Differentially Methylated Loci Distinguish Ovarian Carcinoma Histological Types: Evaluation of a DNA Methylation Assay in FFPE Tissue
Bibliographic record
Abstract
Epigenomic markers can identify tumor subtypes, but few platforms can accommodate formalin-fixed paraffin-embedded (FFPE) tumor tissue. We tested different amounts of bisulfite-converted (bs) DNA from six FFPE ovarian carcinomas (OC) of serous, endometrioid, and clear cell histologies and two HapMap constitutional genomes to evaluate the performance of the GoldenGate methylation assay. Methylation status at each 1,505 CpG site was expressed as β-values. Comparing 400 ng versus 250 ng bsDNA, reproducibility of the assay ranged from Spearman r(2) = 0.41 to 0.90, indicating that β-values obtained with a lower DNA amount did not always correlate well with the higher amount. Average methylation for the six samples was higher using 250 ng (β-value = 0.45, SD = 0.29) than with 400 ng (β-value = 0.36, SD = 0.32). Reproducibility between duplicate HapMap samples (r(2) = 0.76 to 0.92) was also variable. Using 400 ng input bsDNA, THBS2 and ERG were differentially methylated across all histologic types and between endometrioid and clear cell types at <0.1% false discovery rate. Methylation did not always correlate with gene expression (r(2) = -0.70 to 0.15). We found that lower bsDNA overestimates methylation, and, using higher bsDNA amounts, we confirmed a previous report of higher methylation of THBS2 in clear cell OC, which could provide new insight into biological pathways that distinguish OC histological types.
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How this classification was reachedexpand
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 | 0.003 | 0.003 |
| 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 teacher head, 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".