Abstract 5049: Detecting chromosomal copy number variations and point mutations in Glioma using a single assay; sparing tissue while significantly reducing testing time and cost
Notice bibliographique
Résumé
Abstract We aimed to develop a single streamlined assay to detect all Glioma biomarkers. The current diagnosis of Gliomas incorporates molecular biomarkers that include whole chromosomal copy number variations (CNVs) and single nucleotide variations (SNVs). Testing CNVs and SNVs are traditionally done using different large platforms and are performed in large genomic facilities that have the suitable infrastructure. This creates delays in testing, where samples undergo lengthy referrals and the tissue is often split between institutions, which also adds substantially to the costs. We have previously used Nanopore sequencing to develop an assay to detect IDH SNVs from formalin-fixed paraffin-embedded (FFPE) tissue. Nanopore sequencing platform uses small inexpensive tools and can test for both CNVs and SNVs. However, combining both targets efficiently in FFPE tissue remains a challenge. While nanopore whole genome sequencing can detect CNVs, it does not allow for enough coverage to accurately detect small SNVs. Also, raw FFPE DNA is not compatible with the nanopore technology. To fix both challenges we used a PCR based approach that allows enrichment of the SNV targets, as well as creates new nanopore compatible DNA copies. To counteract the bias that PCR creates with uneven amplification, we used a SNP based method to call chromosomal gains and losses. Statistical analysis of probability showed that targeting 15 heterozygous SNPs per chr arm is sufficient for accurate CNV detection (error rate <0.1%). A total of 57 amplicons targeting CNVs on Chr 1,7, 10, 19, and EGFR gene; and IDH1 and IDH2 SNVs were pooled together in one assay and tested on 4 Glioma samples. Variant calling was performed with a custom Python script. All CNV targets were accurately detected on all samples. Losses in chr 1p, 19q and chr 10 were evident with a loss of heterozygosity (LOH) pattern on the SNP analysis. The gain in chromosome 7 was also observed as an allele gain 2:1 SNP pattern on the corresponding samples. The SNV statuses of these samples were also accurately detected when compared to reference results. Finally, the SNV and CNV calling analysis were streamlined using custom shell script, further reducing total turnaround time (including sequencing and data analysis) to less than a day for the entire batch of samples. Initial cost analysis shows <50% of the traditional testing costs for the assay. This work is the first to develop a streamlined single test to detect CNVs and SNVs in Gliomas using FFPE DNA on the nanopore sequencing platform. The shorter testing time, streamlined workflow, and low assay/capital cost could positively influence the care of brain cancer patients. Notably, nanopore-based sequencing tools can also be implemented in other types of tumor diagnosis, especially by smaller labs, helping to overcome many of the existing diagnostic challenges. Citation Format: Mashiat L. Mimosa, Jared T. Simpson, Karel Boissinot, Mora Tiab, Ramzi Fattouh, Rola M. Saleeb. Detecting chromosomal copy number variations and point mutations in Glioma using a single assay; sparing tissue while significantly reducing testing time and cost [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5049.
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 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,003 |
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 ».