Essential Elements For Semi-automating Biological And

Clinical Reasoning In Oncology

Roger S. Day, William E. Shirey, Michele MorrisUniversity of Pittsburgh

Big in Modeling of Cancer

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

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

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 POPULATION DYNAMICS is poor

Tumor heterogeneityA missing link in the big picture

“Cancer GenomeAnatomy”

What happens to patients

????

Population dynamics,Toxicity,

Drug interactions,Doctor/patient,

“Society of cells”,…

INFORMATION SYNTHESIS Reductionism, then holism

OncoTCap 4/Cancer Information Genie

The software platform: “Protégé”

An expert knowledge acquisition system

protégé.stanford.edu

Frame-based KB,compliant with OKBC.

The standard “tabs”Ontology developmentForms editorInstance capture

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

Three workflows

• Knowledge capture

• Mapping from a catalog of statement templates to computer model-driving code

• Building modeling applications like tinker toys

OncoTCap 4 “Tricorn”Knowledge capturework process

Application-buildingwork processCode-mapping

work process

Workflow #1:Information capture

•Automated field capture •Full-text location, script-driven

Workflow #1: Information capture

Assessments

An example of the work flow

.

Workflow #2: Coding catalog

A WT gene locus for gene gene name can mutate to MUT

with rate mutrate

Example of a statement template:

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]

Workflow #3: Model controllers

Workflow #3: A Validation Suite model controller

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.