Epigenetic patterns and methylation-based models for robust outcome prediction in osteosarcoma

ABSTRACT Osteosarcoma (OSA) is a relatively rare but aggressive bone malignancy that primarily affects children and adolescents and has not seen survival improvements in at least three decades. No clinical or pathological prognostic factor has been identified that can lead to improved outcomes. Further, the complex genomic architecture and genetic heterogeneity of OSA and the rare nature of the tumor have significantly hampered identification and validation of molecular predictors. Recent assessment of DNA methylation in OSA has associated patterns with clinically informative outcomes, yet models building on these findings that can be applied in the clinic have not been developed. To address this unmet need, we developed methylation-based models for individualized patient risk prognostication and treatment response prediction at the time of diagnosis. By applying a region-based method to assess methylation, we were able to greatly reduce dimensionality and soften CpG-level noise, yielding stable features for model construction. These models, based on variably methylated regions (VMRs), underwent comprehensive internal cross-validation, and when possible, external validation in a completely independent dataset that was generated using a different technology from the discovery dataset. Further, they were contextualized with an unsupervised exploration of the features, showing widespread global hypermethylation being associated with unfavorable outcomes. Additionally, survival-associated signal appeared to be concentrated within a smaller, more focal subset of features, while in contrast, the chemoresponse signal seemed to come from a larger, more diffuse feature set, potentially suggesting a shift in cell state. An examination of epigenetic age using a methylation-derived epigenetic clock was also shown to provide an orthogonal approach of investigating the methylation landscape with significant association to outcome. Finally, clustering phenotypes were compared between methylation and several other omics platforms (mRNA, miRNA, and copy number variation) and showed very little overlap with one another. Together, these findings establish a foundation for methylation-based outcome stratification at the time of diagnosis and underscore the need for continued investigation including validation in larger and prospective cohorts. Competing Interest Statement The authors have declared no competing interest. Funding Statement Supported by the Amy Chase McMahon Sarcoma Research Fund and the Casper Colson philanthropic donation to Dimitrios Spentzos and the Sarcoma Program at the MGH Cancer Center. The funding bodies had no role in the design of the study, and collection, analysis, and interpretation of the data, or in writing the manuscript. Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The IRB of the Massachusetts General Hospital gave ethical approval for this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Footnotes Author list and affiliations updated; acknowledgements updated Data Availability The datasets analyzed in this study are available the NCI Genomic Data Commons (https://portal.gdc.cancer.gov/projects/TARGET-OS) and the Gene Expression Omnibus repository (GSE59200). All other data are available from the corresponding author upon reasonable request.