Project description:DNA methylation classification reference set (1077) and validation set (428) of 1505 sarcoma samples using Illumina HumanMethylation450 BeadChips or Illumina Infinium HumanMethylation850 BeadChips
Project description:Infinium® HumanMethylation450 BeadChip and EPIC arrays were run with the aim of using the methylation profiles (n=986 in total) for sarcoma subtype classification (Paper: Lyskjær et al, 2021, DNA methylation-based profiling of bone and soft tissue tumours: a validation study of the ‘DKFZ sarcoma Classifier’ ). 500ng of DNA from fresh frozen (FT) or formalin-fixed paraffin-embedded (FFPE) tumour samples were bisulfite converted using the Zymo EZ DNA methylation Gold kit (Zymo Research Corp. Irvine, USA) before hybridisation to the Infinium HumanMethylation450 or EPIC beadchip arrays (Illumina, San Diego, CA) by UCL Genomics. All bisulfite-converted FFPE samples were restored with the Infinium FFPE DNA Restore kit (Illumina).
Project description:DNA methylation profiling has emerged as a valuable tool for tumor classification, exemplified by the German Cancer Research Center's creation of online classifiers for CNS tumors and sarcomas. Identification of rare molecular events, such as TRIO::TERT fusion in undifferentiated sarcomas, through DNA methylation profiling and transcriptome analysis aims to define distinct molecular subgroups within sarcomas of uncertain diagnosis, potentially improving classification and treatment strategies.In this study, we present 8 cases of sarcomas characterized by TRIO::TERT fusion, establishing it as a distinct molecular subtype of sarcomas. This fusion represents a consistent molecular feature across all analyzed tumors, suggesting its pivotal role in sarcomatogenesis. Identifying TRIO::TERT transcript sarcoma as a new tumor type may enhance diagnostic strategies for improved patient management.
Project description:Pediatric sarcomas present heterogeneous morphology, genetics and clinical behavior posing a challenge for an accurate diagnosis. DNA methylation is an epigenetic modification that coordinates chromatin structure and regulates gene expression, determining cell type and function. DNA methylation-based tumor profiling classifier for sarcomas (known as sarcoma classifier) from the German Cancer Research Center (Deutsches Krebsforschungszentrum) was applied to 122 pediatric sarcomas referred to a reference pediatric oncology hospital. The classifiers reported 88.5% of agreement between histopathological and molecular classification confirming the initial diagnosis of all osteosarcomas and Ewing sarcomas. Transcriptome raw data quality was verified with FASTQC. We used STAR-fusion to identify and annotate fusion transcripts based on discordant read alignments with default configurations. ChimeraViz was used to plot fusion-genes.
Project description:We retrospectively identified adult patients who had received anti-PD-1 ICI therapy for recurrent sarcoma and performed DNA methylation profiling using Infinium MethylationEPIC microarrays. Response to anti-PD-1 ICI therapy was correlated with DNA methylation profiles.
Project description:DNA methylation profiling has become a powerful tool for neuro-oncology diagnostics. We investigated the value of using DNA methylation profiling to achieve molecular diagnosis in adult primary diffuse lower-grade gliomas according to WHO 2016 classification system of central nervous system tumors. We further evaluated the use of methylation profiling for improved molecular characterization of the tumors and identify prognostic differences beyond histological grade and molecular markers (IDH mutation and 1p/19q codeletion status).
Project description: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.