Epigenetic Therapy of Non-Small Cell Lung Cancer Using Decitabine (5-Aza-2′-Deoxycytidine)
Bibliographic record
Abstract
Epigenetic analysis shows that many genes that suppress malignancy are silenced by aberrant DNA methylation in lung cancer. Many of these genes are interesting targets for reactivation by the inhibitor of DNA methylation, decitabine (5-aza-2'-deoxycytidine, DAC). A pilot study on intense dose DAC showed promising results in patients with metastatic non-small cell lung cancer (NSCLC). However, subsequent clinical studies using low dose DAC were not very effective against NSCLC and interest in this therapy diminished. Recently, interesting responses were observed in a patient with NSCLC following treatment with a combination of the related inhibitor of DNA methylation, 5-azacytidine, and an inhibitor of histone deacetylation. This finding has generated a renewed interest in the epigenetic therapy of lung cancer. Preclinical studies indicate that DAC has remarkable chemotherapeutic potential for tumor therapy. This epigenetic agent has a delayed and prolonged epigenetic action on tumor cells. This delayed action should be taken into consideration in the design and evaluation of clinical studies on DAC. Future research should be directed at finding the optimal dose-schedule of de DAC for the treatment of NSCLC.
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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) | 0.001 | 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".