Biomarkers hold the key to more targeted and timely patient care, yet current tools for analyzing and identifying them often fall short in specificity, sensitivity, or scalability. The complexity of the human genome and its dynamic epigenetic landscape only compounds this difficulty. To overcome these barriers, scientists are turning to multiomic approaches that capture a bigger picture of disease biology.
Robert Neely, PhD Co-founder, Director, and Chief Scientific Officer Tagomics
In this Innovation Spotlight, Robert Neely, the co-founder, director, and chief scientific officer of Tagomics, discusses the importance of biomarker discovery and highlights how a new platform generates both genomic and epigenomic data for better biomarker assessments. Early results from a lung cancer diagnosis pilot study suggest that this approach could fundamentally improve how diseases are identified and treated.
How can novel biomarker discovery lead to better diagnostics and treatments?
Biomarkers are our reading and interpretation of a biological signal or process, but they can be very blunt instruments. For example, an elevated resting heart rate could be regarded as a biomarker for viral infection but might also be due to countless other factors. A better biomarker is one in which we have more confidence, that provides greater and more accurate diagnostic insight, and that doesn’t point us in the wrong direction when we’re looking to identify an underlying disease. The best biomarkers do two things well. Firstly, they are highly specific to their disease, telling clinicians exactly how to treat that patient, where that disease is, and which medicine it will respond to. Secondly, they’re highly sensitive for the disease, giving clinicians the earliest possible indication that a patient is sick and needs treatment. The goal of biomarker discovery is to improve biomarkers so that a clinician can provide the most targeted and timely treatment possible.
What is the benefit of combining genetic and epigenetic biomarker assessments?
Genetics and epigenetics provide the two sides of the “ideal” biomarker story. For a long time, genetics has been the gold standard for biomarkers, and there are well-known genetic markers that tell clinicians exactly which medications to prescribe for patients with genetically associated diseases such as cancer. The epigenome is all about timeliness. It is a series of chemical switches and other processes in our cells that control where, how, and when the genome is read. Biomarkers that reflect disruptions in epigenomic processes can serve as early indicators of disease—and offer multiple shots on goal—because they target pathways that may be broadly affected across the epigenome, rather than relying on a single mutated base pair among the three billion in the genome.
What are the current gaps or pain points associated with biomarker discovery?
The analytical challenge lies in maximizing the retrieval of molecular information from a DNA sample, including the comprehensive assessment of its methylation landscape. DNA methylation is an epigenomic switch on some regions of our DNA that changes the way the genome is read by the proteins in our cells. It is a critical mediator of cell biology, yet we have very few tools for reading DNA methylation across the whole genome in a way that is easily scalable for most scientific laboratories. This limits the number of samples scientists can include in their biomarker discovery project, which, in turn, will limit the application of a DNA methylation biomarker in the clinic.
Our aim is to make comprehensive multiomic biomarker discovery accessible and scalable, so that we get better biomarkers and better outcomes for patients.
How did your team come up with a way to profile both the genome and the epigenome at the same time?
We built the Tagomics Interlace platform, which profiles genetics and epigenetics from a single sample input, on our unique epigenomic profiling technology. We use nature’s solution for detecting DNA methylation, an enzyme called DNA methyltransferase, to target unmodified sites of the genome. There are about 28 million sites that could be modified in the genome, and our enzyme visits each one but only tags the 20-30 percent of sites that are not methylated. These are typically the active regulatory elements of the genome, and once tagged, we pull them out of solution for analysis using standard next-generation sequencing.
One critical aspect of this enzymatic tagging reaction is that it does not damage or change the genome in any way. Hence, in the same workflow as the tagging reaction, we can create a copy of the whole genome that we use for genetic analysis. This means we put a single sample into the reaction and we get both genetic and epigenetic profiles from one streamlined workflow.
Tagomics’ Interlace platform uses a DNA methyltransferase enzyme to tag unmethylated genomic regions, enabling dual profiling of genetic and epigenetic data from one sample
©iStock, selvanegra
How does the Tagomic workflow work, and how does it solve biomarker discovery challenges?
Tagomics’ Interlace workflow has been validated using Agilent’s SureSelect Comprehensive Genomic Profiling panel as its genetic readout. This means that scientists can put one sample into the workflow, down to a single nanogram of input DNA, and they can generate targeted genetic and genome-wide epigenetic profiles as output. Because our Interlace platform targets a smaller fraction of the epigenome for analysis, the sequencing requirement for a high-quality epigenome profile is only around 1/10th that of a standard epigenomic profiling technology. The workflow is scalable but genome-wide, meaning that scientists get extremely comprehensive, multiomic insights from each experiment.
What does your partnership with Agilent bring to the table?
Agilent bring decades of experience as leaders in the field of target enrichment. They have been a great, very collaborative partner to work with, and as an early-stage biotech company, we appreciate their input on the design and validation of the Interlace platform. Together, we have delivered a unique tool for biomarker discovery that could unlock understanding across a range of diseases, where we can really dig into the combined genomic and epigenomic biology that underpins these conditions.
What results have you seen so far with the Tagomics technology?
We have a pilot study running, together with the Salford Royal NHS Trust in England, looking at the early diagnosis of lung cancer in a clinical setting. The biomarker discovery we have done for that study has yielded some tremendously exciting results. We are finding those methylation markers that are known to be associated with lung cancers, but we’re also seeing a host of robust, epigenomic markers beyond the usual hypermethylated promoters, which our technology is ideally suited to pick up. I think we’ve barely scratched the surface of what is possible with this technology.
What does the future of biomarker discovery hold?
We are obviously entering an era of AI-driven technology development, and biomarker discovery will benefit from this, particularly where AI and machine learning are able to pick out or predict patterns and relationships in multiomic data that we otherwise would not see. Being able to do this successfully will rely on having banked, comprehensive datasets, and that’s where our focus is right now.
