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BMC Medical Informatics and Decision Making
Published in: BMC Medical Informatics and Decision Making 1/2016

Open Access 01-12-2016 | Research article

An information model for computable cancer phenotypes

Authors: Harry Hochheiser, Melissa Castine, David Harris, Guergana Savova, Rebecca S. Jacobson

Published in: BMC Medical Informatics and Decision Making | Issue 1/2016

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Abstract

Background

Standards, methods, and tools supporting the integration of clinical data and genomic information are an area of significant need and rapid growth in biomedical informatics. Integration of cancer clinical data and cancer genomic information poses unique challenges, because of the high volume and complexity of clinical data, as well as the heterogeneity and instability of cancer genome data when compared with germline data. Current information models of clinical and genomic data are not sufficiently expressive to represent individual observations and to aggregate those observations into longitudinal summaries over the course of cancer care. These models are acutely needed to support the development of systems and tools for generating the so called clinical “deep phenotype” of individual cancer patients, a process which remains almost entirely manual in cancer research and precision medicine.

Methods

Reviews of existing ontologies and interviews with cancer researchers were used to inform iterative development of a cancer phenotype information model. We translated a subset of the Fast Healthcare Interoperability Resources (FHIR) models into the OWL 2 Description Logic (DL) representation, and added extensions as needed for modeling cancer phenotypes with terms derived from the NCI Thesaurus. Models were validated with domain experts and evaluated against competency questions.

Results

The DeepPhe Information model represents cancer phenotype data at increasing levels of abstraction from mention level in clinical documents to summaries of key events and findings. We describe the model using breast cancer as an example, depicting methods to represent phenotypic features of cancers, tumors, treatment regimens, and specific biologic behaviors that span the entire course of a patient’s disease.

Conclusions

We present a multi-scale information model for representing individual document mentions, document level classifications, episodes along a disease course, and phenotype summarization, linking individual observations to high-level summaries in support of subsequent integration and analysis.
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Metadata
Title
An information model for computable cancer phenotypes
Authors
Harry Hochheiser
Melissa Castine
David Harris
Guergana Savova
Rebecca S. Jacobson
Publication date
01-12-2016
Publisher
BioMed Central
Published in
BMC Medical Informatics and Decision Making / Issue 1/2016
Electronic ISSN: 1472-6947
DOI
https://doi.org/10.1186/s12911-016-0358-4

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