Standardizing cancer biomarkers criteria: data elements as a foundation for a database. Inflammatory mediator/M-CSF as model marker

The purpose of this position article was to design a set of criteria (data elements) for a wide range of cancer biomarkers (CBs) in an attempt to standardize biomarkers features through a common language as a foundation for a database. Data elements are described as a set of generic criteria, which should characterize nearly all biomarkers introduced in the literature. Data elements were extracted from the review of prominent features that biomarkers represent within various categories. The extracted characteristics of biomarkers produced a short list of shared and unique generic features such as biological nature and history; stage/phase of study; sensitivity and specificity; modes of action; risk assessment; validation status; technology, and recommendation status for diversified biomarkers. To tailor data elements on specific markers, a cytokine, such as macrophage-colony stimulating factor (M-CSF), which has been proposed as a ‹potentially suitable biomarker’ for diagnosis of ovarian, lung, breast, pancreatic, and colorectal cancers, was selected as a Model biomarker. Small scale clinical studies suggested the superior usefulness of M-CSF compared with traditional markers for cancer detection. A key criterion for selecting Model marker and tailoring data elements for detection of cancer was the comparison of data on its specificity and sensitivity with traditional markers. The design of data elements for standardizing CBs criteria is considered a Research Tool and a foundation for developing a comprehensive CBs database useful for oncology researchers for a wide range of biomarkers. Validation, integration and proper packaging, data visualization and recommendation of suitability of CBs, by a panel of experts, for technology development are important challenging next steps toward developing a reliable database, which would allow professionals to effectively retrieve and study integrated information on potentially useful markers; identify important knowledge gaps and limitations of data; and assess state of technologies and commercialization of markers at a point of need. Appropriate use of integrated information on biomarkers in clinical practices would eventually account for more cost-effective characteristics of an individual’s state of health.