Abstract 2121: Overcoming tamoxifen resistance and inhibiting metastatic recurrence in estrogen receptor-positive breast cancer
Bibliographic record
Abstract
Abstract Estrogen receptor (ER)-positive breast cancer accounts for almost 75% of all breast cancers. Tamoxifen has been used for over 40 years to treat early, locally advanced and metastatic ER-positive breast cancer; however, patients develop resistance over time. Furthermore, metastatic recurrence, the major cause of death, is common in ER-positive breast cancer patients whose cancer progressed on tamoxifen and other hormone therapies. In this line, we and others reported epithelial-mesenchymal (EMT)-like changes upon acquisition of tamoxifen resistance hinting towards the co-occurrence of resistance and metastatic abilities of the cells. However, little is known about common molecular mediators of drug resistance and metastatic recurrence. Therefore, we addresses the question of how we concurrently overcome tamoxifen resistance and prevent lethal metastatic recurrence whereby eliminating the mortality associated with metastatic breast cancer. Combining whole-transcriptome sequencing and downstream pathway analysis, we identified cyclic AMP (cAMP) signaling to be the most significantly altered pathway in acquired tamoxifen resistant breast cancer cells. We found that PDE4D (Phosphodiesterase 4D), which hydrolyzes cAMP, was significantly overexpressed in both MCF-7 and T47D tamoxifen-resistant (TamR) cells. We demonstrated that PDE4D inhibition overcomes tamoxifen resistance in cell lines and tumor xenografts via cAMP induced endoplasmic reticulum stress and cell death. Importantly, our TamR cells have partial EMT and enrichment of TGF-beta signaling, and PDE4D inhibition in combination with tamoxifen inhibited cell migration in vitro and blocked TGF-beta induced EMT. RNA-Seq experiment also identified one of the highly oncogenic long non-coding RNAs (lncRNA) to be upregulated in TamR cells, and its targeting inhibits PDE4D and leads to tamoxifen sensitization. We showed that higher expressions of both PDE4D and the candidate lncRNA predict worse survival in tamoxifen-treated breast cancer patients. Currently, we are testing the tamoxifen sensitizer and metastasis blocker functions of PDE4D and our candidate lncRNA using patient-derived xenograft (PDX) models of metastatic ER-positive breast cancer. Overall, our results suggest that targeting PDE4D, or its upstream regulatory lncRNA, can simultaneously overcome tamoxifen resistance and prevent metastatic recurrence in ER-positive breast cancer. This is then expected to dramatically reduce mortality rates among ER-positive breast cancer patients in future. Citation Format: Pelin Gulizar Ersan, Ozge Saatci, Oguzhan Tarman, Rasmi Mishra, Nevin Belder, Unal Metin Tokat, Yasser Riazalhosseini, Ozgur Sahin. Overcoming tamoxifen resistance and inhibiting metastatic recurrence in estrogen receptor-positive breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2121.
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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".