Abstract P3-04-08: Epigenetic Changes Due to DNA Methylation in CpG Islands during Breast Cancer Progression
Bibliographic record
Abstract
Abstract DNA methylation has been associated with several key events of gene regulation and to human cancer. It is not yet known how the epigenome of various populations is associated with clinical manifestations during the course of the disease. In this poster we describe a method for stratifying breast cancer patients from Indian origin using CpG island microarray from the University Healthcare Network (UHN) Toronto (human CpG island 12k microarray chip, HCGI12K). DNA samples were obtained from a prospective study cohort which consisted of 51 female primary breast cancers. All patients had been undergoing treatment in a tertiary care hospital and its associated centers in the southern part of India between 2007 and 2009. We have identified and classified the DNA methylation in CpG islands of patient samples using various clinical parameters such as age of disease onset, menopausal status, hormone receptor status and Her2 status. We present the methods to analyze the data from UHN CpG island arrays used in a high throughput methylation study in order to derive decision rules of stratifying the data into basic classes such as normal and benign conditions, and infiltrating ductal carcinoma. Results will be presented pertaining to differential methylation status of patients in different categories such as age of disease onset, menopausal status, hormone receptor status and Her2 status. Citation Information: Cancer Res 2010;70(24 Suppl):Abstract nr P3-04-08.
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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 | 0.000 |
| 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) | 0.005 | 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".