Personalized Diagnosis of Medulloblastoma Subtypes Across Patients and Model Systems

View Researcher's Other Codes

Disclaimer: The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).

Authors Deena Mohamad Ameen Gendoo, , Petr Smirnov, Mathieu Lupien, , , Benjamin HaibeKains,
Journal/Conference Name GENOMICS
Paper Category
Paper Abstract Molecular subtyping is instrumental towards selection of model systems for fundamental research in tumor pathogenesis, and clinical patient assessment. Medulloblastoma (MB) is a highly heterogeneous, malignant brain tumor that is the most common cause of cancer-related deaths in children. Current MB classification schemes require large sample sizes, and standard reference samples, for subtype predictions. Such approaches are impractical in clinical settings with limited tumor biopsies, and unsuitable for model system predictions where standard reference samples are unavailable. Our developed Medullo-Model To Subtype (MM2S) classifier stratifies single MB gene expression profiles without reference samples or replicates. Our pathway-centric approach facilitates subtype predictions of patient samples, and model systems including cell lines and mouse models. MM2S demonstrates >96% accuracy for patients of well-characterized normal cerebellum, WNT, or SHH subtypes, and the less-characterized Group 4 (86%) and Group 3 (78.2%). MM2S also enables classification of MB cell lines and mouse models into their human counterparts.
Date of publication 2015
Code Programming Language R
Comment

Copyright Researcher 2021