Tumor-specific immunity and antiangiogenesis generated by a DNA vaccine encoding calreticulin linked to a tumor antigen
Bibliographic record
Abstract
Antigen-specific cancer immunotherapy and antiangiogenesis have emerged as two attractive strategies for cancer treatment. An innovative approach that combines both mechanisms will likely generate the most potent antitumor effect. We tested this approach using calreticulin (CRT), which has demonstrated the ability to enhance MHC class I presentation and exhibit an antiangiogenic effect. We explored the linkage of CRT to a model tumor antigen, human papilloma virus type-16 (HPV-16) E7, for the development of a DNA vaccine. We found that C57BL/6 mice vaccinated intradermally with CRT/E7 DNA exhibited a dramatic increase in E7-specific CD8+ T cell precursors and an impressive antitumor effect against E7-expressing tumors compared with mice vaccinated with wild-type E7 DNA or CRT DNA. Vaccination of CD4/CD8 double-depleted C57BL/6 mice and immunocompromised (BALB/c nu/nu) mice with CRT/E7 DNA or CRT DNA generated significant reduction of lung tumor nodules compared with wild-type E7 DNA, suggesting that antiangiogenesis may have contributed to the antitumor effect. Examination of microvessel density in lung tumor nodules and an in vivo angiogenesis assay further confirmed the antiangiogenic effect generated by CRT/E7 and CRT. Thus, cancer therapy using CRT linked to a tumor antigen holds promise for treating tumors by combining antigen-specific immunotherapy and antiangiogenesis.
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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 | 0.001 | 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 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".