MP51-15 PREDICTIVE VALUE AND POTENTIALS FOR CO-TARGETED THERAPY OF STAT1 SIGNALING IN GEMCITABINE/CISPLATIN RESISTANT BLADDER CANCER
Bibliographic record
Abstract
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 ...
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".