The molecular hallmarks of primary and secondary vitreoretinal lymphoma

Vitreoretinal lymphoma (VRL) is a rare subtype of diffuse large B-cell lymphoma (DLBCL) considered a variant of primary central nervous system lymphoma (PCNSL). The diagnosis of VRL requires examination of vitreous fluid, but cytologic differentiation from uveitis remains difficult. Because of its rarity and the difficulty in obtaining diagnostic material, little is known about the genetic profile of VRL. The purpose of our study was to investigate the mutational profile of a large series of primary and secondary VRL. Targeted next-generation sequencing using a custom panel containing the most frequent mutations in PCNSL was performed on 34 vitrectomy samples from 31 patients with VRL and negative controls with uveitis. In a subset of cases, genome-wide copy number alterations (CNAs) were assessed using the OncoScan platform. Mutations in MYD88 (74%), PIM1 (71%), CD79B (55%), IGLL5 (52%), TBL1XR1 (48%), ETV6 (45%), and 9p21/CDKN2A deletions (75%) were the most common alterations, with similar frequencies in primary (n = 16), synchronous (n = 3), or secondary (n = 12) VRL. This mutational spectrum is similar to MYD88mut/CD79Bmut (MCD or cluster 5) DLBCL with activation of Toll-like and B-cell receptor pathways and CDKN2A loss, confirming their close relationship. OncoScan analysis demonstrated a high number of CNAs (mean 18.6 per case). Negative controls lacked mutations or CNAs. Using cell-free DNA of vitreous fluid supernatant, mutations present in cellular DNA were reliably detected in all cases examined. Mutational analysis is a highly sensitive and specific tool for the diagnosis of VRL and can also be applied successfully to cell-free DNA derived from the vitreous

DNA methylation-based classification of central nervous system tumours.

Accurate pathological diagnosis is crucial for optimal management of patients with cancer. For the approximately 100 known tumour types of the central nervous system, 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. Here we present a comprehensive approach for the DNA methylation-based classification of central nervous system tumours across all entities and age groups, and demonstrate its application in a routine diagnostic setting. We show that the availability of this method may have a substantial impact on diagnostic precision compared to 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, the use of which does not require any 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

Glioneuronal tumor with ATRX alteration, kinase fusion and anaplastic features (GTAKA): a molecularly distinct brain tumor type with recurrent NTRK gene fusions

Glioneuronal tumors are a heterogenous group of CNS neoplasms that can be challenging to accurately diagnose. Molecular methods are highly useful in classifying these tumors-distinguishing precise classes from their histological mimics and identifying previously unrecognized types of tumors. Using an unsupervised visualization approach of DNA methylation data, we identified a novel group of tumors (n = 20) that formed a cluster separate from all established CNS tumor types. Molecular analyses revealed ATRX alterations (in 16/16 cases by DNA sequencing and/or immunohistochemistry) as well as potentially targetable gene fusions involving receptor tyrosine-kinases (RTK; mostly NTRK1-3) in all of these tumors (16/16; 100%). In addition, copy number profiling showed homozygous deletions of CDKN2A/B in 55% of cases. Histological and immunohistochemical investigations revealed glioneuronal tumors with isomorphic, round and often condensed nuclei, perinuclear clearing, high mitotic activity and microvascular proliferation. Tumors were mainly located supratentorially (84%) and occurred in patients with a median age of 19 years. Survival data were limited (n = 18) but point towards a more aggressive biology as compared to other glioneuronal tumors (median progression-free survival 12.5 months). Given their molecular characteristics in addition to anaplastic features, we suggest the term glioneuronal tumor with ATRX alteration, kinase fusion and anaplastic features (GTAKA) to describe these tumors. In summary, our findings highlight a novel type of glioneuronal tumor driven by different RTK fusions accompanied by recurrent alterations in ATRX and homozygous deletions of CDKN2A/B. Targeted approaches such as NTRK inhibition might represent a therapeutic option for patients suffering from these tumors

Sarcoma classification by DNA methylation profiling

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