Histone 3.3 hotspot mutations in conventional osteosarcomas: a comprehensive clinical and molecular characterization of six H3F3A mutated cases

Background: Histone 3.3 (H3.3) hotspot mutations in bone tumors occur in the vast majority of giant cell tumors of bone (GCTBs; 96%), chondroblastomas (95%) and in a few cases of osteosarcomas. However, clinical presentation, histopathological features, and additional molecular characteristics of H3.3 mutant osteosarcomas are largely unknown. Methods: In this multicentre, retrospective study, a total of 106 conventional high-grade osteosarcomas, across all age groups were re-examined for hotspot mutations in the H3.3 coding genes H3F3A and H3F3B. H3.3 mutant osteosarcomas were re-evaluated in a multidisciplinary manner and analyzed for genome-wide DNA-methylation patterns and DNA copy number aberrations alongside H3.3 wild-type osteosarcomas and H3F3A G34W/L mutant GCTBs. Results: Six osteosarcomas (6/106) carried H3F3A hotspot mutations. No mutations were found in H3F3B. All patients with H3F3A mutant osteosarcoma were older than 30 years with a median age of 65 years. Copy number aberrations that are commonly encountered in high-grade osteosarcomas also occurred in H3F3A mutant osteosarcomas. Unlike a single osteosarcoma with a H3F3A K27M mutation, the DNA methylation profiles of H3F3A G34W/R mutant osteosarcomas were clearly different from H3.3 wild-type osteosarcomas, but more closely related to GCTBs. The most differentially methylated promoters between H3F3A G34W/R mutant and H3.3 wild-type osteosarcomas were in KLLN/PTEN (p < 0.00005) and HIST1H2BB (p < 0.0005). Conclusions: H3.3 mutations in osteosarcomas may occur in H3F3A at mutational hotspots. They are overall rare, but become more frequent in osteosarcoma patients older than 30 years. Osteosarcomas carrying H3F3A G34W/R mutations are associated with epigenetic dysregulation of KLLN/PTEN and HIST1H2BB

Genome-wide methylation profiling and copy number analysis in atypical fibroxanthomas and pleomorphic dermal sarcomas indicate a similar molecular phenotype

Background: Atypical fibroxanthomas (AFX) and pleomorphic dermal sarcomas (PDS) are lesions of the skin with overlapping histologic features and unspecific molecular traits. PDS behaves aggressive compared to AFX. Thus, a precise delineation, although challenging in some instances, is relevant. Methods: We examined the value of DNA-methylation profiling and copy number analysis for separating these tumors. DNA-methylation data were generated from 17 AFX and 15 PDS using the Illumina EPIC array. These were compared with DNA-methylation data generated from 196 tumors encompassing potential histologic mimics like cutaneous squamous carcinomas (cSCC; n = 19), basal cell carcinomas (n = 10), melanoma metastases originating from the skin (n = 11), leiomyosarcomas (n = 11), angiosarcomas of the skin and soft tissue (n = 11), malignant peripheral nerve sheath tumors (n = 19), dermatofibrosarcomas protuberans (n = 13), extraskeletal myxoid chondrosarcomas (n = 9), myxoid liposarcomas (n = 14), schwannomas (n = 10), neurofibromas (n = 21), alveolar (n = 19) and embryonal (n = 17) rhabdomyosarcomas as well as undifferentiated pleomorphic sarcomas (n = 12). Results: DNA-methylation profiling did not separate AFX from PDS. The DNA-methylation profiles of the other cases, however, were distinct from AFX/PDS. They reliably assigned to subtype-specific DNA-methylation clusters, although overlap occurred between some AFX/PDS and cSCC. Copy number profiling revealed alterations in a similar frequency and distribution between AFX and PDS. They involved losses of 9p (22/32) and 13q (25/32). Gains frequently involved 8q (8/32). Notably, a homozygous deletion of CDKN2A was more frequent in PDS (6/15) than in AFX (2/17), whereas amplifications were non-recurrent and overall rare (5/32). Conclusions: Our findings support the concept that AFX and PDS belong to a common tumor spectrum. We could demonstrate the diagnostic value of DNA-methylation profiling to delineating AFX/PDS from potential mimics. However, the assessment of certain histologic features remains crucial for separating PDS from AFX

Methylation profiling of choroid plexus tumors reveals 3 clinically distinct subgroups

BACKGROUND: Choroid plexus tumors are intraventricular neoplasms derived from the choroid plexus epithelium. A better knowledge of molecular factors involved in choroid plexus tumor biology may aid in identifying patients at risk for recurrence. METHODS: Methylation profiles were examined in 29 choroid plexus papillomas (CPPs, WHO grade I), 32 atypical choroid plexus papillomas (aCPPs, WHO grade II), and 31 choroid plexus carcinomas (CPCs, WHO grade III) by Illumina Infinium HumanMethylation450 Bead Chip Array. RESULTS: Unsupervised hierarchical clustering identified 3 subgroups: methylation cluster 1 (pediatric CPP and aCPP of mainly supratentorial location), methylation cluster 2 (adult CPP and aCPP of mainly infratentorial location), and methylation cluster 3 (pediatric CPP, aCPP, and CPC of supratentorial location). In methylation cluster 3, progression-free survival (PFS) accounted for a mean of 72 months (CI, 55-89 mo), whereas only 1 of 42 tumors of methylation clusters 1 and 2 progressed (P< .001). On stratification of outcome data according to WHO grade, all CPCs clustered within cluster 3 and were associated with shorter overall survival (mean, 105 mo [CI, 81-128 mo]) and PFS (mean, 55 mo [CI, 36-73 mo]). The aCPP of methylation cluster 3 also progressed frequently (mean, 69 mo [CI, 44-93 mo]), whereas no tumor progression was observed in aCPP of methylation clusters 1 and 2 (P< .05). Only 1 of 29 CPPs recurred. CONCLUSIONS: Methylation profiling of choroid plexus tumors reveals 3 distinct subgroups (ie, pediatric low-risk choroid plexus tumors [cluster 1], adult low-risk choroid plexus tumors [cluster 2], and pediatric high-risk choroid plexus tumors [cluster 3]) and may provide useful prognostic information in addition to histopathology

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

Integrated Molecular-Morphologic Meningioma Classification: A Multicenter Retrospective Analysis, Retrospectively and Prospectively Validated

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

A complementary study approach unravels novel players in the pathoetiology of Hirschsprung disease

Hirschsprung disease (HSCR, OMIM 142623) involves congenital intestinal obstruction caused by dysfunction of neural crest cells and their progeny during enteric nervous system (ENS) development. HSCR is a multifactorial disorder; pathogenetic variants accounting for disease phenotype are identified only in a minority of cases, and the identification of novel disease-relevant genes remains challenging. In order to identify and to validate a potential disease-causing relevance of novel HSCR candidate genes, we established a complementary study approach, combining whole exome sequencing (WES) with transcriptome analysis of murine embryonic ENS-related tissues, literature and databas

Integrated Molecular-Morphologic Meningioma Classification: A Multicenter Retrospective Analysis, Retrospectively and Prospectively Validated.

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

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