Abstract A07: DNA methylation predicts response of triple-negative breast cancer to all-trans retinoic acid treatment
Bibliographic record
Abstract
Abstract We have identified a novel, epigenetic-based strategy to identify breast cancer patients who will benefit from an existing anticancer agent, all-trans retinoic acid (atRA). Triple-negative breast cancer (TNBC) is among the most aggressive breast cancers and lacks targeted therapies. TNBCs are a heterogeneous group of breast cancers with a wide range of gene expression profiles and drug sensitivities. Profiling these differences will lead to the development of more effective treatment options for TNBC. We hypothesize that atRA can be an effective therapy for a subset of TNBC patients. To test our hypothesis, we profiled the response of 12 TNBC cell lines to atRA using in vivo tumor growth assays. We found that atRA treatment had a range of effects on the tumor growth of TNBC cell lines: significantly decreasing tumor growth in HCC70, SUM149, and HCC1937, while increasing the growth of MDA-MB-231, MDA-MB-436, and Du4475 tumors. Gene expression and methylation analysis of these 12 cell lines revealed subtype-specific expression of atRA-inducible genes due to silencing by DNA methylation, e.g., of the atRA-inducible tumor-suppressor gene RARRES1. RARRES1 is silenced by methylation in cell lines where atRA promotes tumor growth, but is hypomethylated and expressed in cells that can be effectively treated with atRA. Use of the distinct gene expression and methylation profiles allowed us to accurately predict the response of 4 patient-derived xenografts (PDXs) to RA treatment. Continued classification of TNBCs by gene expression and DNA methylation can predict the response of patient tumors to RA treatment, thus identifying a novel targeted therapy strategy for TNBCs. Citation Format: Krysta M. Coyle, Cheryl A. Dean, Dejan Vidovic, Ian Weaver, Carman Giacomantonio, Lucy Helyer, Paola Marcato. DNA methylation predicts response of triple-negative breast cancer to all-trans retinoic acid treatment [abstract]. In: Proceedings of the AACR Special Conference: Advances in Breast Cancer Research; 2017 Oct 7-10; Hollywood, CA. Philadelphia (PA): AACR; Mol Cancer Res 2018;16(8_Suppl):Abstract nr A07.
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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 | 0.001 | 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) | 0.000 | 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".