Abstract 110: PPARγ loss increases the metastatic potential of HER2+ breast cancer
Bibliographic record
Abstract
Abstract Breast tumors that overexpress human epidermal growth factor receptor 2 (HER2+) grow and spread faster than HER2-negative tumors, resulting in poor patient prognosis. Peroxisome proliferator-activated receptor (PPAR)γ is a nuclear transcription factor that controls the expression of genes essential for normal metabolism of fats and sugars in the body. Our laboratory previously showed that PPARγ expression suppresses environmental carcinogen DMBA-mediated breast tumor progression in vivo, and PPARγ-activating drugs further enhance this effect. However, the role of PPARγ and PPARγ agonists on HER2+ breast tumorigenesis and patient survival is unclear. We hypothesized that PPARγ loss enhances HER2+ breast tumor progression. To test this, this study generated a novel mouse model referred to as NIC:PPARγ-KO, which has targeted PPARγ deletion in the same HER2+ transformed mammary epithelial cells that drive breast tumorigenesis. Compared to NIC:PPARγ-WT mice, NIC:PPARγ-KO mice have increased mammary tumor incidences and lung metastases. Protein analysis of NIC:PPARγ-KO tumors shows PPARγ loss is inversely correlated with increased HER2 phosphorylation at tyrosine 877 (pY877HER2) in primary and metastatic tumorigenic tissue. Immunofluorescence also showed HER2 H-scores were significantly highest among tumors from NIC:PPARγ-KO mice, but also correlated with targeted PPARγ loss in DMBA-induced primary and metastatic mammary tumors among PPARγ-WT and PPARγ-KO mice (p<0.05). To further investigate the role of PPARγ in the metastatic process, we established cell lines from the freshly isolated lung metastatic tumors harvested from the NIC:PPARγ-KO model (NIC:PPARγ-KO-lmet). In vitro analysis of several human HER2+ breast cancer cells lines and our NIC:PPARγ-KO-lmet cells shows migration, invasion and tumorsphere-forming potential were significantly increased after epidermal growth factor (EGF, 20ng/mL) treatment and, more interestingly, that co-treatment with a PPARγ-activating drug (rosiglitazone, 10μM) significantly abrogated these effects (p<0.05). Together, these data provide the first evidence that PPARγ may be a useful prognostic/predictive biomarker for HER2+ breast tumors, and suggest that the novel inclusion of PPARγ-activating drugs may benefit a subset of HER2+ breast cancer patients. Citation Format: Elizabeth D. Lightbody, Kathleen MJ O'Connell, Hailey T. Newton, Rachel R. Rubino, Anthony J. Apostoli, Kevin Ren, Sandip K. SenGupta, Christopher J. Nicol. PPARγ loss increases the metastatic potential of HER2+ breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 110.
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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) | 0.007 | 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".