Elucidate ACKR3 expression and function in glioblastoma-associated microenvironment
Bibliographic record
Abstract
Abstract AIMS Glioblastoma is the most prevalent primary brain tumor in adults, leading to a grim prognosis. The development of efficient therapies is limited by several factors, including the immunosuppressive tumor microenvironment. The chemokine receptor CXCR4 has been described as a key player in glioblastoma progression, participating in tumor cell stemness, invasion, angiogenesis and immunosuppression. Accumulating results indicate that ACKR3, the other receptor for the chemokine CXCL12, might also participate in tumor severity. However, its function in cancer remains to be determined. We aim to characterize the expression and function of ACKR3 in glioblastoma-associated microenvironment and evaluate its potential relevance as therapeutic target. METHODS Experiments were carried on using syngeneic murine glioblastoma models, i.e. intracranial engraftment of GL261 cells. The immune landscape of GL261 tumors was characterized by spectral flow cytometry in untreated conditions, or after pharmacological modulation of ACKR3 and CXCR4. RESULTS In our model, CXCR4 is expressed on murine glioblastoma cells, as well as at the surface of cells from the myeloid lineage and NK cells. ACKR3 is nearly absent on tumor cells, but is highly expressed on myeloid, NK and T cells. Pharmacological modulation of the receptors had a systemic effect on CXCL12 plasma concentration in mice, showing a significant increase after administration of ACKR3 and CXCR4 antagonists. Changes in immune cells recruitment to the brain were observed upon receptor modulation. Modulation of ACKR3 and CXCR4 had opposite effects on the recruitment of TAMs. Unlike CXCR4, modulation of ACKR3 also had an impact on the proportion of CD4+ and CD8+ T cells, suggesting its function in immunosurveillance. CONCLUSION These data support the hypothesis that ACKR3, as well as CXCR4, participate to the immunosuppressive mi- croenvironment of glioblastoma. The physiopathological relevance of these findings remain to be addressed regarding the mobilization of cells and their phenotype, as well as its impact on tumor growth.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".