Targeting CD47-SIRPa axis shows potent preclinical anti-tumor activity as monotherapy and synergizes with PARP inhibition
Bibliographic record
Abstract
The objective was to correlate CD47 gene expression with resistance to immune checkpoint inhibitors (ICI) in tumor tissue of gynecological cancer (GC). Further, we sought to assess the efficacy of targeting CD47 pathway alone and in combination in pre-clinical ovarian cancer (OC) models. We performed transcriptomic analyses in GC treated with ICI. Signaling pathway enrichment analysis was performed using Ingenuity Pathway Analysis. Immune cell abundance was estimated. CD47 expression was correlated with other pathways, objective response, and progression-free survival (PFS). Anti-tumor efficacy of anti-CD47 therapy alone and in combination was investigated both in-vitro and in-vivo using cell-line derived xenograft (CDX) and patient-derived xenograft (PDX) models. High CD47 expression associated with lower response to ICI and trended toward lower PFS in GC patients. Higher CD47 associated negatively with PDL1 and CTLA4 expression, as well as cytotoxic T-cells and dendritic cells but positively with TGF-β, BRD4 and CXCR4/CXCL12 expression. Anti-CD47 significantly enhanced macrophage-mediated phagocytosis of OC cells in-vitro and exhibited potent anti-tumor activity in-vivo in OC CDX and PDX models. In-vitro treatment with PARPi increased CD47 expression. Anti-CD47 led to significantly enhanced in-vitro phagocytosis, enhanced STING pathway and synergized in-vivo when combined with PARP inhibitors in BRCA-deficient OC models. This study provides insight on the potential role of CD47 in mediating immunotherapy resistance and its association with higher TGF-β, BRD4 and CXCR4/CXCL12 expression. Anti-CD47 showed potent anti-tumor activity and synergized with PARPi in OC models. These data support clinical development of anti-CD47 therapy with PARPi in OC.
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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 | 0.000 | 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 | 0.001 |
| 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".