Customized Viral Immunotherapy for HPV-Associated Cancer
Bibliographic record
Abstract
Abstract The viral-transforming proteins E6 and E7 make human papillomavirus–positive (HPV+) malignancies an attractive target for cancer immunotherapy. However, therapeutic vaccination exerts limited efficacy in the setting of advanced disease. We designed a strategy to induce substantial specific immune responses against multiple epitopes of E6 and E7 proteins based on an attenuated transgene from HPV serotypes 16 and 18 that is incorporated into MG1-Maraba virotherapy (MG1-E6E7). Mutations introduced to the transgene abrogate the ability of E6 and E7 to perturb p53 and retinoblastoma, respectively, while maintaining the ability to invoke tumor-specific, multifunctional CD8+ T-cell responses. Boosting with MG1-E6E7 significantly increased the magnitude of T-cell responses compared with mice treated with a priming vaccine alone (greater than 50 × 106 E7-specific CD8+ T cells per mouse was observed, representing a 39-fold mean increase in boosted animals). MG1-E6E7 vaccination in the HPV+ murine model TC1 clears large tumors in a CD8+-dependent manner and results in durable immunologic memory. MG1-Maraba can acutely alter the tumor microenvironment in vivo and exploit molecular hallmarks of HPV+ cancer, as demonstrated by marked infection of HPV+ patient tumor biopsies and is, therefore, ideally suited as an oncolytic treatment against clinical HPV+ cancer. This approach has the potential to be directly translatable to human clinical oncology to tackle a variety of HPV-associated neoplasms that cause significant morbidity and mortality globally. Cancer Immunol Res; 5(10); 847–59. ©2017 AACR.
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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 | 0.000 | 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 | 0.001 |
| 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 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".