Abstract 1719: Integrative genomic analysis of ovarian cancer cell lines points to EMT involvement in the development of cisplatin resistance
Notice bibliographique
Résumé
Abstract Ovarian cancer is the 5th leading cause of cancer related death in women, and has the highest associated mortality rate of any of the gynecological cancers. Approximately 75-80% of women diagnosed will succumb to the disease. The high mortality rate associated with ovarian cancer is due to poor screening and detection methods, resulting in late stage diagnosis, and also to resistance of the tumours to chemotherapeutic drugs. Primary drug resistance occurs in about 20% of patients during their treatment phase while approximately 70-80% of women that initially respond to chemotherapy will develop drug resistance over the course of treatment or at the time of relapse. Being able to accurately predict which patients will not successfully respond to drugs is the first step in developing personalized medicine and preventing useless chemotherapy treatments. In order to discover biomarkers of cisplatin resistance, a multi-platform integrative approach was taken utilizing data from aCGH as well as miRNA and mRNA microarrays. Data were collected from a drug sensitive and drug resistant ovarian cancer cell line pair (A2780 and A2780cis). Working with the paired cell lines provided a homogeneous population with which to build an integration algorithm. Data analysis using GeneSpring and Nexus software showed an enrichment of genes involved in TGFβ and EGF signaling. These signaling pathways lead us to investigate the involvement of the epithelial to mesenchymal transition (EMT) in drug resistance. Interestingly, many of the key genes involved in the regulation of the EMT process were found to be upregulated in our mRNA expression data. Observations from cell culture work support EMT involvement. We have noted a slower growth rate and a change to a fibroblastic appearance in the resistant cells relative to their drug sensitive parents. Immunohistochemistry has also been utilized to show an increase in mesenchymal markers in the resistant cell line. In order to determine if these findings were representative of drug resistance in human ovarian cancer, expression data from cisplatin sensitive and resistant ovarian cancer tumours were mined specifically for these and other EMT genes. A large number of the same EMT genes were consistently shown to be upregulated in the drug resistant tumour tissues relative to the sensitive tumours. Together, these data suggest the potential involvement of the epithelial to mesenchymal transition in the development of cisplatin resistance in ovarian cancer. Further validation of these results in vitro is currently in progress. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 1719. doi:10.1158/1538-7445.AM2011-1719
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 | 0,000 |
| 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 | 0,000 | 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) | 0,003 | 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 ».