Accurate calling of KIAA1549-BRAF fusions from DNA of human brain tumours using methylation array-based copy number and gene panel sequencing data.

AIMS: KIAA1549-BRAF fusions occur in certain brain tumours and provide druggable targets due to a constitutive activation of the MAP-kinase pathway. We introduce workflows for calling the KIAA1549-BRAF fusion from DNA methylation array-derived copy number as well as DNA panel sequencing data. METHODS: Copy number profiles were analysed by automated screening and visual verification of a tandem duplication on chromosome 7q34, indicative of the KIAA1549-BRAF fusion. Pilocytic astrocytomas of the ICGC cohort with known fusion status were used for validation. KIAA1549-BRAF fusions were called from DNA panel sequencing data using the fusion callers Manta, Arriba with modified filtering criteria and deFuse. We screened DNA methylation and panel sequencing data of 7790 specimens from brain tumour and sarcoma entities. RESULTS: We identified the fusion in 337 brain tumours with both DNA methylation and panel sequencing data. Among these, we detected the fusion from copy number data in 84% and from DNA panel sequencing data in more than 90% using Arriba with modified filters. While in 74% the KIAA1549-BRAF fusion was detected from both methylation array-derived copy number and panel sequencing data, in 9% it was detected from copy number data only and in 16% from panel data only. The fusion was almost exclusively found in pilocytic astrocytomas, diffuse leptomeningeal glioneuronal tumours and high-grade astrocytomas with piloid features. CONCLUSIONS: The KIAA1549-BRAF fusion can be reliably detected from either DNA methylation array or DNA panel data. The use of both methods is recommended for the most sensitive detection of this diagnostically and therapeutically important marker.

Transcriptional profiling of medulloblastoma with extensive nodularity (MBEN) reveals two clinically relevant tumor subsets with VSNL1 as potent prognostic marker.

Medulloblastoma with extensive nodularity (MBEN) is one of the few central nervous system (CNS) tumor entities occurring in infants which is traditionally associated with good to excellent prognosis. Some MBEN, however, have been reported with an unfavorable clinical course. We performed an integrated DNA/RNA-based molecular analysis of a multi-institutional MBEN cohort (n = 41) to identify molecular events which might be responsible for variability in patients' clinical outcomes. RNA sequencing analysis of this MBEN cohort disclosed two clear transcriptome clusters (TCL) of these CNS tumors: "TCL1 MBEN" and "TCL2 MBEN" which were associated with various gene expression signatures, mutational landscapes and, importantly, prognosis. Thus, the clinically unfavorable "TCL1 MBEN" subset revealed transcriptome signatures composed of cancer-associated signaling pathways and disclosed a high frequency of clinically relevant germline PTCH1/SUFU alterations. In contrast, gene expression profiles of tumors from the clinically favorable "TCL2 MBEN" subgroup were associated with activation of various neurometabolic and neurotransmission signaling pathways, and germline SHH-pathway gene mutations were extremely rare in this transcriptome cluster. "TCL2 MBEN" also revealed strong and ubiquitous expression of VSNL1 (visinin-like protein 1) both at the mRNA and protein level, which was correlated with a favorable clinical course. Thus, combining mutational and epigenetic profiling with transcriptome analysis including VSNL1 immunohistochemistry, MBEN patients could be stratified into clinical risk groups of potential value for subsequent treatment planning.

Desmoplastic/nodular medulloblastomas (DNMB) and medulloblastomas with extensive nodularity (MBEN) disclose similar epigenetic signatures but different transcriptional profiles.

