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Researchers at the Johns Hopkins Kimmel Cancer Center have developed a novel liquid biopsy approach to identify early-stage cancers by measuring the random variation in DNA methylation patterns, rather than the absolute level of those patterns as in other liquid biopsies. The method, which utilizes 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. The work was supported in part by the National Institutes of Health.
The proof-of-concept findings, published Jan. 27 in Clinical Cancer Research and presented at the 2024 AACR meeting, suggest 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,” says 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.”
Thomas Pisanic, Ph.D., an associate research professor of oncology at the Johns Hopkins Institute for NanoBioTechnology and co-lead on the study, added, “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.
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 signals, then tested that model using cross-validation approaches. 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,” says 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.”
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 and other DNA mutation-based assays, and be used as a potential “secondary triaging measure” for clinical use, says Easwaran. 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.
Additional authors on the paper include Zhicheng Jin, Jacob Blum, Andrei Gurau, Michaël Noë, Robert B. Scharpf, Victor E. Velculescu, Leslie Cope, Malcolm Brock and Stephen Baylin of Johns Hopkins.
This research was supported by the National Cancer Institute (grants R01CA229240 and R01CA230995), the National Institute On Aging (U01AG066101), and the National Institute of Environmental Health Sciences (R01 ES011858). 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.
Scharpf is a founder of and consultant for Delfi Diagnostics. Velculescu is a founder of Delfi Diagnostics, serves on the board of directors, and owns Delfi Diagnostics stock, which is subject to certain restrictions under university policy. Additionally, The Johns Hopkins University owns equity in Delfi Diagnostics. Velculescu divested his equity in Personal Genome Diagnostics (PGDx) to LabCorp in February 2022. He is an inventor on patent applications submitted by The Johns Hopkins University related to cancer genomic and cell-free DNA analyses that have been licensed to one or more entities, including Delfi Diagnostics, LabCorp, Qiagen, Sysmex, Agios, Genzyme, Esoterix, Ventana and ManaT Bio. Under the terms of these license agreements, the University and inventors are entitled to fees and royalty distributions. Velculescu also is an adviser to Viron Therapeutics and Epitope. Baylin consults for MDxHealth. He and JHU are entitled to royalty shares received from license sales. These arrangements have been reviewed and approved by The Johns Hopkins University in accordance with its conflict-of-interest policies.
Information source: Johns Hopkins Medical
