Cytokine screening identifies TNF to potentially enhance immunogenicity of pediatric sarcomas
Bibliographic record
Abstract
Introduction Pediatric sarcomas, including osteosarcoma (OS), Ewing sarcoma (EwS) and rhabdomyosarcoma (RMS) carry low somatic mutational burden and low MHC-I expression, posing a challenge for T cell therapies. Our previous study showed that mediators of monocyte maturation sensitized the EwS cell line A673 to lysis by HLA-A*02:01/CHM1319-specific allorestricted T cell receptor (TCR) transgenic CD8+ T cells (CHM1319 CD8+ T cells). Methods In this study, we tested a panel of monocyte maturation cytokines for their ability to upregulate immunogenic cell surface markers on OS, EwS and RMS cell lines, using flow cytometry. xCELLigence, SRB and ELISpot assays were used to assess whether TNF pretreatment increases CD8+ T cell cytotoxicity. Results We observed that TNF and IL-1β upregulated MHC class I, ICAM-1 as well as CD83 and PD-L1 on the surface of pediatric sarcoma cell lines, while IL-4, GM-CSF, IL-6 and PGE2 failed to induce respective effects. Although pretreatment of pediatric sarcoma cell lines with TNF did not improve unspecific peripheral blood mononuclear cells (PBMCs) cytotoxicity, TNF enhanced specific lysis of 1/3 HLA-A2+ EwS cell lines by CHM1319 CD8+ T cells depending on MHC-I expression and ICAM-1 upregulation. Discussion Our study supports utilization of TNF or TNF-inducing regimens for upregulation of MHC-I and costimulatory surface molecules on pediatric sarcoma cells and for enhancing recognition of responsive HLA-A2+ EwS tumor cells by antigen-specific CD8+ T cells.
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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 | 0.000 |
| 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".