Abstract LB-327: Analytical performance of the nCounter analysis system for gene expression cancer signatures
Notice bibliographique
Résumé
Abstract Background: The nCounter Analysis System is a platform for performing highly multiplexed, digital quantification of hundreds of different nucleic acid species in a single reaction. The system is being developed for use as a platform for in vitro diagnostic applications. The current study aimed to evaluate the analytical performance of the system by implementing a 50-gene signature used for determining the intrinsic subtype and prognostic risk of a breast cancer tumor from formalin-fixed, paraffin-embedded (FFPE) tissue samples. The gene expression profiles can be used to divide breast cancer into four intrinsic subtypes: Basal-like, Luminal A, Luminal B, and HER2 enriched. A normal-like subtype identifies tumor specimens contaminated with a high percentage of normal tissue. A prognostic Risk-Of-Relapse (ROR) score is also calculated. Methods: The nCounter assay is performed by direct, multiplexed hybridization of molecular barcodes to target mRNAs. For this study, a CodeSet containing probes for 50 classification genes and 8 normalizing genes was developed. The intrinsic subtyping algorithm was trained by supervised hierarchical clustering of data from 538 samples. Prototypical centroids for tumor subtypes were chosen as statistically significant clusters, while the normal-like centroid was trained from reduction mammoplasty samples. All samples were correlated to the five prototypical centroids and assigned the subtype with the largest positive correlation. The analytical precision of the assay was measured by testing the same sample across replicates. The precision of the subtyping test was determined by assaying 40 different samples across 9 different combinations of reagent lots. The performance of the assay was also evaluated with varying input levels of RNA. Results: The precision of the nCounter assay was found to be driven by Poisson noise in the digital measurements at low expression levels. When samples were subtyped across multiple reagent lots, the correlations to each of the five subtypes were narrowly distributed. One sample varied in the categorical subtype call between Luminal B and HER2-enriched across all lots, however, this sample was similarly correlated between both Luminal B and HER2-enriched across all lot combinations. Not surprisingly, the variability for the ROR was minimal, even for the case where the categorical subtype call changed. Finally, the subtype call and ROR score were stable across a 10x range of RNA input. Conclusions: The NanoString nCounter system has the sensitivity and precision to be implemented as a distributed platform for multiplexed gene expression profiling of tumors. The sensitivity and direct digital detection without the need for enzymatic amplification make the technology compatible with FFPE tissue samples. The precision should make the technology robust under the many varied conditions seen in testing labs. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr LB-327. doi:10.1158/1538-7445.AM2011-LB-327
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 | 0,006 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 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) | 0,005 | 0,004 |
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 ».