Making the most of phenotypes in ontology-based biomedical knowledge discovery
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The document discusses the importance of computable phenotypes in biomedical knowledge discovery, focusing on methods for comparing phenotypes and their applications in areas like undiagnosed diseases and drug identification. It explores the challenges in diagnosing rare diseases due to limited phenotype annotations and emphasizes the role of semantic similarity measures in improving diagnosis and drug repurposing. Furthermore, it highlights the potential of using model organisms to enhance phenotype mapping and predictive analyses in drug development.
![B6.Cg-Alms1foz/fox/J
increased weight,
adipose tissue volume,
glucose homeostasis altered
ALSM1(NM_015120.4)
[c.10775delC] + [-]
GENOTYPE
PHENOTYPE
obesity,
diabetes mellitus,
insulin resistance
increased food intake,
hyperglycemia,
insulin resistance
kcnj11c14/c14; insrt143/+(AB)
A multi-species inventory of phenotypes
from genetic perturbations
@micheldumontier::Biostats:19-02-1529](https://image.slidesharecdn.com/2015-biostats-phenotypes-150219182556-conversion-gate01/85/Making-the-most-of-phenotypes-in-ontology-based-biomedical-knowledge-discovery-29-320.jpg)
Making the most of phenotypes in ontology-based biomedical knowledge discovery
- 1. Making the most of phenotypes in ontology-based biomedical knowledge discovery 1 Michel Dumontier, Ph.D. Associate Professor of Medicine (Biomedical Informatics) Stanford University @micheldumontier::Biostats:19-02-15
- 2. Topics • Computable Phenotypes • Methods to compare Phenotypes • Cross-Species Phenotype Integration • Applications – Undiagnosed Diseases – Drug Target Identification – Drug Repurposing @micheldumontier::Biostats:19-02-152
- 3. Phenotypes • A phenotype is an observable characteristic of an individual and typically pertains to its morphology, function, and behavior. – qualitative, deals with normal and abnormal phen. – red eye color, abnormal gait, enlarged colon @micheldumontier::Biostats:19-02-153
- 4. Diagnosis uses observable/measured phenotypes “Phenotypic Profile” @micheldumontier::Biostats:19-02-154
- 5. Matching patients to diseases Patient Disease X Differential diagnosis with similar but non-matching phenotypes is difficult Flat back of head Hypotonia Abnormal skull morphology Decreased muscle mass @micheldumontier::Biostats:19-02-155
- 6. Differential diagnosis becomes challenging with rare and complex disorders • Over 7000 rare diseases • < 1 in 1500-2500 • Most have fewer than 50 case reports • Nearly 1 in 10 Americans suffer from one or more rare diseases • Only 250 medicinal products have been approved to diagnose and treat rare diseases @micheldumontier::Biostats:19-02-156 Carpenter Syndrome - acrocephalopolysyndactyly (ACPS) disorder - 40 cases described in the literature - <1 in 1M
- 7. Genotypes + Phenotypes Improves Diagnosis @micheldumontier::Biostats:19-02-157 Remove off-target, common variants, and variants not in known disease causing genes http://compbio.charite.de/PhenIX/ Target panel of 2741 known Mendelian disease genes Compare phenotype profiles from: Clinvar, OMIM, Orphanet Zemojtel et al. Sci Transl Med. 2014. 6(252):252ra123
- 8. PhenIX helped diagnose 11/40 patients @micheldumontier::Biostats:19-02-158
- 9. So how did they do it? 1. Computable representation of phenotypes 2. Methods to compare phenotype profiles 3. Using model organisms to increase coverage of the phenotype space @micheldumontier::Biostats:19-02-159
- 10. Difficult to find all results using text searches @micheldumontier::Biostats:19-02-1510
- 11. The Human Phenotype Ontology: A Computable Representation of Human Phenotypes 11,000+ classes Follows the True Path Rule Used to annotate: • Patients • Disorders/Diseases • Genes, Gene Variants, & Genotypes Reduced pancreatic beta cells Abnormality of pancreatic islet cells Abnormality of endocrine pancreas physiology Pancreatic islet cell adenoma Pancreatic islet cell adenoma Insulinoma Multiple pancreatic beta-cell adenomas Abnormality of exocrine pancreas physiology Köhler et al. Nucleic Acids Res. 2014 Jan 1;42(1):D966-74. @micheldumontier::Biostats:19-02-1511
