In biomedical contexts, a biomarker, or biological marker, is a measurable indicator of some biological state or condition. Biomarkers are often measured and evaluated using blood, urine, or soft tissues [1] to examine normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention. [2] Biomarkers are used in many scientific fields.
Biomarkers used in the medical field, are a part of a relatively new clinical toolset categorized by their clinical applications. The four main classes are molecular, physiologic, histologic and radiographic biomarkers. [3] All four types of biomarkers have a clinical role in narrowing or guiding treatment decisions and follow a sub-categorization of being either predictive, prognostic, or diagnostic.
Predictive molecular, cellular, or imaging biomarkers that pass validation can serve as a method of predicting clinical outcomes. Predictive biomarkers are used to help optimize ideal treatments, and often indicate the likelihood of benefiting from a specific therapy. For example, molecular biomarkers situated at the interface of pathology-specific molecular process architecture and drug mechanism of action promise capturing aspects allowing assessment of an individual treatment response. [4] This offers a dual approach to both seeing trends in retrospective studies and using biomarkers to predict outcomes. For example, in metastatic colorectal cancer predictive biomarkers can serve as a way of evaluating and improving patient survival rates and in the individual case by case scenario, they can serve as a way of sparing patients from needless toxicity that arises from cancer treatment plans. [5]
Common examples of predictive biomarkers are genes such as ER, PR and HER2/neu in breast cancer, BCR-ABL fusion protein in chronic myeloid leukaemia, c-KIT mutations in GIST tumours and EGFR1 mutations in NSCLC. [6]
Diagnostic biomarkers that meet a burden of proof can serve a role in narrowing down diagnosis. This can lead to diagnosis that are significantly more specific to individual patients.
A biomarker can be a traceable substance that is introduced into an organism as a means to examine organ function or other aspects of health. [7]
It can also be a substance whose detection indicates a particular disease state, for example, the presence of an antibody may indicate an infection. [7] More specifically, a biomarker indicates a change in expression or state of a protein that correlates with the risk or progression of a disease, or with the susceptibility of the disease to a given treatment. [7]
One example of a commonly used biomarker in medicine is prostate-specific antigen (PSA). This marker can be measured as a proxy of prostate size with rapid changes potentially indicating cancer. The most extreme case would be to detect mutant proteins as cancer specific biomarkers through selected reaction monitoring (SRM), since mutant proteins can only come from an existing tumor, thus providing ultimately the best specificity for medical purposes. [8]
An example is the traumatic brain injury (TBI) blood-based biomarker test consisted of measuring the levels of neuronal Ubiquitin carboxy-terminal hydrolase L1 (UCH-L1) and Glial fibrillary acidic protein (GFAP) to aid in the diagnosis of the presence of cranial lesion(s) among moderate to mild TBI patients that is(are) otherwise only diagnosable with the use of a CT scan of the head. [9]
Another example is KRAS, an oncogene that encodes a GTPase involved in several signal transduction pathways. Biomarkers for precision oncology are typically utilized in the molecular diagnostics of chronic myeloid leukemia, colon, breast, and lung cancer, and in melanoma. [10]
Digital biomarkers are a novel emerging field of biomarkers, mostly collected by smart biosensors. [11] So far, digital biomarkers have been focusing on monitoring vital parameters such as accelerometer data and heartrate [12] [13] but also speech. [14] Novel non-invasive, molecular digital biomarkers are increasingly available recorded by e.g. on-skin sweat analysis (internet-enabled Sudorology), which can be seen as next-generation digital biomarkers. [15] Collecting and tracking digital biomarkers have become more easily available with the advancement of smartphones and wearables. In Parkinson's disease (PD), for example, finger tapping a mobile phone via counting apps have been used as a method of (self-)evaluating bradykinesia and effectiveness of medication. [16]
Digital biomarkers are currently being used in conjugation with artificial intelligence (A.I.) in order to recognize symptoms for mild cognitive impairment (MCI). [17] One major current use of digital biomarkers involves keeping track of regular brain activity. Specific neural indicators can be measured by devices to evaluate patients for any neuro abnormalities. The data collected can determine the patients disease probability or condition. [18] While patients carryout everyday tasks (IADL), computers are using machine learning to observe and detect any deviation from normal behavior. These markers are used as signs or indicators of cognitive decline. [17]
