Tag: Academic research

For decades, cancer medicine has been driven primarily by genetics—the search for mutations, deletions, amplifications, and other DNA changes that directly alter how cells behave. Precision oncology has largely focused on identifying these genomic drivers and matching them with targeted therapies. But another biological layer has steadily moved from the margins of research into the center of clinical care: epigenetics.

Epigenetics refers to chemical modifications that regulate how genes are turned on or off without changing the underlying DNA sequence itself. One of the best-known examples is DNA methylation, in which small chemical tags called methyl groups attach to DNA and influence whether genes are active or silenced. These marks help explain one of biology’s most fundamental questions: how a brain cell and a heart cell can contain the exact same genetic code yet function in completely different ways.

Rather than rewriting DNA, epigenetics controls how a stretch of DNA is read. It shapes normal development, tissue specialization, immune responses, aging, and disease progression. In cancer, epigenetic disruption can silence tumor-suppressor genes, activate harmful pathways, and create stable molecular fingerprints that reveal where a tumor came from and how it behaves. Increasingly, those fingerprints are becoming clinically actionable.

Today, epigenetics is helping physicians classify brain tumors more accurately, detect pancreatic cancer from a simple blood draw, improve molecular sequencing workflows, and even therapeutically silence disease-causing genes. Across diagnostics and therapeutics, the field is rapidly becoming one of the most practical and powerful tools in precision medicine.

Few people have watched that transformation more closely than Matija Snuderl, MD, a neuropathologist at NYU Langone. He remembers a time when epigenetics barely registered in medical education.

“When I was in medical school,” he said, “there were literally like two pages on epigenetics in the genetics book, and it had no implications for cancer whatsoever.”

That changed quickly.

Brain tumors become a clinical test case

Snuderl became deeply interested in epigenetics around 2013, when researchers began realizing that epigenetic patterns could do far more than explain developmental biology. They could also reveal a tumor’s cell of origin and, in some cancers, directly illuminate the mechanisms driving carcinogenesis.

Brain tumors proved to be one of the most compelling settings for that work. “Brain tumors are only about two percent of all cancers,” Snuderl explained, “but they are disproportionately heterogeneous.”

That heterogeneity is extraordinary. There are now roughly 180 recognized molecular subtypes of central nervous system tumors. Yet for decades, many of those diseases were classified almost entirely by histology—how the tumor looked under a microscope. Tumors with similar visual features were grouped together, even when they were biologically distinct.

The result was predictable. “We ended up putting a lot of tumors that are completely different into the same basket, treating them the same way, and then we were surprised that we didn’t see results,” Snuderl said.

Epigenetics offered a way out of that problem. Because DNA methylation reflects both developmental lineage and disease biology, it provides a stable molecular fingerprint of tumor identity. Unlike RNA, which fluctuates significantly and degrades quickly after tissue collection, methylation patterns are remarkably durable. “The beauty of methylation,” Snuderl said, “is that it’s an incredibly robust biomarker.”

A molecular fingerprint for diagnosis

Snuderl began a collaboration with the German Cancer Research Center (DKFZ) in Heidelberg and proposed a simple but powerful hypothesis: every tumor type has a distinct methylation signature—a recognizable pattern of hypermethylated and hypomethylated regions that functions like a fingerprint.

Using thousands of tumor samples, the team built a machine learning classifier based on a random forest model. The algorithm was trained to recognize these methylation signatures and assign new tumor samples to the most likely disease subtype. “At every decision point,” Snuderl explained, the classifier essentially asks, “Are you methylated or are you unmethylated?”

The system evaluates more than 10,000 decision trees before generating a calibrated confidence score that indicates how certain the classifier is about a diagnosis.

The visual output is particularly striking. On uniform manifold approximation and projections (UMAPs), which is a method of viewing high-dimensional data in a low-dimensional manner, each tumor appears as a colored dot that clusters with biologically similar tumors. Some groups are extremely tight, reflecting genetically simple and highly consistent diseases. Others are more diffuse, showing greater heterogeneity and multiple biological drivers.

These patterns helped reveal that some long-standing diagnostic categories were fundamentally wrong. One major example involved primitive neuroectodermal tumors (PNETs), once considered a distinct diagnosis. Methylation profiling showed that PNET was largely a “waste basket” category: about 80% of those tumors were misclassified, while the remaining cases represented multiple separate molecular diseases, said Snuderl.

Another example came from low-grade epilepsy-associated tumors in young adults, known as polymorphous low-grade neuroepithelial tumor of the young. It emerged as a clearly distinct clinical and pathological entity through methylation analysis.

These discoveries were not simply academic refinements. They changed how patients were diagnosed and treated.

Taking research to the clinic

Snuderl is quick to point out that many important scientific discoveries never reach patients because researchers stop at publication. A high-impact paper may generate excitement, but translating a method into a clinical diagnostic requires an entirely different level of work—regulatory validation, reproducibility testing, and operational rigor.

“There is very little glory in going through the regulatory framework,” he said. Still, his team decided that it was an essential step.

