Prolactin induces BCRP/ABCG2 expression in T‐47D breast cancer cells via activation of multiple signalling cascades
Bibliographic record
Abstract
The drug transporter Breast Cancer Resistance Protein (BCRP/ABCG2) has been shown to be upregulated in certain drug resistant breast cancer cells and in the normal mammary gland during lactation. Given the important role of the lactogenic hormone prolactin in breast cancer and lactation biology, we investigated the effect of prolactin on BCRP expression in T‐47D human breast cancer epithelial cells. Prolactin upregulated BCRP mRNA and protein expression in a dose‐dependent manner. Induction of BCRP mRNA by prolactin was blocked by the transcription inhibitor actinomycin D. To characterize the mechanism responsible for prolactin‐induced BCRP mRNA expression, we systematically inhibited members of the three major pathways activated in prolactin signalling: JAK2/STAT5, MAPK, and PI3K/AKT. Knockdown of JAK2 by siRNA attenuated the prolactin‐BCRP response. Knockdown or pharmacological inhibition of STAT5 by siRNA or small molecule inhibitor, respectively, blunted prolactin‐induced BCRP mRNA expression. Treating cells in the presence of the MAPK pathway inhibitors PD98059 (20μM) and U0126 (10μM), and the PI3K pathway inhibitors LY294002 (10μM) and Wortmannin (25nM), significantly attenuated the effect of prolactin on BCRP mRNA expression. Taken together, our results demonstrate overlapping roles for JAK2/STAT5, MAPK, and PI3K/AKT signalling in prolactin‐induced BCRP expression. Funding provided by CIHR.
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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) | 0.003 | 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".