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.

Tumors with histological features of pilocytic astrocytoma (PA), but with increased mitotic activity and additional high-grade features (particularly microvascular proliferation and palisading necrosis) have often been designated anaplastic pilocytic astrocytomas. The status of these tumors as a separate entity has not yet been conclusively demonstrated and molecular features have only been partially characterized. We performed DNA methylation profiling of 102 histologically defined anaplastic pilocytic astrocytomas. T-distributed stochastic neighbor-embedding (t-SNE) and hierarchical clustering analysis of these 102 cases against 158 reference cases from 12 glioma reference classes revealed that a subset of 83 of these tumors share a common DNA methylation profile that is distinct from the reference classes. These 83 tumors were thus denominated DNA methylation class anaplastic astrocytoma with piloid features (MC AAP). The 19 remaining tumors were distributed amongst the reference classes, with additional testing confirming the molecular diagnosis in most cases. Median age of patients with MC AAP was 41.5 years. The most frequent localization was the posterior fossa (74%). Deletions of CDKN2A/B (66/83, 80%), MAPK pathway gene alterations (49/65, 75%, most frequently affecting NF1, followed by BRAF and FGFR1) and mutations of ATRX or loss of ATRX expression (33/74, 45%) were the most common molecular alterations. All tumors were IDH1/2 wildtype. The MGMT promoter was methylated in 38/83 tumors (45%). Outcome analysis confirmed an unfavorable clinical course in comparison to PA, but better than IDH wildtype glioblastoma. In conclusion, we show that a subset of histologically defined anaplastic pilocytic astrocytomas forms a separate DNA methylation cluster, harbors recurrent alterations in MAPK pathway genes in combination with alterations of CDKN2A/B and ATRX, affects patients who are on average older than those diagnosed with PA and has an intermediate clinical outcome.

pTLIF allows for safe and efficient minimally invasive treatment of single and multilevel degenerative lumbar instability with good clinical results. Further prospective studies investigating long-term functional results are required to assess the definitive merits of percutaneous instrumentation of the lumbar spine.

PURPOSE Meningiomas are the most frequent primary intracranial tumors. Patient outcome varies widely from benign to highly aggressive, ultimately fatal courses. Reliable identification of risk of progression for individual patients is of pivotal importance. However, only biomarkers for highly aggressive tumors are established ( CDKN2A/B and TERT), whereas no molecularly based stratification exists for the broad spectrum of patients with low- and intermediate-risk meningioma. METHODS DNA methylation data and copy-number information were generated for 3,031 meningiomas (2,868 patients), and mutation data for 858 samples. DNA methylation subgroups, copy-number variations (CNVs), mutations, and WHO grading were analyzed. Prediction power for outcome was assessed in a retrospective cohort of 514 patients, validated on a retrospective cohort of 184, and on a prospective cohort of 287 multicenter cases. RESULTS Both CNV- and methylation family–based subgrouping independently resulted in increased prediction accuracy of risk of recurrence compared with the WHO classification (c-indexes WHO 2016, CNV, and methylation family 0.699, 0.706, and 0.721, respectively). Merging all risk stratification approaches into an integrated molecular-morphologic score resulted in further substantial increase in accuracy (c-index 0.744). This integrated score consistently provided superior accuracy in all three cohorts, significantly outperforming WHO grading (c-index difference P = .005). Besides the overall stratification advantage, the integrated score separates more precisely for risk of progression at the diagnostically challenging interface of WHO grade 1 and grade 2 tumors (hazard ratio 4.34 [2.48-7.57] and 3.34 [1.28-8.72] retrospective and prospective validation cohorts, respectively). CONCLUSION Merging these layers of histologic and molecular data into an integrated, three-tiered score significantly improves the precision in meningioma stratification. Implementation into diagnostic routine informs clinical decision making for patients with meningioma on the basis of robust outcome prediction.