Charting a path with biomarker discovery tools

As technological advances have transformed our capacity to probe biological parameters and mechanisms of health and disease, biomarkers have played a growing role in countless aspects of medicine. The Human Genome Project was a tipping point that exemplified a new way of looking at the human body at a resolution never before possible. In the decades since, the growing accessibility of genome sequencing and the advent of multi-omics approaches are elucidating how different biological variables increase and decrease in response to illness.

These deep, multifaceted biomarker discovery tools can help us fit together the puzzle pieces of disease, offering new possibilities for diagnosing illness, understanding prognosis and guiding targeted treatment with groundbreaking therapeutics. Biomarkers provide critical insights into the molecular and genetic mechanisms underlying diseases, enabling researchers to identify therapeutic targets with unprecedented precision. Because many key biomarkers originate from organs struggling with the effects of a disease, such as DNA shed from a tumor, studies of patient samples can yield possible diagnostic and therapeutic targets.

This has accelerated the design of more powerful diagnostic tools and innovative treatments, such as gene-editing therapies tailored to correct specific genetic mutations or cell-based therapies engineered to enhance immune responses. By serving as tools to stratify patient populations, predict treatment responses, and monitor therapeutic safety and efficacy, biomarkers have become indispensable in the creation and optimization of cutting-edge treatments. Biomarker discovery goes hand in hand with drug discovery and development, paving the way for transformative advancements in precision medicine.

Progress in fits and starts

Despite the progress made thus far, the field still faces challenges in biomarker discovery and application. Many of these are rooted in logistical and technological limitations. Methods such as liquid chromatography-mass spectrometry and ligand binding assays can be limited in throughput and selectivity and can be cost-prohibitive for many research groups to leverage fully. Even when promising biomarkers are identified, validation is a further obstacle. For novel diagnostic modalities like liquid biopsy, clinical validation of a low-abundance biomarker is highly dependent on the sensitivity and reliability of analytical methods at your disposal. Additionally, as the drug development space reckons with challenges of research inclusivity and diversity, biomarker discovery and development are subject to the same issues.

But progress has continued, in fits and starts, to unravel complex biomarker classes with evolving tools and methods. One example is the study of glycans — branched carbohydrate chains that join to biomolecules throughout cells and tissues and play a key role in regulating and maintaining normal biological functions. Glycans are a promising class of potential oncology biomarkers, however glycosylation is an extremely complex and dynamic phenomenon, impacted by genetics, epigenetics and disease processes.

This complexity makes glycosylation status equally challenging to study. What’s currently missing are accessible tools that support high-throughput screening and measurement of low-abundance molecules. This will enable characterization of glycosylation differences across massive patient populations and inform our understanding of glycosylation among individuals and within a given disease, powering personalized therapies. As is often the case in science, progress in one area can have a positive impact on other applications as well. Gaining a better understanding of glycosylation and glycan structure/function could inform glycoengineering of antibodies for improved selectivity, empowering biomarker discovery and application in diagnostics.

Future possibilities for biomarker discovery

Artificial intelligence and machine learning will be immensely valuable for building on what we know thus far and facilitating deeper, faster insights. While not a replacement for research, AI can make better use of massive, disparate datasets, uncover new patterns, and improve existing tools for new applications. For example, AI tools could inform recombinant antibody design beyond known sequences to enable engineering of products with better affinity, specificity, manufacturability and more. This, in turn, can improve diagnostic and therapeutic development in addition to basic biomarker discovery research.

The possibilities for biomarker discovery and application are virtually limitless, and progress does not occur in a vacuum. From incremental improvements in research tools to observations of changing biomarkers across disease progression, each achievement builds upon an existing foundation and empowers further discovery. As we continue to leverage innovative tools to understand markers of health and disease at high resolution, we move closer to a future with unprecedented diagnostic precision and groundbreaking therapeutic possibilities.