In the quest to unravel the mysteries of human health and disease, scientists have turned their attention to biomarkers – the microscopic clues that hold immense diagnostic and therapeutic potential. These tiny but mighty indicators offer a window into our biological processes, from proteins and genes to chemical compounds.
To help you understand the intricacies of biomarkers, this article provides a concise overview of the biomarker discovery and validation process, highlighting its critical stages and challenges. Let’s explore how effective biomarker discovery can unlock new opportunities for precision medicine and enhance patient care.
What Is a Biomarker?
In biomedicine, a biological marker (biomarker) is a measurable characteristic indicating the state of an organism’s biological process or disease. It may also denote the severity of a biological response to therapeutic or pathological interventions. Every biomarker possesses either chemical, physical, or biological characteristics that can be measured along functional, physiological, biochemical, cellular, or molecular aspects.
Biomarkers are found in various biological materials such as blood, urine, tissue, and other bodily fluids or may be produced by the tissue itself or other cells in the body in response to a disease, e.g., cancer. Any molecule that indicates something about your health can serve as a biomarker, such as your DNA (genes), proteins, or hormones.
What Are the Different Categories of Biomarkers?
Biomarkers play a crucial role in understanding and monitoring the intricacies of the human body. They offer valuable insights into biological processes, disease diagnosis, treatment effectiveness, and patient outcomes, enabling researchers and clinicians to make informed clinical decisions.
There are various types of biomarkers, each serving a specific purpose and providing unique information about the body’s health and functionality. They are generally classified into the following 7 categories:
1. Diagnostic Biomarkers
Diagnostic biomarkers are used to identify the presence or absence of a disease or condition. They can be specific molecules, proteins, genes, or other measurable entities that indicate the presence of a particular disease.
Example: Prostate-specific antigen (PSA) is a diagnostic biomarker used for the diagnosis of prostate cancer. PSA is a protein produced by the cells of the prostate gland, and elevated levels of PSA in the blood can indicate the presence of prostate cancer.
2. Prognostic Biomarkers
Prognostic biomarkers provide information about the likely outcome or progression of a disease. They can help predict the likelihood of disease recurrence, disease progression, or overall patient survival. Prognostic biomarkers can aid in treatment planning and patient management.
Example: BRAF is a prognostic biomarker, generally mutated in cancers such as melanoma and colorectal cancer. BRAF is a gene that encodes a protein kinase involved in cell signaling pathways regulating cell growth and division. Mutations in the BRAF gene, such as the V600E mutation, are commonly found in these cancers and are associated with a more aggressive disease course.
3. Predictive Biomarkers
Predictive biomarkers are used to determine the likelihood of response to a specific treatment or therapy. They help identify patients more likely to benefit from a particular treatment approach, optimizing treatment selection and improving patient outcomes.
Example: HER2/neu is a predictive biomarker serving in the status prediction of breast cancer. Patients with HER2-positive breast cancer have higher chances of responding to anti-HER2 targeted therapies, such as trastuzumab or pertuzumab. HER2 testing helps determine the appropriate treatment approach for breast cancer patients and improves treatment outcomes.
4. Susceptibility Biomarkers
Susceptibility/Risk biomarkers are indicators that help identify individuals who are at an increased risk of developing a particular disease or condition. These biomarkers can provide insights into the genetic, environmental, or lifestyle factors that contribute to an individual’s susceptibility to a specific health outcome.
5. Monitoring Biomarkers
Monitoring biomarkers track disease progression, treatment response, or the recurrence of a condition over time. These biomarkers are measured repeatedly to monitor the effectiveness of therapies, assess disease activity, and guide treatment decisions.
Example: Brain natriuretic peptide (BNP) is a monitoring biomarker for heart failure. A pressured or enlarged heart releases BNP, which is common in heart failure. Monitoring BNP levels can aid in determining the severity of heart failure for prompt treatment decisions.
6. Safety Biomarkers
Safety biomarkers can assess the safety and potential adverse effects of drugs or therapies. They help identify and monitor any harmful effects or toxicity associated with the treatment. Safety biomarkers ensure patient safety in clinical trials and post-marketing surveillance.
Example: Creatinine is used to assess kidney function. Elevated levels of creatinine in the blood can indicate impaired kidney function or kidney damage. Monitoring creatinine levels help identify potential nephrotoxic effects of drugs or other interventions and enables healthcare providers to make informed decisions regarding dose adjustments or discontinuation to prevent further kidney damage.
7. Pharmacodynamic/Response Biomarkers
Pharmacodynamic/Response biomarkers provide information about the effect of a drug or therapy on the target or the intended biological pathway. They assess whether the treatment is having the desired effect and can be used to monitor drug response and optimize dosing.
