Robust methylation based classification of brain tumors using nanopore sequencing
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE209865 | GEO | 2022/07/30
REPOSITORIES: GEO
Similar Datasets
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: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: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.
2018-01-14 | GSE90496 | 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:We identified hankyphage prophages within B. thetaiotaomicron isolates gathered from French hospitals. We extracted genomic DNA from an overnight culture from a single colony of each strain and sequenced them using Nanopore sequencing using the Plasmidsaurus platform. This long-read approach helped the assembly of the phages and determination of the hankyphage ends. We also improved the annotation of the reference hankyphage (hankyphage p00 from P. dorei HM719) using a structural prediction approach and annotated our B. thetaiotaomicron hankyphages using this new annotation. In this project we upload the genomic raw reads of nanopore sequencing of our hankyphage-bearing B. thetaiotaomicron collection (jmh strains) and the processed assembled hankyphages.
2025-01-24 | E-MTAB-14724 | biostudies-arrayexpress
Project description:Nanopore sequencing of K562 human cell line using the Oxford Nanopore GridION and R9.4.1 version chemistry
2021-09-20 | GSE173688 | GEO
Project description:We used the nanopore Cas9 targeted sequencing (nCATS) strategy to specifically sequence 125 L1HS-containing loci in parallel and measure their DNA methylation levels using nanopore long-read sequencing. Each targeted locus is sequenced at high coverage (~45X) with unambiguously mapped reads spanning the entire L1 element, as well as its flanking sequences over several kilobases. The genome-wide profile of L1 methylation was also assessed by bs-ATLAS-seq in the same cell lines (E-MTAB-10895).
2023-09-22 | E-MTAB-12247 | biostudies-arrayexpress
Project description:Sequencing was performed to assess the ability of Nanopore direct cDNA and native RNA sequencing to characterise human transcriptomes. Total RNA was extracted from either HAP1 or HEK293 cells, and the polyA+ fraction isolated using oligodT dynabeads. Libraries were prepared using Oxford Nanopore Technologies (ONT) kits according to manufacturers instructions. Samples were then sequenced on ONT R9.4 flow cells to generate fast5 raw reads in the ONT MinKNOW software. Fast5 reads were then base-called using the ONT Albacore software to generate Fastq reads.
2019-03-14 | E-MTAB-7778 | biostudies-arrayexpress
Project description:5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC) are modified versions of cytosine in DNA with roles in regulating gene expression. Using whole genomic DNA from mouse cerebellum, we benchmark 5mC and 5hmC detection by Oxford Nanopore Technologies sequencing against other standard techniques. In addition, we assess the ability of duplex base-calling to study strand asymmetric modification. Nanopore detection of 5mC and 5hmC is accurate relative to compared techniques and opens new means of studying these modifications. Strand asymmetric modification is widespread across the genome but reduced at imprinting control regions and CTCF binding sites in mouse cerebellum. Here we demonstrate the unique ability of nanopore sequencing to improve the resolution and detail of cytosine modification mapping.
2025-02-03 | GSE288331 | GEO