Tumor-associated macrophages and macrophage-related immune checkpoint expression in sarcomas
Bibliographic record
Abstract
Early trials for immune checkpoint inhibitors in sarcomas have delivered mixed results, and efforts to improve outcomes now look to combinatorial strategies with novel immunotherapeutics, including some that target macrophages. To enhance our understanding of the sarcoma immune landscape, we quantified and characterized tumor-associated macrophage infiltration and expression of the targetable macrophage-related immune checkpoint CD47/SIRPα across sarcoma types. We surveyed immunohistochemical expression of CD68, CD163, CD47, and SIRPα in tissue microarrays of 1242 sarcoma specimens (spanning 24 types). Non-translocation sarcomas, particularly undifferentiated pleomorphic sarcoma and dedifferentiated liposarcoma, had significantly higher counts of both CD68+ and CD163+ macrophages than translocation-associated sarcomas. Across nearly all sarcoma types, macrophages outnumbered tumor-infiltrating lymphocytes and CD163+ (M2-like) macrophages outnumbered CD68+ (M1-like) macrophages. These findings were supported by data from The Cancer Genome Atlas, which showed a correlation between increasing macrophage contributions to immune infiltration and several measures of DNA damage. CD47 expression was bimodal, with most cases showing either 0% or >90% tumor cell staining, and the highest CD47 scores were observed in chordoma, angiosarcoma, and pleomorphic liposarcoma. SIRPα scores correlated well with CD47 expression. Given the predominance of macrophage infiltrates over tumor-infiltrating lymphocytes, the bias toward M2-like (immunosuppressive) macrophage polarization, and the generally high scores for CD47 and SIRPα, macrophage-focused immunomodulatory agents, such as CD47 or IDO-1 inhibitors, may be particularly worthwhile to pursue in sarcoma patients, alone or in combination with lymphocyte-focused agents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".