A prognostic biomarker provides information about the patients overall outcome, regardless of any treatment or therapeutic intervention. [6] One example of a prognostic biomarkers in clinical research, is the use of mutated PIK3CA in the study of metastatic breast cancer. As illustrated by the graph, the mutation is prognostic since its presence in the patient endure the same outcome regardless of the treatment method used. Women who had the PIK3CA mutation before treatment, had the lowest average survival rate. The decline in the groups containing the mutant occurred quicker and in a much steeper decline. The independent nature of the prognostic factor allows researcher to study the disease or condition in its natural state. This makes it easier to observe these abnormal biological processes and speculate on how to correct them. Prognostic factors are often used in combination with predictive variables in therapeutics studies, to examine how effective different treatments are in curing specific diseases or cancer. As opposed to predictive biomarkers, prognostic do not rely on any explanatory variables, thus allowing for independent examination of the underlying disease or condition. [19]
Nutritional biomarkers (biochemical markers of intake) are used to estimate dietary intake in nutrition research, in particular nutritional epidemiology, but also in other disciplines such as archaeology where reliable dietary information are required. [20] [21] A nutritional biomarker can be any specimen that reflects intake of dietary constituents and is sufficiently specific. [22] [23] Many biomarkers are derived from compounds found in foods, such as sugar or phytochemicals, or combinations thereof using a metabolomics. [20] [24] Another type of nutritional biomarkers, in particular common in archaeology, are stable isotope ratios. [25]
Biomarkers for precision medicine are a part of a relatively new behavioral and clinical toolset. In terms of the behavioral toolset, biomarkers are increasingly being used to motivate health behavior change, particularly in diabetes, cardiovascular diseases, and obesity research. [26] Most research to date uses biomarkers that are easily measured, including weight, blood pressure, and glucose; these biomarkers may reflect the impacts of diet, physical activity, and smoking reduction. However, the methods by which feedback from biomarkers are used in intervention research are varied, and their effectiveness remains unclear. [26]
In reference to the clinical toolset, only two predictive biomarkers are implemented clinically in the case of metastatic colorectal cancer. [5] In this case, the lack of data beyond retrospective studies and successful biomarker-driven approaches may be a factor in using biomarker studies due to the attrition of subjects in clinical trials. [27]
The field of biomarker research is also expanding to include a combinatorial approach to identifying biomarkers from multiple sources. Combining biomarkers from various data allows for the possibility of developing panels that evaluate treatment response based on many biomarkers at a single time. One such area of expanding research in multiple-factor biomarkers is mitochondrial DNA sequencing. Mutations in mitochondrial DNA have been shown to correlate to risk, progression, and treatment response of head and neck squamous cell carcinoma. [28] In this example, a relatively low cost sequencing pipeline was shown to be able to detect low frequency mutations within tumor-associated cells. This highlights the general snapshot capability of mitochondrial DNA-based biomarkers in capturing heterogeneity amongst individuals. [28]
The Early Detection Research Network (EDRN) compiled a list of seven criteria by which biomarkers can be assessed in order to streamline clinical validation. [29]
Previously used to identify the specific characteristics of the biomarker, this step is essential for doing an in situ validation of these benefits. The biologic rationale of a study must be assessed on a small scale before any large scale studies can occur. [29] Many candidates must be tested to select the most relevant ones. [30]
One of the most important steps, it serves to identify specific characteristics of the candidate biomarker before developing a routine test. [31] Several parameters are considered including:
This optimizes the validated protocol for routine use, including analysis of the critical points by scanning the entire procedure to identify and control the potential risks.
In 1997 the National Institute of Health suggested a need for guidelines and legislation development that would regulate the ethical dimensions of biomarker studies. [29]
Ensuring that all of the participants that are included each step of the project (i.e. planning, implementation, and the compilation of the results) are provided with the protection of ethical principles that are put in place prior to beginning the project. These ethical protections should not only protect the participants in the study, but also the non participants, researchers, sponsors, regulators, and all other persons or groups involved in the study. [29] Some ethical protections could include but are not limited to: [29]
In cell biology, a biomarker is a molecule that allows the detection and isolation of a particular cell type (for example, the protein Oct-4 is used as a biomarker to identify embryonic stem cells). [33]
In genetics, a biomarker (identified as genetic marker) is a DNA sequence that causes disease or is associated with susceptibility to disease. They can be used to create genetic maps of whatever organism is being studied.
