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Cancer immunity webinar april 29
- 1. 04.29.2015 Maria Shkrob, PhD Sr. Bioinformatics Scientist R&D Solutions, Elsevier m.shkrob@elsevier.com Cell-Centered Database for Immunology and Cancer Research
- 2. | 2
- 3. | 3 Information avalanche 0 500 1000 1500 2000 2500 3000 3500 2015200519951985197519651955 “Cancer Immunotherapy” in PubMed as of February 12, 2015
- 4. | 4 Below the tip of the iceberg Image source: Tumor-altered dendritic cell function: implications for antitumor immunity. Hargadon KM. Front Immunol. 2013 Jul 11;4:192. Drug Targets Biomarkers Cancer immunotherapy
- 5. Too much information: how to survive Our approach What Pathway Studio has to offer Too much information about cells in particular Examples of problems that the database helps to solve Today:
- 6. | 6 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
- 7. | 7 From text to fact Tregs contribute to the progression of HNSCC Regulatory T cell Head and neck squamous cell carcinoma text fact positive regulation sentence 5.6 M facts 24 M abstracts 3.5 M full texts standard name standard link standard name
- 8. | 8 Find cells involved in HNSCC Pathway Studio: manipulate facts not text 96 publications • type of connection • sign • intersect, combine and expand Visualize Summarize
- 9. | 9 Pathway Studio Overview Pathway Studio ToolsKnowledgebase Manually curated pathways Ontologies Biological relations extracted from literature Experiment analysis: Gene expression Proteomics Metabolomics NGS (beta) Search Summarization Navigation Visualization 24M abstracts 3.5M full texts 4.8M relations +836K new relations
- 10. | 10 Pathway Studio databases standard name standard link standard name Mammal protein-centered ChemEffect drug-centered DiseaseFX biomarker-centered +Biomarker facts +Drugs Proteins + • diseases • clinical parameters • small molecules • cell processes • treatments • genetic change • state change • quantitative change Cells +Cell facts Cells
- 11. Finding cells and facts about cells
- 12. | 12 Have you seen this cell? full name nickname aka formerly known as scars and marks for short
- 13. | 13 Epitope From inconsistent names to standard names Basic cell“Attribute” CD4+ CD25 regulatory T cell T-lymphocyte leukocyte T-cell leucocytehemopoetic hemopoietic haemopoetic haemopoietic hematopoetic hematopoietic haematopoetic haematopoietic regulatory immunoregulatory CD4+CD25+ CD25+FOXP3+ CD4+ CD25+ FOXP3+
- 14. | 14 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
- 15. | 15 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
- 16. | 16 Recognizing cell processes in text • Information about more specific cell types • Doubles the number of cell processes compared to Gene Ontology + EmTree
- 18. | 18 • What cells are involved in the disease of interest, and how? • What proteins/small molecules affect those cells, and how? • What proteins are exposed on the surface of the cell? • What proteins are secreted from the cell? What questions do we want to be answered?
- 19. | 19 - Protein/gene - receptor - ligand - kinase - phosphatase - Complex - Functional class - miRNA protein regulates cell cell expresses protein: • surface • secretion Cells and proteins
- 20. | 20 • 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 and small molecules
- 21. | 21 State is changed Cells are related to disease Quantity is changed Cells play active role in diseases Cells and diseases
- 22. | 22 • General disease properties • Measurable parameters • Scores Cells and clinical parameters Cells affect clinical parameters
- 23. | 23 (beta) Database statistics Objects Cells 617 Cell Processes 7 K Diseases 7.3 K Clinical Parameters 1.5 K Proteins and protein classes 17.7 K Small molecules 13.5 K Relations Regulation 540 K Cell expression 250 K Quantitative Change 10 K State change 7.7 K Functional Association 31.5 K Over 830 K new relations On top of 4.8 million previously extracted relations
- 24. Example 1: Browse the literature
- 25. | 25 How proteins secreted from breast carcinoma affect cells involved in its mechanism? Step 1 What proteins are secreted from breast carcinoma cells?
- 26. | 26 How proteins secreted from breast carcinoma affect cells involved in its mechanism? Step 1 What proteins are secreted from breast carcinoma cells? Disease Secretion Protein
- 27. | 27 Disease Secretion Protein Step 2 What cells are involved in breast carcinoma? Cell Regulation Disease How proteins secreted from breast carcinoma affect cells involved in its mechanism? Step 1
- 28. | 28 Step 3 Find proteins that stimulate pro-disease cells and inhibit anti-disease cells Protein Regulation Cell How proteins secreted from breast carcinoma affect cells involved in its mechanism? Step 2 Step 1 Disease Secretion Protein Cell Regulation Disease
- 29. | 29 How proteins secreted from breast carcinoma affect cells involved in its mechanism?
- 30. Example 2 Analyze experiments: • Gene expression • Metabolomics • Proteomics • NGS
- 31. | 31 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.
- 32. | 32 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
- 33. | 33 Experiment analysis in Pathway Studio
- 34. | 34 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?
- 35. | 35 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
- 36. | 36 Additional cell processes – additional results
- 37. | 37 Does this new information make sense? Known connections between immune cells identified in analysis and brain tumors
- 38. | 38 • New database is focused on cell-centered facts • It adds over 800K new relations and 4.4K entities to the knowledgebase • New data can be used to answer complex biological questions and analyze experiments • Flexible recognition of cells and cell processes allows customization Summary
- 39. Thank you