Essential Elements for Semi-Automating Clinical Oncology Reasoning

1. Essential Elements For Semi-automating Biological And Clinical Reasoning In OncologyRoger S. Day, William E. Shirey, Michele MorrisUniversity of Pittsburgh

2. Big in Modeling of Cancer Q What are cancer models good for? • Discovering general principles • Professional training • Prediction for planning experiments • Description of natural history, distinguishing mechanisms & explanations • Prediction for individualizing treatments

3. Educational Resource for Tumor Heterogeneity“ERTH” • Develop a computer “playground” for thinking broadly about cancer • Develop wide range of learning applications • Field test, evaluate, deploy, disseminate  Oncology Thinking Cap “OncoTCap” software

4. Why is tumor heterogeneity important? • Spatial heterogeneity  metastasis It kills people. • Genetic/epigenetic heterogeneity within tumors  survival of the fittest • immortalization, motility, invasion, metastatic potential, recruitment of blood vessel, resistance to apoptosis, resistance to therapy • resistance to patient’s defenses • Natural intuition about POPULATIONDYNAMICS is poor

5. Tumor heterogeneityA missing link in the big picture ???? Population dynamics, Toxicity, Drug interactions, Doctor/patient, “Society of cells”, … “Cancer Genome Anatomy” What happens to patients INFORMATION SYNTHESIS Reductionism, then holism

6. OncoTCap 4/Cancer Information GenieThe software platform: “Protégé”An expert knowledge acquisitionsystemprotégé.stanford.edu • Frame-based KB, • compliant with OKBC. • The standard “tabs” • Ontology development • Forms editor • Instance capture

7. OncoTCap 4:mission creep is a good thing • Clinical trials bottleneck: • Accrual • Time • Expense • Far “faaar” too many hypotheses to test • Choosing which trials to do… today: • Due diligence information gathering– by hand • Model-building and prediction – by intuition • What if… • Information gathering is empowered • Model-building/validation/prediction is empowered

8. Three workflows • Knowledge capture • Mapping from a catalog of statement templates to computer model-driving code • Building modeling applications like tinker toys

9. Knowledge capture work process Application-building work process Code-mapping work process OncoTCap 4 “Tricorn”

10. Workflow #1: • Information capture • Automated field capture • Full-text location, script-driven

11. Workflow #1: Information captureAssessments

13. Workflow #2: Coding catalog Example of a statement template: A WT gene locus for gene gene name can mutate to MUT with rate mutrate Representation in statement bundles: The gene [gene name] has values WT/WT, WT/MUT, MUT/MUT. The mutation rate for [gene name] from WT/WT to WT/MUT is 2 times [mutrate] The mutation rate for [gene name] from WT/MUT to MUT/MUT is [mutrate]

14. Workflow #3: Model controllers

15. Workflow #3: A Validation Suite model controller

16. NLP and OncoTCap? • Plug in new tools for locating published resources (like MedMiner, EDGAR). • Parse captured text, identify concepts, map to keyword tree. • Provide a conduit to other Ontologies, to import portions into our Keyword tree. • Replace user-defined Keywords with standard terms from other Ontologies. • Suggest “interpretations”– mappings into catalog of StatementTemplates.