OX40 and 4-1BB delineate distinct immune profiles in sarcoma
Bibliographic record
Abstract
Systemic relapse after radiotherapy and surgery is the major cause of disease-related mortality in sarcoma patients. Combining radiotherapy and immunotherapy is under investigation as a means to improve response rates. However, the immune contexture of sarcoma is understudied. Here, we use a retrospective cohort of sarcoma patients, treated with neoadjuvant radiotherapy, and TCGA data. We explore therapeutic targets of relevance to sarcoma, using genomics and multispectral immunohistochemistry to provide insights into the tumor immune microenvironment across sarcoma subtypes. Differential gene expression between radioresponsive myxoid liposarcoma (MLPS) and more radioresistant undifferentiated pleomorphic sarcoma (UPS) indicated UPS contained higher transcript levels of a number of immunotherapy targets (CD73/NT5E, CD39/ENTPD1, CD25/IL2RA, and 4–1BB/TNFRSF9). We focused on 4–1BB/TNFRSF9 and other costimulatory molecules. In TCGA data, 4–1BB correlated to an inflamed and exhausted phenotype. OX40/TNFRSF4 and 4–1BB/TNFRSF9 were highly expressed in sarcoma subtypes versus other cancers. Despite OX40 and 4–1BB being described as Treg markers, we identified that they delineate distinct tumor immune profiles. This was true for sarcoma and other cancers. While only a limited number of samples could be analyzed, spatial analysis of OX40 expression identified two diverse phenotypes of OX40+ Tregs, one associated with and one independent of tertiary lymphoid structures (TLSs). Patient stratification is of intense interest for immunotherapies. We provide data supporting the viewpoint that a cohort of sarcoma patients, appropriately selected, are promising candidates for immunotherapies. Spatial profiling of OX40+ Tregs, in relation to TLSs, could be an additional metric to improve future patient stratification.
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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 | 0.001 | 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".