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Text mining For Precision Cancer Medicine
- 1. Text mining for precision cancer medicine Jake Lever BC Cancer Agency & University of British Columbia
- 2. Hello http://www.bccrc.ca/homepagecontent/dh-building.png https://commons.wikimedia.org/wiki/File:English_Bay,_Vancouver,_BC.jpg PhD Student at the BC Cancer Agency & University of British Columbia in Vancouver, Canada Visiting Bioinformatician with Ben Busby at NCBI
- 5. POG = Personalised OncoGenomics http://www.ctvnews.ca/health/b-c-doctors-turning-to-dna-to-develop-personalized-cancer-treatments-1.2583777 http://www.theglobeandmail.com/news/british-columbia/bc-government-investing-3-million-in-advanced-cancer-treatment/article28133652/
- 7. The Job of the POG Bioinformatician https://openclipart.org/detail/191435/line-drawing-of-man-at-computer http://www.ncbi.nlm.nih.gov/pubmed https://commons.wikimedia.org/wiki/File:Microsoft_PowerPoint_2013_logo.svg https://openclipart.org/detail/249841/first-responder-doctor Single nucleotide variants Indels Amplifications & Deletions Structural variants Expression data
- 8. Driver versus passenger mutations Stratton, Michael R., Peter J. Campbell, and P. Andrew Futreal. "The cancer genome." Nature 458.7239 (2009): 719-724.
- 9. The Puzzle Gene Event CTNND1 Deleted BIP Extra Copy SLC3946 Overexpressed KIF20A Point Mutation SCYL1BP1 Underexpressed SRSF5 Deleted CXCR4 Fused with ABL1 TUSC3 Overexpressed MKK4 Overexpressed ... ?
- 11. Driver Valuable for diagnosis Oncogenic Expect high expression and/or amplification Tumor suppressive Expect low expression and/or deletions
- 12. Context is everything The significance of these mutations in HNSCC requires further validation; nevertheless, they implicate NOTCH1 as a tumor suppressor in HNSCC. (PMID:21948802) NOTCH1 is the most commonly mutated oncogene in T-ALL (PMID:22538478)
- 14. There is a demand for an automatically curated knowledge base of genes linked with cancer types based on medical literature
- 16. Hypothesis A substantial amount of valuable information on genes linked with cancer types is found in medical literature. e.g. “Among these, GATA3, CCNE1, CDK4 and GRB7 are known drivers of breast cancer” (PMID:24194916)
- 17. Plans Identify sentences of interest Annotate sentences with relations Train relation classifier Apply classifier across PubMed
- 18. What is a sentence of interest? A sentence that mentions a type of cancer in conjunction with a gene, with words that suggest driving, oncogenic or tumor suppressive relations Identify sentences of interest
- 20. Ontologies Identify sentences of interest
- 21. Relation Types Driver Recent studies reported S100A2 protein is a molecular driver in TGF-β induced cell invasion and migration in hepatic carcinoma.(PMID:25591983) Oncogenic In summary, our work suggests a new direction for understanding the oncogenic function of TRAF4 in breast cancer. (PMID:25738361) Tumor Suppressive In present report, the tumor suppressive role of DMTF1 was studied and confirmed in bladder cancer. (PMID:25965824) Identify sentences of interest
- 22. Annotation system Annotate sentences with relations
- 23. Summary of annotated relations Annotate sentences with relations Relation Type Count Driver 152 Oncogenic 653 Tumor Suppressive 655 None ~2383 Annotations in 1500 sentences 3 annotators
- 24. VERSE Train relation classifier ● Bag-of-words ● Dependency parse (using CoreNLP) ● SVM or Logistic Regression
- 25. VERSE Train relation classifier https://commons.wikimedia.org/wiki/File:Logistic-curve.svg VERSE Logistic Threshold Parameter
- 26. Kindred
- 27. Recall Conservative Predictions ● Only make prediction of CIViC relation if very certain Precision F0.1 Train relation classifier
- 28. Cross-validation Results (with only one set of annotations) Measure Score Precision 0.941 Recall 0.128 Average from 2-fold cross-validation Train relation classifier
- 29. Knowledge Base Results Apply classifier across PubMed # of analysed sentences 60,464 # of gene terms 155,646 # of cancer terms 79,290 # of driver annotations 1,967 # of oncogenic annotations 6,877 # of tumor suppressive annotations 3,075 MEDLINE PubMed Central 2010-2016
- 30. Knowledge Base Apply classifier across PubMed
- 31. Design Decisions 1. Not to waste analyst’s time a. High precision, low recall is preferable 2. Iterative design a. Work with analysts b. Adapt to their ontology decisions 3. Flexible approach a. Allow integration of newer relation types (e.g. prognostic markers) b. Requires flexible annotation system and relation classifier 4. Citation is required a. PMID/PMCID needed for all relations
- 33. The Challenge of Interpretation in Precision Medicine Good, Benjamin M., et al. "Organizing knowledge to enable personalization of medicine in cancer." Genome biology 15.8 (2014): 438.
