Tumor Apoptotic Bodies Inhibit CTL Responses and Antitumor Immunity via Membrane-Bound Transforming Growth Factor-β1 Inducing CD8+ T-Cell Anergy and CD4+ Tr1 Cell Responses
Bibliographic record
Abstract
Tumor cell apoptosis induced by radiation therapy results in apoptotic tumor cells and apparition of membrane blebs termed apoptotic bodies (APB). The immune responses induced by apoptotic tumor cells have been extensively studied. However, the role of APB in modulation of tumor immune responses is elusive. In this study, we induced apoptosis in 90% ovabumin-expressing EG7 tumor cells by in vitro irradiation (9,000 rad) of tumor cells with a subsequent cell culture for 9 hours. APB purified from irradiation-induced apoptotic EG7 cell culture supernatant by differential ultracentrifugation were vesicles with 50 to 90 nm in diameter and expressed apoptosis-specific Annexin V, 14-3-3, and Histone H3. We then investigated its potential modulation in DC(OVA)-induced T-cell responses and antitumor immunity. We found that EG7-derived APB were tolerogenic and capable of suppressing DC(OVA)-stimulated CD8+ CTL responses and antitumor immunity via its induction of CD8+ T-cell anergy and type 1 regulatory CD4+ T-cell responses. Analysis of apoptotic tumor cells and APB revealed the expression of membrane-bound transforming growth factor (TGF)-beta1 associated with irradiation-induced apoptosis formation, which is a result from activation of transcriptional factor NF-AT specific for TGF-beta1 promoters. Our data further elucidate that it is the membrane-bound TGF-beta1 expression on APB that contributes to its in vitro antiproliferative effect as shown by using neutralizing TGF-beta1-specific antibody. Administration of anti-TGF-beta1 antibody in vivo also blocked APB-mediated immune suppression of CD8+ CTL responses and antitumor immunity. Therefore, our study may have great impact in designing a combined radiation therapy with immunotherapy of cancer.
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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 | 0.000 | 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".