Abstract PO1-24-06: Prolactin Hormone-Induced Mammary Differentiation and Anti-tumorigenic Role in Breast Cancer
Bibliographic record
Abstract
Abstract The premise of differentiation therapy in cancer is a strategy that aims at engaging-forward differentiation and cellular reprograming restricting the proliferative, tumor repopulation, stemness, EMT and metastatic capacities of tumor cells leading to the cessation of the aggressive tumor phenotype and offering the cancer patients improved survival for decades. Therefore, deciphering molecular mechanisms deriving differentiation in normal mammary and breast cancer cells may allow the discovery of innovative differentiation-based biomarkers and therapeutics in breast cancer. Lactation is an intricate process that results in the ability of the breast cells to produce a complex nutritious biological fluid to nurse the new-born. While the beneficial effects of breastfeeding to the infant is irrefutable, its effects on maternal health are less investigated especially in relation to breast cancer that still affects 1 in 8 women in high income countries and 1 in 20 globally. Importantly, epidemiological studies have linked breastfeeding to reduced risk of breast cancer by promoting terminal differentiation of the breast epithelial cells. While breastfeeding process is regulated by multiple hormones, growth factors, and transcriptional regulators, the lactation hormone prolactin (PRL) is known to be directly implicated in the hormonal control of breastfeeding. Extensive research including our own work has shown that PRL/PRL receptor (PRLR) pathway derives mammary gland development and importantly mammary epithelial cell terminal differentiation allowing successful lactation. In contrast the role of PRL in breast cancer is still to be fully defined. Importantly, we have accumulated compelling evidence using in-silico publicly available patient data sets and molecular data implicating this pathway as a clinically relevant pro-differentiation pathway driving differentiation and cellular reprograming suppressing stemness, EMT, metastasis and tumorigenesis in breast cancer. To better characterize PRL signaling mediating its pro-differentiation effects, we used large-scale unbiased proteomics analysis of PRLR/interacting proteins in breast cancer cells. Our analyses revealed several novel PRLR-interactors. We have further confirmed these interactions through co-immunoprecipitations/western blotting analyses in breast cancer cells representative the different breast cancer subtypes. We also examined their functional contributions to mammary differentiation and tumorigenesis using in vitro and in vivo assays. Moreover, we defined their potential clinical relevance using bioinformatics in silico databases of breast cancer. Together our study delineates novel PRL/PRLR-downstream signaling mediators important for mammary differentiation and tumorigenesis and may thus be useful as biomarkers and/or therapeutic targets in breast cancer. Citation Format: Dana Hamam, Suhad Ali. Prolactin Hormone-Induced Mammary Differentiation and Anti-tumorigenic Role in Breast Cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-24-06.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".