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 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) | 0.001 | 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".