Robust methylation based classification of brain tumors using nanopore sequencing
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ABSTRACT:
PROVIDER: PRJNA862740 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Using a public reference data set of 82 unique entities, 382 nanopore-sequenced brain tumor samples were classified based on their methylation status through an ad hoc random forest algorithm. As a measure of confidence, score recalibration was performed and platform-specific thresholds were defined.
2022-07-30 | GSE209865 | GEO
Project description:Background: DNA methylation-based classification of cancer provides a comprehensive molecular approach to diagnose tumours. In fact, DNA methylation profiling of human brain tumours already profoundly impacts clinical neuro-oncology. However, current implementations using hybridization microarrays are time-consuming and costly. We recently reported on shallow nanopore whole-genome sequencing for rapid and cost-effective generation of genome-wide 5-methylcytosine profiles as input to supervised classification. Here, we demonstrate that this approach allows to discriminate a wide spectrum of primary brain tumours.
Results: Using public reference data of 82 distinct tumour entities, we performed nanopore genome sequencing on N=382 tissue samples covering 46 brain tumour (sub)types. Using bootstrap sampling in a cohort of N = 56 cases, we find that a minimum set of 1,000 random CpG features is sufficient for high-confidence classification by ad hoc random forests. We implemented score recalibration as confidence measure for interpretation in a clinical context and empirically determined a platform-specific threshold in a randomly sampled discovery cohort (N = 185). Applying this cut-off to an independent validation series (N = 184) yielded 148 classifiable cases (sensitivity 80.4%) and demonstrated 100 % specificity. Cross-lab validation demonstrated robustness with concordant results across four laboratories in 10/11 (90.9%) cases. In a prospective benchmarking (N = 15), median time to results was 21.1 hours.
Conclusions: In conclusion, nanopore sequencing allows robust and rapid methylation-based classification across the full spectrum of brain tumours. Platform-specific confidence scores facilitate clinical implementation for which prospective evaluation is warranted and ongoing.
| EGAS00001006540 | EGA
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:Background: Medulloblastoma (MB) is one of the most prevalent embryonal malignant brain tumors. Current classification organizes these tumors into four molecular groups (WNT-activated, SHH-activated and TP53-wild type, SHH-activated and TP53-mutant, and non-WNT/non-SHH). Recently, a comprehensive classification has been established, identifying numerous subgroups, some of which exhibit a poor prognosis. It is critical to establish effective subgrouping methods for accurate diagnosis and patient’s management that strikes a delicate balance between improving outcomes and minimizing the risk of comorbidities. Methods: We evaluated the ability of Nanopore sequencing to provide clinically relevant methylation and copy number profiles of MB. Nanopore sequencing was applied to an EPIC discovery cohort of frozen MB, benchmarked against the gold standard EPIC array, and validated further evaluated on an integrated diagnosis cohort of MB.
2024-11-16 | GSE280038 | GEO
Project description:DNA methylation-based classification of sinonasal tumors
| PRJNA803926 | ENA
Project description:DNA methylation-based classification of common renal tumors
| PRJNA1064051 | ENA
Project description:DNA methylation-based classification of sinonasal tumors [test set]
| PRJNA803928 | ENA
Project description:DNA methylation-based classification of sinonasal tumors [reference set]
| PRJNA784516 | ENA
Project description:DNA methylation-based classification of human central nervous system tumors
| PRJNA430715 | ENA
Project description:The diagnosis of sinonasal tumors is challenging due to a heterogeneous spectrum of various differential diagnoses as well as poorly defined, disputed entities such as sinonasal undifferentiated carcinomas (SNUCs). In this study, we apply machine learning algorithm based on DNA methylation patterns to classify sinonasal tumors with clinical-grade reliability. We further show that sinonasal tumors with SNUC morphology are not as undifferentiated as their current terminology suggests but rather reassigned to four distinct molecular classes defined by epigenetic, mutational and proteomic profiles. This includes two classes with neuroendocrine differentiation, characterized by IDH2 or SMARCA4/ARID1A mutations with an overall favorable clinical course, whereas tumors that are driven by SMARCB1-deficiency and tumors that represent previously misclassified adenoid cystic carcinomas are highly aggressive. Our findings have the potential to dramatically improve the diagnostic classification of sinonasal tumors and will fundamentally change the current perception of SNUCs.
2022-09-25 | GSE196227 | GEO