Cross-Platform DNA Methylation Classifier for the Eight Molecular Subtypes of Group 3 & 4 Medulloblastoma

Omer Abid, Gholamreza Rafiee * Abstract -- Medulloblastoma is a malignant pediatric brain cancer, and the discovery of molecular subgroups is enabling personalized treatment strategies. In 2019, a consensus identified eight novel subtypes within Groups 3 and 4, each displaying heterogeneous chara cteristics. Classifiers are essential for translating these findings into clinical practice by supporting clinical trials, personalized therapy development and application, and patient monitoring. This study presents a DNA methylation - based, cross - platform machine learning classifier capable of distinguishing these subtypes on both HM450 and EPIC methylation array samples . Across two independent test sets, the model achieved weighted F1 = 0.95 and balanced accuracy = 0.957, consistent across platforms. As the first cross - platform solution, it provides backward compatibility while extending applicability to a newer platform, also enhancing accessibility. It also has the potential to become the first publicly available classifier for these subtypes once deployed through a web application, as planned in the future . Th is work overall takes steps in the direction of advancing precision medicine and improving clinical outcomes for patients within the majority prevalence medulloblastoma subgroups, g roups 3 and 4. Keywords -- Medulloblastoma, Molecular Subgroup Classification, Machine Learning, AI for Health Medulloblastoma is a malignant brain cancer widely known for its prevalence in children. Through extensive treatment strategies based on surgery, chemotherapy and radiation therapy, approximately 75% of the patient are able to survive in the long term [1]. These treatments whi le crucial also come along with negative side effects, effecting patients' li ves [1] [2], especially considering the implications on the growing children. However, with advancement in genomics, molecular subgroups have been discover ed within the disease . T hese subgroups have shown to be heterogenous in clinical, biological and outcomes perspective [3] . These in fact are now considered better definition of disease behaviour than conventional techniques [3] .