Expression of t-DARPP Mediates Trastuzumab Resistance in Breast Cancer Cells
Bibliographic record
Abstract
PURPOSE: We have investigated the role of t-DARPP in trastuzumab resistance in ERBB2-amplified and overexpressed breast cancer cell lines. EXPERIMENTAL DESIGN: We have used the HR-5 and HR-6 trastuzumab-resistant cells that were established from tumors that recurred in the presence of trastuzumab therapy following xenografts of BT-474 cells in nude mice. In addition, SKBR-3 cells, engineered for stable expression of t-DARPP, and HCC-1569 cells, which have constitutive expression of t-DARPP and are de novo resistant to trastuzumab, were used. RESULTS: We reported > or =15-fold up-regulation of mRNA and protein levels of t-DARPP in HR-5 and HR-6 cells compared with their progenitor BT-474 trastuzumab-sensitive cells. The t-DARPP expression was not regulated by changes in its promoter DNA methylation levels. The SKBR-3 cells stably expressing t-DARPP developed resistance to trastuzumab compared with their parental cells and empty vector controls (P < 0.01). The trastuzumab-resistant cell lines showed a significant increase in pAKT (Ser(473)) and BCL2 protein levels. The small interfering RNA knockdown of t-DARPP in all trastuzumab-resistant cells led to a significant reduction in ERBB2, pAKT (Ser(473)), and BCL2 protein levels with a significant decrease in cell viability (P < or = 0.001) and an increase in cleaved caspase-3 levels, indicating the progression of these cells toward apoptosis. The t-DARPP protein was associated with both heat shock protein 90 and ERBB2 forming a potential protein complex. This association may play a role in regulating ERBB2 protein in trastuzumab-resistant cells. CONCLUSION: We conclude that t-DARPP is a novel molecular target that can mediate the therapeutic resistance to trastuzumab in breast cancer cells.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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 teacher head, 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".