An engineered Accum‐E7 protein‐based vaccine with dual anti‐cervical cancer activity
Bibliographic record
Abstract
Abstract Worldwide prevalence of cervical cancer decreased significantly with the use of human papilloma virus (HPV)‐targeted prophylactic vaccines. However, these multivalent antiviral vaccines are inert against established tumors, which leave patients with surgical ablative options possibly resulting in long‐term reproductive complications and morbidity. In an attempt to bypass this unmet medical need, we designed a new E7 protein‐based vaccine formulation using Accum™, a technology platform designed to promote endosome‐to‐cytosol escape as a means to enhance protein accumulation in target cells. Prophylactic vaccination of immunocompetent mice using the Accum‐E7 vaccine (aE7) leads to complete protection from cervical cancer despite multiple challenges conducted with ascending C3.43 cellular doses (0.5‐, 1.0‐, and 2.0 × 106 cells). Moreover, the humoral response induced by aE7 was higher in magnitude compared with naked E7 protein vaccination and displayed potent inhibitory effects on C3.43 proliferation in vitro. When administered therapeutically to animals with pre‐established C3.43 or Tal3 tumors, the vaccine‐induced response synergized with multiple immune checkpoint blockers (anti‐PD‐1, anti‐CTLA4, and anti‐CD47) to effectively control tumor growth. Mechanistically, the observed therapeutic effect requires cross‐presenting dendritic cells as well as CD8 T cells predominantly, with a non‐negligible role played by both CD4+ and CD19+ lymphocytes. good laboratory practice (GLP) studies revealed that aE7 is immunogenic and well tolerated by immunocompetent mice with no observed adverse effects despite the use of a fourfold exceeding dose. In a nutshell, aE7 represents an ideal vaccine candidate for further clinical development as it uses a single engineered protein capable of exhibiting both prophylactic and therapeutic activity.
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 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 | 0.000 |
| 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 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".