Presenting Author Yale University, Yale University
Introduction: Mortality rates from hepatocellular carcinoma (HCC) have been steadily increasing in the US over the past 15 years. Most patients are diagnosed at advanced stages when the mortality rate is as high as 89%. Early diagnostic indicators are not currently available, and unfortunately, invasive liver core biopsies or resections are the standard of care to confirm HCC diagnosis. While no mutations have have proven to be tumor drivers, novel genome wide methylation studies show clear, cancer-specific patterns of methylation and identified deregulation of epigenetic signatures. Epigenetic clocks have been shown to be a versatile tool for the prediction of a variety of patient phenotypes. Most commonly used for the prediction of age and mortality risk, these have also been used as indicators of alcohol consumption, smoking, and proxies for various blood-based biomarkers. Acceleration in a number of clocks has been correlated to increased all-cause mortality, as well as cancer risk. While much focus in HCC has been on clocks trained to precisely predict chronological age, confidence in the characterization of the notable epigenetic landscape and target region identification will be improved through the use of clocks trained to instead predict biological age with low signal to noise ratio. The goal of this study is to use epigenetic age acceleration to develop early cancer risk detection, improve treatment options, and to understand the relationship between accelerated liver tissue aging and tumorigenesis. Epigenetic aging events are potentially reversible adjuvant. Methylated DNA is also far more stable in serum and tissue than un-methylated nucleic acids. These factors make epigenetic events attractive candidates for serum and biopsy biomarkers.
Methods: Genomic DNA was extracted from IRB- approved, post-operative or snap frozen, clinical patient liver resections or explants. DNA methylation (DNAm) was collected using Illumina BeadChip arrays. DNAm was used to calculate epigenetic age according to PCPhenoAge in over 600 liver samples, 8 tumor infiltrated NK-cell isolates and 50 cell culture samples. PCPhenoAge is an improved version of the original biological aging clock with increased reliability and reduced susceptibility to technical noise. Multiple regression was used to residualize PCPhenoAge with respect to age and multiple phenotypes, such as alcohol consumption, known to affect epigenetic age. These residuals capture the epigenetic age acceleration independent of known behavioral risk factors for HCC.
Results: Preliminary analysis of 27 of our matched HCC patient tissues--14 Alcohol and 13 Hepatitis C virus (HCV) associated HCC patients--show higher PCPhenoAge in tumor versus non-tumor tissues. Further, alcohol-related HCC tumor tissue has significantly higher PCPhenoAge than HCV-related HCC tumor tissue.
Rutgers University/ New Jersey Medical School in collaboration with Mayo Clinic
