Optimization and Validation of Nanopore Based Sequencing Method for Molecular Testing of CNS Tumours
Notice bibliographique
Résumé
Background The World Health Organization (WHO) introduced molecular identifiers for the diagnosis and prognosis of CNS tumors including the mutational status of isocitrate dehydrogenase or IDH genes in glial tumors. Currently used immunohistochemistry (IHC) is not capable of detecting the non‐canonical mutations, and sequencing is often required as a follow‐up. Current next‐generation sequencing (NGS) technologies used in tumor molecular marker detection introduce key challenges including high capital cost, complex infrastructure requirements, and long turnaround times. These challenges considerably limit the ability to perform NGS testing in many pathology laboratories. In this study, we aimed to use third generation nanopore sequencing technology to resolve these limitations. The Oxford Nanopore MinION, a pocket‐sized nanopore sequencing device, has minimal capital costs and infrastructural requirements, and shorter turnaround times. However, the nanopore technology has not been validated in clinical practice and has not been optimized on formalin‐fixed paraffin‐embedded (FFPE) tissue. Methods DNA extraction of selective tumor areas was performed from the corresponding FFPE tissue blocks from a cohort of gliomas with confirmed IDH1 and IDH2 gene statuses (n=65). A PCR amplicon‐based approach was used to amplify hot spots of the IDH1 and IDH2 genes, starting with 30ng DNA material. The amplicon libraries were sequenced for 2 hours in multiplex on the MinION device and IDH SNPs were called with the Nanopolish software. ASIP Abstract Mashiat Mimosa Results 26 IDH mutant samples were identified: 21 IDH1 R132H, 2 IDH1 R132G, 2 IDH2 R172G, and 1 IDH2 D177H. All cases showed concordant IDH mutational status when compared to the reference methods (IHC or NGS) and both analytical sensitivity and specificity were 100%. Precision analysis of variant allele frequency (VAF) showed the coefficient of variation was less than 5% (both inter and intra runs), and the limit of detection for VAF was 3%. The range of read depth obtained was 882X to 43,000x with an average of 20,000x. This assay revealed a $50‐$100 material cost per sample, and the time taken from extracted nucleic acid to final result generation was 1‐2 business days. Conclusion This project is the first to optimize and validate an approach to detect SNP mutations in FFPE samples using nanopore technology. It has demonstrated the feasibility and efficacy of the nanopore amplicon sequencing method in cancer FFPE tissue with excellent test performance characteristics, significantly shorter turnaround times at considerably lower costs and without any infrastructural needs. Thus, it can be used to circumvent challenges to current NGS testing platforms and can be the milestone that would make cancer NGS testing available for every laboratory.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».