Abstract B46: Targeting EZH2 reactivates a breast cancer subtype-specific antimetastatic transcriptional program
Bibliographic record
Abstract
Abstract Emerging evidence has illustrated the importance of epigenomic reprogramming in cancer, with altered post-translational modifications of histones contributing to the pathogenesis of many tumor types, including breast cancer. However, the contributions of histone modifiers to breast cancer progression are unclear, and how these processes vary between molecular subtypes has yet to be adequately addressed. In this study, we demonstrate that genetic or pharmacologic targeting of the epigenetic modifier Ezh2 dramatically hinders metastatic behavior in a mouse model of breast cancer reflective of the luminal B subtype. We also employ patient-derived xenografts from different intrinsic subtypes of breast cancer to confirm that pharmacologic inhibition of Ezh2 activity hinders metastasis specifically in the luminal B subtype. We further define a molecular mechanism intrinsic to the luminal B subtype whereby EZH2 maintains H3K27me3-mediated repression of the FOXC1 gene, thereby inactivating a FOXC1-driven, antiinvasive transcriptional program. We demonstrate that higher FOXC1 levels are predictive of favorable outcome specifically in luminal B breast cancer patients and establish the use of EZH2 methyltransferase inhibitors as a viable strategy to block metastasis in luminal B breast cancer, where options for targeted therapy are currently limited. Citation Format: Alison Hirukawa, Harvey Smith, Dongmei Zou, Paul Savage, Radia Johnson, Guillaume Bourque, Vincent Giguere, Mark Basik, Cathy Dufour, Morag Park, William Muller. Targeting EZH2 reactivates a breast cancer subtype-specific antimetastatic transcriptional program [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 B46.
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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) | 0.001 | 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 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".