A modified MethyLight assay predicts the clinical outcomes of anti‐epidermal growth factor receptor treatment in metastatic colorectal cancer
Bibliographic record
Abstract
DNA methylation status correlates with clinical outcomes of anti-epidermal growth factor receptor (EGFR) treatment. There is a strong need to develop a simple assay for measuring DNA methylation status for the clinical application of drug selection based on it. In this study, we collected data from 186 patients with metastatic colorectal cancer (mCRC) who had previously received anti-EGFR treatment. We modified MethyLite to develop a novel assay to classify patients as having highly methylated colorectal cancer (HMCC) or low-methylated colorectal cancer (LMCC) based on the methylation status of 16 CpG sites of tumor-derived genomic DNA in the development cohort (n = 30). Clinical outcomes were then compared between the HMCC and LMCC groups in the validation cohort (n = 156). The results showed that HMCC had a significantly worse response rate (4.2% vs 33.3%; P = .004), progression-free survival (median: 2.5 vs 6.6 mo, P < .001, hazard ratio [HR] = 0.22), and overall survival (median: 5.6 vs 15.5 mo, P < .001, HR = 0.23) than did LMCC in patients with RAS wild-type mCRC who were refractory or intolerable to oxaliplatin- and irinotecan-based chemotherapy (n = 101). The DNA methylation status was an independent predictive factor and a more accurate biomarker than was the primary site of anti-EGFR treatment. In conclusion, our novel DNA methylation measurement assay based on MethyLight was simple and useful, suggesting its implementation as a complementary diagnostic tool in a clinical setting.
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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 | 0.001 | 0.002 |
| 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) | 0.000 | 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 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".