Analysis of Protein Biology to Increase Efficacy of Aurora Kinase Inhibitors for Multiple Myeloma Therapy.
Notice bibliographique
Résumé
Abstract Despite improvement in therapeutic regimes which have extended survival of patients with multiple myeloma, the search for novel targets to further advance treatment options continues. It is critical to the success of targeted cancer therapy that the rational selection of drugs be derived from the molecular and cellular biology of protein targets. Furthermore, studies have indicated that focused combination drug therapy could produce remarkable improvement in patient outcomes. Aurora kinases A/B/C (AURK), components of the centrosome and important for cell cycle function, are promising new targets in the treatment of numerous types of cancer. We have previously validated AURKs as a therapeutic target in pre-clinical models of multiple myeloma. In this study our aim is to better define the biology of AURKs in both normal and malignant cells, with the goal of identifying rational strategies for improving the therapeutic potential of Aurora kinase inhibitors (AKIs) as anti-cancer agents. We use a systematic two-pronged approach to map post-translational modifications and novel protein-protein interactions that define the interactome of AURKs. We also examine whether Aurora kinase interactions and posttranslational modifications are significantly different in cancer cells compared to normal cells. Increased understanding of Aurora kinase biology will help develop mechanisms to intensify the anti-cancer effects of AKIs. SUMOylation is a recently discovered posttranslational modification of numerous proteins that has been linked to many cancer types. A small ubiquitin-like modifier (SUMO) group is conjugated at the canonical ubiquitin site of a protein to alter its function, stability or sub-cellular localization. We believe that Aurora kinases may be differentially SUMOylated in cancer cells compared to normal cells, and that SUMOylation of Aurora kinases in cancer cells may alter their function and their susceptibility to AKI therapy. Here we have determined putative SUMOylation sites of all three AURKs in silico and also have evidence of AURK SUMOylation using an in vitro assay and also in human HEK293 cells. Furthermore, the gene encoding the central SUMO conjugating enzyme - UBC9 - is expressed at higher levels in CD138+ malignant plasma cells compared to other CD138−bone marrow cells in myeloma patients. As part of our strategy, we have also used Tandem Affinity Purification (TAP) technology to identify novel protein-protein interactions of Aurora kinases. In the initial stage, we have established stable expressing clones in a human cancer cell line and a normal line that express TAP-tagged AURKA/B or C. In pilot experiments we have successfully isolated pure recombinant AURKA along with interacting proteins using the TAP method. Using mass spectrometry (MS/MS) protein ID, we have identified putative protein interaction partners with AURKA which have implications in general cancer biology and centrosomal structure. In the future, these interactions will be validated using conventional biochemical methods before being tested in myeloma cell lines.
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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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 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) | 0,002 | 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 ».