ING1 and 5-Azacytidine Act Synergistically to Block Breast Cancer Cell Growth
Bibliographic record
Abstract
BACKGROUND: Inhibitor of Growth (ING) proteins are epigenetic "readers" that recognize trimethylated lysine 4 of histone H3 (H3K4Me3) and target histone acetyl transferase (HAT) and histone deacetylase (HDAC) complexes to chromatin. METHODS AND PRINCIPAL FINDINGS: Here we asked whether dysregulating two epigenetic pathways with chemical inhibitors showed synergistic effects on breast cancer cell line killing. We also tested whether ING1 could synergize better with chemotherapeutics that target the same epigenetic mechanism such as the HDAC inhibitor LBH589 (Panobinostat) or a different epigenetic mechanism such as 5-azacytidine (5azaC), which inhibits DNA methyl transferases. Simultaneous treatment of breast cancer cell lines with LBH589 and 5azaC did not show significant synergy in killing cells. However, combination treatment of ING1 with either LBH589 or 5azaC did show synergy. The combination of ING1b with 5azaC, which targets two distinct epigenetic mechanisms, was more effective at lower doses and enhanced apoptosis as determined by Annexin V staining and cleavage of caspase 3 and poly-ADP-ribose polymerase (PARP). ING1b plus 5azaC also acted synergistically to increase γH2AX staining indicating significant levels of DNA damage were induced. Adenoviral delivery of ING1b with 5azaC also inhibited cancer cell growth in a murine xenograft model and led to tumor regression when viral concentration was optimized in vivo. CONCLUSIONS: These data show that targeting distinct epigenetic pathways can be more effective in blocking cancer cell line growth than targeting the same pathway with multiple agents, and that using viral delivery of epigenetic regulators can be more effective in synergizing with a chemical agent than using two chemotherapeutic agents. This study also indicates that the ING1 epigenetic regulator may have additional activities in the cell when expressed at high levels.
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) | 0.001 | 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 | 0.001 |
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".