Example: One example of a pharmacodynamic biomarker is blood pressure reduction in response to antihypertensive medication. In patients with hypertension, the pharmacodynamic biomarker would be the measurement of systolic and diastolic blood pressure before and after treatment. The reduction in blood pressure serves as an indicator of the desired pharmacological effect of the medication, demonstrating its ability to lower blood pressure and control hypertension.
5 Major Applications of Biomarkers
Biomarkers find their usage in numerous applications across diverse domains, some of which include:
- Disease Diagnosis & Screening: Biomarkers have become vital for detecting the presence of diseases or conditions, enabling early diagnosis and intervention.
- Prognosis & Treatment Prediction: Biomarkers can indicate disease progression, treatment response, and the likelihood of recurrence. This assessment aids healthcare providers in personalizing treatment plans and making informed decisions about patient management.
- Therapeutic Monitoring: Biomarkers can monitor treatment efficacy and adjust therapy as needed. They provide real-time information on the response to a specific treatment, allowing healthcare providers to modify the treatment regimen or dosage to optimize patient outcomes.
- Drug Development & Clinical Trials: Biomarkers can identify potential drug targets, assess drug safety and efficacy, and stratify patient populations. They help in selecting appropriate candidates for clinical trials, monitoring drug effects, and predicting treatment response.
- Personalized Medicine: Biomarkers enable personalized medicine approaches by guiding treatment selection based on an individual’s unique characteristics. They can help improve treatment outcomes, and minimize adverse effects.
How Are Biomarkers Developed & Implemented?
The process of searching for and implementing biomarkers for healthcare improvement is long and complex. It involves a multi-stage biomarker development process encompassing the discovery and validation of biomarkers in the human body.
Biomarker Discovery Process
Biomarker discovery refers to the identification of potential biomarkers that are associated with a particular disease or condition. This involves the use of high-throughput technologies such as genomics, proteomics, or metabolomics to screen and analyze biological samples.
It is an iterative and dynamic process involving multidisciplinary collaboration among researchers, clinicians, bioinformaticians, statisticians, and data analysts to ensure reliable outcomes.
The process involves several steps and methodologies that vary depending on the context and the type of biomarker being sought. Here are some steps that outline a typical biomarker discovery workflow:
- Study Design Selection: Define the research question, scope, and study objectives. Select a well-defined patient cohort or population, including disease-affected individuals and controls.
- Sample Collection: Collect biological samples from the selected patients, such as blood, urine, tissue, or other relevant specimens. Ensure appropriate sample handling and storage to maintain sample integrity.
- Biomarker Identification: Employ various high-throughput techniques to identify potential biomarkers. This may include genomics, transcriptomics, proteomics, metabolomics, or other omics approaches. Analyze the collected samples using technologies like microarrays, next-generation sequencing, mass spectrometry, or other specialized platforms.
- Data Standardization: Many biomedical datasets derived from non-targeted molecular profiling or high-throughput imaging approaches are noisy, biased, and often not harmonized across patient cohorts. To address this problem, quality control, data curation, annotation, and standardization are critical initial steps in biomedical data processing pipelines.
- Data Analysis: Process and analyze the generated data using bioinformatics and statistical methods. Identify patterns, correlations, and potential candidate biomarkers that distinguish between the affected and control groups.
- Internal Validation: Validate the identified candidate biomarkers using independent sample sets or cohorts. Utilize targeted validation techniques, such as immunoassays, PCR-based assays, or other specific assays, to quantify and confirm the presence of the biomarkers.
- Clinical Utility Assessment: Evaluate the clinical relevance and utility of the validated biomarkers. Determine their potential for disease diagnosis, prognosis, prediction, and monitoring treatment response. Assess their added value compared to existing diagnostic or prognostic methods.
Biomarker Validation Process
Following their identification in the biomarker discovery stage, the candidate biomarkers are subjected to two kinds of validation methods, namely, analytical and clinical validation.
1. Analytical Biomarker Validation
Analytical validation involves assessing the performance characteristics of a biomarker, such as sensitivity, specificity, accuracy, precision, and interlaboratory reproducibility, following a predetermined protocol. The statistical analysis methods employed during analytical validation are similar to those used in biomarker discovery.
The following are the key steps involved in the analytical validation of biomarkers:
- Performing analytical validation to determine the performance characteristics of the biomarker assay.
- Evaluating sensitivity, specificity, precision, accuracy, linearity, and the lower detection limit.
- Determining the robustness and stability of the assay under various conditions.
The objective of analytical validation is to evaluate the technical performance of a biomarker, ensuring that it consistently provides reliable measurements without assessing its clinical utility or usefulness.
2. Clinical Biomarker Validation
Clinical validation focuses on establishing a meaningful association between a biomarker and the specific endpoint of interest, aiming to determine the biomarker’s utility and usefulness in a clinical context.