Desmoplastic/nodular medulloblastomas (DNMB) and medulloblastomas with extensive nodularity (MBEN) were outlined in the current WHO classification of tumors of the nervous system as two distinct histological MB variants. However, they are often considered as cognate SHH MB entities, and it is a reason why some clinical MB trials do not separate the patients with DNMB or MBEN histology. In the current study, we performed an integrated DNA/RNA-based molecular analysis of 83 DNMB and 36 MBEN to assess the etiopathogenetic relationship between these SHH MB variants. Methylation profiling revealed "infant" and "children" SHH MB clusters but neither DNMB nor MBEN composed separate epigenetic cohorts, and their profiles were intermixed within the "infant" cluster. In contrast, RNA-based transcriptional profiling disclosed that expression signatures of all MBEN were clustered separately from most of DNMB and a set of differentially expressed genes was identified. MBEN transcriptomes were enriched with genes associated with synaptic transmission, neuronal differentiation and metabolism, whereas DNMB profiling signatures included sets of genes involved in phototransduction and NOTCH signaling pathways. Thus, DNMB and MBEN are distinct tumor entities within the SHH MB family whose biology is determined by different transcriptional programs. Therefore, we recommend a transcriptome analysis as an optimal molecular tool to discriminate between DNMB and MBEN, which may be of benefit for patients' risk stratification in clinical trials. Molecular events identified in DNMB by RNA sequencing could be considered in the future as potent molecular targets for novel therapeutic interventions in treatment-resistant cases.

Routine RNA sequencing of formalin-fixed paraffin-embedded specimens in neuropathology diagnostics identifies diagnostically and therapeutically relevant gene fusions.

Molecular markers have become pivotal in brain tumor diagnostics. Mutational analyses by targeted next-generation sequencing of DNA and array-based DNA methylation assessment with copy number analyses are increasingly being used in routine diagnostics. However, the broad variety of gene fusions occurring in brain tumors is marginally covered by these technologies and often only assessed by targeted assays. Here, we assessed the feasibility and clinical value of investigating gene fusions in formalin-fixed paraffin-embedded (FFPE) tumor tissues by next-generation mRNA sequencing in a routine diagnostic setting. After establishment and optimization of a workflow applicable in a routine setting, prospective diagnostic application in a neuropathology department for 26 months yielded relevant fusions in 66 out of 101 (65%) analyzed cases. In 43 (43%) cases, the fusions were of decisive diagnostic relevance and in 40 (40%) cases the fusion genes rendered a druggable target. A major strength of this approach was its ability to detect fusions beyond the canonical alterations for a given entity, and the unbiased search for any fusion event in cases with uncertain diagnosis and, thus, uncertain spectrum of expected fusions. This included both rare variants of established fusions which had evaded prior targeted analyses as well as the detection of previously unreported fusion events. While the impact of fusion detection on diagnostics is highly relevant, it is especially the detection of "druggable" fusions which will most likely provide direct benefit to the patients. The wider application of this approach for unbiased fusion identification therefore promises to be a major advance in identifying alterations with immediate impact on patient care.

Papillary glioneuronal tumor (PGNT) exhibits a characteristic methylation profile and fusions involving PRKCA.

Papillary glioneuronal tumor (PGNT) is a WHO-defined brain tumor entity that poses a major diagnostic challenge. Recently, SLC44A1-PRKCA fusions have been described in PGNT. We subjected 28 brain tumors from different institutions histologically diagnosed as PGNT to molecular and morphological analysis. Array-based methylation analysis revealed that 17/28 tumors exhibited methylation profiles typical for other tumor entities, mostly dysembryoplastic neuroepithelial tumor and hemispheric pilocytic astrocytoma. Conversely, 11/28 tumors exhibited a unique profile, thus constituting a distinct methylation class PGNT. By screening the extended Heidelberg cohort containing over 25,000 CNS tumors, we identified three additional tumors belonging to this methylation cluster but originally histologically diagnosed otherwise. RNA sequencing for the detection of SLC44A1-PRKCA fusions could be performed on 19 of the tumors, 10 of them belonging to the methylation class PGNT. In two additional cases, SLC44A1-PRKCA fusions were confirmed by FISH. We detected fusions involving PRKCA in all cases of this methylation class with material available for analyses: the canonical SLC44A1-PRKCA fusion was observed in 11/12 tumors, while the remaining case exhibited a NOTCH1-PRKCA fusion. Neither of the fusions was found in the tumors belonging to other methylation classes. Our results point towards a high misclassification rate of the morphological diagnosis PGNT and clearly demonstrate the necessity of molecular analyses. PRKCA fusions are highly diagnostic for PGNT, and detection by RNA sequencing enables the identification of rare fusion partners. Methylation analysis recognizes a unique methylation class PGNT irrespective of the nature of the PRKCA fusion.

