Dasatinib inhibits mammary tumour development in a genetically engineered mouse model
Bibliographic record
Abstract
Src family kinase activity is elevated in a number of human cancers including breast cancer. This increased activity has been associated with aggressive disease and poor prognosis. Src inhibitors are currently in clinical development with a number of trials currently assessing their activity in breast cancer. However, the results to date have been disappointing and a further evaluation of the preclinical effects of Src inhibitors is required to help establish whether these agents will be useful in the treatment of breast cancer. In this study we investigate the effects of dasatinib, which is a potent inhibitor of Src family kinases, on the initiation and development of breast cancer in a genetically engineered model of the disease. The mouse model utilized is driven by expression of activated ErbB-2 under the transcriptional control of its endogenous promoter coupled with conditional loss of Pten under the control of Cre recombinase expressed by the BLG promoter. We show that daily oral administration of dasatinib delays tumour onset and increases overall survival but does not inhibit the proliferation of established tumours. The striking difference between the dasatinib-treated group of tumours and the vehicle controls was the prominent squamous metaplasia that was seen in six out of 11 dasatinib-treated tumours. This was accompanied by a dramatic up-regulation of both E-cadherin and β-catenin and down-regulation of ErbB-2 in the dasatinib-treated tumours. Dasatinib also inhibited both the migration and the invasion of tumour-derived cell lines in vitro. Together these data support the argument that benefits of Src inhibitors may predominate in early or even pre-invasive disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".