Abstract B023: Identifying molecular programs of progesterone-driven mammary stem cell expansion
Bibliographic record
Abstract
Abstract Lifetime exposure to ovarian hormones plays a crucial role in determining a woman's risk for breast cancer: the risk of developing breast cancer is positively correlated with the number of ovarian-hormone-dependent menstrual cycles. Progesterone is an ovarian steroid hormone that peaks during the luteal phase of the female menstrual cycle. Recent research has revealed that progesterone is a key mediator of cellular changes in the mammary gland that likely underlies the correlation between ovarian hormones and breast cancer risk. These studies have shown that progesterone can exert mitogenic effects through paracrine signaling between specific mammary epithelial populations and control mammary stem cell (MaSC) expansion. Such findings provide new insights into MaSC dynamics and underscore the involvement of ovarian hormones in regulating fundamental mammary epithelial changes. Given the differentiation potential of luminal progenitors and MaSCs, they are the proposed cellular targets of transformation in breast cancer. Although it is known that progesterone can induce luminal and basal cell expansion, the underlying mechanisms driving hormone action within the different cellular compartments (basal, luminal and stromal) of the mammary gland are yet to be defined. Therefore, I hypothesize that the transcriptional response to progesterone will reveal important paracrine signaling pathways involved in MaSC changes. To test my hypothesis, mRNA expression profiles were generated from the different mammary cellular compartments under defined hormone treatments. More specifically, basal, luminal and stromal cells were FACS purified after 2 weeks of hormone stimulation with progesterone, estrogen, progesterone plus estrogen, or vehicle control and subjected to microarray analyses using the Agilent platform. To analyze this microarray data, an optimal pre-processing method was generated before any downstream analysis. After pre-processing, significantly altered genes under each hormone treatment were investigated and cross compared within different cellular compartments. Our lab is interested in examining progesterone-mediated ligand and receptor expression changes in the different epithelial compartments that may play a paracrine role in altering the MaSC population. Once significantly altered ligand-receptor pairs are identified, I will validate specific pathways through both in vivo and in vitro experiments utilizing knockout mice to test their functional significance and investigate the effects of aberrant signaling in these pathways in cell culture assays. Progesterone is believed to play a crucial role in MaSC regulation and this might in part explain why a greater number of reproductive cycles and hormone replacement therapy using progestins contribute to a higher risk of developing breast cancer. MaSCs are postulated to be involved in breast cancer initiation, hence elucidating the mechanisms that induce MaSC expansion will allow us to identify putative targets that can be harnessed to control stem/progenitor cells and limit cellular transformation. Citation Format: Yu-Jia Shiah, Purna A. Joshi, Alexander G. Beristain, Michelle Chan-Seng-Yue, Paul C. Boutros, Rama Khokha. Identifying molecular programs of progesterone-driven mammary stem cell expansion. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Breast Cancer Research: Genetics, Biology, and Clinical Applications; Oct 3-6, 2013; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Res 2013;11(10 Suppl):Abstract nr B023.
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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 | 0.000 | 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 | 0.001 | 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".