- 34. CIViC A community curated resource that links ‘omic events with clinically relevant information (sourced from published literature)
- 35. CIViC Example
- 36. CIViC Example
- 37. CIViC Example
- 39. The CIViC community ● Primarily used by analysts looking for clinical understanding of variants ● Worst case: Everyone builds their own internal knowledge base ● Encourage them to read a paper and share its results through CIViC https://civic.genome.wustl.edu
- 40. Challenge Analysts add new items to CIViC as they observe them in publications A method is needed to identify the most important items to add to CIViC
- 42. Our Question Can text mining prioritise documents that contain relevant information for a CIViC evidence item?
- 43. Example Sentence “In colorectal cancer, KRAS mutations were found to be associated with cetuximab resistance.” Morris et al 2011. PMID: 22248868
- 44. Why sentences? Information extraction approach allows for prioritisation of papers based on frequency of statements of the same knowledge Mentioned in 9 unique papers and not yet in CIViC CXCR4 colorectal cancer is a prognostic marker in
- 45. Pipeline MEDLINE PMC Parsing & Entity Recognition WikiData UMLS Disease Ontology NCBI Gene Info Annotation Platform VERSE Relation Extraction Tool Prioritisation Sentence Selection Sentences with CIViC-relevant knowledge and associated PMIDs Sentences with CIViC-relevant knowledge and associated PMIDs Sentences with CIViC-relevant knowledge and associated PMIDs
- 46. Entities Cancer Type (Disease Ontology) ● e.g. esophageal squamous cell carcinoma ● Removed broad terms (e.g. carcinoma) ● Complement with UMLS synonyms Gene Names (NCBI Gene) ● e.g. EGFR ● Removed common English words Drug Names (WikiData) ● e.g. erlotinib ● Instance of “pharmaceutical drug” (Q12140) Parsing & Entity Recognition WikiData UMLS Disease Ontology NCBI Gene Info
- 48. What do the annotations look like? Variants (and other aberrations, e.g. methylation) will be associated with genes with additional set of annotations Relation Type Entity 1 Entity 2 Entity 3 Diagnostic Gene Cancer Type Prognostic/Predictive Gene Cancer Type Drug Predisposing Gene Cancer Type
- 49. Example Output “ZAP-70 expression is a recognized prognostic marker in chronic lymphocytic leukemia (CLL).” Corcoran et al. 2005. PMID: 16079107
- 50. Evaluation of prototype (using single annotator) ● CIViC editor examined prioritised list and found multiple items that should be added to CIViC ● Feedback given on new predisposing evidence type and refining diagnostic evidence type ● Require mention of mutation in sentence CIViC Hackathon/Curation Jamboree in December 2016 Annotator is ongoing
- 52. PubMed Abstracts PubRunner Text mining tool Public FTP or Zenodo Private Server Download XML files Upload results Run tool Get results www.pubrunner.org website Update status of tool and location of latest results Keeping text mining results up-to-date
- 53. Thank You & Acknowledgements ● Steven Jones ● Martin Jones ● Eric Zhao ● Jasleen Grewal ● Jones Lab @ UBC ● Marco Marra ● Janessa Laskin ● POG analysts https://github.com/jakelever/ ● Obi Griffith ● Malachi Griffith ● Kilannin Krysiak ● Arpad Danos ● The Griffith Lab @ WashU @jakelever0