An <i>in vivo</i> genome‐wide shRNA screen identifies BCL6 as a targetable biomarker of paclitaxel resistance in breast cancer
Bibliographic record
Abstract
Paclitaxel is a common breast cancer drug; however, some tumors are resistant. The identification of biomarkers for paclitaxel resistance or sensitivity would enable the development of strategies to improve treatment efficacy. A genome-wide in vivo shRNA screen was performed on paclitaxel-treated mice with MDA-MB-231 tumors to identify genes associated with paclitaxel sensitivity or resistance. Gene expression of the top screen hits was associated with tumor response (resistance or sensitivity) among patients who received neoadjuvant chemotherapy containing paclitaxel. We focused our validation on screen hit B-cell lymphoma 6 (BCL6), which is a therapeutic target in cancer but for which no effects on drug response have been reported. Knockdown of BCL6 resulted in increased tumor regression in mice treated with paclitaxel. Similarly, inhibiting BCL6 using a small molecule inhibitor enhanced paclitaxel treatment efficacy both in vitro and in vivo in breast cancer models. Mechanism studies revealed that reduced BCL6 enhances the efficacy of paclitaxel by inducing sustained G1/S arrest, concurrent with increased apoptosis and expression of target gene cyclin-dependent kinase inhibitor 1A. In summary, the genome-wide shRNA knockdown screen has identified BCL6 as a potential targetable resistance biomarker of paclitaxel response in breast cancer.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".