- 12. HPO has unique terms @micheldumontier::Biostats:19-02-1512 Winnenburg and Bodenreider, ISMB PhenoDay, 2014
- 13. Increased numbers of diseases are described using the HPO @micheldumontier::Biostats:19-02-1513 Phenotype annotations per species http://www.monarchinitiative.org
- 14. Phenotype “BLAST”: Which phenotypic profile is most similar? Disease X Patient Disease Y @micheldumontier::Biostats:19-02-1514
- 15. Phenotips: Getting high quality patient phenotypes @micheldumontier::Biostats:19-02-1515 Girdea et al. (2013), PhenoTips: Patient Phenotyping Software for Clinical and Research Use. Hum. Mutat., 34: 1057–1065. doi: 10.1002/humu.22347
- 16. Semantic Similarity • Semantic similarity is a metric defined over a set of terms, where the distance between them is based on their meaning. • It can be estimated by examining, for instance, – Topological similarity – Information content – Statistical co-occurrence • Widely used in bioinformatics for gene enrichment, function prediction, network screening, clustering, etc. @micheldumontier::Biostats:19-02-1516
- 18. Measures of Semantic Similarity Edge-Based Measures – Shortest path (Rada) – Common path – Scaling by depth, etc. • Requires uniform distribution of nodes and edges Node-based Measures – Shared terms – Common ancestors – Information content (IC) • Better able to account for structural heterogeneity Set comparisons • Pairwise – Max/average/sum – All or best pairs • Groupwise – Set, graph, vector – Various combinations Implementations – Semanticmeasureslibrary.org – OWL-SIM @micheldumontier::Biostats:19-02-1518 Semantic Similarity in Biomedical Ontologies PLoS Comput Biol. 2009 Jul; 5(7): e1000443.
- 19. Term specificity @micheldumontier::Biostats:19-02-1519 𝑖𝑐 𝑡 = −log(𝑃 𝑡 ) 𝑖𝑐 𝑡 = 𝑑𝑒𝑝𝑡ℎ 𝑡 𝑥 1 − log 𝑑𝑒𝑠𝑐 𝑡 + 1 log 𝑡𝑜𝑡𝑎𝑙 𝑡𝑒𝑟𝑚𝑠 Structure-based Corpus-based by: Heiko Muller, CSIRO
- 20. Group-wise Similarity @micheldumontier::Biostats:19-02-1520 𝐽 𝐴, 𝐵 = |𝐴 ∩ 𝐵| |𝐴 ∪ 𝐵| 𝐽 g1, g2 = 6 11 = 0.55
- 21. Group-wise Semantic Similarity @micheldumontier::Biostats:19-02-1521 IC(g1) = 10.66 IC(g1) = 9.79 IC(g1 ⊕ g2) = 2.79 ------------------- sim(g1,g2)=0.27 𝑠𝑖𝑚 g1, g2 = 1 2 𝐼𝐶(g1 ⊕ g2) 𝐼𝐶(g1) + 𝐼𝐶(g1 ⊕ g2) 𝐼𝐶(g2) X. Chen et al. Gene. 2012. 509(1):131-5
- 22. Robustness of phenotype annotations @micheldumontier::Biostats:19-02-1522
- 23. Image credit: Viljoen and Beighton, J Med Genet. 1992 Schwartz-jampel Syndrome, Type I Schwartz-jampel Syndrome, Type I Caused by Hspg2 mutation, a proteoglycan ~100 phenotype annotations @micheldumontier::Biostats:19-02-1523
- 24. Similarity of Schwartz-jampel Syndrome derivations @micheldumontier::Biostats:19-02-1524
- 25. Semantic similarity is robust in the face of missing information 92% of derived profiles are most similar to original disease profile Profile Similarity Derived Profile Rank @micheldumontier::Biostats:19-02-1525
- 26. Semantic similarity algorithms are sensitive to specificity of information The more general the phenotype, the poorer the match the disease Profile Similarity Derived Profile Rank @micheldumontier::Biostats:19-02-1526
- 28. Problem: less than 40% of human genes are annotated with phenotypes GWAS + ClinVar + OMIM @micheldumontier::Biostats:19-02-1528
- 29. B6.Cg-Alms1foz/fox/J increased weight, adipose tissue volume, glucose homeostasis altered ALSM1(NM_015120.4) [c.10775delC] + [-] GENOTYPE PHENOTYPE obesity, diabetes mellitus, insulin resistance increased food intake, hyperglycemia, insulin resistance kcnj11c14/c14; insrt143/+(AB) A multi-species inventory of phenotypes from genetic perturbations @micheldumontier::Biostats:19-02-1529
- 30. Down Syndrome Mouse @micheldumontier::Biostats:19-02-1530 Ts65Dn mice survive to adulthood and express some characteristics of Down syndrome such as developmental delay, hyperactivity, weight problems, craniofacial dysmorphology, impaired learning, and behavior deficit
- 31. Each species uniquely covers a different set of phenotypes Provides an opportunity to use this information to inform human disease @micheldumontier::Biostats:19-02-1531
- 32. Human and model phenotypes can be linked to >75% human genes @micheldumontier::Biostats:19-02-1532
