Interleukin-10 production by tumor infiltrating macrophages plays a role in Human Papillomavirus 16 tumor growth
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Human Papillomavirus, HPV, is the main etiological factor for cervical cancer. Different studies show that in women infected with HPV there is a positive correlation between lesion grade and number of infiltrating macrophages, as well as with IL-10 higher expression. Using a HPV16 associated tumor model in mice, TC-1, our laboratory has demonstrated that tumor infiltrating macrophages are M2-like, induce T cell regulatory phenotype and play an important role in tumor growth. M2 macrophages secrete several cytokines, among them IL-10, which has been shown to play a role in T cell suppression by tumor macrophages in other tumor models. In this work, we sought to establish if IL-10 is part of the mechanism by which HPV tumor associated macrophages induce T cell regulatory phenotype, inhibiting anti-tumor activity and facilitating tumor growth. RESULTS: TC-1 tumor cells do not express or respond to IL-10, but recruit leukocytes which, within the tumor environment, produce this cytokine. Using IL-10 deficient mice or blocking IL-10 signaling with neutralizing antibodies, we observed a significant reduction in tumor growth, an increase in tumor infiltration by HPV16 E7 specific CD8 lymphocytes, including a population positive for Granzyme B and Perforin expression, and a decrease in the percentage of HPV specific regulatory T cells in the lymph nodes. CONCLUSIONS: Our data shows that in the HPV16 TC-1 tumor mouse model, IL-10 produced by tumor macrophages induce regulatory phenotype on T cells, an immune escape mechanism that facilitates tumor growth. Our results point to a possible mechanism behind the epidemiologic data that correlates higher IL-10 expression with risk of cervical cancer development in HPV infected women.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it