Abstract 1150: Genome-wide DNA methylation profiling of cell-free serum DNA in esophageal adenocarcinoma and Barrett's esophagus
Bibliographic record
Abstract
Abstract Aberrant DNA methylation (DNAm) is a feature of most types of cancers. Genome-wide DNAm profiling has been performed successfully on DNA extracted from many types of tumor tissues. However, the invasive procedure and the likely existence of tissue heterogeneity limit the utility of tumor tissue for epidemiological studies. While recent data indicate that cell-free circulating DNA methylation (cfDNAm) profiles reflect DNAm profiles in matched tumor tissues, no studies have examined the association of cfDNAm with cancer or precursors on a genome-wide scale. The objective of this pilot study was to evaluate the putative significance of genome-wide cfDNAm profiles in esophageal adenocarcinoma (EA) and Barrett's esophagus (BE, EA precursor). We performed genome-wide DNAm profiling in EA tissue DNA (n=8) and matched serum DNA (n=8), in serum DNAs of BE (n=12) and healthy controls (n=12), using the Infinium Humanmethylation27 Beadchip that covers 27,578 CpG loci in 14,495 genes. We found that cfDNAm profiles were highly correlated to DNAm profiles in matched tumor tissue DNAs (r=0.92) in patients with EA. We selected the most differentially methylated loci (a β-value difference of >0.2 and a P-value of 6 x 10 −7 between groups) to perform hierarchical clustering analysis. We found that 911 loci can perfectly discriminate between EA and control samples; 554 loci can separate EA from BE samples; and 46 loci can distinguish BE from control samples. These results suggest that genome-wide cfDNAm profiles are highly consistent with DNAm profiles detected in corresponding tumor tissues. Differential cfDNAm profiling may be a useful approach for the non-invasive screening of EA and EA premalignant lesions. Supported by grants: Flight Attendant Medical Research Institute (FAMRI) grant# 062459; NIH grants CA92824, CA74386, CA90578, and CA119650); the Kevin Jackson Memorial Fund and Alan Brown Chair of Molecular Genomics. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 1150. doi:1538-7445.AM2012-1150
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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 | 0.001 | 0.001 |
| 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".