A Biomarker Data Model for Standardization of FDA-Approved Cancer Laboratory Tests

Identifying new disease biomarkers has become increasingly challenging due to the lack of validated reference values and standardized classifications, which have hindered clinical use. To better understand cancer heterogeneity exploration of its genomic and proteomic landscape through omics-based technology has fueled cancer biomarker research in recent years. With the rapid expansion of omics technologies, strategies and standardization techniques have accelerated high-throughput data availability. In this context, bridging the gap between biologics products (lab tests, therapeutic protein, reagents) and understanding key cancer drivers should be considered. Therefore, there is a need for a standardized basis for reporting biomarker data for application in clinical settings.The Foods and Drugs Administration (FDA) and the National Institute of Health (NIH) working group developed BEST (Biomarkers, EndpointS, and other Tools) as a guide to define biomarkers by the context of use (COU). An open-access database currently populated with COVID19, cancer, and diabetes mellitus biomarkers ( https://data.oncomx.org/allbiomarkers), classifies biomarkers using BEST as the top-level categories. The FDA has approved over a hundred genetic tests analyzing DNA and RNA sequence variations to diagnose various medical conditions, including cancers. Targeted therapy relies significantly on biomarker validation, allowing for valuable and interpretable data models. Increased representativeness of biomarker data will allow for targeted efforts towards meaningful clinical applications contributing to advancements in precision medicine. We curated 40 companion diagnostic tests associated with FDA-approved cancer tests modeled into individual markers or panels. This task has allowed for the identification of 24 panels and 14 single biomarkers that encompass 158 unique biomarker entities represented by gene/protein expression, differential expression, and mutations. This work provides the basis for curating cancer biomarkers that are part of FDA-approved tests.

Author

• Coulibaly, Hawa

Language

• en

Date created

• 2022

Type of Work

• Master's Thesis

License

• All rights reserved

Rights statement

• In Copyright

GW Unit

• Bioinformatics and Molecular Biochemistry

Degree

• M.S.

Advisor

• Mazumder, Raja

Committee Member(s)

• Lababidi, Samir
• Sepulveda, Jorge

Persistent URL

•  https://scholarspace.library.gwu.edu/etd/hq37vp38m