BET and BRAF inhibitors act synergistically against <i>BRAF‐</i>mutant melanoma
Bibliographic record
Abstract
Despite major advances in the treatment of metastatic melanoma, treatment failure is still inevitable in most cases. Manipulation of key epigenetic regulators, including inhibition of Bromodomain and extra-terminal domain (BET) family members impairs cell proliferation in vitro and tumor growth in vivo in different cancers, including melanoma. Here, we investigated the effect of combining the BET inhibitor JQ1 with the BRAF inhibitor Vemurafenib in in vitro and in vivo models of BRAF-mutant melanoma. We performed cytotoxicity and apoptosis assays, and a xenograft mouse model to determine the in vitro and in vivo efficacy of JQ1 in combination with Vemurafenib against BRAF-mutant melanoma cell lines. Further, to investigate the molecular mechanisms underlying the effects of combined treatment, we conducted antibody arrays of in vitro drug-treated cell lines and RNA sequencing of drug-treated xenograft tumors. The combination of JQ1 and Vemurafenib acted synergistically in BRAF-mutant cell lines, resulting in marked apoptosis in vitro, with upregulation of proapoptotic proteins. In vivo, combination treatment suppressed tumor growth and significantly improved survival compared to either drug alone. RNA sequencing of tumor tissues revealed almost four thousand genes that were uniquely modulated by the combination, with several anti-apoptotic genes significantly down-regulated. Collectively, our data provide a rationale for combined BET and BRAF inhibition as a novel strategy for the treatment of melanoma.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".