Abstract PO4-24-05: Fluid forces and hormone levels during mammary gland development drive changes in breast epithelium that are relevant to the progression of postpartum breast cancer
Bibliographic record
Abstract
Abstract Postpartum breast cancer (PPBC), diagnosed in the 5-10 years after childbirth, has an elevated risk of metastasis and death. Poor outcomes are thought to be due to factors involved in mammary gland involution, an important stage of mammary gland development. Involution functions to return the mammary gland to the normal post-lactation state and involves activation of multiple processes such as inflammation, wound healing, and lymphangiogenesis. Previously, these processes have been linked to mechanical forces induced by fluid flowing past cells, known as fluid shear stress (FSS). We and others have shown FSS impacts processes associated with lactation and breast cancer metastasis. Herein, we describe the results of a cell model for mimicking lactation and the cessation of lactation as an investigative tool for probing the mechanisms involved in PPBC development and progression. We identified changing levels of FSS as a potential physiologic biomarker through which pathways associated with the progression of PPBC could be identified. The role of fluid shear stress in the progression of cancer remains relatively unexplored, mostly due to practical research barriers. Since FSS cannot be accurately measured in vivo, most research to date has relied on in vitro modelling. In contrast, the role of the involuting mammary gland on the progression of breast cancer has often been studied using rodent models. As a result, studying the interactions between fluid shear stress and involution poses a novel engineering challenge. To address this problem, we developed a bioreactor cell model to enable cell exposure to fluid flow, mimicking forces experienced during lactation, in presence of lactogenic hormones (dexamethasone, insulin, prolactin). Previous work has induced FSS using parallel plate flow chambers, and induced lactation in mammary epithelial cells grown in vitro using lactogenic hormone treatment. To our knowledge, this is the first time these experimental conditions have been studied in combination. We established three distinct stages of treatment, consistent with the fluid shear stress and hormonal levels expected in (1) lactation, (2) cessation of lactation, and (3) involution. To determine whether the model mimicked expected in vivo conditions in the postpartum mammary gland, we used morphological markers and measured the protein expression of β-casein (CSN2), a milk protein that is an established marker of lactation. Each stage was validated and analyzed using proteomic and genomic sequencing. Generated datasets were further analyzed using unsupervised clustering, combining publicly available datasets with our data. We determined that β-casein levels were highest when protein was collected after the lactation stage, followed by an initial drop-off in β-casein expression when fluid shear stress were lowered during the cessation period. The β-casein levels continued at a constant, low level during the involution period, despite a reintroduction of fluid shear stress. This suggests that breast cancer cells are initially responsive to shear stress stimulation, consistent with what would be expected in the postpartum mammary gland in vivo. Proteomic and genomic datasets generated from cells exposed to FSS demonstrated that multiple pathways associated with metastasis are upregulated, as compared to static controls. When combined with publicly available involution datasets, pathways involved in extracellular matrix remodeling, inflammation, and lymphangiogenesis emerged. Multiple targets identified through this analysis have been previously linked to metastatic activities during breast cancer, suggesting that FSS and involution are relevant physiologic biomarkers in the context of PPBC. Work to validate hormonal and fluid shear stress conditions in combination is ongoing. Future work will use the validated model to test relevant targets identified from analysis of generated datasets. Citation Format: Maya Stibbards-Lyle, Kristina Rinker, Laura Hall, Seleem Badawy, Kathy Zhan. Fluid forces and hormone levels during mammary gland development drive changes in breast epithelium that are relevant to the progression of postpartum 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 PO4-24-05.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".