Pleomorphic xanthoastrocytoma is a heterogeneous entity with pTERT mutations prognosticating shorter survival.

Pleomorphic xanthoastrocytoma (PXA) in its classic manifestation exhibits distinct morphological features and is assigned to CNS WHO grade 2 or grade 3. Distinction from glioblastoma variants and lower grade glial and glioneuronal tumors is a common diagnostic challenge. We compared a morphologically defined set of PXA (histPXA) with an independent set, defined by DNA methylation analysis (mcPXA). HistPXA encompassed 144 tumors all subjected to DNA methylation array analysis. Sixty-two histPXA matched to the methylation class mcPXA. These were combined with the cases that showed the mcPXA signature but had received a histopathological diagnosis other than PXA. This cohort constituted a set of 220 mcPXA. Molecular and clinical parameters were analyzed in these groups. Morphological parameters were analyzed in a subset of tumors with FFPE tissue available. HistPXA revealed considerable heterogeneity in regard to methylation classes, with methylation classes glioblastoma and ganglioglioma being the most frequent mismatches. Similarly, the mcPXA cohort contained tumors of diverse histological diagnoses, with glioblastoma constituting the most frequent mismatch. Subsequent analyses demonstrated the presence of canonical pTERT mutations to be associated with unfavorable prognosis among mcPXA. Based on these data, we consider the tumor type PXA to be histologically more varied than previously assumed. Histological approach to diagnosis will predominantly identify cases with the established archetypical morphology. DNA methylation analysis includes additional tumors in the tumor class PXA that share similar DNA methylation profile but lack the typical morphology of a PXA. DNA methylation analysis also assist in separating other tumor types with morphologic overlap to PXA. Our data suggest the presence of canonical pTERT mutations as a robust indicator for poor prognosis in methylation class PXA.

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.

Molecular profiling-based decision for targeted therapies in IDH wild-type glioblastoma.

BACKGROUND: Molecular profiling allows tumor classification as well as assessment of diagnostic, prognostic, and treatment-related molecular changes. Translation into clinical practice and relevance for patients has not been demonstrated yet. METHODS: We analyzed clinical and molecular data of isocitrate dehydrogenase wild-type glioblastoma patients with sufficient clinical follow-up from the Heidelberg Neuro-Oncology Center and with molecular analysis of tumor tissue that consisted of DNA methylation array data, genome-scale copy number variations, gene panel sequencing, and partly mTOR immunohistochemistry between October 2014 and April 2018. RESULTS: Of 536 patients screened, molecular assessment was performed in 253 patients (47%) in a prospective routine clinical setting with further clinical appointments. Therapy decision was directly based on the molecular assessment in 97 (38%) patients. Of these, genetic information from MGMT (n = 68), EGFR (n = 7), CDKN2A/B (n = 8), alterations of the PI3K-AKT-mTOR pathway (n = 5), and BRAF (n = 3) have been the most frequently used for decision making with a positive overall survival signal for patients with glioblastoma harboring an unmethylated MGMT promoter treated according to the molecular assignment. Based on detected molecular alterations and possible targeted therapies, we generated an automated web-based prioritization algorithm. CONCLUSION: Molecular decision making in clinical practice was mainly driven by MGMT promoter status in elderly patients and study inclusion criteria. A reasonable number of patients have been treated based on other molecular aberrations. This study prepares for complex molecular decisions in a routine clinical decision making.