Caitlin Smith has a B.A. in biology from Reed College, a Ph.D. in neuroscience from Yale University, and completed postdoctoral work at the Vollum Institute.
Ask anyone working in translational medicine about biomarkers of disease, and they will probably light up—and may begin talking animatedly. This intriguing field is where science meets medicine, where bench research reaches out to heal patients. Though the applications of such tools are only beginning, biomarker researchers have reason to be excited. Here’s a look at some technologies helping researchers analyze biomarkers today.
Nature of biomarkers
An important quality of biomarkers is that they come in many flavors—a biomarker can be any measurable indication associated with a condition or disease. Thus, when analyzing biomarkers, one of the first steps is to consider its nature. Urban Kiernan, senior scientist at Thermo Scientific, says it is prudent to begin by considering the bigger picture: “Holistically, what is the biomarker, what is the sample, and what is the question that needs to be answered?”
The very nature of biomarkers can affect how they [perform] and how they should be analyzed, yet there are wide variations in biomarker types, including small molecules; peptides; larger proteins; variants of proteins whether by SNPs, splice variants, or post-translational modifications; and even changes in brain structure. “These factors need to be accounted for, as they will directly influence both the performance of the biomarker and the specific analytics required,” says Kiernan. “Both go hand in hand.”
Different diseases also may pose distinct challenges, according to Brian Burke, business development manager at Horizon Discovery. In oncology, for example, biomarkers are especially challenging to study because of tumor heterogeneity. “This leads to cancer-relevant genotypes being present at variable allelic frequencies, which has a direct effect on the ability of researchers to detect such biomarkers,” Burke says.
Sample type
The type of sample matrix used (such as plasma, serum or urine, for example) also influences the analysis of the biomarkers the matrix contains. “Each matrix brings unique challenges, [including] analyte concentration, complexation and stability, for example,” says Kiernan. Thermo Fisher Scientific’s Mass Spectrometric Immunoassay (MSIA) workflow is designed with the flexibility to handle a wide variety of biomarkers and sample types.
ICON’s Gyros Gyrolab is ideal for biomarker analysis in instances “where sample matrix is very precious in terms of volume, such as with tears or cerebrospinal fluid,” says John Allinson, vice president of biomarker lab services and development solutions at ICON. Allinson also notes the importance of simplifying the often-complex procedures for collecting samples, which are “usually the major source of poor data due to sample integrity issues. And often this may be blind to the analytical laboratory, and hence results generated may then be interpreted inappropriately.”
Data throughput
Biomarker analysis on a larger scale is now possible using multiplexing technology. Rizwan Farooqui, global large volume and custom manager at Thermo Scientific, says the Thermo Scientific Human in vitro Translation System can be used, for example, to express subproteomes to screen patient samples for biomarkers. “In this method, known as Nucleic Acid Programmable Protein Arrays (NAPPA), hundreds of biomarkers can be measured from a single human sample,” says Farooqui. This is especially important to physicians using biomarkers of diseases, where more data gives improved diagnostic power. “Recent advances in NAPPA technology enables clinicians to detect a host of biomarkers in a single patient sample,” says Farooqui, “leading to more confident diagnosis and ultimately better patient care.”
An alternative high-throughput method, which is based on magnetic particles, is better for screening many patients for one biomarker. “Researchers often need to probe multiple patient samples for specific biomarkers and need to arrive at a final concentration value of a biomarker in patient serum,” says Farooqui. For this, you can use magnetic particles in immunoaffinity assays for particular biomarkers. “Magnetic particles can be handled in high-throughput fashion using a magnetic particle processor, such as the Thermo Scientific Kingfisher instrument,” says Farooqui. ICON also offers its high-throughput Randox Evidence, a fully automated system designed for conducting multiplex biomarker assays using many sample types.
Oncology labs, too, are turning to multiplex platforms for biomarker analysis. “As the challenge of tumor drug resistance becomes better understood, it is clear that physicians need to review a range of biomarkers over time in order to identify the best solutions for patients,” says Burke. Horizon’s quantitative multiplex DNA reference standard is designed to support this research. “[It] has 11 different oncology-specific genotypes across six genes at a range of allelic frequencies from [approximately] 1% to [approximately] 22%,” says Burke. “This complex reference standard has helped laboratories test the integrity of their multiplex workflow, ensuring confidence around detection levels for different allelic events.”
The challenge of integrating labs and clinics
A challenge facing biomarker scientists is effective communication between lab researchers and clinicians. “Many laboratories do not necessarily have scientists with a clinical background,” says Allinson. “I see this situation as a major hindrance to those labs that want to conduct biomarker research where clinical or biomedical scientists do not exist.”
Burke agrees that expertise in more than one discipline is required to combat cancer, for example. “There are some challenges between different professional groups as it becomes clear there is not just one skill set required,” says Burke. “You need pathologists, oncologists, molecular biologists and genetic counselors, amongst others, in order to successfully diagnose and treat cancer patients. This requires consensus to be established between these different groups. Early studies have shown that next-generation sequencing generates more data than can easily be interpreted—how do you communicate this effectively to patients and make the right decisions as a result of it?”