In 2019, NYU became the first CLIA-certified laboratory in the United States to clinically implement DNA methylation–based brain tumor classification. It was also the first to use machine learning as a primary diagnostic tool for cancer classification.

That achievement was especially significant because New York State has some of the strictest molecular diagnostic regulations in the country. “The benefit of that,” Snuderl said, “is the tests approved in New York State are really rigorous.”

The clinical validation study made clear just how necessary the assay was. Rather than testing only difficult or ambiguous cases, the team prospectively profiled nearly 2,000 consecutive brain tumors to determine real-world utility across all diagnoses.

The results were striking. Approximately 15% of tumors experienced a complete change in diagnosis. Another seven percent were further sub-stratified into clinically meaningful categories. In cases where pathologists could not make a diagnosis at all, methylation profiling resolved the case in more than 85% of patients.

This meant that patients who might have received unnecessary radiation and chemotherapy could avoid overtreatment, while those with aggressive disease who might otherwise have been undertreated were correctly escalated to more appropriate care.

Liquid biopsies and the rise of 5hmC

While brain tumors demonstrate the power of methylation in tissue diagnostics, other companies are applying epigenetics to blood-based cancer detection.

At ClearNote Health, Jeffrey Venstrom, MD, CMO, focuses not on traditional 5-methylcytosine (5mC), but on 5-hydroxymethylcytosine (5hmC), a related epigenetic mark that provides a different kind of biological signal.

“While much of the scientific focus on methylation has centered around the 5mC mark,” he said, “we have found the 5hmC mark to be more relevant to the detection of cancer.”

The distinction is important. While 5mC often reflects silenced genes, 5hmC marks genes that are actively being expressed. That makes it particularly useful for identifying active cancer biology, including the upregulated genetic programs associated with tumor growth.

There are also major technical advantages. Because much more of the genome is silenced than active, there is less 5hmC signal to analyze, requiring less sequencing depth to achieve reliable results. Unlike 5mC analysis, 5hmC detection avoids bisulfite conversion, a harsh chemical step that can damage DNA and complicate sequence mapping. Scientific studies have shown that 5hmC is a reliable signal even in early-stage cancers and carries tissue-specific signatures that help identify where a tumor originated.

ClearNote’s platform analyzes cell-free DNA (cfDNA) from a standard blood draw, integrating 5hmC signals with other genomic features and applying machine learning to detect cancer. “Our machine learning model has now been trained on thousands of patient samples and independently validated,” Venstrom said. That platform powers the company’s Avantect^® tests for one of the most clinically important targets in oncology: pancreatic cancer.

Pancreatic cancer remains a highly lethal malignancy, largely because it is rarely detected early enough for effective intervention. Standard imaging often misses disease during the narrow window when curative treatment is still possible.

ClearNote designed the Avantect Pancreatic Cancer Test specifically for individuals at elevated risk, including people over age 50 with newly diagnosed type 2 diabetes, those with BRCA mutations, and patients with a strong family history of pancreatic cancer.

The test is a simple blood draw, but its performance is substantial. It now achieves 82.6% sensitivity and 97.5% specificity—metrics that Venstrom says will supplement standard imaging technologies for early detection.

The assay is supported by a robust body of evidence, including multiple conference presentations and peer-reviewed scientific publications, representing more than 20,000 test runs across more than 7,000 patients. It is also being deployed in major global studies, including the U.K.’s National Health Service-led SAFE-D study and the international Pancreatic Cancer Early Detection PRECEDE Consortium.

ClearNote is expanding into ovarian cancer and multi-cancer early detection. Its Avantect Multi-Cancer Detection Test is designed to simultaneously identify eight cancers while predicting tissue of origin. That capability is crucial because, as Snuderl notes of metastatic brain tumors, knowing that cancer is present is only the beginning—knowing where it came from determines how it should be treated. This assay was one of only two selected for the National Cancer Institute’s Vanguard Study, which is evaluating real-world implementation across up to 24,000 participants. For cancers that currently lack practical screening tools, multi-cancer detection might represent the next major leap forward.

Reliable epigenetics in clinics

From Zymo Research, Xiaojing Yang, PhD, epigenetics group leader, and Paolo Piatti, PhD, director of applied epigenetics, pointed out that one of the biggest barriers to using epigenetics in clinics is ensuring that DNA methylation testing is “reliable, reproducible, and cost-effective,” especially when working with poor-quality samples like cfDNA from plasma, stool, or urine. These samples are often scarce, degraded, and contain only trace amounts of DNA, making it difficult to accurately detect rare epigenetic signals.

The researchers note that bisulfite-based methods continue to outperform newer approaches in difficult clinical settings. Comparative studies, they say, show these methods provide “higher reproducibility and more stable performance” and align better with reference methods such as EPIC arrays. Although newer enzymatic approaches are promising, they might create systematic biases like methylation overestimation. Because of this, they argue that “bisulfite conversion chemistry continues to be reinforced as the clinical gold standard for DNA methylation analysis.”

