Abstract 539: Characterization of the functional roles of RET isoforms in breast cancer.
Notice bibliographique
Résumé
Abstract REarranged during Transfection (RET) is a receptor tyrosine kinase crucial for normal development of the kidneys, endocrine tissues and nervous system. RET is normally activated though binding of both a ligand from the Glial Cell-line Derived Neurotrophic Factor (GDNF) family, and a co-receptor of the GDNF Family Receptor alpha (GFRα) proteins. Abnormal RET signalling caused by germline activating mutations of RET,or somatic gene rearrangements are known to play an important role in tumorigenesis and disease progression in thyroid cancers. However, expression of wildtype RET and its ligands have also been linked to other tumour types such as pancreatic cancer, where the expression and activation of RET may lead to more aggressive disease. RET is also expressed in 25-30% of invasive breast cancers, with relatively more frequent expression in hormone receptor-positive sub-types. RET has two major distinct protein isoforms, called RET9 and RET51, that share the first 1062 residues but differ in their C-terminal amino acids. RET9 and RET51 are highly conserved across species, and both isoforms are normally co-expressed in the kidneys and in neural crest-derived tissues during development. Previous studies have begun to elucidate certain isoform-specific differences including: differential phosphorylation patterns after activation, unique target gene expression patterns, and distinct trafficking properties. The functional differences between RET9 and RET51 in breast cancer, however, have not yet been explored. As such, our overarching research objective has been to investigate the roles of individual RET isoforms in breast cancer progression. We used quantitative real-time reverse transcription PCR to assess expression of RET9, RET51, pan-RET (all isoforms of RET) and two GFRα co-receptors, GFRα1 and GFRα3, in both estrogen receptor (ER) negative and ER positive breast cancer cell lines. RET9 and RET51 protein expression were verified by Western blotting. Our data suggest that RET9 is more highly expressed than RET51 in breast cancer cells. We are currently conducting proliferation, migration and invasion assays with tumour cell lines expressing single RET isoforms to explore individual roles of RET9 and RET51 in breast cancer. Further, using well-characterized isoform-specific antibodies, we have examined expression of RET9 and RET51 in two large cohort breast cancer TMAs(>150 samples/array). Analyses are currently ongoing to explore the expression of each individual isoform in tumours. This study may shed light to potential functional differences between RET9 and RET51 in breast cancer, furthering our understanding of RET isoform-specific differences. Citation Format: Piriya Yoganathan, Ami Wang, Eric Lian, Keyue Ding, Victor A. Tron, Lois M. Mulligan. Characterization of the functional roles of RET isoforms in breast cancer. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 539. doi:10.1158/1538-7445.AM2013-539
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) | 0,002 | 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 ».