Transgenic IGF-IR overexpression induces mammary tumors with basal-like characteristics, whereas IGF-IR-independent mammary tumors express a claudin-low gene signature
Bibliographic record
Abstract
Molecular profiling has allowed a more precise classification of human cancers. With respect to breast cancer, this approach has been used to identify five subtypes; luminal A, luminal B, HER2-enriched, basal-like and claudin-low. In addition, this approach can be used to determine the type of tumor represented by particular cell lines or transgenic animal models. Therefore, this approach was utilized to classify the mammary tumors that develop in MTB-IGFIR transgenic mice. It was determined that the primary mammary tumors, which develop due to elevated expression of the type I insulin-like growth factor receptor (IGF-IR) in mammary epithelial cells, most closely resemble murine tumors with basal-like or mixed gene expression profiles and with human basal-like breast cancers. Downregulation of IGF-IR transgene in MTB-IGFIR tumor-bearing mice leads to the regression of most of the tumors, followed by tumor reappearance in some of the mice. These tumors that reappear following IGF-IR transgene downregulation do not express the IGF-IR transgene and cluster with murine mammary tumors that express a mesenchymal gene expression profile and with human claudin-low breast cancers. Therefore, IGF-IR overexpression in murine mammary epithelial cells induces mammary tumors with primarily basal-like characteristics, whereas tumors that develop following IGF-IR downregulation express a gene signature that most closely resembles human claudin-low breast tumors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".