Addition of the Akt inhibitor triciribine overcomes antibody resistance in cells from ErbB2/Neu-positive/PTEN-deficient mammary tumors
Bibliographic record
Abstract
Trastuzumab resistance is a challenging problem in ErbB2/HER2-positive breast cancers. Multiple mechanisms of resistance have been proposed and, thus, may require the development of more personalized therapies. In this study, we report the establishment of a mouse mammary cancer cell line, designated MT104T, obtained from spontaneous tumors in genetically engineered FVB/N-ErbB2/Neu-positive-PTEN-deficient mice. The critical molecular phenotype of MT104T cells was confirmed by genotyping and western blot analysis. This cell line was tumorigenic in immunologically intact syngeneic mice, forming tumors of generally similar histology as its origin. PTEN loss led to hyperactivation of Akt and conferred resistance to anti-ErbB2/Neu antibody treatment in MT104T cells. Addition of the Akt inhibitor triciribine (TCN) inhibited the viability of MT104T cells in a dose- and time-dependent manner as evaluated by MTT assay. ErbB2/Neu antibody and TCN combination treatment greatly induced apoptosis of MT104T cells as indicated by Annexin V-FITC staining. Moreover, this combination treatment also significantly reduced both Akt and Erk activities, which are responsible for the inhibitory effect on MT104T cells. Therefore, MT104T cells could represent an alternative model system to investigate the nature of ErbB2‑positive breast cancer and for the experimental therapeutics studies of this disease. Our findings also suggest that combination of TCN may be a potential strategy for the treatment of trastu-zumab-resistant breast cancer mediated by PTEN loss or PI3K hyperactivation, which may facilitate the development of more personalized therapies for breast cancer patients.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".