Integrated molecular analysis suggests a three-class model for low-grade gliomas: a proof-of-concept study.

摘要: Abstract Introduction: We used an integrated molecular analysis strategy to perform class discovery on a population of low-grade gliomas (astrocytomas, oligodendrogliomas, and mixed gliomas) improve our understanding the relationships among these tumors reconcile genotypic with current histologic strategies for tumor classification. Methods: Gene expression profiling was performed cross-section World Health Organization (WHO) grades I–II gliomas. Unsupervised algorithms identified validated clusters similarity, data were chromosomal copy number assays RT-PCR define subclasses. Machine learning models allowed accurate, prospective classification unknown into subgroups. This model compared pathologic (chromosome 1p 19q deletions, p53 alterations, Ki-67 expression) methods glioma Results: Molecular suggested three-class One discrete cluster pilocytic astrocytomas, second grouped 1p/19q codeleted mixture remaining intact gliomas, including oligoastrocytomas, formed third pattern expression. Conclusions: Integration genomic, transcriptomic, morphologic suggests Class I represents similarity II are similar III infiltrative is clinical paradigms gliomas; work basis such models. may supplement or serve as alternative grading schemes highlight potential targets future biologically based treatments trials.