Development of New Models to Study Human Her-2 Positive Breast Cancer
Bibliographic record
Abstract
To study the evolution of human Her-2 positive breast cancer, we evaluated a model of three dimensional spheroid co-culture using H2N human breast cancer cells that express high amounts of Her-2 protein, with the Her-2 negative MDA-231 cells. Human HCC1419, which can form compact spheroids, were used as controls. In tissue culture plates, H2N cells were found to have a doubling rate of about 48 hours, compared to around 144 hours for MDA231. Since H2N cells were engineered to express a fluorescent protein, we could note that these cells did not immediately overgrow the non-fluorescent MDA231 cells in a mixed three dimensional mass – suggesting that this model could allow for the study of long term evolution of specific subpopulation of tumor cells within a heterogeneous tumor mass.\nWhen the breast cancer cells were grown on tissue culture plastic, we evaluated a drug screen of newly synthesized potential anticancer compounds. One compound, CLM29 was observed to inhibit the growth of all three breast cancer cell lines. Furthermore, 25 micromolar treatment of H2N or of MDA231, but not of HCC1419 cells, lead over 4 days to the formation of cellular neurite-like extensive protrusions, suggesting the activation of a differentiation of breast cancer cells.\nOur results show the effective use of three different human breast cancer cell lines to develop models to follow the evolution of specific subtypes of breast cancer within a tumor mass, and to test new compounds with potential anti-breast cancer activity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".