Chromatin-focused genetic and chemical screens identify BRPF1 as a targetable vulnerability in Taxol-resistant triple-negative breast cancer
Why this work is in the frame
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Bibliographic record
Abstract
Triple-negative breast cancer (TNBC) is a particularly aggressive and frequently recurring form of breast cancer, where chemotherapy is the primary treatment approach. Unfortunately, the development of resistance to chemotherapy poses a considerable challenge, restricting the already limited therapeutic alternatives for recurrent cases. Here, we generated two Taxol-resistant TNBC cell lines with a dose-escalation method to mimic chemotherapy resistance in vitro. These cells exhibited reduced growth rates, altered morphology and evasion of apoptosis. Transcriptome analysis uncovered elevated ABCB1 expression and multidrug-resistant phenotype in these resistant cells. To comprehensively investigate the key epigenetic regulators of Taxol resistance, we conducted chromatin-focused genetic and chemical screens and pinpointed Bromodomain and PHD Finger Containing 1 (BRPF1) as a novel regulator of Taxol resistance. Knockout of BRPF1, the reader protein in the MOZ-MORF histone acetyltransferase complex, but not the other complex members, sensitized resistant cells to Taxol. In addition, BRPF1 inhibitors, PFI-4 and OF-1, in combination with Taxol significantly reduced cell viability. Transcriptome analysis upon BRPF1 loss or inhibition revealed a negative impact on ribosome biogenesis-related gene sets, resulting in a global decrease in protein translation in Taxol-resistant cells. CUT&RUN-qPCR analysis demonstrated that BRPF1 directly binds to the ABCB1 promoter, enhancing its expression toward inducing a multidrug-resistant phenotype. Conversely, knockout or inhibition of BRPF1 leads to decreased ABCB1 expression. Our findings uncover a comprehensive molecular framework, highlighting the pivotal role of epigenetic reader protein BRPF1 in Taxol resistance and providing potential avenues for therapeutic intervention in TNBC.
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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) | 0.000 | 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) | 0.000 | 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 it