Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1189694 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR31477700.fastq.gz | Fastqsanger.gz | |||
| SRR31477701.fastq.gz | Fastqsanger.gz | |||
| SRR31477702_1.fastq.gz | Fastqsanger.gz | |||
| SRR31477702_2.fastq.gz | Fastqsanger.gz | |||
| SRR31477703.fastq.gz | Fastqsanger.gz |
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Project description:Cerebrospinal fluid (CSF) liquid biopsies serve as a rich source of tumor-derived cell-free DNA (cfDNA) for evaluating patients with central nervous system (CNS) tumors. However, challenges stemming from trace cfDNA yields and low mutational burden have hindered sensitivity, whereas first-generation clinical assays have relied on genetic alterations as biomarkers. Leveraging the diagnostic utility of DNA methylation classification in CNS tumors, we developed M-PACT (Methylation-based Predictive Algorithm for CNS Tumors), a robust deep neural network that accurately classifies tumors from sub-nanogram input cfDNA methylomes acquired through enzymatic methylation sequencing. In addition to tumor classification, this workflow enables methylation-based cellular deconvolution and sensitive copy number variation (CNV) detection. We benchmark our methodology in pediatric CNS embryonal tumors and further demonstrate accurate classification of intra-operative CSF, balanced tumor genomes, and secondary malignancies. Altogether, we provide a blueprint for CNS tumor classification from low input cfDNA methylomes, motivating prospective validation for future clinical implementation.
2025-11-17 | GSE292312 | GEO
Project description:We used the Illumina Infinium HumanMethylation EPIC beadchip array to profile 31 pediatric tumors with the histological diagnosis of anaplastic pilocytic astrocyoma (PA). PA with anaplasia has been defined in the 2016 WHO classification as a tumor with PA morphology and >4 mitoses per 10 HPF, with or without necrosis with a potential worse outcome. DNA Methylation based studies performed mainly in adults have put forward this subtype as a distinct entity. After performing the same techniques on pediatric samples, our findings argue that the diagnostic histomolecular criteria established for anaplasia in adult PA are not of diagnostic or prognostic value in a pediatric setting.
2019-11-08 | E-MTAB-8390 | biostudies-arrayexpress
Project description:Pediatric central nervous system (CNS) tumors are the second most common type of cancer in children and the leading cause of cancer-related death in this age group. Diagnosing these tumors based solely on histopathology is challenging and can lead to misdiagnosis, even among experienced pathologists. DNA methylation profiling classifies CNS tumors into molecular classes providing a more reliable diagnosis with the potential to impact patient’s management. This approach allows for faster and more accurate diagnoses combined with histopathology and other biomarkers. In this study, we performed DNA methylation analysis using the Infinium MethylationEPIC BeadChip (Illumina®) on a cohort of 182 pediatric CNS tumor samples from a reference center for pediatric cancer treatment in the countryside of São Paulo, Brazil. The data were analyzed using the DKFZ/Heidelberg CNS tumor platform (v12.8). Of the 182 tumors, excluding cases classified as control tissue, 82.8% (135 of 163) achieved a satisfactory calibrated score of ≥ 0.9 for a methylation family. Methylation profiling refined the initial diagnosis in 88 cases (65.7%) and provided a new diagnosis for 28 (20.9%) Several rare tumors only identified by methylation profile disclose the importance of analyzing full and consecutive samples, which allows the heterogeneity assessment and provide a more comprehensive insight into CNS tumors. Our work highlights the value of DNA methylation profiling as a molecular classification tool in a pediatric cohort from a middle-income country, which is often underrepresented in general databases.
2025-11-13 | GSE296487 | GEO
Project description:Pediatric adrenocortical carcinomas (ACCs) are aggressive; the overall survival of pediatric patients with ACCs is 40%-50%. Appropriate staging and histologic classification are crucial as children with incompletely resected tumors or metastatic disease have a dismal prognosis. The clinical course of pediatric adrenocortical tumors (ACTs) is difficult to predict using the current classification schemas, which rely heavily on subjective microscopic and gross macroscopic variables. Recent advances in adult ACT studies have revealed distinct DNA-methylation patterns with prognostic significance that have not been systematically interrogated in the pediatric population.
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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.
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Project description:To assess their utility in routine neuropathology, we prospectively integrated DNA methylation-based CNS tumor classification and targeted gene panel sequencing of tumor and constitutional DNA with blinded neuropathological reference diagnostics for a population-based cohort of > 1,200 newly-diagnosed pediatric patients.
