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
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».