Abstract 5144: Exploring the potential of a novel multiplex proteomics technology to identify personalized biomarkers of tumor burden in ovarian cancer patients
Notice bibliographique
Résumé
Abstract Objective: Owing to tumor heterogeneity, we hypothesize that each tumor secretes a unique mixture of tumor-related proteins into the bloodstream, which when quantified can serve as personalized markers of tumor load. We previously introduced the concept of "personalized tumor markers", which are highly sensitive for monitoring tumor load in a small subpopulation (5-30%) of patients. Screening against a robust panel of such personalized markers could identify the most effective markers of relapse in each patient. With the advent of multiplex proteomics technologies, for the first time we can screen serum for tumor-related proteins in individual patients. In this pilot study, we explored the potential of one of the leading new multiplex proteomics technologies for identifying personalized markers of tumor burden in the most common and deadliest type of ovarian cancer - high grade serous carcinoma (HGSC). Serum CA125 is the only widely used marker for HGSC but it is not informative of relapse in 30-60% of cases. With up-and-coming immunotherapies and precision medicines, there is a timely need for personalized markers to monitor for relapse and find the optimal timing for 2nd-line treatment in each patient. Methods: We leveraged the multiplex immuno-PCR assay, Proseek Panels (Olink, Sweden), to concurrently measure 1,196 proteins in sera obtained pre- and post-surgically from 10 HGSC patients. Sera from two healthy individuals, with blinded technical duplicates, were used as controls. To validate the multiplexed results, we used independent, clinical grade ELISA immunoassays to measure three candidate proteins in sera collected pre- and post-surgically from five of the same 10 HGSC patients. Results: Scatterplot analysis of 1,196 protein measurements in the technical duplicates showed high correlation and excellent assay precision. For candidate selection, we eliminated proteins that showed greater than two-fold change in the controls. We identified a panel of eight proteins that dropped more than 10-fold post-surgery in at least two patients. The panel as a whole was informative of tumor burden for 80% (8/10) of the patients tested. Using independent ELISAs, we observed high concordance in the post-surgical fold decrease between the Proseek panels and ELISA results, confirming the reliability of the novel platform. Further literature search demonstrated the relevance to tumorigenesis and ovarian cancer for the eight candidate personalized tumor markers. Significance: With future validation in larger, longitudinal studies, our goal is to develop a panel of personalized HGSC biomarkers of relapse to aid in individualizing patient monitoring and ameliorating survival outcome. Our study also showed the utility of an innovative high throughput biotechnology that is promising for measuring tumor-derived proteins, which can be adapted for a myriad of biomarker studies, particularly in prevalent cancers with no reliable serum biomarkers such as lung cancer. Citation Format: Annie Ren, Ioannis Prassas, Antoninus Soosaipillai, Marcus Bernardini, Vathany Kulasingam, Eleftherios Diamandis. Exploring the potential of a novel multiplex proteomics technology to identify personalized biomarkers of tumor burden in ovarian cancer patients [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5144.
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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| 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 | 0,000 | 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 ».