Assessing the impact of CD73 inhibition on overcoming anti-EGFR resistance in glioma cells
Bibliographic record
Abstract
Objectives: Glioblastoma (GB) is a grade IV glial tumor characterized by high malignancy and dismal prognosis, primarily due to high recurrence rates and therapeutic resistance. The epidermal growth factor receptor (EGFR), a receptor tyrosine kinase (RTK), regulates signaling pathways, including cell growth, proliferation, survival, migration, and cell death. Many cancers utilize immune checkpoints (ICs) to attenuate immune responses. CD73 is an enzyme that functions as an IC by hydrolyzing AMP to adenosine, suppressing immune cells in the tumor microenvironment. However, the role of CD73 in resistance to EGFR inhibitors is poorly understood. This study aims to elucidate the resistance mechanisms induced by anti-EGFR treatment and to evaluate an anti-CD73 approach to overcome resistance mediated by anti-EGFR monotherapy. Methods: . Results: We observed high EGFR, PD-L1, and CD73 expression in human GB cells. The treatment with AG1478 increased the expression of resistance markers MRP-1, PD-L1, and CD73, whereas it decreased CTLA-4. The combination of AG1478 and APCP did not alter proliferation or apoptosis but interfered with cell cycling, arresting the cells in the G1 phase, decreasing cell motility and partially reversing MRP-1 overexpression. Conclusion: studies are needed, as the inhibition of both EGFR and CD73 affects cells in the tumor microenvironment and could potentially enhance anti-tumor immunity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".