Abstract 628: Determinants of quality of next-generation sequencing output from the strand-specific TruSight Tumor Sequencing Panel in a clinical diagnostic setting
Notice bibliographique
Résumé
Abstract The use of Next-Generation Sequencing (NGS) technologies is increasingly prevalent within diagnostic labs. As genomic regions are sequenced to greater depth in cancer diagnostics, it is critical to differentiate clinically actionable variants from artifacts arising from sequencing-errors, sample- processing or sample-age, and to identify samples that will be difficult to evaluate. We sought to determine whether strand-specific sequencing approaches, such as the TruSight Tumor Sequencing Panel (Illumina) could enable sample and variant triage in a clinical diagnostic settingTruSight Tumor Sequencing Panel allows for paired-end sequencing of individual strands of DNA and analyzing them either together (Paired) or separately (Pool A and Pool B). Variants identified in one pool, but not the other, are putative artifacts; variants identified in both pools are considered true calls. Combined analysis of both pools was performed in two ways: By summing variant calls across pools, and by informatically determining overlapping variant calls between pools. In a test cohort of 44 FFPE samples of varying age and tumor type, we assessed whether age of sample, strand bias, and fixation impacted the detection of high confidence variants using the TruSight Tumor Sequencing panel. Data were compared to the results of analysis of the same samples using the established Illumina TruSeq Amplicon Cancer Panel and/or Sanger Sequencing.Sample age, tumor cellularity, tumor type and template DNA quality were not found to be associated with quality of NGS output in our study. We also evaluated the overall transition/transversion (Ti/Tv) ratios for variants detected either uniquely in one pool or in combined analysis. Interestingly, for variants detected in both pools, the Ti/Tv ratio was 1.97, compared to 0.52-0.60 for those detected in only 1 pool (p < 0.001). Strikingly, samples that sequenced successfully but gave inconclusive and difficult to interpret variant lists were associated with%G>A:C>T transition > 62.5% and Ti/Tv ratios of > 4.0 (p < 0.001). G>A:C>T transitions were significantly over-represented in these samples. The overall%G>A:C>T transitions were equivalent (44-52%) in individual pools or in paired analysis. However, when inconclusive samples were accounted for, the%G>A:C>T transitions differed between the two analyses: 49.7% (paired) vs. 30.1-32.1% (individual pools). In summary, the Ti/Tv ratio can act as a critical determinant of variant call quality - Ti/Tv ratios ∼0.5 represent sequencing artifacts, while Ti/Tv ratios > 4.0 are indicative of inconclusive sequencing output.We conclude that variant Ti/Tv ratio as well as%G>A:C>T transition in variants detected by the TruSight Tumor Sequencing Panel may be helpful evaluators of quality and clinical utility of sequencing output for FFPE tumor samples tested in a clinical diagnostic setting. Citation Format: Swati Garg, Mahadeo A. Sukhai, Mariam Thomas, Michelle Mah, Tong Zhang, Trevor Pugh, Suzanne Kamel-Reid, Tracey L. Stockley. Determinants of quality of next-generation sequencing output from the strand-specific TruSight Tumor Sequencing Panel in a clinical diagnostic setting. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 628. doi:10.1158/1538-7445.AM2015-628
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 | 0,009 | 0,035 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».