Machine-learning operations streamlined clinical workflows of DNA methylation-based CNS tumor classification.

Background: The diagnosis and grading of central nervous system (CNS) tumors, which was traditionally relied on histology, has been enhanced significantly by molecular testing, including DNA methylation profiling, which has been widely adopted for tumor classification. Clinical laboratories, however, are hindered when changes, such as the introduction of the Illumina Infinium MethylationEPIC v2.0 BeadChip, make existing classifiers incompatible due to shifts in targetable CpG sites among array versions. The aim of this study is to provide a scalable CNS tumor classification solution that empowers molecular laboratories and pathology teams to respond swiftly to these challenges. Methods: We employed machine-learning operational methods including continuous integration and continuous training using 228 in-house MethylationEPICv1 array samples and two publicly available data sources to train and validate a DNA-methylation CNS classification pipeline that is compatible with Methylation450k, MethylationEPICv1, and MethylationEPICv2 BeadChips. We optimized CNS tumor classification by validating a multi-modal machine-learning classifier using a combination of a random forest and k-nearest neighbor model framework. Results: We demonstrated an increase of accuracy, sensitivity, and specificity of CNS classification at the superfamily, family, and class level (class-level AUC = 0.90) after employing machine-learning operational methods to our clinical workflow. Our classification pipeline outperformed the DKFZv12.8 classifier in classifying pediatric CNS tumor types and subtypes when using the Illumina Infinium MethylationEPIC v2.0 BeadChip (concordance = 92%). Conclusion: By leveraging machine-learning operational principles, we demonstrate a practical clinical solution for clinical molecular laboratories to employ for improved accuracy and adaptability in DNA methylation-based CNS tumor diagnostics. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study did not receive any funding. ### 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: All data was collected and anonymized in compliance of policies set by Children's Hospital Los Angeles institutional review board. 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 All code and model data has been deployed in a publicly at (https://github.com/alex-markowitz/pipeline-mmaa) and is available as an open-source pipeline via a Docker container (https://hub.docker.com/r/alexmarkowitz/mmaa_pipeline).