- 33. Problem: Clinical and model phenotypes are described differently @micheldumontier::Biostats:19-02-1533
- 34. lung lung lobular organ parenchymatous organ solid organ pleural sac thoracic cavity organ thoracic cavity abnormal lung morphology abnormal respiratory system morphology Mammalian Phenotype Mouse Anatomy FMA abnormal pulmonary acinus morphology abnormal pulmonary alveolus morphology lung alveolus organ system respiratory system Lower respiratory tract alveolar sac pulmonary acinus organ system respiratory system Human development lung lung bud respiratory primordium pharyngeal region Problem: Each organism uses different vocabularies develops_from part_of is_a (SubClassOf) surrounded_by @micheldumontier::Biostats:19-02-1534
- 35. Reduced pancreatic beta cells Abnormality of pancreatic islet cells Abnormality of endocrine pancreas physiology Pancreatic islet cell adenoma Pancreatic islet cell adenoma Insulinoma Multiple pancreatic beta-cell adenomas Abnormality of exocrine pancreas physiology abnormal pancreatic beta cell mass abnormal pancreatic beta cell morphology abnormal pancreatic islet morphology abnormal endocrine pancreas morphology abnormal pancreatic beta cell number abnormal pancreatic alpha cell morphology abnormal pancreatic alpha cell differentiation abnormal pancreatic alpha cell number @micheldumontier::Biostats:19-02-1535
- 36. Enhance lexical approach with OWL bridging axioms • Key idea: – Describe the phenotype in a machine-interpretable way • Break it down into digestible chunks! • Logical definition – The machine will then be able to help you • Match phenotypes • Automate ontology checking and addition of new terms • Approach: – Use Web Ontology Language (OWL), a description logic to describe phenotypes – Use OWL reasoning to find connections Mungall et al. (2012). Genome Biology, 13(1), R5 Köhler et al. (2014) F1000Research 2:30 Haendel et al. (2014) JBMS 5:21 Hoendorf et al. (2011). NAR 39(18):e119 Hoendorf et al. (2011) Bioinformatics 27(7):1001 @micheldumontier::Biostats:19-02-1536
- 37. abnormal pancreatic beta cell mass abnormal pancreatic beta cell morphology abnormal pancreatic islet morphology type B pancreatic cell islet of Langerhans endocrine pancreas part of part of abnormal endocrine pancreas morphology part of @micheldumontier::Biostats:19-02-1537
- 38. abnormal pancreatic beta cell mass abnormal pancreatic beta cell morphology abnormal pancreatic islet morphology type B pancreatic cell islet of Langerhans endocrine pancreas part of part of abnormal endocrine pancreas morphology part of mass morphology quality @micheldumontier::Biostats:19-02-1538
- 39. Reduced pancreatic beta cells Abnormality of pancreatic islet cells Abnormality of endocrine pancreas physiology Pancreatic islet cell adenoma Pancreatic islet cell adenoma Insulinoma Multiple pancreatic beta-cell adenomas Abnormality of exocrine pancreas physiology abnormal pancreatic beta cell mass abnormal pancreatic beta cell morphology abnormal pancreatic islet morphology abnormal endocrine pancreas morphology abnormal pancreatic beta cell number abnormal pancreatic alpha cell morphology abnormal pancreatic alpha cell differentiation abnormal pancreatic alpha cell number inferred from CL inferred from PATO ‘abnormal phenotype’ and has_entity some ‘type B pancreatic cell’ and has_quality some amount ‘abnormal phenotype’ and has_entity some ‘type B pancreatic cell’ and has_quality some ‘reduced amount’ @micheldumontier::Biostats:19-02-1539
- 41. PhenomeDrug Computational methods that use phenotypes to predict drug targets, drug effects, and drug indications @micheldumontier::Biostats:19-02-1541
- 42. animal models provide insight for on target effects • In the majority of 100 best selling drugs ($148B in US alone), there is a direct correlation between knockout phenotype and drug effect • Immunological Indications – Anti-histamines (Claritin, Allegra, Zyrtec) – KO of histamine H1 receptor leads to decreased responsiveness of immune system – Predicts on target effects : drowsiness, reduced anxiety @micheldumontier::Biostats:19-02-1542 Zambrowicz and Sands. Nat Rev Drug Disc. 2003.
