DNA methylation landscape of triple-negative ductal carcinoma in situ (DCIS) progressing to the invasive stage in canine breast cancer
Bibliographic record
Abstract
Triple-negative breast cancer (TNBC) is a subtype of breast cancer unresponsive to traditional receptor-targeted treatments, leading to a disproportionate number of deaths. Invasive breast cancer is believed to evolve from non-invasive ductal carcinoma in situ (DCIS). Detection of triple-negative DCIS (TN-DCIS) is challenging, therefore strategies to study molecular events governing progression of pre-invasive TN-DCIS to invasive TNBC are needed. Here, we study a canine TN-DCIS progression and investigate the DNA methylation landscape of normal breast tissue, atypical ductal hyperplasia (ADH), DCIS and invasive breast cancer. We report hypo- and hypermethylation of genes within functional categories related to cancer such as transcriptional regulation, apoptosis, signal transduction, and cell migration. DNA methylation changes associated with cancer-related genes become more pronounced at invasive breast cancer stage. Importantly, we identify invasive-only and DCIS-specific DNA methylation alterations that could potentially determine which lesions progress to invasive cancer and which could remain as pre-invasive DCIS. Changes in DNA methylation during TN-DCIS progression in this canine model correspond with gene expression patterns in human breast tissues. This study provides evidence for utilizing methylation status of gene candidates to define late-stage (DCIS and invasive), invasive stage only or DCIS stage only of TN-DCIS progression.
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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) | 0.001 | 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".