We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

LabMedica

Download Mobile App
Recent News Expo Clinical Chem. Molecular Diagnostics Hematology Immunology Microbiology Pathology Technology Industry Focus

New DNA Methylation-Based Method Predicts Cancer Progression

By LabMedica International staff writers
Posted on 18 Sep 2025

Cancer often develops silently for years before diagnosis, making it difficult to trace its origins and predict its progression. Traditional approaches to studying cancer evolution have lacked the precision needed to forecast how the disease will advance in individual patients. Now, a new method that deciphers the epigenetic history of tumors can help understand the origin and evolution of cancer, making it possible to predict its future clinical course.

The new technique, developed by an international team led by Clínic-IDIBAPS-UB (Barcelona, Spain) and the Institute of Cancer Research, London (London, UK), is based on DNA methylation analysis. This approach focuses on fluctuating methylation, a type of epigenetic mark that captures the identity of the original tumor cell and evolves as the cancer grows and diversifies. The researchers created an algorithm called EVOFLUx to interpret these patterns, reconstructing the origin and development of tumors much like a “black box” records flight data.


Image: From left to right, experts Iñaki Martín-Subero and Martí Duran-Ferrer (Photo courtesy of Universitat De Barcelona)
Image: From left to right, experts Iñaki Martín-Subero and Martí Duran-Ferrer (Photo courtesy of Universitat De Barcelona)

The EVOFLUx algorithm was applied to 2,000 samples from patients with leukemias and lymphomas. By analyzing previously collected epigenetic data, the team uncovered detailed evolutionary histories of cancers that had been hidden within what was once considered background noise. The study, published in Nature, demonstrates that this method can reveal when tumors began, how quickly they grew, and whether they generated cellular diversity, offering key insights into disease biology.

The method was further validated in patients with lymphoid cancers, including pediatric acute lymphoblastic leukemia and adult chronic lymphoblastic leukemia. By correlating tumor history with aggressiveness, the researchers found that initial cancer growth strongly influenced future progression. This knowledge allows prediction of clinical outcomes years in advance and could guide more personalized treatment, with potential applications across all types of cancer.

“Cancers change over time, which complicates their treatment,” said Trevor Graham from the Institute of Cancer Research, who coordinated the research. “We discovered that the initial growth of the cancer determines how it will evolve in the future, allowing us to predict how the disease will progress in each patient. This is a big step in personalized disease management.”

“In the case of chronic lymphocytic leukemia, a type of cancer that does not always require immediate treatment, with this new test we can predict when the disease will need to be treated years in advance,” added UB adjunct professor Iñaki Martín-Subero. “Although in this study we analyzed leukemia and lymphoma samples, we believe that this methodology could work with all types of cancer.”

Related Links:
Clínic-IDIBAPS-UB
Institute of Cancer Research


New
Gold Member
Cardiovascular Risk Test
Metabolic Syndrome Array I & II
Collection and Transport System
PurSafe Plus®
New
Rapid Molecular Testing Device
FlashDetect Flash10
New
Human Estradiol Assay
Human Estradiol CLIA Kit

Latest Molecular Diagnostics News

Urine Test Could Predict Outcome of Cartilage Transplant Surgery
18 Sep 2025  |   Molecular Diagnostics

2-Hour Cancer Blood Test to Transform Tumor Detection
18 Sep 2025  |   Molecular Diagnostics

Ultrasensitive Test Could Identify Earliest Molecular Signs of Metastatic Relapse in Breast Cancer Patients
18 Sep 2025  |   Molecular Diagnostics