Summary
Accurate pathological diagnosis is crucial for optimal management of cancer patients. For the ~100 known central nervous system (CNS) tumour entities, standardization of the diagnostic process has been shown to be particularly challenging - with substantial inter-observer variability in the histopathological diagnosis of many tumour types. We herein present the development of a comprehensive approach for DNA methylation-based CNS tumour classification across all entities and age groups, and demonstrate its application in a routine diagnostic setting. We show that availability of this method may have substantial impact on diagnostic precision compared with standard methods, resulting in a change of diagnosis in up to 12% of prospective cases. For broader accessibility we have designed a free online classifier tool (www.molecularneuropathology.org) requiring no additional onsite data processing. Our results provide a blueprint for the generation of machine learning-based tumour classifiers across other cancer entities, with the potential to fundamentally transform tumour pathology.
Glioblastoma (GBM) is a devastating tumor and few patients survive beyond 3 years. Defining the molecular determinants underlying long-term survival is essential for insights into tumor biology and biomarker identification. We therefore investigated homogeneously treated, IDH (wt) long-term (LTS, n = 10) and short-term survivors (STS, n = 6) by microarray transcription profiling. While there was no association of clinical parameters and molecular subtypes with long-term survival, STS tumors were characterized by differential polarization of infiltrating microglia with predominance of the M2 phenotype detectable both on the mRNA and protein level. Furthermore, transcriptional signatures of LTS and STS predicted patient outcome in a large, IDH (wt) cohort (n = 468). Interrogation of overlapping genomic alterations identified concurrent gain of chromosomes 19 and 20 as a favorable prognostic marker. The strong association of this co-gain with survival was validated by aCGH in a second, independent cohort (n = 124). Finally, FISH and gene expression data revealed gains to constitute low-amplitude, clonal events with a strong impact on transcription. In conclusion, these findings provide important insights into the manipulation of the innate immune system by particularly aggressive GBM tumors. Furthermore, we genomically characterize a previously unknown, clinically relevant subgroup of glioblastoma, which can easily be identified through modern neuropathological workup.
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