IMMU-21. GLIOBLASTOMA CELLS EXPORT TENASCIN-C VIA MICROVESICLES TO SUPPRESS T CELL RESPONSES
Bibliographic record
Abstract
The dismal prognosis of glioblastoma is contributed in part by the existence of stem-like brain tumor-initiating cells (BTICs) that are more radio- and chemo-resistant than their differentiated transformed progenies. This emphasizes the need for new approaches, particularly on therapies that instruct immune cells against BTICs. However, effective immunotherapies in glioblastoma remain elusive as immune cells such as T lymphocytes are suppressed by tumor cells. We have shown that BTICs and differentiated glioblastoma cells produce large amounts of tenascin-C (TNC), an extracellular matrix protein that promotes BTIC growth (Sarkar et al., Cancer Res 2017); whether and how TNC regulates T cell responses remain to be established. We stained human glioblastoma brain specimens and found TNC to be widely deposited including in vicinity of BTICs and T cells. In co-cultures, glioblastoma patient-derived BTICs inhibit the activation of T-cells, in part by the secretion of TNC as determined through neutralization by a series of monoclonal antibodies against TNC. Analysis of downstream signaling pathways of T cells following interaction with TNC showed attenuated phospho-protein levels. Of interest, TNC is mainly exported out of BTICs by microvesicles and microvesicle-depleted conditioned media were reduced in their capacity to suppress T cell responses. Moreover, TNC-depleted microvesicles had lower inhibitory effects on T cell activation. Finally, we found that circulating microvesicles from glioblastoma patients contained more TNC than those from control individuals. Collectively, our study reveals a novel pathway for therapeutic intervention: the suppression of T lymphocyte activity by BTICs through TNC cargoed within BTIC-secreted microvesicles.
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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) | 0.002 | 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; 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".