Personalized diagnosis of medulloblastoma subtypes across patients and model systems

Deena Mohamad Ameen Gendoo; Petr Smirnov; Mathieu Lupien; Benjamin Haibe-Kains

doi:10.1016/j.ygeno.2015.05.002

Personalized diagnosis of medulloblastoma subtypes across patients and model systems

Genomics. 2015 Aug;106(2):96-106. doi: 10.1016/j.ygeno.2015.05.002. Epub 2015 May 12.

Authors

Deena Mohamad Ameen Gendoo¹, Petr Smirnov², Mathieu Lupien³, Benjamin Haibe-Kains⁴

Affiliations

¹ 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.
² Bioinformatics and Computational Genomics Laboratory, Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, Canada.
³ 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.
⁴ 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.

PMID: 25979459
DOI: 10.1016/j.ygeno.2015.05.002

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.

Keywords: Cancer; Classification; Human; Medulloblastoma; Mouse model; Subtypes.

Publication types

Evaluation Study
Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Animals
Cell Line, Tumor
Cerebellar Neoplasms / classification*
Cerebellar Neoplasms / diagnosis*
Classification / methods
Gene Expression Profiling*
Humans
Medulloblastoma / classification*
Medulloblastoma / diagnosis*
Mice
Precision Medicine
Software