This validation process typically involves external validation, which can be achieved through retrospective analysis of clinical trial data or prospective clinical trials. Retrospective use of clinical trial data involves evaluating the biomarker’s performance in a study initially designed for a different purpose, serving as an external validation approach.
The following are the key steps involved in the clinical validation of biomarkers:
- Evaluating the biomarker’s performance using patient samples from the selected cohort.
- Assessing the biomarker’s ability to distinguish between relevant clinical groups, for example, diseased vs. healthy, responder vs. non-responder, etc.
- Analyzing the biomarker’s correlation with clinical outcomes or relevant endpoints.
Clinical validation aims to assess the clinical relevance and practical value of the biomarker in improving patient outcomes. If the biomarker shows promising results in clinical validation, regulatory approvals may be sought, depending on the intended use. Next, standardized protocols and guidelines are developed for biomarker measurement, and their implementation in clinical practice or research studies is considered.
Once a biomarker is approved and implemented, ongoing monitoring and evaluation are essential to assess its performance and impact in clinical practice. Real-world data are collected to evaluate the biomarker’s long-term effectiveness, safety, and value in improving patient outcomes.
What Are the Challenges Associated With Biomarker Discovery & Validation?
Biomarker discovery and validation are complex procedures that deal with a vast network of biomedical datasets with complicated and intricate relationships. This can hinder the discovery process for biomarker researchers in a number of ways:
1. Data Integration
Biomarker discovery and validation require the integration and analysis of diverse data types, including genomics, proteomics, clinical data, and imaging data. Managing and integrating these complex datasets can be challenging, particularly when dealing with large-scale studies involving multiple sources.
2. Sample Size & Heterogeneity
Obtaining an adequate sample size and addressing sample heterogeneity is crucial for biomarker discovery and validation. Variability in patient populations, disease subtypes, and treatment responses can impact the identification and validation of robust biomarkers.
3. Reproducibility & Generalizability
Reproducing biomarker findings across different studies or populations is a significant challenge. Biomarkers may exhibit variability in their performance due to factors such as assay variability or differences in patient populations, making it essential to validate biomarkers across multiple independent populations.
4. Knowledge Gap
Understanding the underlying biological mechanisms and interpreting complex biomarker data requires extensive domain knowledge. Bridging the gap between biomarker data and biological knowledge is critical for accurate interpretation and meaningful translation into clinical applications.
Role of Knowledge Graphs in Addressing Biomarker Discovery & Validation Challenges
A knowledge graph is a structured representation of interconnected knowledge that captures relationships and semantics between entities, enabling advanced reasoning, inference, and data integration. It is a powerful tool with diverse applications in biomedicine, including biomarker discovery and validation.
Using knowledge graphs for biomarker discovery and validation can enable a more accurate and meaningful interpretation of biomarker findings. Here is a sample illustration of a knowledge graph for discovering cancer biomarkers.
Here’s how knowledge graphs can enhance biomarker discovery and validation:
Knowledge graphs enable the integration of diverse biomarker-related data sources, allowing researchers to navigate and explore the interconnected information easily. They provide:
- A unified view of data
- Facilitating efficient data integration
- Hypothesis generation
- Identification of novel biomarker candidates
Semantic Integration & Inference
Knowledge graphs capture the semantics and relationships among biomarker-related entities, such as genes, proteins, diseases, and pathways. This semantic integration allows for advanced reasoning and inference, aiding in identifying potential biomarker candidates and their connections to disease mechanisms.
Data Harmonization & Standardization
Knowledge graphs provide a common framework for harmonizing and standardizing biomarker data, ensuring consistency and interoperability. They enable the mapping of heterogeneous data to common ontologies and terminologies, facilitating data sharing and collaboration across different studies and datasets.
Knowledge-Driven Biomarker Discovery
Knowledge graphs help incorporate existing biological knowledge and prior research findings into biomarker discovery and validation processes. This can assist researchers in conveniently generating hypotheses, validating biomarker candidates, and gaining a deeper understanding of the underlying biology.
Leverage Wisecube’s Knowledge Graph Engine For Biomarker Discovery & Validation
Wisecube’s Knowledge Graph Engine is built using cutting-edge technology and ground-breaking AI and NLP algorithms on top of our scalable and open AI platform.
With an easy-to-use, GraphQL-based API, Wisecube is designed to give unified access to biomedical data and insights through a knowledge graph. You can use Wisecube to:
- Access and utilize billions of biomedical facts across disparate data sources
- Enable predictive recommendations for discovering biomarkers, leads & targets
- Query the graph in natural language using GPT with referenceable sources.
Revolutionize your biomarker discovery journey with Wisecube’s cutting-edge knowledge graph technology. Contact us today to get started.