A biomarker can be any kind of molecule indicating the existence, past or present, of living organisms. In the fields of geology and astrobiology, biomarkers, versus geomarkers, are also known as biosignatures. The term biomarker is also used to describe biological involvement in the generation of petroleum. Biomarkers were used in the geo-chemical investigation of an oil spill in the San Francisco Bay, California in 1988. [34] On April 22–23 around 400,000 gallons of crude oil was accidentally released into the San Joaquin Valley by a refinery and manufacturing complex of the Shell Oil Company. The oil affected many surrounding areas. Samples of the crude oil were collected in the various regions where it had spread and compared to samples that were unreleased in an attempt to distinguish between the spilled oil and the petrogenic background present in the spill area. [34] Mass Spectra was performed to identify biomarkers and cyclic aliphatic hydrocarbons within the samples. Variations in the concentration of constituents of the crude oil samples and sediments were found. [34]
Biomarkers are being used to identify the effects of water contamination on aquatic organisms. Benthic macro-invertebrates reside in the sediment on the bottoms of streams, which is where many contaminants settle. These organisms have high exposure to the contamination, which makes them good study species when detecting pollutant concentrations and pollution impacts on an ecosystem. [35] There are a variety of biomarkers within an aquatic organism that can be measured, depending on the contaminant or the response in question. There are also a variety of contaminants within water bodies. To analyze the impact of a pollutant on an organism, the biomarker must respond to a specific contaminant within a specific time frame or at a certain concentration. [36] The biomarkers used to detect pollution in aquatic organisms can be enzymatic or non-enzymatic. [37] [38]
Rachel Carson, the author of Silent Spring , raised the issue of using organochlorine pesticides and discussed the possible negative effects that said pesticides have on living organisms. [39] Her book raised ethical issues against chemical corporations that were controlling the general reception of the effect of pesticides on the environment, which pioneered the need for ecotoxicological studies. Ecotoxicologial studies could be considered the precursors to biomarker studies. [40] Biomarkers are used to indicate an exposure to or the effect of xenobiotics which are present in the environment and in organisms. The biomarker may be an external substance itself (e.g. asbestos particles or NNK from tobacco), or a variant of the external substance processed by the body (a metabolite) that usually can be quantified.
The widespread use of the term "biomarker" dates back to as early as 1980. [41] The manner in which the environment was monitored and studied near the end of the 1980s was still mainly reliant on the study of chemical substances that were considered dangerous or toxic when found in moderate concentrations in water, sediments, and aquatic organisms. [40] The methods used to identify these chemical compounds were chromatography, spectrophotometry, electrochemistry, and radiochemistry. [40] Although these methods were successful in elucidating the chemical makeup and concentrations present in the environment of the contaminants and the compounds in question, the tests did not provide data that was informative on the impact of a certain pollutant or chemical on a living organism or ecosystem. It was proposed that characterizing biomarkers could create a warning system to check in on the well being of a population or an ecosystem before a pollutant or compound could wreak havoc on the system. Now, due to the development of biomarker studies, biomarkers can be used and applied in the fields of human medicine and in the detection of diseases. [40]
The term "biological marker" was introduced in 1950s. [42] [43]
De Kock and Kramer developed the concept of active biomonitoring in 1994. Active biomonitoring is a comparison of the chemical/biological properties of a sample that has been relocated to a new environment that contains different conditions than its original environment. [50]
In clinical trials, a surrogate endpoint is a measure of effect of a specific treatment that may correlate with a real clinical endpoint but does not necessarily have a guaranteed relationship. The National Institutes of Health (USA) defines surrogate endpoint as "a biomarker intended to substitute for a clinical endpoint".
A tumor marker is a biomarker that can be used to indicate the presence of cancer or the behavior of cancers. They can be found in bodily fluids or tissue. Markers can help with assessing prognosis, surveilling patients after surgical removal of tumors, and even predicting drug-response and monitor therapy.
Tissue microarrays consist of paraffin blocks in which up to 1000 separate tissue cores are assembled in array fashion to allow multiplex histological analysis.
Personalized medicine, also referred to as precision medicine, is a medical model that separates people into different groups—with medical decisions, practices, interventions and/or products being tailored to the individual patient based on their predicted response or risk of disease. The terms personalized medicine, precision medicine, stratified medicine and P4 medicine are used interchangeably to describe this concept, though some authors and organizations differentiate between these expressions based on particular nuances. P4 is short for "predictive, preventive, personalized and participatory".
