CTL-Dependent and -Independent Antitumor Immunity Is Determined by the Tumor Not the Vaccine
Bibliographic record
Abstract
Previously, we compared the efficiency of direct injection with an adenovirus (Ad) expressing human gp100 (hgp100) to immunization with dendritic cells (DC) loaded with the same vector ex vivo. The DC vaccine provided the greatest protection against challenge with B16F10 melanoma, and antitumor immunity was found to be CD8(+) T cell-independent. In the current study, we sought to determine whether lack of CD8(+) T cell-mediated antitumor immunity was a function of the vaccine platform or the tumor line. Both Ad and DC/Ad vaccines elicited CD8(+) CTL reactive against hgp100 and provided protection against B16F10 engineered to express hgp100 demonstrating that both vaccination platforms can effectively generate protective CD8(+) T cell-mediated immunity. The hgp100-induced CTL cross-reacted with murine gp100 (mgp100) and lysed B16F10 cells pulsed with mgp100 peptide indicating that the resistance of B16F10 cells to CTL elicited by hgp100 vaccination may be due to a defect in processing of the endogenous mgp100. Indeed, introduction of the TAP-1 cDNA into B16F10 rendered the cells sensitive to lysis by gp100-specific CTL. Furthermore, gp100-immunized mice were protected from challenge with B16F10-TAP1 cells through a mechanism dependent upon CD8(+) T cells. These results demonstrate that tumor phenotype, not the vaccination platform, ultimately determines CD8(+) or CD4(+) T cell-mediated tumor clearance.
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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) | 0.001 | 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) | 0.001 | 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".