Sensitivity of human lung adenocarcinoma cell lines to targeted inhibition of BET epigenetic signaling proteins
Bibliographic record
Abstract
Bromodomain and extra terminal domain (BET) proteins function as epigenetic signaling factors that associate with acetylated histones and facilitate transcription of target genes. Inhibitors targeting the activity of BET proteins have shown potent antiproliferative effects in hematological cancers through the suppression of c-MYC and downstream target genes. However, as the epigenetic landscape of a cell varies drastically depending on lineage, transcriptional coactivators such as BETs would be expected to have different targets in cancers derived from different cells of origin, and this may influence the activity and mechanism of action of BET inhibitors. To test this hypothesis, we treated a panel of lung adenocarcinoma (LAC) cell lines with the BET inhibitor JQ1 and found that a subset is acutely susceptible to BET inhibition. In contrast to blood tumors, we show that LAC cells are inhibited by JQ1 through a mechanism independent of c-MYC down-regulation. Through gene expression profiling, we discovered that the oncogenic transcription factor FOSL1 and its targets are suppressed by JQ1 in a dose-dependant manner. Knockdown of BRD4 also decreased FOSL1 levels, and inhibition of FOSL1 phenocopied the effects of JQ1 treatment, suggesting that loss of this transcription factor may be partly responsible for the cytotoxic effects of BET inhibition in LAC cells, although ectopic expression of FOSL1 alone did not rescue the phenotype. Together, these findings suggest that BET inhibitors may be useful in solid tumors and that cell-lineage-specific differences in transcriptional targets of BETs may influence the activity of inhibitors of these proteins in different cancer types.
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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 | 0.001 |
| 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".