DNA methylation signatures as predictive biomarkers for chemotherapy (CT) resistance and survival in pancreatic ductal adenocarcinoma (PDA).

Abstract

4151 Background: Predicting innate treatment resistance to traditional CT in PDA can optimize therapeutic strategies and improve patient outcomes. This study investigates methylation changes in genes encoding proteins implicated in preclinical models (PDA cell lines or mouse models) influencing drugs commonly used to treat PDA, including 5-fluorouracil (5FU), oxaliplatin, irinotecan, gemcitabine (Gem), nanoliposomal irinotecan, and nab-paclitaxel. Using a comprehensive literature review (1970–2024), we curated a panel of relevant genes and analyzed their methylation patterns in The Cancer Genome Atlas (TCGA) database. We hypothesized that DNA methylation changes affect gene expression and protein production, contributing to chemotherapy resistance. Methods: PDA patient methylation data were accessed from the TCGA database. Survival analyses were performed using elastic net multivariate regression to identify significant methylation signatures, followed by Kaplan-Meier analysis. Model parameters, including alpha (α) and lambda (λ), were optimized through 100 iterations to minimize error. Our curated panel consisted of 138 genes, predominantly Gem-specific or Gem + 5FU (n = 93). Results: The TCGA database provided methylation data for 184 PDA patients (106 had Gem or Gem-based therapy), with 133/138 genes in our analysis. Our analysis identified 23 cytosines followed by guanine residue (CpG) methylation signatures within the panel, ranging from 1 to 23 CpG sites. The best-performing signature, containing 21 CpG sites, stratified patients into significantly different survival groups (17 months (m) vs. not evaluable, p = 0.004). The second-best signature, with 8 CpG sites, stratified survival as 17m vs. 66.94m (p = 0.03). Interestingly, signatures with the most (n = 23) and least (n = 1) CpG sites also demonstrated strong stratification, with survival differences of 15.15m vs. 30.02m (p = 0.01) and 18.67m vs. 44.38m (p = 0.01), respectively. Many signatures included multiple CpG sites from single genes. A Gem or Gem + 5FU-specific panel (n = 93) applied to patients treated with Gem-based therapy identified an 8-CpG signature distinguishing high-risk patients (20.84m vs. 49.38m, p = 0.02). Conclusions: This study highlights the potential of CpG methylation signatures to predict treatment outcomes in PDA. These findings may guide the identification of high-risk patients and the optimization of CT regimens for improved survival. The identification of methylation signatures associated with genes implicated in chemotherapy resistance provides valuable insights into the underlying mechanisms of innate treatment resistance in PDA. Further validation of these methylation signatures could contribute to more effective and targeted therapeutic approaches in clinical practice.