DNA methylation in melanoma immunotherapy: mechanisms and therapeutic opportunities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abnormal DNA methylation is a hallmark of cancer and a nearly universal feature of melanoma. DNA methylation plays well-appreciated melanoma cell-intrinsic roles, including silencing tumor-suppressor genes, regulating genomic stability, deregulating expression of oncogenes to potentiate proliferative signaling and tumor migration. With the recent success of immunological therapies for melanoma, important roles for DNA methylation are also emerging at the interface between melanoma and immune cells with the potential to regulate the anti-tumor immune response. These newly recognized roles for DNA methylation in controlling melanoma cell immunogenicity, expression of MHC and immune checkpoint molecules as well as T cell phenotypes in the tumor microenvironment raise the possibility of using DNA methylation to develop improved therapies and methylation-based biomarkers. In addition to reviewing the "immune dimension" of DNA methylation, we summarize recent developments with potential clinical applications in melanoma, such as targeted DNA methylation editing, single-cell methylation approaches, and measurement of circulating methylated DNA. An improved understanding of the immune roles of DNA methylation presents an exciting opportunity for continued improvement of care and outcomes for patients with 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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it