A Single Nucleotide Polymorphism Chip-Based Method for Combined Genetic and Epigenetic Profiling: Validation in Decitabine Therapy and Tumor/Normal Comparisons
Bibliographic record
Abstract
Progress on several unresolved issues in cancer epigenetics will benefit from rapid and standardized methods for profiling DNA methylation genome-wide. In the area of epigenetic therapy, the demethylating drug decitabine (5-aza-2'-deoxycytidine) is increasingly used to treat acute myelogenous leukemia and myelodysplastic syndrome, but the mechanisms of its anticancer activity have remained unclear. Given the clinical efficacy of decitabine and the uncertainties about its mode of action, it will be useful to optimize methods for following DNA methylation as a biochemical response in individual patients. Here, we describe a single nucleotide polymorphism (SNP) chip-based method (MSNP) for profiling DNA methylation. Using this procedure, the extent of demethylation in bone marrow aspirates from patients with leukemia receiving decitabine can be assessed genome-wide using commercially available (Affymetrix) SNP chips. We validated the accuracy of MSNP by comparing the results with combined bisulfite restriction analysis and by sequencing cloned PCR products from bisulfite-converted DNA. We further validated MSNP in a Wilms' tumor/normal kidney comparison, comparing the results with methylation-sensitive Southern blotting. MSNP simultaneously detects aberrations in DNA copy number and loss of heterozygosity, making it a generally useful approach for combined genetic and epigenetic profiling in tissue samples from cancer patients.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".