Suppression of Ovarian Cancer Cell Tumorigenicity and Evasion of Cisplatin Resistance Using a Truncated Epidermal Growth Factor Receptor in a Rat Model
Bibliographic record
Abstract
The overexpression of the epidermal growth factor receptor (EGFR) is associated with a poor prognosis in ovarian cancer. The dominant-negative EGFR (EGFR-DNR) is a truncated receptor that lacks the tyrosine kinase domain and is devoid of signaling capability. This study tested the effects of a EGFR-DNR approach in ovarian cancer cells. NuTu-19, a rat ovarian cancer cell line was rendered resistant to cisplatin. Both NuTu-19 and resistant cells were infected with a retroviral vector containing the EGFR-DNR. NuTu-19 and NuTu-DNR (NuTu-19 cells expressing the EGFR-DNR) were injected into Fisher 344 immunocompetent rats. Western blot analyses were used to assess signal transduction pathways. All rats injected with NuTu-DNR cells remained healthy following tumor injection. In contrast, 100% of the rats injected with the NuTu-19 and NuTu-Sham (NuTu-19 cells expressing an empty vector) died of disease progression at the end of 15 weeks (P = 0.00009). On Western blot analysis, both NuTu-19 and NuTu-Sham cells showed a strong activation of mitogen-activated protein kinase (MAPK) after exposure to EGF. Cisplatin-resistant cell lines showed an enhanced EGF stimulatory effect via the MAPK pathway compared with parental cells. The EGFR-DNR significantly reduced the ability of EGF to induce cell signaling through the MAPK pathway. Lastly, the EGFR-DNR can partially reverse cisplatin resistance in drug-resistant cells. The EGFR-DNR approach suggests that EGFR confers a growth advantage to NuTu-19 cells in vivo. Thus, EGFR blockade may ultimately prove to be a useful therapeutic tool in the treatment of cisplatin-sensitive and cisplatin-resistant ovarian cancers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".