Abstract 5405: PPARγ-agonist can render bladder tumor sensitive to EGFR inhibition
Bibliographic record
Abstract
Abstract Abstract Purpose: The two signalling molecules that are extremely attractive for targeted therapy are the epidermal growth factor receptor (EGFR) and the peroxisome proliferator-activated receptor γ (PPARγ). We evaluate the integration of combined drugs against these targets in the management of bladder cancer therapy. Methods: The effect of EGFR inhibitor (Gefitinib) and PPARγ-agonist DIM-C, on cell growth, were screened in a panel of 9 human urothelial carcinoma cell lines derived from well-differentiated superficial bladder tumors as well as from high-grade invasive tumors. Cell proliferation was determined with an MTT assay after incubation with varying concentrations of the targeted agents (10-3 to 102 mM) for 72 h. Antiproliferative effects of combined therapy (Gefitinib and DIM-C) compared to each drug alone were monitored in vitro, by MTT assays. Levels of expression of EGFR and PPARγ were evaluated by Western Blot analysis at baseline and after pre-treatment with EGFR inhibitor (Gefitinib). Immunofluorescence was then used to determine PPARγ nuclear accumulation mediated by Gefitinib. In vivo, the effect of Gefitinib, DIM-C alone and combined were determined in nude mice bearing human KU-7 subcutaneous xenografts. Results: Several bladder cancer cell lines were demonstrated to be relatively resistant to Gefitinib. Induction of PPARγ expression was observed in response to different concentrations of Gefitinib for 24 h. Moreover, nuclear accumulation of PPARγ was observed following its upregulation. MTT assay shows that PPARγ agonist significantly sensitized bladder cancer cell lines that were resistant to EGFR inhibition in a schedule-specific manner. These findings were confirmed in vivo where combination therapy maximally inhibited tumor growth in contrast to each treatment alone when compared to control (p<0.04). Conclusions: Preliminary results suggest that PPARγ-agonist DIM-C can render bladder tumor sensitive to EGFR inhibition and combination efficacy might be achieved in a schedule-specific manner. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 5405.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".