MammaPrint is a prognostic and predictive diagnostic test for early stage breast cancer patients that assess the risk that a tumor will metastasize to other parts of the body. It gives a binary result, high-risk or low-risk classification, and helps physicians determine whether or not a patient will benefit from chemotherapy. Women with a low risk result can safely forego chemotherapy without decreasing likelihood of disease free survival. MammaPrint is part of the personalized medicine portfolio marketed by Agendia.
Biomarker discovery is a medical term describing the process by which biomarkers are discovered. Many commonly used blood tests in medicine are biomarkers. There is interest in biomarker discovery on the part of the pharmaceutical industry; blood-test or other biomarkers could serve as intermediate markers of disease in clinical trials, and as possible drug targets.
In medicine, a biomarker is a measurable indicator of the severity or presence of some disease state. It may be 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." More generally a biomarker is anything that can be used as an indicator of a particular disease state or some other physiological state of an organism. According to the WHO, the indicator may be chemical, physical, or biological in nature - and the measurement may be functional, physiological, biochemical, cellular, or molecular.
14-3-3 protein eta also referred to as 14-3-3η is a protein that in humans is encoded by the YWHAH gene.
Neurofilament light polypeptide, also known as neurofilament light chain, abbreviated to NF-L or Nfl and with the HGNC name NEFL is a member of the intermediate filament protein family. This protein family consists of over 50 human proteins divided into 5 major classes, the Class I and II keratins, Class III vimentin, GFAP, desmin and the others, the Class IV neurofilaments and the Class V nuclear lamins. There are four major neurofilament subunits, NF-L, NF-M, NF-H and α-internexin. These form heteropolymers which assemble to produce 10 nm neurofilaments which are only expressed in neurons where they are major structural proteins, particularly concentrated in large projection axons. Axons are particularly sensitive to mechanical and metabolic compromise and as a result axonal degeneration is a significant problem in many neurological disorders. The detection of neurofilament subunits in CSF and blood has therefore become widely used as a biomarker of ongoing axonal compromise. The NF-L protein is encoded by the NEFL gene. Neurofilament light chain is a biomarker that can be measured with immunoassays in cerebrospinal fluid and plasma and reflects axonal damage in a wide variety of neurological disorders. It is a useful marker for disease monitoring in amyotrophic lateral sclerosis, multiple sclerosis, Alzheimer's disease, and more recently Huntington's disease. It is also promising marker for follow-up of patients with brain tumors. Higher levels of blood or CSF NF-L have been associated with increased mortality, as would be expected as release of this protein reflects ongoing axonal loss. Recent work performed as a collaboration between EnCor Biotechnology Inc. and the University of Florida showed that the NF-L antibodies employed in the most widely used NF-L assays are specific for cleaved forms of NF-L generated by proteolysis induced by cell death. Methods used in different studies for NfL measurement are sandwich enzyme-linked immunosorbent assay (ELISA), electrochemiluminescence, and high-sensitive single molecule array (SIMOA).
CA 242 is a tumor marker for sialylated Lewis carbohydrates associated with adenocarcinomas and e-selectin-mediated metastatic risk. It is commonly tested along with CEA, CA19-9, and CA242 for detecting pancreatic cancer. The specificity of CA 242 is higher than similar markers. Current research dictates that diagnostic efficiency is highest when various tumor markers are tested for at once.
A gene signature or gene expression signature is a single or combined group of genes in a cell with a uniquely characteristic pattern of gene expression that occurs as a result of an altered or unaltered biological process or pathogenic medical condition. This is not to be confused with the concept of gene expression profiling. Activating pathways in a regular physiological process or a physiological response to a stimulus results in a cascade of signal transduction and interactions that elicit altered levels of gene expression, which is classified as the gene signature of that physiological process or response. The clinical applications of gene signatures breakdown into prognostic, diagnostic and predictive signatures. The phenotypes that may theoretically be defined by a gene expression signature range from those that predict the survival or prognosis of an individual with a disease, those that are used to differentiate between different subtypes of a disease, to those that predict activation of a particular pathway. Ideally, gene signatures can be used to select a group of patients for whom a particular treatment will be effective.
Cancer is a category of disease characterized by uncontrolled cell growth and proliferation. For cancer to develop, genes regulating cell growth and differentiation must be altered; these mutations are then maintained through subsequent cell divisions and are thus present in all cancerous cells. Gene expression profiling is a technique used in molecular biology to query the expression of thousands of genes simultaneously. In the context of cancer, gene expression profiling has been used to more accurately classify tumors. The information derived from gene expression profiling often helps in predicting the patient's clinical outcome.
