Abstract B16: Response and resistance to BET bromodomain inhibitors in triple-negative breast cancer
Bibliographic record
Abstract
Abstract Triple negative breast cancer (TNBC) is a heterogeneous and clinically aggressive disease for which there is no targeted therapy. Cancer genome sequencing studies focusing on TNBC failed to identify novel recurrently mutated cancer-driving genes, obviating immediate opportunities for targeted therapeutic development. Here we have identified BET bromodomain inhibitors (BBDIs) as promising novel therapeutic agents in TNBC. Specifically, we have found that TNBC cells are significantly and preferentially growth inhibited by BBDIs (e.g., JQ1) with IC50s in the low nM range compared to luminal breast cancer cells. The growth of established xenografts derived from TNBC cell lines and primary patient samples were also efficiently inhibited by BBDIs treatment. BRD4, a target of BBDIs, is highly expressed in TNBCs compared to luminal cell lines and its downregulation using shRNAs inhibits TNBC cell growth in cell culture and induces tumor regression in vivo. In line with the role of BRD4 in transcription restart after G2/M, BBDI treatment prevented cell cycle re-entry, arrested TNBC cells in early G1, apoptosis, and induced luminal epithelial cell differentiation. Using integrated epigenomic analysis, we have identified the direct transcriptional targets of BBDI in TNBC. BBDI was found to efficiently displace chromatin-bound BRD4 in sensitive TNBC cells. This effect was more pronounced at genomic regions exhibiting elevated Bio-JQ1 binding by Chem-seq, implying strong association between BRD4 and JQ1 genomic binding patterns. By genome-wide study with ChIP-seq and RNA-seq, we have also found that selective disruption of super-enhancers associated genes by JQ1, leading to deregulation of coordinated transcriptional pathways involved in cell proliferation, invasion, and survival. The sensitivity of TNBC and other tumor types to BET inhibition establishes a rationale for clinical investigation and a motivation to understand mechanisms of resistance. After selecting derivatives with acquired resistance to BET inhibition in previously sensitive TNBCs, we identified a unique mechanism of epigenomic resistance to this epigenetic therapy. Resistant cells remained dependent on BRD4. However, TNBC cells adapt to BET bromodomain inhibition by maintaining recruitment of unmutated BRD4 to super-enhancers in a bromodomain-independent manner. Differential super-enhancer analysis found a significant gain in the number of SEs in resistant cells, and a less pronounced loss of fewer SEs. The gain of Bio-JQ1 SEs was associated with gain of BRD4 binding to these genomic loci and also with increased transcription of the associated genes. Among the top gained super-enhancers in resistant cells was an upstream and intragenic region of H3K27ac enrichment at the BCL-xL locus. Notably, BCL-xL was among the few highly up-regulated genes in resistant cells by expression profiling. Proteomic studies of resistant TNBC identify strong association with MED1 and hyper-phosphorylation of BRD4 due to decreased activity of PP2A that we identified as a BRD4 phosphatase. We have defined that hyper-phosphorylation of BRD4 increases the recruitment of BRD4 to MED1 and contributes to BBDI resistance. To explore the translational relevance of our findings, we conducted synergy screens with JQ1 and molecules targeting BCL-XL (ABT737), a gained super-enhancer in resistant cells, and modulators of BRD4 phosphorylation, a CK2 inhibitor CX-4945, and PP2A activator perphenazine (PPZ). We observed significant synergy between JQ1 and ABT737, CX-4945, and also PPZ implying that these drug combinations will likely achieve even higher efficacy in TNBCs than BBDIs alone. Together, these studies provide a rationale for BET inhibition in TNBC and argue for combination strategies to anticipate clinical drug resistance. Citation Format: Shaokun Shu, Charles Lin, Housheng Hansen He, Robert Witwicki, Justin Roberts, Doris Tabassum, Yi Liang, Muhammad Ekram, Ernest Doherty, Jonathan Brown, Hisham Mohammed, Clive D'Santos, Michael McKeown, Christopher Ott, Jun Qi, Min Ni, Prakash Rao, Melissa Duarte, Shwu-Yuan Wu, Cheng-Ming Chiang, Richard Young, Jason Carroll, Henry Long, Myles Brown, Shirley X. Liu, Clifford Meyer, James Bradner, Kornelia Polyak. Response and resistance to BET bromodomain inhibitors in triple-negative breast cancer. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Breast Cancer Research; Oct 17-20, 2015; Bellevue, WA. Philadelphia (PA): AACR; Mol Cancer Res 2016;14(2_Suppl):Abstract nr B16.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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 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".