title: Personalized diagnosis of medulloblastoma subtypes across patients and model systems.
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.
authors.affiliation: Bioinformatics and Computational Genomics Laboratory, Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, Canada; Medical Biophysics Department, University of Toronto, Toronto, Ontario, Canada. Electronic address: deena.gendoo@utoronto.ca.
authors.forename: Deena Mohamad Ameen
authors.initials: DM
authors.lastname: Gendoo
authors.affiliation: Bioinformatics and Computational Genomics Laboratory, Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, Canada.
authors.forename: Petr
authors.initials: P
authors.lastname: Smirnov
authors.affiliation: Bioinformatics and Computational Genomics Laboratory, Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, Canada; Medical Biophysics Department, University of Toronto, Toronto, Ontario, Canada; Ontario Institute for Cancer Research, Toronto, Ontario M5G 1L7, Canada.
authors.forename: Mathieu
authors.initials: M
authors.lastname: Lupien
authors.affiliation: Bioinformatics and Computational Genomics Laboratory, Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, Canada; Medical Biophysics Department, University of Toronto, Toronto, Ontario, Canada. Electronic address: bhaibeka@uhnresearch.ca.
authors.forename: Benjamin
authors.initials: B
authors.lastname: Haibe-Kains
mesh_terms: D000465:Algorithms; D000818:Animals; D045744:Cell Line, Tumor; D002528:Cerebellar Neoplasms / Q000145:classification* / Q000175:diagnosis*; D002965:Classification / Q000379:methods; D020869:Gene Expression Profiling; D006801:Humans; D008527:Medulloblastoma / Q000145:classification* / Q000175:diagnosis*; D051379:Mice; D057285:Precision Medicine; D012984:Software
keywords: Cancer; Classification; Human; Medulloblastoma; Mouse model; Subtypes