Dissecting genetic requirements of human breast tumorigenesis in a tissue transgenic model of human breast cancer in mice
Bibliographic record
Abstract
Breast cancer development is a complex pathobiological process involving sequential genetic alterations in normal epithelial cells that results in uncontrolled growth in a permissive microenvironment. Accordingly, physiologically relevant models of human breast cancer that recapitulate these events are needed to study cancer biology and evaluate therapeutic agents. Here, we report the generation and utilization of the human breast cancer in mouse (HIM) model, which is composed of genetically engineered primary human breast epithelial organoids and activated human breast stromal cells. By using this approach, we have defined key genetic events required to drive the development of human preneoplastic lesions as well as invasive adenocarcinomas that are histologically similar to those in patients. Tumor development in the HIM model proceeds through defined histological stages of hyperplasia, DCIS to invasive carcinoma. Moreover, HIM tumors display characteristic responses to targeted therapies, such as HER2 inhibitors, further validating the utility of these models in preclinical compound testing. The HIM model is an experimentally tractable human in vivo system that holds great potential for advancing our basic understanding of cancer biology and for the discovery and testing of targeted therapies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".