DNA Methylation Signatures Could be Reliable Biomarkers

Researchers in Tampa report the identification of cancer biomarkers based on methylation that predict the type of tumor immune environment and patient outcomes. The discovery also unlocks potential new targets for personalized treatment.

Biomarkers are often used during the management of cancer to guide treatment decisions and inform patient outcomes. One such biomarker, cytolytic activity score, predicts the presence of tumor-infiltrating lymphocytes based on gene expression. Tumor-infiltrating lymphocytes are a type of immune cell that have moved out of the blood and into a tumor. The presence of a high number of tumor-infiltrating lymphocytes in a tumor is typically associated with improved patient prognosis.  

However, it is often difficult to identify appropriate biomarkers that can be consistently reproduced and are easily analyzed with limited patient specimens.

Recently, studies have suggested that patterns of methylation—a type of epigenetic modification in which a methyl chemical group is attached to a particular region of a gene—could be potential biomarkers. The use of methylation as a biomarker has several advantages over commonly used gene expression biomarkers, including reproducibility and stability that enables it to be easily used in clinical settings, fewer variations among different testing techniques, and the ability to use tissue specimens that are either frozen or fixed in formaldehyde and embedded in paraffin.

A team at Moffitt Cancer Center proposed in Cancer Research that tumor-based expression quantitative trait methylation, which correlates gene methylation patterns with gene expression, could be used to identify potential biomarkers. The team focused on melanoma as a disease model and assessed whether they could identify a particular methylation signature that could predict what a tumor’s immune environment looks like and patient outcomes.

The team discovered that methylation sequences in melanoma samples could serve as a surrogate biomarker for the cytolytic activity score and predict the type of immune environment in a tumor. By performing a more targeted analysis, they determined that the methylation of a single gene called TCF7 could predict whether T cells in a tumor had anti-tumor properties, and they found that the TCF7 signature combined with the cytolytic activity score predicted patient outcomes. Specifically, melanoma patients with a low TCF7 signature and a high cytolytic activity score had longer survival than other signature combinations.

The researchers confirmed the association between low TCF7/high cytolytic activity scores in other tumor types, including kidney carcinoma, esophageal carcinoma, glioma, sarcoma and lung cancer. Additionally, the researchers determined that the TCF7 signature could predict patient outcomes independent of other variables.

“While additional studies need to be performed, these analyses suggest that determining immunoepignomic status through tumor-based expression quantitative trait methylation screening could allow for an accurate prediction of patient outcomes. The discovery unlocks potential new targets for personalized treatment decisions. It is similar to a fingerprint or iris scan,” said Xuefeng Wang, Ph.D., associate member of the Department of Biostatistics and Bioinformatics and a group lead for computational immunology at Moffitt.