Taxol Resistance in Breast Cancer Cells Is Mediated by the Hippo Pathway Component TAZ and Its Downstream Transcriptional Targets<i>Cyr61</i>and<i>CTGF</i>
Bibliographic record
Abstract
Taxol (paclitaxel) resistance represents a major challenge in breast cancer treatment. The TAZ (transcriptional co-activator with PDZ-binding motif) oncogene is a major component of the novel Hippo-LATS signaling pathway and a transcriptional coactivator that interacts with and activates multiple transcription factors to regulate various biological processes. Here, we report that elevated levels of TAZ found in human breast cancer cells are responsible for their resistance to Taxol. DNA microarray analysis identified the oncogenes Cyr61 and CTGF as downstream transcriptional targets of TAZ. Short hairpin RNA-mediated knockdown of both Cyr61 and CTGF reversed TAZ-induced Taxol resistance in breast cancer cells. Interaction of TAZ with the TEAD family of transcription factors was essential for TAZ to activate the Cyr61/CTGF promoters and to induce Taxol resistance. Our findings define the TAZ-TEAD-Cyr61/CTGF signaling pathway as an important modifier of the Taxol response in breast cancer cells, as well as highlighting it as a novel therapeutic target to treat drug-resistant breast cancers that arise commonly at advanced stages of disease.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".