Abstract 1764: Accurate and reproducible detection of fusions and exon skipping events in NSCLC-derived samples using a comprehensive, targeted RNA-Seq system across multiple laboratories
Notice bibliographique
Résumé
Abstract Introduction: Reliable assessment of cancer-associated RNA markers in lung cancer produced by gene-fusions or exon skipping events by next-generation sequencing requires integrated reagents, protocols, and interpretive software that can harmonize procedures and ensure consistent results across laboratories. We evaluated a comprehensive system for targeted RNA-Seq that includes reagents for nucleic acid quantification, library prep, run controls, and companion bioinformatics software. The reproducibility of this system was evaluated in a multi-phase study design at 5 independent laboratories. Methods: Total nucleic acid (TNA) was isolated from formalin-fixed, paraffin-embedded (FFPE) residual non-small cell lung cancer (NSCLC) tumor biopsies and cell-lines (RT-112, H596, HCC78). These TNA isolates were used to prepare a set of 30 test samples, including a dilution series to assess assay sensitivity. A non-template control and kit positive control were also included. The sample set was evaluated using the QuantideX® NGS RNA Lung Cancer Kit RUO (Asuragen) and sequenced on the MiSeq® system (Illumina) at Asuragen and 4 independent laboratories. Analyses were conducted using QuantideX® NGS Reporter RUO (Asuragen), a software suite that includes a FASTQ processing pipeline and incorporates pre-analytical QC information into the fusion-caller algorithm and reporting tool. Results: Laboratories were trained on the assay workflow and companion bioinformatics software in less than two days followed by independent library preparation and sequencing workflows. A total of 266 sample libraries were evaluated from inputs down to <10 ng. A single library was excluded from sequencing due to a failed QC status. Specific targeted fusions (ALK, ROS1, FGFR3) and splice variants (MET exon 14 skipping) were detected in 132 libraries and were concordant at all sites. Designs to detect 3’/5’ expression imbalances reported the presence of gene fusions for ALK and ROS1 in a total of 96 libraries with one aberrant call and one missed call, which occurred in libraries flagged as “at risk” by the interpretive software. Conclusions: The accuracy of this novel targeted NGS assay for RNA fusions and splice variants in NSCLC was demonstrated in a multi-site laboratory evaluation using clinically-relevant specimens and low inputs of TNA. The ability of the panel to detect both common and rare gene fusion transcripts and exon skipping events within an integrated wet- and dry-bench workflow provides a foundation for the reliable detection of oncogenic RNA fusions and aberrant splicing events that can respond to current and emerging targeted therapies. This study highlighted the ease of implementation and consistent performance that can be achieved in different laboratories when the process from sample-to-report is highly integrated. Citation Format: Gary J. Latham, Richard Blidner, Brian C. Haynes, Shobha Gokul, Maria L. Aguirre, Stephen Hyter, Ziyan Y. Pessetto, Maria Curtis, Dan Su, Tom Halsey, Victor Weigman, Patrick Hurban, Andrew K. Godwin, Leon C. van Kempen. Accurate and reproducible detection of fusions and exon skipping events in NSCLC-derived samples using a comprehensive, targeted RNA-Seq system across multiple laboratories [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1764. doi:10.1158/1538-7445.AM2017-1764
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 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| 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) | 0,003 | 0,002 |
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 ».