Abstract 3575: The OncoNetwork Consortium: A global collaborative research study on the development and verification of an Ion AmpliSeq RNA gene lung fusion panel
Notice bibliographique
Résumé
Abstract Chromosomal translocations and corresponding gene fusions play an important role in the initiation of tumorigenesis and these processes have been strongly associated with distinct tumor subtypes. The recent association of ALK, ROS and RET fusion transcripts as lung tumor therapy predictive biomarkers has increased the need of a technology that could detect these biomarkers starting from limited amount of material. Life Technologies and OncoNetwork consortium collaborated for the development of a lung fusion panel based on Ion AmpliSeq™ RNA chemistry. The OncoNetwork consortium is comprised of twelve -translational cancer research institutes with many years of experience in adopting the latest molecular techniques for lung therapy research. Material and Methods: Consortia's requirements for the panel development : 1) detect all variants of ALK, ROS1, or RET fusion transcripts described in COSMIC in a single reaction using 10 ng of total RNA 2) Include 5′ and 3′ ALK, ROS1, RET gene expression assays as an indicator of a translocation at this gene. 3) Include endogenous RNA assay controls to determine if the quality of the results could be affected by RNA quality. 4) Provide similar results on archived FFPE samples tested by FISH. Human lung adenocarcinoma cell lines H2228 (EML4-ALK positive), HCC78 (SLC4A2-ROS1 positive ) ,LC-2/ad (CCDC6-RET positive) and Ambion® FirstChoice® Human Brain Reference (HBR) RNA was used as positive and negative control for the study. Formalin fixed paraffin embedded (FFPE) tissue was isolated using different extraction methods. After amplification using Ion AmpliSeq™ RNA chemistry samples were sequenced on the Ion Torrent PGM™ sequencer using the Ion PGM™ 200 Sequencing Kit. The presence of the fusions was confirmed by custom TaqMan® gene expression assays when possible. Preliminary results of the panel on ALK or ROS1 or RET positive cell lines and FFPE archived cancer research samples gave good concordance with FISH results. Expected negative samples were confirmed negative. We found a KIF5B-RET fusion positive sample in a sample not previously tested for RET fusions. Two of the expected positive samples by FISH were found negative due to limited amount to tumor cells present in the sample. Cell line RNA dilutions were performed to determine the panel's limit of detection. We demonstrate a limit of detection of 1 % tumor RNA in the presence of 99 % normal RNA using the panel with 10 ng of RNA extracted by cell lines. Gene expression controls work well as an indicator of the RNA quality and of the translocation presence. The Ion AmpliSeq™ RNA lung cancer fusion panel workflow is easier and faster to perform in comparison to the FISH method. The results obtained to date are highly encouraging for panel to be used in the clinical research setting. More data needs to be analyzed before a final conclusion is made. Citation Format: Susan M. Magdaleno, Angie Cheng, Rosella Petraroli, Orla Sheils, Bastiaan Tops, Delphine Le Corre, Henriette Kurth, Helene Blons, Eliana Amato, Andrea Mafficini, Anna Maria Rachiglio, Anne Reimann, Christoph Noppen, Chrysanthi Ainali, Jin Katayama, Renato Franco, Harriet Feilotter, Jeoffrey Schageman, Ian Cree, Andrew Felton, Jose Luis Costa, Alain Rico, Aldo Scarpa, Jose Carlos Machado, Kazuto Nishio, Nicola Normanno, Marjolijn Ligtenberg, Cecily P. Vaughn, Ludovic Lacroix, Pierre Laurent-Puig. The OncoNetwork Consortium: A global collaborative research study on the development and verification of an Ion AmpliSeq RNA gene lung fusion panel. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 3575. doi:10.1158/1538-7445.AM2014-3575
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 | 0,040 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,003 | 0,007 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 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 ».