Altered gene expression due to aberrant DNA methylation correlates with responsiveness to anti‐EGFR antibody treatment
Bibliographic record
Abstract
The cetuximab gene expression signature and DNA methylation status of colorectal cancer (CRC) are predictive of the therapeutic effects of anti-epidermal growth factor receptor (EGFR) antibody therapy. As DNA methylation is a means of regulating gene expression, it may play an important role in the expression of cetuximab signature genes. This study aims to determine the effects of aberrant DNA methylation on the regulation of cetuximab signature gene expression. Comprehensive DNA methylation and gene expression data were retrieved from CRC patients in three tumor tissue (TT) cohorts and three normal colorectal mucosa/tumor tissue paired (NCM-TT) cohorts. Of the 231 cetuximab signature genes, 57 exhibited an inverse correlation between the methylation of promoter CpG sites and gene expression level in multiple cohorts. About two-thirds of the promoter CpG sites associated with the 57 genes exhibited this correlation. In all 57 gene promoter regions, the methylation levels in NCMs did not differ according to comparisons based on cetuximab signature or DNA methylation status classification of matched TTs. Thus, the altered expression of 57 genes was caused by aberrant DNA methylation during carcinogenesis. Analysis of the association between cetuximab signature or DNA methylation status and progression-free survival (PFS) of anti-EGFR antibody agents in the same cohort showed that DNA methylation status was most associated with PFS. In conclusion, we found that aberrant DNA methylation regulates specific gene expression in cetuximab signature during carcinogenesis, suggesting that it is one of the important determinants of sensitivity to anti-EGFR antibody agents.
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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 | 0.001 |
| 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".