Abstract A045: EZH2 inhibition re-sensitizes drug resistant triple-negative breast cancer PDX models to Eribulin
Bibliographic record
Abstract
Abstract Background: Triple negative breast cancer (TNBC) is the most aggressive subtype of breast cancer predominantly affecting younger women. The mainstay of treatment is chemotherapy, to which most patients eventually stop responding. After demonstrating improved overall survival in the metastatic setting, eribulin, a microtubule inhibitor, was approved for the treatment of late-stage, heavily pretreated breast cancer. While frequently used in the clinic, most patients either do not respond initially or they stop responding to eribulin. Considering that there is a lack of oncology drugs including targeted therapies to treat TNBCs, patients are therefore left with few therapeutic options after eribulin. There is a paucity of data on mechanisms of acquired resistance to eribulin, and few potential uncovered targetable alterations. The goal of our study is to determine mechanisms of acquired resistance to Eribulin using isogenic acquired-resistant PDX models and to attempt to reverse this resistance with novel combinations. Methods: PDX models are grown as mammary fat pad engraftments of patient tumours in immuno-compromised (NGS) mice. To determine drug response, we treat tumour-bearing mice with weekly eribulin, measure the tumor volume once per week, then classify response based on mRECIST 1.1 criteria. Acquired resistance was generated in vivo in TNBC PDXs via the continuous or interrupted treatment of Eribulin in sensitive models that regressed on Eribulin. RNA-Seq analysis was performed on isogenic pairs of Eribulin sensitive and resistant PDX tumors and analyzed for differential gene expression and pathway enrichments. Drug combinations targeting candidate regulators of enriched pathways across our models were assessed in vivo in resistant PDXs. Drug trials were performed with n=5-7 mice per treatment arm until endpoint (tumour volume reaches 2000mm^3 or BW loss of 20%). Statistical analysis included t-tests for tumour volume and Wilcoxon test for survival. Results: 6 isogenic pairs of Eribulin acquired resistant TNBC PDX models were generated in vivo and molecular analysis was performed by comparing transcriptomic profiles of sensitive and resistant tumours. Pathway analysis revealed that, across 5 of 6 acquired resistant models, differentially expressed genes were significantly enriched for targets of SUZ12 (BH p-value<0.01). As SUZ12 makes up a core component of the PRC2 complex, we attempted to target EZH2, the catalytic component of PRC2, in vivo. Combining Tazemetostat, an FDA-approved EZH2 inhibitor, with Eribulin in one PDX model, resulted in tumor regression and improved overall survival (p<0.005) in one model. We will be testing this combination in more models, and performing differential chromatin accessibility studies to identify candidate driver genes in these regions. Conclusion: Our results suggest that combining an approved EZH2 inhibitor, Tazemetostat, with Eribulin can re-sensitize acquired-resistant TNBC PDXs to Eribulin. This approach could have significant clinical benefit in late stage hard to treat TNBCs. Citation Format: Kathryn Bozek, Cedric Darini, Cathy Lan, Marguerite Buchanan, Catherine Chabot, Josiane Lafleur, Juliet Guay, Eva Filosa, Adriana Aguilar-Mahecha, Mark Basik. EZH2 inhibition re-sensitizes drug resistant triple-negative breast cancer PDX models to Eribulin [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr A045.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".