Study: New Blood Test Measuring Epigenetic Instability may help in the Early Detection of Cancer

Researchers at the Johns Hopkins Kimmel Cancer Center have developed a novel liquid biopsy approach to identify early-stage cancers by measuring random variation in DNA methylation patterns, rather than the absolute levels of those patterns, as in other liquid biopsies.

The method, which uses a new metric called the Epigenetic Instability Index (EII), successfully distinguished, with high accuracy, patients with early-stage lung and breast cancers from healthy individuals.

A robust and universal biomarker
The proof-of-concept study was supported in part by the National Institutes of Health (NIH), the National Institute on Aging, and the National Institute of Environmental Health Sciences (R01 ES011858), published in the January 27, 2026 edition of Clinical Cancer Research and presented at the 2024 AACR meeting. [1][2]

The study suggests that quantifying the randomness of the cancer epigenome – a phenomenon the authors describe as ‘epigenetic instability‘ – could provide a more robust and universal biomarker for early cancer detection than currently available methods.

“This is the first study where we are trying to really implement measuring that variation, or stochasticity, into a diagnostic tool,” noted lead study author Hariharan Easwaran, Ph.D., M.Sc., an associate professor of oncology at the Johns Hopkins University School of Medicine.

“We immediately found that measuring DNA methylation variation performs better than just measuring DNA methylation by itself,” Easwaren added.

Thomas Pisanic, Ph.D., an associate research professor of oncology at the Johns Hopkins Institute for NanoBioTechnology and co-lead on the study, further noted, “We hypothesize that early-stage tumors and precancerous lesions that exhibit high degrees of methylation variation, or epigenetic instability, may be more resistant to intrinsic cancer-protective mechanisms and progress more rapidly.”

Blood tests called liquid biopsies that measure DNA methylation typically detect specific, absolute changes in methylation, a chemical reaction in which a methyl group is added to DNA at individual sites in the genome.

However, these tests are typically developed through studying a specific cohort of people — who are similar in age, race or disease development, for example — and tend to work for that cohort of people but fail to perform as well in broader, more diverse populations.

A broader diagnostic tool
To develop a better, broader diagnostic tool for cancer screening, Sara-Jayne Thursby, a postdoctoral researcher in Easwaran’s lab, analyzed publicly available cancer DNA methylation datasets from 2,084 samples to identify a panel of 269 specific genomic regions, known as CpG islands, which captured most DNA methylation variability across multiple cancer types. Those regions could now be used to design biomarker panels.

“We identified specific genomic regions that tend to be the most variable in DNA methylation marks during cancer,” says Thursby, first author on the paper. “In cell-free DNA in the blood, that variability shouldn’t be high, but if it is, it is indicative of a developing cancerous phenotype.”

Next, the team trained a machine learning model to distinguish cancer signals from healthy ones, then tested it using cross-validation. The resulting tool performed with remarkable accuracy across numerous cancer types. In lung adenocarcinoma, the EII differentiated stage 1A cancer with 81% sensitivity at 95% specificity. Sensitivity refers to how good a cancer test is at finding cancer when it is truly present. A highly sensitive test produces few false negative results. Specificity refers to how accurate a cancer test is at ruling out cancer when it is not present. A highly specific test produces few false positive results.

Additionally, the tool detected early-stage breast cancer with approximately 68% sensitivity at 95% specificity. It also showed promise in detecting signals from colon, brain, pancreatic, and prostate cancers.

“Our hypothesis is that during the earliest stages of cancer development, methylation starts shifting,” explained Easwaran.

“We can try to pick those signals using these stochasticity metrics, even of early cancer stages, as long as the DNA is shed in the blood.” Pisanic adds, “By leveraging these metrics, we may be able to better identify and intercept tissues in the early stages of carcinogenesis,” he said.

Expanding and Improving
Toward this end, the team is now expanding and improving upon the method to continue developing the EII into a diagnostic tool.

“While further validation in larger, long-term clinical cohorts is required, the EII could complement existing screening tools developed at Johns Hopkins, such as DELFI (DNA evaluation of fragments for early interception) and other DNA mutation-based assays, and be used as a potential “secondary triaging measure” for clinical use,” Easwaran explained.

“For example, if a patient has a high PSA (prostate-specific antigen) test, which often yields false positives, an EII blood test could help determine if a follow-up biopsy is truly necessary,” concluded.

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Note: * Additional funding for this research was supported by Samuel Waxman Cancer Research Foundation Collaboration for a Cure Grant, The Commonwealth Foundation, The Evelyn Grollman Glick Scholar Award, and the Dr. Miriam and Sheldon G. Adelson Medical Research Foundation.

Reference
[1] Thursby SJ, Jin Z, Blum J, Gurau A, Noë M, Scharpf RB, Velculescu VE, Cope L, Brock M, Baylin S, Pisanic T, Easwaran H. Epigenetic Instability-Based Metrics in Cell-Free DNA for Early Cancer Detection. Clin Cancer Res. 2026 Jan 27. doi: 10.1158/1078-0432.CCR-25-3384. Epub ahead of print. PMID: 41591979.
[2] Thursby SJ, Jin Z, Baylin SB, Brock M, Pisanic T, Easwaran H. Multi-cancer early detection using metrics of DNA methylation-based epigenetic instability [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl): Abstract nr 3666.

Featured image: A conceptual representation of cell-free DNA being filtered through a cell-free DNA cancer detection algorithm. Before filtering, the cell-free DNA resembles a “cloudy mess,” making it unclear whether it is cell-free DNA or circulating tumor DNA. After filtering, cell-free DNA is sorted clearly into cell-free DNA and circulating tumor DNA. Image Generated by AI using Perplexity.Photo courtesy: © 2024 – 2026 The Johns Hopkins University. Used with permission.

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