Abstract IA12: The Ovarian Tumour Tissue Analysis Consortium: Stratified Prognosis of Ovarian Tumors (OTTA-SPOT) signature for high-grade serous ovarian cancer
Notice bibliographique
Résumé
Abstract Background: High-grade serous ovarian cancer (HGSOC) is the most common and aggressive epithelial ovarian cancer. HGSOC has poor survival, with five-year survival of less than 40%, as it is often diagnosed at a late stage. The median overall survival (OS) is approximately four years. Prognostic gene expression signatures for HGSOC have been inconsistent and have not been translated into clinical practice. The aim of this study was to develop a robust prognostic signature for HGSOC, in order to identify high-risk patients requiring alternative treatments. Methods: A meta-analysis of 1,455 tumors was performed and 200 prognostic genes were selected for expression profiling. An additional 315 candidate genes were selected from the literature, drug-targetable pathways, and other hypotheses. NanoString expression analysis of the 513 genes was performed on formalin-fixed, paraffin-embedded (FFPE) tumor tissue from 3,769 women with HGSOC from the Ovarian Tumor Tissue Analysis (OTTA) Consortium. A prognostic model for 5-year survival was developed using regression-based and machine learning methods. The model was trained on 2,702 tumors and evaluated on an independent set of 1,067 tumors. A NanoString expression analysis was performed on 2,692 additional tumors, for a subset of 218 of the genes, including the prognostic signature. Results: Expression of 276 genes was associated with OS (false discovery rate (FDR) < 0.05) in single-gene analyses adjusted for age, stage, and race. The top five genes were TAP1, ZFHX4, CXCL9, FBN1, and PTGER3 (p << 0.001). A 101-gene prognostic signature was established, where a gain of one standard deviation in the gene expression score conferred a greater than two-fold increase in risk of death (HR (hazard ratio) = 2.35 [2.02, 2.71]; p << 0.001). Median survival by quintile group was 9.5, 5.4, 3.8, 3.2, and 2.3 years. Sensitivity analyses, applying the signature to specific patient groups, showed that the prognostic power of the signature is not explained by residual disease, treatment, BRCA status, or CD8 score. The prognostic signature was enriched in pathways with treatment implications, such as PI3K, GPCR, and immune. This signature is suitable for use in FFPE tumor material and future cases can be assigned an HR from the gene expression score. The signature was developed in cases that did not receive neoadjuvant treatment (NACT); however, application to NACT cases was assessed in the second NanoString expression analysis. Conclusion: The OTTA-SPOT (Ovarian Tumor Tissue Analysis consortium – Stratified Prognosis of Ovarian Tumors) gene expression signature may improve risk stratification in clinical trials by identifying patients who are least likely to achieve 5-year survival. Citation Format: Joshua Millstein, Timothy Budden, Ellen L. Goode, Michael S. Anglesio, Aline Talhouk, OTTA consortium, David G. Huntsman, David D. Bowtell, James D. Brenton, Jennifer A. Doherty, Paul P.D. Pharoah, Susan J. Ramus. The Ovarian Tumour Tissue Analysis Consortium: Stratified Prognosis of Ovarian Tumors (OTTA-SPOT) signature for high-grade serous ovarian cancer [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research; 2019 Sep 13-16, 2019; Atlanta, GA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(13_Suppl):Abstract nr IA12.
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 | 0,005 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,003 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 ».