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.

Sarcomas are malignant soft tissue and bone tumours affecting adults, adolescents and children. They represent a morphologically heterogeneous class of tumours and some entities lack defining histopathological features. Therefore, the diagnosis of sarcomas is burdened with a high inter-observer variability and misclassification rate. Here, we demonstrate classification of soft tissue and bone tumours using a machine learning classifier algorithm based on array-generated DNA methylation data. This sarcoma classifier is trained using a dataset of 1077 methylation profiles from comprehensively pre-characterized cases comprising 62 tumour methylation classes constituting a broad range of soft tissue and bone sarcoma subtypes across the entire age spectrum. The performance is validated in a cohort of 428 sarcomatous tumours, of which 322 cases were classified by the sarcoma classifier. Our results demonstrate the potential of the DNA methylation-based sarcoma classification for research and future diagnostic applications.

Epileptogenic tumors affecting children and young adults are a morphologically diverse collection of neuroepithelial neoplasms that, as a group, exhibit varying levels of glial and/or neuronal differentiation. Recent advances in molecular profiling technology, including comprehensive DNA sequencing and methylation analysis, have enabled the application of more precise and biologically relevant classification schemes to these tumors. In this report, we describe a morphologically and molecularly distinct epileptogenic neoplasm, the polymorphous low-grade neuroepithelial tumor of the young (PLNTY), which likely accounts for a sizable portion of oligodendroglioma-like tumors affecting the pediatric population. Characteristic microscopic findings most notably include infiltrative growth, the invariable presence of oligodendroglioma-like cellular components, and intense immunolabeling for cluster of differentiation 34 (CD34). Moreover, integrative molecular profiling reveals a distinct DNA methylation signature for PLNTYs, along with frequent genetic abnormalities involving either B-Raf proto-oncogene (BRAF) or fibroblast growth factor receptors 2 and 3 (FGFR2, FGFR3). These findings suggest that PLNTY represents a distinct biological entity within the larger spectrum of pediatric, low-grade neuroepithelial tumors.Electronic supplementary materialThe online version of this article (doi:10.1007/s00401-016-1639-9) contains supplementary material, which is available to authorized users.

Patients with DICER1 predisposition syndrome have an increased risk to develop pleuropulmonary blastoma, cystic nephroma, embryonal rhabdomyosarcoma, and several other rare tumor entities. In this study, we identified 22 primary intracranial sarcomas, including 18 in pediatric patients, with a distinct methylation signature detected by array-based DNA-methylation profiling. In addition, two uterine rhabdomyosarcomas sharing identical features were identified. Gene panel sequencing of the 22 intracranial sarcomas revealed the almost unifying feature of DICER1 hotspot mutations (21/22; 95%) and a high frequency of co-occurring TP53 mutations (12/22; 55%). In addition, 17/22 (77%) sarcomas exhibited alterations in the mitogen-activated protein kinase pathway, most frequently affecting the mutational hotspots of KRAS (8/22; 36%) and mutations or deletions of NF1 (7/22; 32%), followed by mutations of FGFR4 (2/22; 9%), NRAS (2/22; 9%), and amplification of EGFR (1/22; 5%). A germline DICER1 mutation was detected in two of five cases with constitutional DNA available. Notably, none of the patients showed evidence of a cancer-related syndrome at the time of diagnosis. In contrast to the genetic findings, the morphological features of these tumors were less distinctive, although rhabdomyoblasts or rhabdomyoblast-like cells could retrospectively be detected in all cases. The identified combination of genetic events indicates a relationship between the intracranial tumors analyzed and DICER1 predisposition syndrome-associated sarcomas such as embryonal rhabdomyosarcoma or the recently described group of anaplastic sarcomas of the kidney. However, the intracranial tumors in our series were initially interpreted to represent various tumor types, but rhabdomyosarcoma was not among the typical differential diagnoses considered. Given the rarity of intracranial sarcomas, this molecularly clearly defined group comprises a considerable fraction thereof. We therefore propose the designation "spindle cell sarcoma with rhabdomyosarcoma-like features, DICER1 mutant" for this intriguing group.

Solid papillary carcinoma with reverse polarity (SPCRP) is a rare breast cancer subtype with an obscure etiology. In this study, we sought to describe its unique histopathologic features and to identify the genetic alterations that underpin SPCRP using massively parallel whole-exome and targeted sequencing. The morphologic and immunohistochemical features of SPCRP support the invasive nature of this subtype. Ten of 13 (77%) SPCRPs harbored hotspot mutations at R172 of the isocitrate dehydrogenase IDH2, of which 8 of 10 displayed concurrent pathogenic mutations affecting PIK3CA or PIK3R1. One of the IDH2 wild-type SPCRPs harbored a TET2 Q548* truncating mutation coupled with a PIK3CA H1047R mutation. Functional studies demonstrated that IDH2 and PIK3CA hotspot mutations are likely drivers of SPCRP, resulting in its reversed nuclear polarization phenotype. Our results offer a molecular definition of SPCRP as a distinct breast cancer subtype. Concurrent IDH2 and PIK3CA mutations may help diagnose SPCRP and possibly direct effective treatment.

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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.

SUMMARY Low-grade astrocytomas (LGA) carry neomorphic mutations in Isocitrate Dehydrogenase (IDH), concurrently with P53 and ATRX loss. To model LGA formation, we introduced R132H IDH1, P53 shRNA and ATRX shRNA in human neural stem cells (NSCs). These oncogenic hits blocked NSC differentiation, increased invasiveness in vivo and led to a DNA methylation and transcriptional profile resembling IDH1-mutant human LGAs. The differentiation block was caused by transcriptional silencing of transcription factor SOX2, secondary to disassociation of its promoter from a putative enhancer. This occurred due to reduced binding of the chromatin organizer CTCF to its DNA motifs and disrupted chromatin looping. Our human model of IDH-mutant LGA formation implicates impaired NSC differentiation due to repression of SOX2 as an early driver of gliomagenesis.