1. wKinMut An integrated tool for the analysis and interpretation of mutations in human protein kinases José MG Izarzugaza 1 Spanish National Cancer Research Center (CNIO) 2 Center for Biological Sequence Analysis (CBS)

2. 2 Protein Kinases: Metabolic switches of the cell José MG Izarzugaza - txema@cbs.dtu.dk Cell Cycle Regulation Signal Transduction Angiogenesis Immune Evasion Proliferation Anti-apoptosis Metastasis … PK Functions Disease / Cancer

3. 3 Kinases and Cancer: Some examples José MG Izarzugaza - txema@cbs.dtu.dk ~30,000 articles refer to ‘kinase AND mutation AND cancer’ in PubMed.

4. 4 wKinMut: Interpreting Kinase Mutations José MG Izarzugaza - txema@cbs.dtu.dk http://wkinmut.bioinfo.cnio.es

5. 5 wKinMut: Interpreting Kinase Mutations José MG Izarzugaza - txema@cbs.dtu.dk

6. 6 wKinMut: Gene & Protein info José MG Izarzugaza - txema@cbs.dtu.dk

7. 7 wKinMut: PFAM Domain Information José MG Izarzugaza - txema@cbs.dtu.dk

8. 8 wKinMut: Mutations onto structures José MG Izarzugaza - txema@cbs.dtu.dk (Izarzugaza et al., BMC Bioinformatics 2009)

9. 9 wKinMut: Annotation databases José MG Izarzugaza - txema@cbs.dtu.dk Mutations occurring at the same position, different Aa allowed Uniprot  General information, experimental characterization SAAPdb  Structural consequences of mutations KinMutBase  Kinase mutations and disease COSMIC  Somatic Mutations in Cancer

10. 10 wKinMut: Assessing the Pathogenicity José MG Izarzugaza - txema@cbs.dtu.dk

11. 11 Methods to predict the pathogenicity of Muts. José MG Izarzugaza - txema@cbs.dtu.dk MutationAssessor Polyphen-2 PMUT SNAP SIFT Torkamani SNPs&GO

12. 12 KinMut: A Kinase-Specific Predictor José MG Izarzugaza - txema@cbs.dtu.dk (Izarzugaza et al., BMC Genomics 2012) Mutation selection criteria - Source db  Uniprot - Human protein kinases - Experimentally classified - Non-synonymous, non-truncating. Dataset Description - Disease set: 865 muts, 65 kinases - Neutral set: 2627 muts, 447 kinases - Best dataset according to Care et al., 2007 - Unevenly distributed in kinase groups.

13. 13 Features: General & Kinase-Specific (Izarzugaza et al., BMC Genomics 2012) - Membership to KinBase groups - “Propensity to disease” of GO terms Gene Level - Amino acid types - Hydrophobicity changes - Sequence conservation (SIFT) - Functional annotations (SW, FireDB) - Phosphorylation propensity - Specificity Determining Positions (SDPs) Residue Level - Mutation within PFAM domains Domain Level José MG Izarzugaza - txema@cbs.dtu.dk -Mutations  vectors of features -General + Kinase-specific features

14. 14 Machine Learning: SVM (Izarzugaza et al., BMC Genomics 2012) Support Vector Machine (SVM) - Kernel (ϕ)  Radial Basis Function - Optimized Parameters: Soft margin (C=8) Radius (ϒ=6x10-4) - 10 K-fold cross-validation ϒ C José MG Izarzugaza - txema@cbs.dtu.dk Soft Margin (C) Gaussian Parameter (γ)

15. 15 KinMut: Kinase-Specific Prioritization of Muts. José MG Izarzugaza - txema@cbs.dtu.dk (Izarzugaza et al., BMC Genomics 2012) MethodScopeAccuracyPrecisionRecallMCC SNP&GOGenome-wide82.0%83.0%78.0%0.7 MutationAssessorGenome-wide79.0%--- SNAPGenome-wide78.2%76.7%80.2%- SIFTGenome-wide68.3%66.1%56.5%0.3 MethodScopeAccuracyPrecisionRecallMCC KinMutKinase83.3%60.0%75.2%0.6 SNP&GO Kinase (re-run with our dataset) 82.3%62.8%77.4%0.6 TorkamaniKinase (from paper)77.0%--0.5 MutationAssessor Kinase (re-run with our dataset) 53.8%41.6%95.6%0.5 SNAP Kinase (re-run with our dataset) 49.4%34%93.1%0.4 SIFT Kinase (re-run with our dataset) 77.6%37.8%27.9%0.2

16. 16 Top most informative features (Izarzugaza et al., BMC Genomics 2012) José MG Izarzugaza - txema@cbs.dtu.dk 1.Disease propensity of GO terms (log odds ratio) 2.Specificity determining positions 3.Change in Hydrophobicity 4.Amino acid type 5.PFAM domains (Any, Pkinase_tyr, Fn…) 6.SIFT 7.Protein Kinase group (TK, CAMK, CK1, TKL…) 8.Uniprot annotations 9.Phosphorylation propensity (bold: Kinase-specific features) Specificity determining positions Kinbase groups

