Sarcomas: Immune biomarker expression and checkpoint inhibitor trials
Bibliographic record
Abstract
Sarcomas are a heterogenous group of mesenchymal cancers comprising over 100 subtypes. Current chemotherapy for all but a very few subtypes has limited efficacy, resulting in 5-year relative survival rates of 16% for metastatic patients. While sarcomas have often been regarded as an "immune cold" tumor category, recent biomarker studies have confirmed a great deal of immune heterogeneity across sarcoma subtypes. Reports from the first generation of clinical trials treating sarcomas with immunotherapy demonstrate a few positive responses, supporting efforts to stratify patients to optimize response rates. This review summarizes recent advances in knowledge around immune biomarker expression in sarcomas, the potential use of new technologies to complement these study results, and clinical trials particularly of immune checkpoint inhibitor therapy in sarcomas. Each of the immune biomarkers assessed was reviewed for subtype-specific expression patterns and correlation with prognosis. Overall, there is extensive heterogeneity of immune biomarker presence across sarcoma subtypes, and no consensus on the prognostic effect of these biomarkers. New technologies such as multiplex immunohistochemistry and high plex in situ profiling may offer more insights into the sarcoma microenvironment. To date, clinical trials using immune checkpoint inhibitor monotherapy have not shown compelling clinical benefits. Combination therapy with dual checkpoint inhibitors or in combinations with other agents has yielded more promising results in dedifferentiated liposarcoma, undifferentiated pleomorphic sarcoma, angiosarcoma and alveolar soft-part sarcoma. Better understanding of the sarcoma immune status through biomarkers may help decipher the reasons behind differential responses to immunotherapy.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".