Methylation Biology of a Blood-Based MDS Risk Stratification Test

Background: Myelodysplastic syndromes (MDS) are a heterogeneous group of myeloid malignancies associated with a myriad of deleterious outcomes and a 5-year relative survival of 37%. MDS risk stratification is key for optimal treatment decisions. We previously demonstrated that methylation-based markers may help stratify MDS risk and achieved comparable performance relative to IPSS-R (the Revised International Prognostic Scoring System) by assessing the risk prediction ability of a classifier trained with features from either Bone Marrow (BM) whole-genome bisulfite sequencing (WGBS) or serum targeted methylation (TM) sequencing. Here, we elucidate the biological underpinnings of the methylation-based classifier and explore the survival stratification performance of our previously reported classifier.

Methods: Utilizing datasets from BM WGBS and matched Serum TM sequencing of 127 patients with MDS (N=104) and secondary AML (N=23), we delineated the features used by the classifier by identifying differentially methylated regions (DMRs) that distinguished patients with overall survival of >3 years from those with <3 years. Our approach also included a functional enrichment and driver gene analysis associated with these DMRs, as well as cluster analysis across patients. We performed further evaluations of the survival outcomes of our BM WGBS and matched Serum TM prognostic classifier using concordance indices and log-rank tests.

Results: Our analysis revealed 7,742 DMRs in BM WGBS and 14,093 DMRs in the Serum TM that significantly separated the long and short-term survival groups. Unsupervised hierarchical clustering of differential methylation patterns in both BM WGBS and Serum TM highlighted three distinctive subgroups of samples: short-term survivors, long-term survivors, and a group with intermediate survival outcomes. Notably, the short-term survivor group exhibited extensive hypermethylation across a majority of DMRs, which were linked to key biological pathways including calcium signaling, cAMP, MAPK, Rap1, and PI3K-Akt. Enrichment analysis of the BM WGBS DMRs between individuals with <3 and >3 years of survival underscored the differential hypermethylation of promoters, CpG islands, and CpG shores in association with worse survival outcomes. Our analysis of BM WGBS DMR driver genes via Hypergeometric Optimization of Motif EnRichment (HOMER) revealed potential contributors to differential survival outcomes, including known genes associated with MDS/AML, such as EWS/FLI-1, ERG, Pu.1, and RUNX1. Both pathway and driver gene analysis recapitulated previously known drivers of MDS to AML progression. The methylation classifier achieved comparable risk stratification performance for both BM WGBS comparing with IPSS-R (C-index: 0.69 [95% CI: 0.61-0.77] for methylation vs 0.71 [95% CI: 0.64-0.77] for IPSS-R) and Serum TM (C-index: 0.68 [95% CI: 0.60-0.75] for methylation vs. 0.70 [95% CI: 0.63-0.78] for IPSS-R). At a specificity threshold of 0.8, both BM WGBS and Serum TM prognostic classifiers were able to stratify between predicted short and predicted long survivors (BM WGBS HR: 2.83, p<0.0001; Serum TM HR: 3.01, p=0.0008).

Conclusions: Our findings generated biological insights into the methylation-based risk stratification of MDS, shedding light on the specific genomic features and biological pathways that underlie these methylation patterns. We also demonstrated that methylation-based MDS risk stratification performs comparably with IPSS-R. These insights not only expand our understanding of the complexity of MDS, but also reinforce the potential of methylation-based sequencing as a viable, possibly less-invasive, alternative for MDS risk stratification compared to traditional IPSS-R methods.