Expression of CD47 protein in hematolymphoid neoplasms: Implications for CD47-mediated cancer immunotherapy
Bibliographic record
Abstract
OBJECTIVES: Recent studies show that blocking CD47-SIRPα interactions is a promising target in checkpoint inhibition for cancer immunotherapy. However, to date, the expression of CD47 is not well characterized in various hematolymphoid neoplasms. METHODS: This study evaluates CD47 expression in a wide range of hematolymphoid neoplasms using immunohistochemistry on 834 cases. RESULTS: Results show variable but widespread CD47 expression among tumor types and within individual samples in both intensity and percentage. The highest CD47 expressions in both percentage of positive lymphoma cells and intensity was seen in small B-cell lymphomas, particularly chronic lymphocytic leukemia/small lymphocytic lymphoma, mantle cell, marginal zone, and follicular lymphomas. T and B lymphoblastic, diffuse large B-cell, peripheral T-cell, γδ T-cell, angioimmunoblastic T-cell lymphomas and myelodysplastic syndrome showed moderate CD47 expression. Acute and chronic myeloid leukemia as well as classic Hodgkin, anaplastic large cell, and natural killer/T-cell lymphomas showed low expression. Burkitt lymphoma is a notable standout, with little to no CD47 expression in all 14 cases examined. CONCLUSIONS: Understanding the prevalence of CD47 expression in hematolymphoid neoplasms is crucial for identifying potential therapeutic targets and selecting patients who may benefit from CD47-targeted therapies. Additionally, CD47 may serve as a valuable diagnostic marker in neoplasms such as Burkitt lymphoma.
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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".