17. 17 wKinMut: Assessing the Pathogenicity José MG Izarzugaza - txema@cbs.dtu.dk

18. SNP2L: -Automatic literature mining. -Kinase mutation mentions. -Mapping to protein sequences. -False positive filtering. 18 wKinMut: Literature searches with SNP2L (Krallinger et al., BMC Bioinformatics 2009)

19. 19 SNP2L: A Text Mining Pipeline José MG Izarzugaza - txema@cbs.dtu.dk (Krallinger et al., BMC Bioinformatics 2009)

20. 20 SNP2L: Simplified Pipeline José MG Izarzugaza - txema@cbs.dtu.dk Abstracts  643 different muts in 128 kinases Full Texts  6970 different muts in 325 kinases … V600E B-Raf, which confers increased kinase activity, … Abstracts / Fulltext Mutation Detection Protein Detection Mutation  Protein Filtering: Sequence Match … V600E B-Raf, which confers increased kinase activity, … V600E  B-Raf 583 GDFGLAT V KSRWSG 606 600 (Krallinger et al., BMC Bioinformatics 2009)

21. 21 SNP2L: Manual Validation (100 abstracts) José MG Izarzugaza - txema@cbs.dtu.dk ~72% Mutation extractions proved correct… … including ~49% that were not already in databases. Correct, Manual Validation (41%) Orthologues (8%) Already in DBs (23%) Wrong Mutation to Protein Assignment (23%) Wrong Mutation Detection (3%) Ambiguous cases (2%) (Krallinger et al., BMC Bioinformatics 2009)

22. 22 SNP2L: Structural distribution of mentions José MG Izarzugaza - txema@cbs.dtu.dk - Literature mentions distributed all over the PK domain. - Higher frequency associated to functional hot-spots. (Krallinger et al., BMC Bioinformatics 2009)

23. 23 SNP2L: From Mutations to Literature José MG Izarzugaza - txema@cbs.dtu.dk Phenotype Biochemical mechanism Organism/Tissue Experimental conditions … Kinase Mutations  SNP2L  Evidence (Krallinger et al., BMC Bioinformatics 2009)

24. José MG Izarzugaza - txema@cbs.dtu.dk SNP2L: -Automatic literature mining. -Kinase mutation mentions. -Mapping to protein sequences. -False positive filtering. 24 wKinMut: Literature searches with SNP2L (Krallinger et al., BMC Bioinformatics 2009)

25. 25 wKinMut: Interactions from Literature (iHop) José MG Izarzugaza - txema@cbs.dtu.dk (Hoffman & Valencia, Nature Methods 2004)

26. 26 wKinMut: Integration of mutation information Gene/Protein Features Domain Features Info in other Databases Residue Features iHop Interactions SNP2L Literature EGFR Gly 719 Ala : Lung cancer, somatic mutation Located in ATP binding pocket Confers resistance to gefitinib EGFR Gly 719 Ala : Lung cancer, somatic mutation Located in ATP binding pocket Confers resistance to gefitinib http://wkinmut.bioinfo.cnio.es

27. 27 Personalized (Stratified) Medicine José MG Izarzugaza - txema@cbs.dtu.dk “Here is my sequence” Sequencing and Genome Analysis

28. 28 Simplified Personalized Med. Pipeline José MG Izarzugaza - txema@cbs.dtu.dk Mutations Blood (normal tissue) Cancer cells Treatment Personalized Medicine Educated Decision Cancer Patient Cancer Patient Clinician Information Integration wKinMut

29. 29 Integrate Information, help the clinician decide José MG Izarzugaza - txema@cbs.dtu.dk (Courtesy of M.Vazquez)

30. Endocrine Cancer Pancreas Cancer SIFT Polyphen-2 MutationAssessor KinMut (kinases) wKinMut as part of our Pers. Med. Pipeline

31. 31 Acknowledgements Pathogenicity Prediction & Personalized Medicine (Izarzugaza et al., BMC Genomics 2012) Alfonso Valencia Miguel Vazquez Angela del Pozo Victor de la Torre Text Mining (Izarzugaza et al., Frontiers 2012) (Krallinger et al., BMC Bioinformatics 2009) Martin Krallinger Mutation Mapping & Distribution Analysis (Izarzugaza et al., Proteins 2009) (Izarzugaza et al., BMC Bioinformatics 2009) (Izarzugaza et al., BMC Bioinformatics 2011) Gonzalo Lopez Antonio Rausell Christine Orengo’s Group (UCL) Ollie Redfern Corin Yeats Benôit Desailly Andrew Martin’s Group (UCL) Anja Baresic Lisa Hopcroft José MG Izarzugaza - txema@cbs.dtu.dk