To address these issues, Zymo developed CE IVD-marked bisulfite conversion kits such as the EZ-96 DNA Methylation Lightning MagPrep. Designed for high-throughput clinical workflows, the kit works with automation platforms like Hamilton, KingFisher Flex, and Tecan Fluent. Its magnetic bead-based system improves DNA recovery and supports downstream PCR, microarrays, and next-generation sequencing.

Beyond products, Zymo offers full-service epigenetic workflows like assay optimization, sequencing, and bioinformatics. Their whole-genome bisulfite sequencing service is optimized for low-input and degraded samples like cfDNA and formalin-fixed, paraffin-embedded tissue. These services are supported by “rigorous quality control,” Yang and Piatti noted, and can extend into CLIA/CAP-compliant environments through partner laboratories.

Looking ahead, the company is developing NGS-based workflows that preserve fragmentomic information from cfDNA. This could enable more sensitive tests for “early disease detection and longitudinal monitoring,” Yang and Piatti said, combining both epigenetic and fragmentomic insights for stronger clinical decision-making.

Measuring epigenetics more directly

As epigenetic diagnostics expand, another challenge becomes increasingly important: how to measure these signals accurately and efficiently.Cora Vacher, PhD, director of segment marketing for human genetics at Oxford Nanopore Technologies, argues that traditional workflows often make epigenetics unnecessarily complex.“Oxford Nanopore sequencing can read native DNA and RNA directly, without PCR amplification or chemical conversion,” she said.That means methylation and other epigenetic modifications can be detected alongside the sequence from the same molecule, in the same experiment.

Instead of stitching together multiple assays for sequencing, structural variation, and methylation analysis, researchers can capture all this information simultaneously. “Our technology reads individual DNA or RNA molecules directly as they pass through a nanopore,” Vacher explained, using changes in electrical current to identify both nucleotide sequence and chemical modifications in real time.

Because amplification and chemical conversion are not required, the method preserves long reads and native biological context. It also enables the detection of multiple epigenetic modifications beyond traditional forms of methylation.

In cancer and rare-disease research, this creates a much richer picture of disease biology by linking sequence variation, haplotype phasing, structural variants, and epigenetic regulation on the same molecule. “Because epigenetic information is captured directly,” she said, “the technology is well-suited to developing simpler, more integrated assays that could support future clinical workflows.”

Therapeutics move beyond diagnosis

Epigenetics is not only improving diagnostics, it is also increasingly becoming the therapy itself. At nChroma Bio, CSO Melissa Bonner, PhD, and CSO Jenny Marlowe, PhD, are developing treatments based on epigenetic silencing rather than gene editing. “Epigenetic silencing enables potent, durable gene silencing without cutting or permanently altering the DNA sequence,” they explained. Instead of editing DNA directly, the approach uses the cell’s own epigenetic machinery—particularly DNA methylation—to turn genes off in a controlled and durable way.

Their lead program, CRMA-1001, an investigational, clinical-stage therapy, targets chronic hepatitis B, where viral persistence depends on both episomal covalently closed circular DNA (cccDNA) and integrated viral DNA. By permanently methylating both forms, the therapy aims to durably silence viral gene expression and potentially deliver a functional cure.

In preclinical studies, CRMA-1001 produced durable suppression of hepatitis B virus (HBV) biomarkers, with up to 90% of animals achieving complete loss of HBV surface antigen and DNA.

The company is also expanding into cardiometabolic disease, central nervous system, and oncology programs. The shift reflects a larger truth across precision medicine: epigenetics is moving from classification to direct intervention.

The next phase of precision medicine

For Snuderl, the next frontier is clear. After spending more than a decade improving diagnosis, today’s goal is to use that molecular knowledge to improve therapy. “Now we know what we are dealing with and, hopefully, we can find better therapies,” he said.

Future applications of epigenetics might resolve other challenges in today’s healthcare. For example, epigenetics might help identify the origins of metastatic brain tumors when physicians are unable to determine where the cancer began. Epigenetics could also improve clinical-trial enrollment by ensuring that the right patients are matched with the most appropriate studies. “If 15% of patients on your clinical trial have a different type of cancer,” Snuderl said, “there’s a statistically very good chance your trial will fail.”

Precision medicine depends on precision diagnosis. Without accurate classification, targeted therapies and biomarker-driven trials cannot succeed. That is where epigenetics has become indispensable. It provides not just more molecular data, but biological context—revealing lineage, vulnerability, treatment response, and increasingly, therapeutic opportunity. What was once a forgotten footnote in a medical school textbook has become one of the most clinically transformative tools in modern oncology.

Mike May, PhD, is a freelance writer and editor with more than 30 years of experience. He earned an MS in biological engineering from the University of Connecticut and a PhD in neurobiology and behavior from Cornell University. He worked as an associate editor at American Scientist, and he is the author of more than 1,000 articles for clients that include GEN, Nature, Science, Scientific American, and many others. In addition, he served as the editorial director of many publications, including several Nature Outlooks and Scientific American Worldview.

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