Protein Biomarker Discovery Methods

Protein biomarkers are proteins within an organism that act as indicators of a disease and its stage or progression. They can even predict future disease. Aside from their uses in diagnosis or predicting disease, biomarkers can also be used to select patients for clinical trials or to evaluate the safety and efficacy of a drug.

However, protein biomarkers can be difficult to identify and validate. “Identifying important biomarkers to analyze can be a formidable task due to the number of potential targets and the complexity of the biology,” says Vanitha Margan, Global Product Manager, Immunoassays at Bio-Rad, which develops Luminex xMAP bead-based assays for protein biomarker discovery.

So what makes a good biomarker? “A good biomarker should be readily accessible (i.e., saliva or blood rather than biopsy or lumbar puncture), offer clear differentiation between healthy state and either presence of a disease or its state of progression, and have high-quality and specific antibodies available for detection,” says Iain McWilliam, CEO of Arrayjet. In order for a protein to be considered a biomarker, its abundance should ideally change predictably in a statistically significant way in response to a disease or state.

How are protein biomarkers discovered?

For a protein to be considered a biomarker, it first needs to be identified and then validated using various methods to ensure reproducibility and association with the disease or biological state. During the discovery phase, thousands of proteins are considered from a small number of samples. Proteins identified as biomarker candidates move to the validation phase where only a few proteins are analyzed in a larger number of samples. Validating a biomarker includes using orthogonal methods to confirm measurements and testing thousands of samples to ensure biological relevance. “No single best tool exists for protein biomarker discovery as it depends on the research needs,” says Margan.

Oftentimes, multiple proteins need to be considered together for diagnosis of disease. “It is increasingly difficult to find silver-bullet biomarkers that offer diagnosis of a disease or the degree of its progression by quantifying a single protein,” says McWilliam. “Commonly, we see panels of biomarkers being used to accurately diagnose complex diseases, and naturally this necessitates multiplex diagnostic tools.”

Mass spectrometry

Protein biomarkers have been widely investigated with mass spectrometry-based proteomics. This technique is accurate and specific, but it is best for medium-to-high abundant proteins. This can be an issue as many protein biomarkers are low in abundance including those that predict early stages of cancer and neurological diseases, or infectious diseases. Mass spectrometry is also expensive and requires technical expertise to operate. This technique can also be used to detect post-translational modifications, which are known to have roles in signaling pathways of many diseases.

(More information on the pros and cons of using mass spectrometry for protein biomarker discovery can be found here.)

Single-molecule proteomics

In contrast to mass spectrometry-based methods, single-molecule proteomics methods that examine individual proteins have the ability to provide a more detailed look into low-abundance protein biomarkers and can detect rare proteoforms that may be indicators of disease. However, like for mass spectrometry, it can be difficult to attribute which peptides come from which protein if the single-molecule proteomics methods used analyze peptides and not intact proteins.

Array-based methods

Array-based methods for protein biomarker discovery involve immobilizing molecules onto a surface to capture proteins of interest. Arrays can include a few dozen spots or up to thousands of spots. The advantage of array-based technologies is that they are high-throughput and require low sample input. However, array-based methods can be challenging for biomarker discovery and validation because of their dependence on antibody quality and availability, and they can have the same problems as mass spectrometry for samples with proteins at widely different concentrations.

“Commonly used protein biomarker discovery assays include reverse phase protein arrays, protein arrays, and antibody arrays,” says McWilliam, whose company uses inkjet printing to create microarrays for various applications.

• Protein microarrays: Protein microarrays involve immobilizing proteins on a solid surface. These microarrays are used to detect interactions with other proteins, including antibodies.
• Antibody microarrays: Antibody microarrays are a type of protein array with antibodies spotted on a solid surface. They are used to detect protein expressed from a given sample (ex: serum, lysates).
• Reverse phase protein arrays (RPPA): RPPAs contain spotted cell lysate or protein extract on a solid support. These lysates are then probed with antibodies against targets of interest.
• Tissue microarrays: These arrays contain small tissue cores from multiple samples. They can analyze protein expression in a large number of tissues at once and are widely used for biomarker validation in cancer research.

Challenges of protein biomarker discovery: Verification and validation

Validating protein biomarkers is difficult because of biological and methodological challenges. “The underlying diversity in how the same disease presents between different individuals is an ever-present challenge,” says McWilliam. “To get around this, we will have to get comfortable with measuring and interpreting broader panels of biomarkers and leaning into computing power for validation.”

In terms of biomarker discovery technologies, Margan notes four reasons why biomarker validation is difficult: protein abundance varies across a wide dynamic range, cross-reactivity of antibodies with non-target proteins, reproducibility issues, and data variability between different technologies.

Future of biomarker discovery

While protein biomarkers may be elusive, there are reasons to be optimistic about protein biomarker discovery in the future. Technologies are advancing on several fronts. “Many disease-causing protein biomarkers are present at a very low concentration,” says Margan. “Therefore, technologies with dynamic concentration range with high sensitivity will contribute to the improvement of protein biomarker discovery.” In addition, label-free screening technologies, such as SPR imaging, could overcome challenges due to antibody reliability, says McWilliam.

As many diseases have multiple protein biomarkers associated with them, the adoption of new methods and tools can be used to better pinpoint signs of disease. “With the acceptance of machine learning algorithms increasing I think we will see diagnostics with increasing complexity and numbers of biomarkers,” says McWilliam.