Novel syngeneic mouse mammary carcinoma cell lines from aggressive ErbB2/Neu-overexpressing/PTEN-deficient tumors
Bibliographic record
Abstract
Breast cancer cell lines and mouse models are valuable tools for investigating the biology of and developing potential therapeutics for human breast carcinoma. The PTEN-/-/NIC mouse is a genetically engineered mouse model for ErbB2/Neu-overexpressing/‑PTEN deficient breast carcinoma with histopathological and molecular features relevant to the luminal subtype of primary human breast cancer. However, the PTEN-/-/NIC model develops multifocal and aggressive mammary tumors with a short life-span, which greatly impedes its preclinical usage. To complement the genetic engineering approach and to facilitate the future application of this model, in the present study, two newly established cell lines, NICP20 and NICP21, from PTEN-/-/NIC mammary tumors are described. These NICP20 and NICP21 cells retained the crucial molecular phenotype similar to the origin, as confirmed by genotyping and western blot analysis. These cells induced tumors in immunocompetent syngeneic mice by mammary fat pad injection and produced lung metastasis when injected intravenously. Tumors induced by these cells displayed luminal‑like histologic morphology and hyperactivation of Akt which are similar to PTEN-/-/NIC tumors. Immunohistochemical staining also revealed that tumors induced by the NICP20 and NICP21 cells showed a high proliferative level, comparable angiogenesis and T-cell infiltration properties similar to PTEN-/-/NIC tumors. Therefore, these NICP20 and NICP21 cells represent an alternative and useful model system to enhance our understanding of the nature of ErbB2-positive breast cancers, particularly accompanying PTEN loss and to facilitate further experimental therapeutic studies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".