2023-01-30 | GSE215240 | GEO
Project description:Ependymal tumors across age groups have been classified solely by histopathology. It is, however, commonly accepted that this classification has limited clinical utility based on its poor reliability. We aimed at establishing a reliable and reproducible molecular classification using DNA methylation fingerprints of the tumors. Studying a cohort of 500 tumors allowed for the delineation of nine robust molecular subgroups, three in each anatomic compartment of the central nervous system (CNS). Two of the supratentorial subgroups are characterized by prototypic fusion genes involving RELA and YAP1, respectively. Regarding clinical associations, the molecular classification proposed herein outperforms the current histopathological classification by far and thus might serve as a basis for the upcoming update of the WHO classification of CNS tumors. DNA methylation patterns in tumors have been shown to represent a very stable molecular memory of the respective cell of origin throughout the disease course, thus making them particularly suitable for tumor classification purposes. Methylation fingerprinting of a large series of ependymal tumors of all grades revealed a highly reliable way of classifying this clinically extremely heterogeneous group of malignancies. In fact, out of nine highly reproducible molecular subgroups identified in the supratentorial, infratentorial and spinal regions, only two harbor the vast majority of clinical high-risk patients (mostly children) for whom novel therapeutic concepts are desperately needed. Since this analysis can be performed from minute amounts of DNA extracted from archived material, it is ideally suited for routine clinical application. We investigated a set of 562 ependymal tumors using the Illumina 450k methylation array.
2015-05-21 | E-GEOD-65362 | biostudies-arrayexpress
Project description:Ependymal tumors across age groups are currently classified and graded solely by histopathology. It is, however, commonly accepted that this classification scheme has limited clinical utility based on its lack of reproducibility in predicting patients' outcome. We aimed at establishing a uniform molecular classification using DNA methylation profiling. Nine molecular subgroups were identified in a large cohort of 500 tumors, 3 in each anatomical compartment of the CNS, spine, posterior fossa, supratentorial. Two supratentorial subgroups are characterized by prototypic fusion genes involving RELA and YAP1, respectively. Regarding clinical associations, the molecular classification proposed herein outperforms the current histopathological classification and thus might serve as a basis for the next World Health Organization classification of CNS tumors.
2015-05-21 | GSE65362 | GEO
Project description:Background: Modern neuropathology is challenged by an increasing number of clinically-relevant CNS tumor subgroups that require assessment of a multitude of molecular markers for classification, as well a highly trained medical staff. Failure to meet this challenge leads to tumor misclassification, which can have severe consequences for affected patients. Methods: We compiled a cohort of genome-wide DNA methylation profiles of 2,682 tumors from 82 histologically and/or molecularly distinct CNS tumor classes across all ages and histologies that served as reference for a Random Forest-based diagnostic classifier. This classifier was used to prospectively investigate a further 1,104 CNS tumor samples in order to determine its clinical utility. Results: The classifier was able to reliably assign tumor samples to a given diagnostic category with a misclassification rate of less than 2%. The system functioned robustly across laboratories and using different DNA methylation profiling techniques. Prospective application to clinical samples resulted in a reclassification of 12% of tumors compared with standard practice alone. A further 12% could not be classified by methylation profiling – this subset was highly enriched for unusual syndrome-associated tumors and likely novel entities. Conclusion: This study represents a proof-of-concept for the application of machine learning approaches in molecular diagnostics using a single, easy-to-use assay. The reference cohort and Random Forest-based classifier are available online as a valuable community tool for improving precision in brain tumor diagnostics. We expect that approaches similar to the one presented herein will rapidly restructure diagnostic practice in neurooncology and across tumor pathology.
2018-01-18 | GSE109379 | GEO
Project description:Background: Modern neuropathology is challenged by an increasing number of clinically-relevant CNS tumor subgroups that require assessment of a multitude of molecular markers for classification, as well a highly trained medical staff. Failure to meet this challenge leads to tumor misclassification, which can have severe consequences for affected patients. Methods: We compiled a cohort of genome-wide DNA methylation profiles of 2,682 tumors from 82 histologically and/or molecularly distinct CNS tumor classes across all ages and histologies that served as reference for a Random Forest-based diagnostic classifier. This classifier was used to prospectively investigate a further 1,104 CNS tumor samples in order to determine its clinical utility. Results: The classifier was able to reliably assign tumor samples to a given diagnostic category with a misclassification rate of less than 2%. The system functioned robustly across laboratories and using different DNA methylation profiling techniques. Prospective application to clinical samples resulted in a reclassification of 12% of tumors compared with standard practice alone. A further 12% could not be classified by methylation profiling – this subset was highly enriched for unusual syndrome-associated tumors and likely novel entities. Conclusion: This study represents a proof-of-concept for the application of machine learning approaches in molecular diagnostics using a single, easy-to-use assay. The reference cohort and Random Forest-based classifier are available online as a valuable community tool for improving precision in brain tumor diagnostics. We expect that approaches similar to the one presented herein will rapidly restructure diagnostic practice in neurooncology and across tumor pathology.
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