5-hydroxymethylcytosine sequencing in plasma cell-free DNA of prostate cancer patients
BackgroundCurrently, no biomarkers are available to identify resistance to androgen-deprivation therapies (ADT) in men with hormone-naive prostate cancer. Since 5-hydroxymethylcytosines (5hmC) in gene body are associated with gene activation, in this study, we evaluated whether 5hmC signatures in cell-free DNA (cfDNA) predicts early resistance to ADT.ResultsWe collected a total of 139 serial plasma samples from 55 prostate cancer patients receiving ADT at three time points including baseline (prior to initiating ADT, N=55), 3-month (after initiating ADT, N=55), and disease progression (N=15) within 24 months or 24-month if no progression was detected (N=14). To quantify 5hmC abundance across the genome, we used selective chemical labeling sequencing and mapped sequence reads to individual genes. Differential methylation analysis in baseline samples identified significant 5hmC difference in 1,642 of 23,433 genes between patients with and without progression (false discovery rate, FDR<0.1). Patients with disease progression showed significant 5hmC enrichments in multiple hallmark gene sets with androgen responses as top enriched gene set (FDR=1.19E-13). Interestingly, this enrichment was driven by a subgroup of patients featuring a significant 5hmC hypermethylation in the gene sets involving AR, FOXA1 and GRHL2. To quantify overall activities of these gene sets, we developed a gene set activity scoring algorithm and observed significant association of high activity scores with poor progression-free survival (P<0.05). Longitudinal analysis showed that the high activity scores were significantly reduced after 3-months of initiating ADT (P<0.0001) but returned to higher levels when the disease was progressed (P<0.05).ConclusionsThis study demonstrates that 5hmC-based activity scores from gene sets involved in AR, FOXA1 and GRHL2 may be used as biomarkers to determine early treatment resistance, monitor disease progression, and potentially identify patients who would benefit from upfront treatment intensification.