Biomarker (cell)

A biomarker, or biological marker, is defined as a "cellular, biochemical or molecular alteration in cells, tissues or fluids that can be measured and evaluated to indicate normal biological processes, pathogenic processes, or pharmacological responses to a therapeutic intervention."Biomarkers characterize disease progression starting from the earliest natural history of the disease. Biomarkers assess disease susceptibility and severity, which allows one to predict outcomes, determine interventions and evaluate therapeutic responses. From a forensics and epidemiologic perspective, biomarkers offer unique insight about the relationships between environmental risk factors.
Classes of Biomarkers.
Three broad classes of biomarkers are prognostic biomarkers, predictive biomarkers and pharmacodynamic biomarkers.
Prognostic Biomarkers.
Prognostic biomarkers give intervention-independent information on disease status through screening, diagnosis and disease monitoring. Prognostic biomarkers can signify individuals in the latent period of a disease's natural history, allowing optimal therapy and prevention until the disease's termination. Prognostic biomarkers give information on disease status by measuring the internal precursors that increase or decrease the likelihood of attaining a disease. For example, blood pressure and cholesterol are biomarkers for CVD. Prognostic biomarkers can be direct or indirect to the causal pathway of a disease. If a prognostic biomarker is a direct step in the causal pathway, it is one of the factors or products of the disease. A prognostic biomarker could be indirectly associated with a disease if it is related to a change caused by the exposure, or related to an unknown factor connected with the exposure or disease.
Predictive Biomarkers.
Predictive biomarkers measure the effect of a drug and tell if the drug is having its expected activity, but do not offer any direct information on the disease. Predictive biomarkers are highly sensitive and specific; therefore they increase diagnostic validity of a drug or toxin's site-specific effect by eliminating recall bias and subjectivity from those exposed. For example, when an individual is exposed to a drug or toxin, the concentration of that drug or toxin within the body, or the biological effective dose, provides a more accurate prediction for the effect of the drug or toxin compared to an estimation or measurement of the toxin from the origin or external environment.
Pharmacodynamic Biomarkers.
Pharmacodynamic (PD) biomarkers can measure the direct interaction between a drug and its receptor. Pharmacodynamic biomarkers reveal drug mechanisms, if the drug has its intended effect on the biology of the disease, ideal biological dosing concentrations, and physiologic response/resistance mechanisms. Pharmacodynamic biomarkers are particularly relevant in drug mechanisms of tumor cells, where pharmacodynamic endpoints for drug interventions can be assessed directly on tumor tissues. For example, protein phosphorylation biomarkers indicate alterations in target protein kinases and activation of downstream signaling molecules.
Clinical Applications for Biomarkers.
Biomarkers can be classified on their clinical applications as molecular biomarkers, cellular biomarkers or imaging biomarkers.
Molecular Biomarkers.
Four of the main types of molecular biomarkers are genomic biomarkers, transcriptomic biomarkers, proteomic biomarkers and metabolic biomarkers.
Genomic Biomarkers.
Genomic biomarkers analyze DNA by identifying irregular sequences in the genome, typically a single nucleotide polymorphism. Genetic biomarkers are particularly significant in cancer because most cancer cell lines carry somatic mutations. Somatic mutations are distinguishable from hereditary mutations because the mutation is not in every cell; just the tumor cells, making them easy targets.
Transcriptomic Biomarkers.
Transcriptomic biomarkers analyze all RNA molecules, not solely the exome. Transcriptomic biomarkers reveal the molecular identity and concentration of RNA in a specific cell or population. Pattern-based RNA expression analysis provides increased diagnostic and prognostic capability in predicting therapeutic responses for individuals. For example, distinct RNA subtypes in breast cancer patients have different survival rates.
Proteomic Biomarkers.
Proteomics permits the quantitative analysis and detection of changes to proteins or protein biomarkers. Protein biomarkers detect a variety of biological changes, such as protein-protein interactions, post-translational modifications and immunological responses. 
Cellular Biomarkers.
Cellular biomarkers allow cells to be isolated, sorted, quantified and characterized by their morphology and physiology. Cellular biomarkers are used in both clinical and laboratory settings, and can discriminate between a large sample of cells based on their antigens. An example of a cellular biomarker sorting technique is Fluorescent-activated cell sorting.
Imaging Biomarkers.
Imaging biomarkers allow earlier detection of disease compared to molecular biomarkers, and streamline translational research in the drug discovery marketplace. For example, one could determine the percent of receptors a drug targets, shortening the time and money of research during the new drug development stage. Imaging biomarkers also are non-invasive, which is a clinical advantage over molecular biomarkers. Some of the image-based biomarkers are X-Ray, Computed Tomography (CT), Positron Emission Tomography (PET), Single Photo Emission Computed Tomography (SPECT) and Magnetic Resonance Imaging (MRI).