Abstract DP-002: COMPARISON OF POTENTIAL OVARIAN CANCER BIOMARKERS BY MASS SPECTROMETRY-BASED PROTEOMIC ANALYSIS OF RESIDUAL PAP TEST FLUID, CERVICAL SWABS, AND TUMOR TISSUE FROM AN OVARIAN CANCER PATIENT
Notice bibliographique
Résumé
Abstract Early detection is the key to increased survival for women with ovarian cancer, yet a screening tool has yet to be developed that is adequately sensitive and specific enough for use in the general population. In contrast, screening for cervical cancer by Pap tests has been routinely performed for over 50 years. In the liquid-based Pap test, cells are collected from the cervix and placed into an alcohol-based fixative and then examined for abnormal cells. Since ovarian cancer cells have been observed in Pap tests, we reasoned that ovarian cancer peptide biomarkers may also be present. Our central hypothesis is that proteins shed by ovarian cancer cells can be detected during routine Pap tests by mass spectrometry (MS)-based proteomics. In particular, when collected at the time of cervical cancer Pap test screening, the alcohol-based Pap test fixative and cervical swabs are ideal for biomarker discovery since they are derived from a site near the ovarian cancer (i.e. proteins may be secreted or shed from the tumor and flow through the fallopian tube into the uterus and out the cervical opening). Recently, the fimbria of the fallopian tube have been suggested to be the true precursor to ovarian cancer, strengthening our hypothesis that ovarian cancer proteins will be found in the lower genital tract, perhaps even at early stages. To demonstrate the feasibility of using Pap tests as a biospecimen for proteomics, we previously examined the proteins present in residual Pap test fixative samples from women with normal cervical cytology by MS and described 152 proteins in the “Normal Pap Proteome.” The objective of this study was to identify and compare the proteins from three different sources from the same ovarian cancer patient: (i) the residual Pap test fixative, (ii) a Merocel swab of the cervix, and (iii) the primary ovarian cancer tumor tissue. Proteins were concentrated from the cell-free supernatant of the Pap test fixative or eluted from the swab, and then trypsin digested using the filter-aided sample preparation method. A total protein extract from the patient's tumor tissue was digested by standard in-solution trypsin digestion. The samples were run on 2D-liquid chromatography MS/MS, followed by bioinformatics integration. We identified over 5000 proteins total in the three samples. More than 2000 proteins were expressed in all three ovarian cancer samples, including several known ovarian cancer biomarkers such as CA125. By Scaffold analysis of the Gene Ontology nomenclature of the proteins, we classified the proteins by both cellular localization and biological processes. Additional matched samples from patients will be used to build a library of proteins and peptides that are specific to ovarian cancer for use in the development of targeted MS assays. We conclude that quantification of proteins from Pap test fixatives and cervical swabs will prove to be a rich source of biomarkers for ovarian cancer detection. Citation Format: Kristin L.M. Boylan, Anna C. Rogers, Melissa A. Geller, Peter A. Argenta, Timothy J. Griffin, and Amy P.N. Skubitz. COMPARISON OF POTENTIAL OVARIAN CANCER BIOMARKERS BY MASS SPECTROMETRY-BASED PROTEOMIC ANALYSIS OF RESIDUAL PAP TEST FLUID, CERVICAL SWABS, AND TUMOR TISSUE FROM AN OVARIAN CANCER PATIENT [abstract]. In: Proceedings of the 12th Biennial Ovarian Cancer Research Symposium; Sep 13-15, 2018; Seattle, WA. Philadelphia (PA): AACR; Clin Cancer Res 2019;25(22 Suppl):Abstract nr DP-002.
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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».