Aberrant Epigenetic Regulation: A Central Contributor to Lung Carcinogenesis and a New Therapeutic Target
Bibliographic record
Abstract
Carcinogenesis is driven by a combination of genetic and epigenetic abnormalities. Aberrancies in gene promoter methylation patterns and histone acetylation are associated with silencing of tumor suppressor genes in lung cancer and other solid tumors. Identification of key epigenetic modifications has been shown to be prognostic in early-stage non-small cell lung cancer. Previous clinical trials aimed at modifying the epigenome with single-agent demethylating agents or histone deacetylase inhibitors given at maximally tolerated doses have provided disappointing results. A recent clinical trial using a combination of a demethylating agent and a histone deacetylase inhibitor at “epigenetically targeted” doses concomitantly has shown promising results, including a patient with a complete objective response. Biomarkers associated with this clinical trial suggest that patients who undergo robust demethylation, as detected in the peripheral blood after a month on treatment, identifies those who gain the most benefit from this novel treatment strategy. Based on observations of unusually durable responses to subsequent therapy after administration of combined epigenetic therapy, epigenetic therapy may also play a role in “priming” patients to better respond to standard cytotoxic therapy or immunotherapy. This manuscript will review the data on the role of epigenetics in lung cancer and the history of epigenetic treatments in lung cancer spanning over the last 40 years.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".