Abstract A170: High-content screening for inhibitors of oncogenic transcription by c-Myc
Notice bibliographique
Résumé
Abstract There is evidence that Myc partners with another molecule called TRRAP to regulate genes required for transformation. Our hypothesis is that blocking the interaction of Myc with TRRAP will selectively kill breast cancer cells. Traditional drug screening approaches to identify compounds that will break apart two interacting proteins have rarely been successful. However, our approach is to screen compounds in live breast cancer cells for those that prevent Myc and TRRAP from binding to each other. Since the cell is constantly replacing Myc, a compound that prevents it from binding TRRAP should kill even established tumors. Importantly, we previously mapped the places on Myc and TRRAP that bind them together and showed they don't involve the parts of the protein involved in other important functions. Therefore, compounds we find that prevent Myc-TRRAP binding should not be toxic to normal cells. Methods: We have established a new assay in which Myc and TRRAP are expressed as fusion proteins to Cerulean and Citrine fluorescence proteins, respectively. We are developing two different but related assays for the interaction between the two proteins. In one we rely on proximity resulting in complementation between non-fluorescent fragments of the Cerulean fluorescence protein. In this assay heterodimerization results in increased fluorescence. In the other assay proximity results in fluorescence resonance energy transfer (FRET) between the Cerulean and Citrine fluorescence proteins. We detect FRET by fluorescence lifetime imaging (FLIM). Results: A novel robotic microscope that can automatically perform high speed FLIM and thereby detect Myc-TRRAP binding quickly and accurately has been assembled and tested. Our automated microscope has had an environmental stage for live cell imaging fitted and tested. Both FRET standards and constructs based on the Cerulean-Myc / TRRAP-Citrine pair have been measured to validate the assay. Cell lines are being optimized for use for screening. Conclusions: High speed FLIM can be used to detect FRET between Cerulean-Myc and TRRAP-Citrine in live cells. Using our automated microscope we can test individually the effect of large numbers of small molecules in live breast cancer cells. This will let us identify compounds that are not only effective but that get into breast cancer cells and are not toxic to normal cells. Relevance/Impact: To stimulate the development of new drugs effective against a wide spectrum of cancers, we are identifying small drug like molecules that disrupt the function of a particularly potent cell growth gene called Myc, which is often misregulated in breast cancer. Citation Information: Mol Cancer Ther 2009;8(12 Suppl):A170.
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) | 0,001 | 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) | 0,004 | 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 ».