Abstract 1570: Early Detection Research Network (EDRN) validation of circulating ovarian cancer biomarkers
Notice bibliographique
Résumé
Abstract Developing non-invasive blood-based tests is extremely appealing for early detection of cancers through screening asymptomatic subjects. This is particularly true for epithelial ovarian cancer in which the majority of women are diagnosed at a late stage when frontline therapy is less effective. To date there are no FDA approved biomarkers for ovarian cancer screening. To address this limitation, 165 proteins and 14 autoantibodies, identified as candidate circulating ovarian cancer biomarkers in previous studies at participating EDRN sites, were evaluated for their ability to discriminate ovarian cancer patient samples from those associated with benign ovarian disease. First, an in silico approach was used to prioritize candidate biomarkers likely to be over-expressed in ovarian cancer and predicted to be secreted. In parallel, high performance quantitative tandem mass spectrometry analyses of pooled plasma from serous ovarian cancer cases and serous benign ovarian disease controls were used to confirm candidate detectability in plasma and triage candidates by differential expression. A total of 61 proteins had sufficient evidence from one or more approaches to warrant further evaluation. 32 of the 61 proteins were evaluated using antibody-free selected reaction monitoring mass spectrometry (SRM-MS) assays. 29 of the 61 proteins could only be detected using high-pressure high-resolution separations with intelligent selection and multiplexing (PRISM)-SRM, due to the required analytical sensitivity. Because of its low prevalence, early detection of ovarian cancer requires very high specificity (≥99.6%), achievable when a blood test at 98% specificity is followed by trans-vaginal ultrasound. Therefore, sensitivity was estimated at 98% specificity for all candidates by quantifying candidates in serum from serous ovarian cancer cases (n = 20) and serous benign ovarian disease controls (n = 20). All 14 autoantibody candidates were similarly evaluated by ELISA using an expanded set of 50 serous ovarian cancer cases and 50 serous benign ovarian disease controls. The use of benign ovarian disease controls ensured similar conditions of blood sample acquisition and avoided selection of candidates that are elevated in the presence of benign disease. Candidates with 5% or greater sensitivity were identified as potential members of a panel of ovarian cancer biomarkers. These included WFDC2, SPON1, CBPA4, IBP2, and A2GL proteins, and autoantibodies CTAG2, p53, CTAG1A, and PTPRA. In summary, a multi-pronged approach identified five circulating proteins and four autoantibodies that warrant further evaluation in longitudinal pre-diagnostic plasma or sera from cases detected in screening studies and matched controls. Candidates successful in this future validation may provide the foundation for a new blood-based biomarker panel for the early detection of ovarian cancer. Citation Format: Steven J. Skates, Karen S. Anderson, Tao Liu, Vathany Kulasingam, Dustin Rabideau, Chaochao Wu, Michael Gillette, Andrew K. Godwin, Nicole Urban, Anna Lokshin, Jeffrey Marks, Eleftherios Diamandis, Zhen Zhang, Sudhir Srivastava, Jacob Kagan, Christos Patriotis, Karin Rodland. Early Detection Research Network (EDRN) validation of circulating ovarian cancer biomarkers. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1570. doi:10.1158/1538-7445.AM2015-1570
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 | 0,011 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».