Cancer biomarkers relying on methylation could forecast tumor type

In cancer management, biomarkers are commonly used to guide treatment decisions and evaluate patient outcomes. Moreover, it is commonly hard to determine appropriate biomarkers that can be consistently reproduced and easily analyzed with patient specimens.

Cancer biomarkers relying on methylation could forecast tumor type, new study shows
Xuefeng Wang, Ph.D. Department of Biostatistics and Bioinformatics. Image Credit: Moffitt Cancer Center

Moffitt Cancer Center scientists observed on their discovery of biomarkers relying on methylation, a type of genetic modification that forecasts the type of tumor immune environment and patient experience, in a new research appeared on the cover of the May 1, 2022, issue of the journal Cancer Research.

Unique adjustments that can either promote or impede gene expression are heavily controlled. Methylation is a type of epigenetic modification that includes the connection of a methyl chemical group to specific regions of a gene. Elevated concentrations of methylation at gene promoter regions are generally linked to lower levels of gene expression.

Latest research has indicated that patterns of genetic methylation may be useful biomarkers. Methylation as a biomarker has many benefits over frequently used gene expression biomarkers, such as reproducibility and stability, which allows it to be used in medical settings, fewer variations among various testing methods, and the capacity to use tissue specimens that are either frozen or corrected in formaldehyde and embedded in paraffin.

The cytolytic activity score is a well-established biomarker that anticipates the existence of tumor-infiltrating lymphocytes based on gene expression. Tumor-infiltrating lymphocytes are immune cells that have moved from the bloodstream into a tumor.

A massive proportion of tumor-infiltrating lymphocytes in a tumor is usually related to better prognosis for the patient. By analyzing gene expression patterns, the cytolytic activity score forecasts the existence of tumor-infiltrating lymphocytes, particularly cytotoxic T cells.

The Moffitt researchers aim to find an improved biomarker than the cytolytic activity score (for recognizing hot/cold tumors) in their research. They recommended tumor-based expression quantitative trait methylation, which corresponds gene methylation patterns with gene expression, in order to detect potential biomarkers.

The scientists used melanoma as a disease model to see if they could find a specific methylation signature that could anticipate how a tumor’s immune scene looks and determine patient outcomes.

The team of researchers found that methylation sequences in melanoma samples could be used as a surrogate biomarker for the cytolytic activity score and to forecast the type of immune environment in a tumor.

They discovered that the methylation of a single gene named TCF7 could forecast whether T cells in a tumor had anti-tumor properties, and that the TCF7 signature coupled with the cytolytic activity score anticipated patient outcomes by conducting a more targeted analysis. Melanoma patients with a reduced TCF7 signature and an elevated cytolytic activity score lived longer than those with other signature combinations.

The studies confirm the link between low TCF7 and strong cytolytic activity in other tumor types, such as kidney carcinoma, esophageal carcinoma, glioma, sarcoma, and lung cancer. Furthermore, the scientists found that the TCF7 signature could anticipate patient outcomes in the absence 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.”

Xuefeng Wang, PhD, Associate Member, Biostatistics and Bioinformatics, Moffitt Cancer Center

The discovery unlocks potential new targets for personalized treatment decisions. It is similar to a fingerprint or iris scan, as featured in the cover art for the journal,” said Wang.

Wang was also the group lead for computational immunology at Moffitt.

Source:
Journal reference:

Yu, X., et al. (2022) Tumor Expression Quantitative Trait Methylation Screening Reveals Distinct CpG Panels for Deconvolving Cancer Immune Signatures. Cancer Research. doi.org/10.1158/0008-5472.CAN-21-3113.

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