MP51-15 PREDICTIVE VALUE AND POTENTIALS FOR CO-TARGETED THERAPY OF STAT1 SIGNALING IN GEMCITABINE/CISPLATIN RESISTANT BLADDER CANCER
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Résumé
You have accessJournal of UrologyBladder Cancer: Basic Research & Pathophysiology I (MP51)1 Apr 2019MP51-15 PREDICTIVE VALUE AND POTENTIALS FOR CO-TARGETED THERAPY OF STAT1 SIGNALING IN GEMCITABINE/CISPLATIN RESISTANT BLADDER CANCER Tetsutaro Hayashi, Kenichiro Ikeda*, Roland Seiler, Robert H Bell, Susan Ettinger, Kendric Wang, Htoo Zarni Oo, Hamidreza Abdi, Wolfgang Jaeger, Tilman Todenhoefer, Colin Collins, Akio Matsubara, and Peter C Black Tetsutaro HayashiTetsutaro Hayashi More articles by this author , Kenichiro Ikeda*Kenichiro Ikeda* More articles by this author , Roland SeilerRoland Seiler More articles by this author , Robert H BellRobert H Bell More articles by this author , Susan EttingerSusan Ettinger More articles by this author , Kendric WangKendric Wang More articles by this author , Htoo Zarni OoHtoo Zarni Oo More articles by this author , Hamidreza AbdiHamidreza Abdi More articles by this author , Wolfgang JaegerWolfgang Jaeger More articles by this author , Tilman TodenhoeferTilman Todenhoefer More articles by this author , Colin CollinsColin Collins More articles by this author , Akio MatsubaraAkio Matsubara More articles by this author , and Peter C BlackPeter C Black More articles by this author View All Author Informationhttps://doi.org/10.1097/01.JU.0000556457.60096.07AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVES: Gemcitabine (GEM) and cisplatin (CDDP) combination chemotherapy (GC) is the standard treatment for patients with advanced bladder cancer (BC), but responses have been reported in only 60% of patients, and these are rarely durable. We aimed to use a genomic analysis to determine mechanisms of resistance to GC. METHODS: Three chemo-sensitive BC cell lines were treated serially with increasing concentrations of CDDP or GEM in order to establish acquired resistance. Gene expression of the resistant cells was compared to the sensitive parental cells. Results were validated in The Cancer Genome Atlas (n=405) and in a patient cohort treated with neoadjuvant GC (n=223). Immunohistochemistry (IHC) was performed in 14 patient tumors before and after neoadjuvant GC and in 37 patients with metastatic BC treated with GC. Correlative in vitro experiments were conducted to explore the mechanism of acquired chemo-resistance. RESULTS: Gene expression analysis revealed that STAT1 and six interferon-regulated genes were among the most highly up-regulated genes in resistant cells. In the TCGA dataset, STAT1 expression correlated with the expression of the other 6 genes (P<0.001). Highest STAT1 expression was observed in basal/squamous and luminal infiltrated subtypes. Five-year survival in these patients treated without neoadjuvant GC was 49.7% and 47.7% in tumors with high and low STAT1 expression (compared the median), respectively. In a cohort of patients treated with neoadjuvant GC, the corresponding survival was 62.7% and 78.9%. Nuclear STAT1 expression by IHC was absent in tumors prior to GC but detected in 29% after GC, suggesting that GC activates STAT1 in a subset of patients. In patients with metastatic BC, STAT1 expression was higher in patients with progressive disease (P=0.078) and high STAT1 expression correlated with worse prognosis (P=0.012). Knockdown of STAT1 in resistant cells without CDDP/GEM treatment increased cell growth by cell cycle progression, which was accompanied by increased SKP2 and decreased p27. However, STAT1 knockdown with CDDP/GEM treatment decreased cell growth and increased apoptosis, suggesting that STAT1 silencing restored sensitivity to GC. Conclusions: STAT1 signaling is activated in a subset of BC patients and is associated with acquired chemotherapy resistance. Pending further validation, STAT1 may be considered as potential target in combination with GC, as well as a predictive marker of response to GC. Source of Funding: None Hiroshima, Japan; Vancouver, Canada; Bern, Switzerland; Vancouver, Canada; Hiroshima, Japan; Vancouver, Canada© 2019 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 201Issue Supplement 4April 2019Page: e729-e729 Advertisement Copyright & Permissions© 2019 by American Urological Association Education and Research, Inc.MetricsAuthor Information Tetsutaro Hayashi More articles by this author Kenichiro Ikeda* More articles by this author Roland Seiler More articles by this author Robert H Bell More articles by this author Susan Ettinger More articles by this author Kendric Wang More articles by this author Htoo Zarni Oo More articles by this author Hamidreza Abdi More articles by this author Wolfgang Jaeger More articles by this author Tilman Todenhoefer More articles by this author Colin Collins More articles by this author Akio Matsubara More articles by this author Peter C Black 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 | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».