Immune profiling of dedifferentiated liposarcoma and identification of novel antigens for targeted immunotherapy
Bibliographic record
Abstract
Dedifferentiated liposarcoma (DDLS) is an aggressive, recurring sarcoma with limited treatments. T-cell immunotherapies selectively target malignant cells, holding promise against DDLS. The development of successful immunotherapy for DDLS requires a thorough evaluation of the tumor immune microenvironment and the identification and characterization of targetable immunogenic tumor antigens. To assess the complexity of the human DDLS tumor immune microenvironment and to identify target antigens, we used the nCounter NanoString platform, analyzing gene expression profiles across 29 DDLS and 10 healthy adipose tissue samples. Hierarchical clustering of tumors based on expression of tumor inflammation signature genes revealed two distinct groups, consisting of 15 inflamed tumors and 14 non-inflamed tumors, demonstrating tumor heterogeneity within this sarcoma subtype. Among the identified antigens, PBK and TTK exhibited substantial upregulation in mRNA expression compared to healthy adipose tissue controls, further corroborated by positive protein expression by IHC. This data shows considerable inter-tumoral heterogeneity of inflammation, which should be taken into consideration when designing an immunotherapy for DDLS, and provides a novel targetable antigen in DDLS. The results of this study lay the groundwork for the development of a novel immunotherapy for this highly aggressive sarcoma.
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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".