An imaging biomarker is a biologic feature, or biomarker detectable in an image. In medicine, an imaging biomarker is a feature of an image relevant to a patient's diagnosis. For example, a number of biomarkers are frequently used to determine risk of lung cancer. First, a simple lesion in the lung detected by X-ray, CT, or MRI can lead to the suspicion of a neoplasm. The lesion itself serves as a biomarker, but the minute details of the lesion serve as biomarkers as well, and can collectively be used to assess the risk of neoplasm. Some of the imaging biomarkers used in lung nodule assessment include size, spiculation, calcification, cavitation, location within the lung, rate of growth, and rate of metabolism. Each piece of information from the image represents a probability. Spiculation increases the probability of the lesion being cancer. A slow rate of growth indicates benignity. These variables can be added to the patient's history, physical exam, laboratory tests, and pathology to reach a proposed diagnosis. Imaging biomarkers can be measured using several techniques, such as CT, PET, SPECT, ultrasound, electroencephalography, magnetoencephalography, and MRI.
DirectHit is a pharmacodiagnostic test used to determine the tumor sensitivity or resistance to drug regimens recommended for the treatment of breast cancer by the National Comprehensive Cancer Network. It is a noninvasive test performed on small amounts of tissue removed during the original surgery lumpectomy, mastectomy, or core biopsy. DirectHit was developed by CCC Diagnostics Inc., a biotechnology company established by former researchers from Johns Hopkins University. DirectHit was launched on 14 January 2010. Currently, it is the only available test for predicting treatment outcomes for anticancer chemotherapy drugs for breast cancer.
A cancer biomarker refers to a substance or process that is indicative of the presence of cancer in the body. A biomarker may be a molecule secreted by a tumor or a specific response of the body to the presence of cancer. Genetic, epigenetic, proteomic, glycomic, and imaging biomarkers can be used for cancer diagnosis, prognosis, and epidemiology. Ideally, such biomarkers can be assayed in non-invasively collected biofluids like blood or serum.
Thymidine kinase is an enzyme, a phosphotransferase : 2'-deoxythymidine kinase, ATP-thymidine 5'-phosphotransferase, EC 2.7.1.21 that catalyzes the reaction:
In the field of medicine, radiomics is a method that extracts a large number of features from medical images using data-characterisation algorithms. These features, termed radiomic features, have the potential to uncover tumoral patterns and characteristics that fail to be appreciated by the naked eye. The hypothesis of radiomics is that the distinctive imaging features between disease forms may be useful for predicting prognosis and therapeutic response for various cancer types, thus providing valuable information for personalized therapy. Radiomics emerged from the medical fields of radiology and oncology and is the most advanced in applications within these fields. However, the technique can be applied to any medical study where a pathological process can be imaged.
Prognostic markers are biomarkers used to measure the progress of a disease in the patient sample. Prognostic markers are useful to stratify the patients into groups, guiding towards precise medicine discovery. The widely used prognostic markers in cancers include stage, size, grade, node and metastasis. In addition to these common markers, there are prognostic markers specific to different cancer types. For example estrogen level, progesterone and HER2 are markers specific to breast cancer patients. There is evidence showing that genes behaving as tumor suppressors or carcinogens could act as prognostic markers due to altered gene expression or mutation. Besides genetic biomarkers, there are also biomarkers that are detected in plasma or body fluid which can be metabolic or protein biomarkers.
The Immunologic Constant of Rejection (ICR), is a notion introduced by biologists to group a shared set of genes expressed in tissue destructive-pathogenic conditions like cancer and infection, along a diverse set of physiological circumstances of tissue damage or organ failure, including autoimmune disease or allograft rejection. The identification of shared mechanisms and phenotypes by distinct immune pathologies, marked as a hallmarks or biomarkers, aids in the identification of novel treatment options, without necessarily assessing patients phenomenologies individually.
Tumour mutational burden is a genetic characteristic of tumorous tissue that can be informative to cancer research and treatment. It is defined as the number of non-inherited mutations per million bases (Mb) of investigated genomic sequence, and its measurement has been enabled by next generation sequencing. High TMB and DNA damage repair mutations were discovered to be associated with superior clinical benefit from immune checkpoint blockade therapy by Timothy Chan and colleagues at the Memorial Sloan Kettering Cancer Center.