Functional Characterization of HC11 Mouse Mammary Epithelial Cells Transduced with <i>Abcg2</i>
Bibliographic record
Abstract
The Breast Cancer Resistance Protein (BCRP/ABCG2) is a multidrug efflux transporter that is highly expressed in certain cancer cells and at biological barriers such as the intestine. ABCG2 is also dramatically upregulated in mammary epithelial cells during lactation where it plays a role in the transfer of drugs and toxins into breast milk. Despite the large number of cell lines that overexpress ABCG2, none are suitable models for ABCG2‐mediated drug excretion into breast milk. We report here the creation of a non‐tumorigenic mouse mammary epithelial cell line that expresses high levels of functional ABCG2. Prolactin‐responsive HC11 mouse mammary epithelial cells, which form globular acinar‐like structures with a central lumen (called mammosphere) in Matrigel®, were transduced with a lentiviral vector encoding mouse Abcg2 . One of the 48 clones isolated (designated HC11.Abcg2.C10) showed high levels of ABCG2. HC11.Abcg2.C10 displayed significantly lower intracellular retention of ABCG2 substrates BODIPY‐Prazosin and Hoechst 33342 compared to parent HC11 cells. We then identified an optimal Matrigel‐based culture condition, which supports these cells to form mammosphere structures. Future work will focus on developing methods to measure drug accumulation in the acinar lumen as a model for ABCG2‐mediated drug excretion into breast milk. This work is supported by the Canadian Institutes of Health Research.
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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) | 0.001 | 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".