Chromatin-focused genetic and chemical screens identify BRPF1 as a targetable vulnerability in Taxol-resistant triple-negative breast cancer
Bibliographic record
Abstract
ABSTRACT Triple-negative breast cancer (TNBC) stands out as a particularly aggressive and frequently recurring form of breast cancer. Due to the absence of hormone receptors, the available treatment avenues are constrained, making chemotherapy the primary approach. Unfortunately, the development of resistance to chemotherapy poses a significant challenge, further restricting the already limited therapeutic alternatives for recurrent cases. Understanding the molecular basis of chemotherapy resistance in TNBC is pivotal for improving treatment outcomes. Here, we generated two different Taxol-resistant TNBC cell lines with a dose-escalation method to mimic chemotherapy resistance in vitro . These cells exhibited hallmark features of resistance, including reduced cell growth, altered morphology, and evasion of apoptosis. Transcriptome analysis uncovered elevated ABCB1 expression and multidrug-resistant phenotype in the 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 in TNBC cells. Knockout of BRPF1, the reader protein in the MOZ/MORF histone acetyl-transferase complex, but not the other complex members, sensitized resistant cells to Taxol. Additionally, 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. Our ChIP-qPCR analysis demonstrated that active BRPF1 directly interacts with the ABCB1 promoter, enhancing its expression towards inducing a multidrug-resistant phenotype. Conversely, knockout or inhibition of BRPF1 leads to decreased ABCB1 expression. This dual mechanism critically sensitizes Taxol-resistant TNBC cells to chemotherapy. 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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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) | 0.002 | 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".