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J Am Med Inform Assoc. 2016 Jul;23(4):750-7. doi: 10.1093/jamia/ocw009. Epub 2016 Mar 24.

Extracting genetic alteration information for personalized cancer therapy from ClinicalTrials.gov.

Author information

1
School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA.
2
Institute for Personalized Cancer Therapy, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
3
Institute for Personalized Cancer Therapy, University of Texas MD Anderson Cancer Center, Houston, TX, USA Department of Investigational Cancer Therapeutics, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
4
School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA Division of General Internal Medicine, Department of Internal Medicine, Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA.
5
School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA hua.xu@uth.tmc.edu.

Abstract

OBJECTIVE:

Clinical trials investigating drugs that target specific genetic alterations in tumors are important for promoting personalized cancer therapy. The goal of this project is to create a knowledge base of cancer treatment trials with annotations about genetic alterations from ClinicalTrials.gov.

METHODS:

We developed a semi-automatic framework that combines advanced text-processing techniques with manual review to curate genetic alteration information in cancer trials. The framework consists of a document classification system to identify cancer treatment trials from ClinicalTrials.gov and an information extraction system to extract gene and alteration pairs from the Title and Eligibility Criteria sections of clinical trials. By applying the framework to trials at ClinicalTrials.gov, we created a knowledge base of cancer treatment trials with genetic alteration annotations. We then evaluated each component of the framework against manually reviewed sets of clinical trials and generated descriptive statistics of the knowledge base.

RESULTS AND DISCUSSION:

The automated cancer treatment trial identification system achieved a high precision of 0.9944. Together with the manual review process, it identified 20 193 cancer treatment trials from ClinicalTrials.gov. The automated gene-alteration extraction system achieved a precision of 0.8300 and a recall of 0.6803. After validation by manual review, we generated a knowledge base of 2024 cancer trials that are labeled with specific genetic alteration information. Analysis of the knowledge base revealed the trend of increased use of targeted therapy for cancer, as well as top frequent gene-alteration pairs of interest. We expect this knowledge base to be a valuable resource for physicians and patients who are seeking information about personalized cancer therapy.

KEYWORDS:

clinical trial; natural language processing; personalized cancer therapy

PMID:
27013523
PMCID:
PMC4926744
DOI:
10.1093/jamia/ocw009
[Indexed for MEDLINE]
Free PMC Article

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