examples of problems that we help to solve Today:
- 4. | 4 If only we knew what we know… Amorphous information Structured information Image Source: http://www.thesocialleader.com/wp-content/uploads/2011/03/paper-piles.jpg Text mining: analyzing text to extract information that is useful for particular purposes Text mining • Hard to deal with • Hard to deal with algorithmically • Not scalable • Search • Visualize • Network analysis • Scalable • Compressed 20km
- 5. | 5 Manipulate facts not text to maximize your experience with literature Increased PARP activity has been reported in HCC as compared with healthy liver tissue Quantitative Change activity positive liver tissue Regulation Our results suggest that a natural immune response mediated by γδ T lymphocytes may contribute to the immunosurveillance of melanoma 24 M abstracts 3.5 M full texts 6.2 M relations 284 K entities
- 6. | 6 Explore the fact and it’s context • Type of connection • Sign • intersect, combine and expand Visualize Summarize
- 7. | 7 Pathway Studio Tools Experiment analysis: • Gene expression • Proteomics • Metabolomics • NGS Search Summarization Navigation Visualization Knowledgebase Manually curated pathways Ontologies Annotations Biological relations extracted from literature Variation annotation from public sources 24M abstracts 3.5M full texts 6.2 M relations Pathway Studio Overview
- 8. | 8 Core databases Disease and biomarkers-centered Mammal Chem Effect Disease FX Plant Cell Effect Plant proteins, processes, pathways and ontologies Chemicals-centered Standard database, protein-centered Cell-centered NEW
- 9. Finding cells and facts about cells
- 10. | 10 Have you seen this cell? full name nickname aka formerly known as scars and marks for short
- 11. | 11 Epitope From inconsistent names to standard names Basic cell“Attribute” CD4+ CD25+ regulatory T cell T-lymphocyte leukocyte T-cell leucocyte hemopoetic hemopoietic haemopoetic haemopoietic hematopoetic hematopoietic haematopoetic haematopoietic regulatory immunoregulatory CD4+CD25+ CD25+FOXP3+ CD25+ CD4+ FOXP3+
- 12. | 12 From inconsistent names to standard names Epitopes “Attribute” Basic cell Standard cell name Image source: http://www.biooncology.com/images/therapeutic-targets/b-cell.png Combination Frequency Standard name Ep1+ Ep2- BC3 1000 BC4 Ep5+ BC6 2 BC6 Combination Frequency Standard name Ep1+ Ep2- BC3 1000 BC4 Ep5+ BC6 100 EP5+ BC6
- 13. | 13 Adding cell processes to the mixture proliferation of death of migration of human polarization cytotoxicity quantity It allows to • Have more cell processes in the database • Assign cell processes to rare cells • Quickly introduce changes if needed Standard cell name
- 14. | 14 Recognizing cell processes in text • Information about more specific cell types • Doubles the number of cell processes compared to Gene Ontology + EmTree
- 16. | 16 protein regulates cell cell expresses protein: • surface • secretion Cells are connected to proteins
- 17. | 17 • chemicals naturally occurring in human body • pharmaceuticals • biologically active peptides • antibody drugs • environmental chemicals, • products of metabolism Small molecules affect cells Cell produces small molecule Cells are connected to small molecules
- 18. | 18 State is changed Cells are disease biomakers Quantity is changed Cells play active role in diseases Cells are connected to diseases and clinical parameters Cells affect clinical parameters Cells are somehow related to diseases
- 19. | 19 Database statistics Objects Cells 764 Cell Processes 8.7 K Diseases 16 K Clinical Parameters 4.3 K Proteins and protein classes 148 K Small molecules 107 K Relations Binding 187 K Biomarker 36 K CellExpression 277 K ChemicalReaction 54 K ClinicalTrial 40 K DirectRegulation 133 K Expression 584 K FunctionalAssociation 4 K GeneticChange 248 K MolSynthesis 39 K MolTransport 14 K PromoterBinding 32 K ProtModification 52 K QuantitativeChange 238 K Regulation 3.6 M StateChange 20 K miRNA Effect 37 K Over 830 K new relations TOTAL: 284 K entities 6.2 M relations
- 21. | 21 How melanoma cells suppress local immune response? Step 1 Disease Secretion Protein What proteins are secreted from melanoma cells?
- 22. | 22 Querying the database
- 23. | 23 Disease Secretion Protein Step 2 What cells are negatively regulated by these proteins? Protein Regulation Cell How melanoma cells suppress local immune response? Step 1
- 24. | 24 Step 3 Find cells that negatively regulate melanoma Cell Regulation Disease Step 2 Step 1 Disease Secretion Protein Protein Regulation Cell How melanoma cells suppress local immune response?
- 25. | 25 How melanoma cells suppress local immune response?
- 26. Example 2 Analyze experiments: • Gene expression • Metabolomics • Proteomics • NGS
- 27. | 27 High grade astrocytoma survival Background: 3-5% of glioblastoma patients survive longer than 3 years What are the predictors of long survival? High grade astrocytoma patient survival: brain tumor (GSE33331) Comparison of surgical brain tumors of the long survival vs average survival Increased Immune Gene Expression and Immune Cell Infiltration in High Grade Astrocytoma Distinguish Long from Short-Term Survivors Donson AM, Birks DK, Schittone SA, Kleinschmidt-DeMasters BK, Sun DY, Hemenway MF, Handler MH, Waziri AE, Wang M, Foreman NK. J Immunol. 2012 Aug 15; 189(4): 1920–1927.
- 28. | 28 What is needed for long survival? Survival over 70 months vs Functionally annotate genes What processes are they involved in? What pathways are they involved in? Standard survival Results depend on the quality and granularity of the database
- 29. | 29 Experiment analysis in Pathway Studio
- 30. | 30 Analysis of genes positively correlating with prognosis Genes positively correlated with survival Gene Set Enrichment Analysis using Gene Ontology Immune system is involved, but can we be more specific?
- 31. | 31 Dig deeper into immune component Gene Ontology Mammal ChemEffect DIseaseFX Mammal ChemEffect DIseaseFX+cells 28 55 63 T cells B cells Leukocytes Lymphocytes Neutrofils + Macrophages Dendritic cells Monocytes T helpers + Th1 cells Th2 cells Th17 cells Tregs CD8+ T cells immune processes in top 100 immune cells in top 100
- 32. | 32 Additional cell processes – additional results red – significantly up-regulated genes
- 33. | 33 Does this new information make sense? Known connections between immune cells identified in analysis and brain tumors
- 34. | 34 • New database is focused on cell-centered facts • It adds over 800K new relations to Pathway Studio Knowledgebase which now contains 6.2 M relations • New data can be used to answer complex biological questions and analyze experiments Summary
- 35. Thank you