Regression of Engineered Tumor Cells Secreting Cytokines Is Related to a Shift in Host Cytokine Profile from Type 2 to Type 1
Bibliographic record
Abstract
The precise role of the endogenous immune response in modulating cancer development remains unclear. In this study, three mouse tumor cell lines were used to elucidate the immune mechanisms for tumor regression versus tumor growth. These cell lines were (1) the poorly immunogenic VKCK cell line and (2) its two derived cell lines VKCK/RM4-tumor necrosis factor-alpha (TNF-alpha) and VKCK/RM4-interferon-gamma (IFN-gamma) engineered to secrete TNF-alpha and IFN-gamma, respectively. Our data showed that VKCK tumors grew aggressively in syngeneic BALB/c mice, and vaccination of irradiated VKCK cells failed to protect the mice from a subsequent challenge with the same tumor. In contrast, engineered VKCK tumor cells lost their tumorigenicity, and vaccination of engineered VKCK cells induced a protective immunity against VKCK cells that was mediated with VKCK-specific CD8+T cells. Susceptible mice developed a Th2-dominant response, whereas resistant mice developed a Th1-dominant response to VKCK. The T cell proliferative response and cytolytic activity against VKCK developed in both resistant and susceptible mice, but in the susceptible mice, these responses were much weaker compared with those in the resistant mice. Our results indicate that regression of tumor cells engineered to secrete cytokines TNF-alpha and IFN-gamma is related to a shift from a host type 2 to a type 1 cytokine profile. Our results further suggest that the failure of unmodified VKCK to generate efficacious T cells is not due to an inability to recognize tumor antigens but, rather, to the nature and magnitude of the antitumor immune response that develops. A better understanding of the mechanisms by which tumor cells modulate the host immune system may result in newer approaches for manipulating host-tumor interactions that favor the development of a protective antitumor immune response.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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; both teacher heads agree on what is shown here.
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".