Monoclonal Antibodies Against Human Papillomavirus E6 and E7 Oncoproteins Inhibit Tumor Growth in Experimental Cervical Cancer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Nearly all cases of cervical cancer are initiated by persistent infection with high-risk strains of human papillomavirus (hr-HPV). When hr-HPV integrates into the host genome, the constitutive expression of oncogenic HPV proteins E6 and E7 function to disrupt p53 and retinoblastoma regulation of cell cycle, respectively, to favor malignant transformation. HPV E6 and E7 are oncogenes found in over 99% of cervical cancer, they are also expressed in pre-neoplastic stages making these viral oncoproteins attractive therapeutic targets. Monoclonal antibodies (mAbs) represent a novel potential approach against the actions of hr-HPV E6 and E7 oncoproteins. In this report, we describe the utilization of anti-HPV E6 and HPV E7 mAbs in an experimental murine model of human cervical cancer tumors. We used differential dosing strategies of mAbs C1P5 (anti-HPV 16 E6) and TVG701Y (anti-HPV E7) administered via intraperitoneal or intratumoral injections. We compared mAbs to the action of chemotherapeutic agent Cisplatin and demonstrated the capacity of mAbs to significantly inhibit tumor growth. Furthermore, we investigated the contribution of the immune system and found increased complement deposition in both C1P5 and TVG701Y treated tumors compared to irrelevant mAb therapy. Taken together, the results suggest that anti-HPV E6 and E7 mAbs exert inhibition of tumor growth in a viral-specific manner and stimulate an immune response that could be exploited for an additional treatment options for patients.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it