Abstract 7408: Robust profiling of cancer-related gene fusions: Analytical validation of PANCARNA for multiple cancer types
Notice bibliographique
Résumé
Abstract The impact of examining gene fusion presence in cancer patients is often overlooked, and as such, no well-established workflow currently exists for robust profiling of tumor samples in this area. We established a targeted RNA sequencing panel (PANCARNA) for investigating gene fusions across multiple cancer types and performed analytical validation to confirm performance. Furthermore, we report on the applicability of using targeted RNA sequencing for the purposes of gene expression profiling. This panel covers 117 genes associated with solid tumors. Thirty-nine FFPE samples with gene fusion status validated using an orthogonal method were used to evaluate analytical performance. Of the 39 standard samples, 33 samples were positive for gene translocation events and 6 were negative controls. Samples were collected from across 10 cancer types. To examine the robustness of the panel, 12 samples with known fusions were selected to undergo testing. Each sample was tested in triplicate across two experimental batches. Seraseq Fusion RNA Mix V4 (SeraCare, Cat. 0710-0497) standard samples were used to test the limit of detection for PANCARNA. Additionally, we explored a cohort of 22 samples with over expressed genes and 4 samples without overexpressed genes to test the expression profiling capabilities of PANCARNA and compared the results to immunohistochemistry stains. Of the 39 samples tested using the orthogonal method and targeted RNA-Seq, 38 were found to have concordant fusion calls in both methods (Positive percent agreement = 97.0%, 32/33; Negative percent agreement = 100%, 6/6; Overall percent agreement = 97.4%, 38/39). In reproducibility studies, the assay produced consistent intrarun and interrun results in all samples (Repeatability = 100%, 12/12; Reproducibility = 100%, 12/12). The limit of detection for multiple fusions of interest, such as EML4-ALK, CD74-ROS1, TPM3-NTRK1, and SLC34A2-BRAF, is found to be 0.5-1 copies per ng of RNA. ETV6-NTRK1 can be reliably detected at 2-4 copies per ng of RNA. MET ex14 skipping events can also be detected in a few as 1-2 copies per ng of RNA. To validate the capability of PANCARNA to quantify gene expression, we tested 26 samples with known gene expression levels for three genes of interest (ERBB2, EGFR, MET). ERBB2, EGFR and MET amplification detection through PANCARNA was found to have a high concordance to IHC results (Sensitivity = 96.2%, 100%, 94.7%, respectively). In conclusion, PANCARNA demonstrated highly accurate and sensitive detection of clinically relevant gene fusions. This assay produces reliable results when detecting fusions and offers high concordance to results obtained through IHC/FISH, indicating that targeted RNA seq analysis can assist with profiling gene fusion events in a clinical setting. Citation Format: Haimeng Tang, Hua Bao, Rui Liu, Shuyu Wu, Sisi Liu, Xue Wu, Yang Shao. Robust profiling of cancer-related gene fusions: Analytical validation of PANCARNA for multiple cancer types [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 7408.
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 | 0,003 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| 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 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».