- 43. Identifying drug targets from mouse knock-out phenotypes @micheldumontier::Biostats:19-02-1543 drug gene phenotypes effects human gene non-functional gene model ortholog similar inhibits Main idea: if a drug’s phenotypes matches the phenotypes of a null model, this suggests that the drug is an inhibitor of the gene
- 44. Terminological Interoperability (we must compare apples with apples) Mouse Phenotypes Drug effects (mappings from UMLS to DO, NBO, MP) Mammalian Phenotype OntologyPhenomeNet PhenomeDrug @micheldumontier::Biostats:19-02-15 poor coordination decreased gut peristalsis axon degeneration decreased stride length erotypic ehavior Abnormal EEG failure to find food Unstable posture Constipation Neuronal loss in Substantia Nigra Shuffling gait Resting tremors REM disorder Hyposmia poor rotarod performance decreased gut peristalsis axon degeneration decreased stride length sterotypic behavior abnormal EEG failure to find food abnormal coordination abnormal digestive physiology CNS neuron degeneration abnormal locomotion abnormal motor function sleep disturbance abnormal olfaction
- 45. Semantic Similarity @micheldumontier::Biostats:19-02-1545 Given a drug effect profile D and a mouse model M, we compute the semantic similarity as an information weighted Jaccard metric. The similarity measure used is non-symmetrical and determines the amount of information about a drug effect profile D that is covered by a set of mouse model phenotypes M.
- 46. Loss of function models predict targets of inhibitor drugs • 14,682 drugs; 7,255 mouse genotypes • Validation against known and predicted inhibitor-target pairs – 0.76 ROC AUC for human targets (DrugBank) – 0.81 ROC AUC for mouse targets (STITCH) • diclofenac (STITCH:000003032) – NSAID used to treat pain, osteoarthritis and rheumatoid arthritis – Drug effects include liver inflammation (hepatitis), swelling of liver (hepatomegaly), redness of skin (erythema) – 49% explained by PPARg knockout • peroxisome proliferator activated receptor gamma (PPARg) regulates metabolism, proliferation, inflammation and differentiation, • Diclofenac is a known inhibitor – 46% explained by COX-2 knockout • Diclofenac is a known inhibitor @micheldumontier::Biostats:19-02-15 Hoehndorf R, Hiebert T, Hardy NW, Schofield PN, Gkoutos GV, Dumontier M. Mouse model phenotypes provide information about human drug targets. Bioinformatics. 2014 Mar 1;30(5):719-25
- 47. Computational Drug Repurposing • Similarity – Guilt by association – If drug i is similar to drug j, and drug i treats disease x, then drug j may treat disease x • Complementarity – if the signature of drug i complements/counters the signature of disease x, then drug i may treat disease x @micheldumontier::Biostats:19-02-1547
- 48. PhenomeDrug: phenotypic complementarity • Extends the idea to match opposing drug- disease phenotypes – Drugs that induce hypotension may be useful in treating hypertension • Problem: We don’t have any information about phenotypic complementarity – We generated over 300 antonym pairs for the Human Phenotype Ontology – Developed a measure to compute phenotypic complementarity @micheldumontier::Biostats:19-02-1548
- 50. Preliminary Results • Suggest that for some well annotated diseases, we recapitulate top drug candidates • Quality of drug annotation is an issue – Some drugs have insufficient annotations to find “good” matches • Full assessment underway • Pulmonary Arterial Hypertension @micheldumontier::Biostats:19-02-1550
- 51. Summary • Ontologies provide the structure and semantics by which phenotypes can be accurately represented and computed with • Measures of semantic similarity in combination with terminological integration enable a broad diversity of ontology-based analyses, including – Diagnosis of rare diseases – Identifying human drug targets – Drug repositioning @micheldumontier::Biostats:19-02-1551
- 52. Acknowledgements Dumontier Lab • Tanya Hiebert • Joachim Baran PhenomeDrug • Robert Hoehndorf • George Gkoutos Monarch Initiative • Melissa Haendel • Peter Robinson • Chris Mungall • the Monarch Team @micheldumontier::Biostats:19-02-1552