MP16-06 EXPLOITING NOVEL THERAPEUTIC TARGETS TO BLOCK METASTASIS OF CLEAR CELL RENAL CELL CARCINOMA (RCC)
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Résumé
You have accessJournal of UrologyKidney Cancer: Basic Research & Pathophysiology I (MP16)1 Apr 2019MP16-06 EXPLOITING NOVEL THERAPEUTIC TARGETS TO BLOCK METASTASIS OF CLEAR CELL RENAL CELL CARCINOMA (RCC) Jan Rudzinski*, Natasha Govindasamy, Konstantin Stoletov, Adrian Fairey, and John Lewis Jan Rudzinski*Jan Rudzinski* More articles by this author , Natasha GovindasamyNatasha Govindasamy More articles by this author , Konstantin StoletovKonstantin Stoletov More articles by this author , Adrian FaireyAdrian Fairey More articles by this author , and John LewisJohn Lewis More articles by this author View All Author Informationhttps://doi.org/10.1097/01.JU.0000555342.90934.fcAboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVES: Metastatic RCC is incurable, and the current therapeutic paradigm is centered around controlling the disease burden to prolong overall survival. We conducted a whole human genome short hairpin RNA (shRNA) screen on human squamous cell carcinoma (Hep3) cells to identify a panel of novel functional genes that are required for productive cell motility and successful metastatic dissemination. One such novel protein target encodes for chromosome 14 open reading frame 142 (C14orf42), which has been demonstrated to be up regulated in metastatic clear cell RCC. The objective of our study was to characterize the impact of C14orf142 on clear cell RCC in vitro motility, invasion, and in vivo vascular extravasation. METHODS: Benign proximal convoluted tubule cells (PCT) and clear cell RCC cell lines 786-0 (derived from renal tissue) were obtained from American Type Culture Collection (ATCC). Targeted genomic editing to knockout (KO) C14orf142 was achieved with CRISPR-Cas9 system. Successful protein knockdown was validated using western blot analysis. To measure impact of gene KO on in vitro invasion we conducted the FITC-gelatin degradation assay. To measure the combined effect of invasion and productive cell migration in vitro we utilized the modified Boyden chamber assay coated with 0.1% gelatin. To study cancer cell vascular extravasation in vivo, 786-0 cells were injected IV into fertilized avian embryos. For statistical analysis, t-test was used to evaluate differences between groups with p value of ≤0.05 accepted as statistically significant. RESULTS: The baseline expression of C14orf142 was significantly higher in 786-0 (1.078AUD±0.11) compared to PCT(0.12AUD±0.04) (p≤0.05). The CRISPR-Cas9 targeted genomic editing resulted in generation of 786-0 clones with complete KO of C14orf142. The FITC-gelatin degradation assay demonstrated significant difference in gelatin degradation between 786-0 scramble and 786-0 CRISPR KO clones (100% vs 27%±5.56%, respectively, p≤0.05). The modified Boyden chamber assay also showed significant difference in productive cell migration between 786-0 scramble and 786-0 CRISPR KO clones (100% vs 42±3.46%, respectively, p≤0.05). Compared to 786-0 scramble clones, 786-0 CRISPR KO clones demonstrated significant reduction in extravasation on the avian embryo model (100% vs 46.33±8.37%, respectively, p≤0.05). CONCLUSIONS: Up regulation of novel target protein, C14orf142, in 786-0 clear cell RCC cells may play an important role in cancer cell invasion, productive cell migration, and vascular extravasation. Source of Funding: Kidney Cancer Research Network (KCRN); Kidney Cancer Canada (KCC); Canadian Institute of Health Research (CIHR) Edmonton, Canada© 2019 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 201Issue Supplement 4April 2019Page: e207-e208 Advertisement Copyright & Permissions© 2019 by American Urological Association Education and Research, Inc.MetricsAuthor Information Jan Rudzinski* More articles by this author Natasha Govindasamy More articles by this author Konstantin Stoletov More articles by this author Adrian Fairey More articles by this author John Lewis More articles by this author Expand All Advertisement PDF downloadLoading ...
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 | 0,000 |
| 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 | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 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 ».