Abstract 3076: Post-transcriptional regulatory mechanisms contributing to the evolution of paclitaxel resistance in breast cancer model cell line, SKBR3
Notice bibliographique
Résumé
Abstract Introduction: Breast Cancer (BCa) treatments often include taxanes in the treatment regimens and resistance to these drugs is common in a clinical setting. Several mechanisms were put forth to explain the resistance. These include induction of multidrug (MDR) resistance family of transporters, differential regulation of tubulin isotypes, microRNA (miR) mediated post-transcriptional regulation and autophagy. Aims: (i) to create a panel of breast cancer cell lines from SKBR3 (Her2+ and ER-ve) with varying levels of drug resistance to paclitaxel; (ii) to profile differentially expressed miRs in these panels of cell lines to understand the evolution of drug resistance with common and unique signatures associated with each of the cell lines in the panel and (iii) to assess β-tubulin (TUBB3) expression and its regulation by miRs. Methods: Drug resistant SKBR-3 cells were generated by a stepwise increase in drug concentration to the parental cells until a maximum tolerable dose was reached. Control cells were those without any treatment (wild type, WT) and serial passage of SKBR3 cells for the same numbers of generations as in each of the drug resistant lines. Total RNA extracted from the cell lines was subjected to small RNAome profiling (with focus on miRs) using Next Generation Sequencing (Illumina Genome Analyzer IIx). Partek Genomics Suite 6.6 was used for analyzing sequencing files. Data normalized (RPKM) from triplicate experiments (three independent cell growth experiments) were adjusted for potential batch effects. We report miRs showing statistically significant differences in expression levels (>2-fold and FDR adjusted p<0.05) between WT SKBR3 and drug resistant cell line(s) with concordant profiles from triplicate sequencing experiments. Protein expression levels of β-tubulin (TUBB3) were assessed using Western blots. Results and conclusions: The resistant cell lines expressed TUBB3 (graded up-regulation with the level of resistance) as expected as well as down-regulation of hsa-miR-200c, a known miR directly regulating the expression of TUBB3. The resistant cell lines also showed up-regulation of miRs 221-3p and 99a known to modulate ER and its mediated gene regulatory pathways, consistent with the ER-ve phenotype of SKBR3 cells. Overall, 27 miRs are differentially expressed when all resistant cells are considered (irrespective of the level of resistance conferred). As expected, some of the identified miRs were reported to have a role in Epithelial-Mesenchymal Transition vis-à-vis acquisition of cancer stem cell properties. We also observed unique miRs in cells expressing graded levels of resistance and the data is subject to further analysis for potential hierarchy of programmed expression of miRs facilitating transitioning of cells from sensitive to highly drug resistant phenotype. Citation Format: Sambasivarao Damaraju, Preethi Krishnan, Marc St George, Jack Tuszynski, Carol Cass, IngSwie Goping, Olga Kovalchuk. Post-transcriptional regulatory mechanisms contributing to the evolution of paclitaxel resistance in breast cancer model cell line, SKBR3. [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 3076. doi:10.1158/1538-7445.AM2015-3076
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 | 0,000 | 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 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,002 |
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 ».