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Record W4408344006 · doi:10.1093/ajcp/aqaf018

Expression of CD47 protein in hematolymphoid neoplasms: Implications for CD47-mediated cancer immunotherapy

2025· article· en· W4408344006 on OpenAlexaff
Jingjing Zhang, Philip L. Bulterys, Sebastian Fernandez‐Pol, Sheren Younes, Shuchun Zhao, Adnan Mansoor, Yasodha Natkunam

Bibliographic record

VenueAmerican Journal of Clinical Pathology · 2025
Typearticle
Languageen
FieldImmunology and Microbiology
TopicPhagocytosis and Immune Regulation
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCD47LymphomaChronic lymphocytic leukemiaMantle cell lymphomaCancer researchMedicineImmunotherapyCancerFollicular lymphomaImmunologyLeukemiaPathologyAntibodyInternal medicine

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.603
Threshold uncertainty score0.451

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.027
GPT teacher head0.388
Teacher spread0.361 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2025
Admission routes1
Has abstractyes

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