Identification of Tumorsphere- and Tumor-Initiating Cells in HER2/Neu-Induced Mammary Tumors
Bibliographic record
Abstract
A variety of human malignancies, including breast cancer, are thought to be organized in a hierarchy, whereby a relatively minor population of tumor initiating cells (TIC) is responsible for tumor growth and the vast majority of remaining cells is nontumorigenic. Analysis of TICs in model systems of breast cancer would offer uniform and accessible source of tumor cells and the power of mouse genetics to dissect these rare cells. The HER2/Neu proto-oncogene is overexpressed in an aggressive form of human breast cancer. Mouse mammary tumor virus (MMTV)-Neu transgenic mice develop mammary tumors that mimic human HER2 subtype breast cancer. Here, we report on the functional identification of mouse HER2/Neu TICs that can induce tumors after transplantation into the mammary gland of recipient mice. Secondary tumors formed after injecting MMTV-Neu TICs resemble primary tumors in the original transgenic mice and are organized in a hierarchy containing TICs as well as their nontumorigenic descendants. To study MMTV-Neu TICs in vitro, we grew tumorspheres under nonadherent culture conditions. Tumorsphere forming units (TFU) capable of producing tumorspheres retained tumorigenic potential and were indistinguishable by several criteria from TICs. Interestingly, MMTV-Neu TICs and TFUs were committed to the luminal cell fate when induced to differentiate in vitro. Our data define reproducible characteristics of the MMTV-Neu TIC and TFU, which help to explain marker expression profiles of HER2-positive breast cancer. In addition, the similarity between TICs and TFUs in this system provides a rationale for TFU-based screens to target tumor-initiating cells in HER2(+) breast cancer.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".