Abstract TMEM-035: GLYCOMARKERS FOR PREDICTING PLATINUM–DRUG RESPONSE IN OVARIAN CANCER
Notice bibliographique
Résumé
Abstract Platinum-based drugs (pt-drugs) continue to be the mainstay of first-line therapy for a wide range of cancers including ovarian carcinoma. Despite common applications, resistance to pt-drugs is an ongoing dilemma in cancer treatments, because tumor cells have different molecular characteristics that affect their responses to drugs. A molecular test that can accurately identify pt-drug resistance would provide invaluable guidance for chemotherapy strategy and reduce the random use of ineffective drugs. This would be a significant advancement in treatment management. Our studies address this unmet need in cancer care by introducing an innovative method to predict pt-drug resistance, before administrating the drug. Our method is based on a new concept linking glycan cell surface expression with drug reactivity. We have identified a glycan structure, namely Glycomarker 1, whose expression level on the cancer cell surface is associated with a response to first-line chemotherapy. A patent for this discovery has been issued by the US patent office in 2009(1). Flow cytometry with a series of fluorescent lectins was initially used to profile the cell surface glycans on three isogenic pairs of ovarian carcinoma cell lines, each pair consisting of parental chemosensitive cell lines, 2008, A2780, IGROV-1, and their corresponding resistant cell lines 2008/C13*5.25, A2780/CP and IGROV-1/CP, respectively. The cell surface glycan comparison revealed that the expression levels of Glycomarker 1 on resistant phenotypes were at least ten times lower than that of sensitive parental cells, on all three pairs of cell lines. Further experiments by various methods of fluorescent confocal microscopy (with fluorescently labeled cisplatin), colony forming assay, glycan modifications, and mass spectrometry confirmed the association of Glycomarker 1 with drug uptake by ovarian carcinoma cell lines(1). Lectin histochemistry (LHC) was adopted as a clinical method to test the Glycomarker 1 on human ovarian tissue samples. The LHC was optimized and validated for reproducibility, specificity, and sensitivity by colorimetric and fluorescent staining systems. The feasibility of LHC as a clinical test was evaluated on a training panel of 64 ovarian tissue sections. In retrospective studies, LHC correctly predicted drug-response in 22 out of 27 (81.4%) cancer specimens from patients with a known history of response to first-line chemotherapy. In the course of testing the clinical samples for Glycomarker 1, we identified another glycan structure, namely Glycomarker 2, whose expression pattern was strongly similar to that of the Glycomarker 1, suggesting the association of two glycan motifs with drug response. A patent for this discovery is pending in the US, Canada and European patent office(2). The ultimate objective of this work is to translate the assays to clinical tests used to predict platinum response in ovarian cancer. This is a novel direct predictive method, truly different from existing procedures, which will provide molecular information for treatment strategy. Thus far, our experiments have focused on ovarian cancer. However, evidence indicates that this method may also apply to other cancers that are treated with pt-drugs. 1) US patent #7585503 2) PCT patent # 20150024409, 01/22/2015 pending in the US, Canada & Europe. Citation Format: Nahid Razi, Afshin Bahador, Nathalie Scholler and Nissi Varki. GLYCOMARKERS FOR PREDICTING PLATINUM–DRUG RESPONSE IN OVARIAN CANCER [abstract]. In: Proceedings of the 11th Biennial Ovarian Cancer Research Symposium; Sep 12-13, 2016; Seattle, WA. Philadelphia (PA): AACR; Clin Cancer Res 2017;23(11 Suppl):Abstract nr TMEM-035.
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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 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 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 ».