Inhibition of FGFR Reactivates IFNγ Signaling in Tumor Cells to Enhance the Combined Antitumor Activity of Lenvatinib with Anti-PD-1 Antibodies
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Bibliographic record
Abstract
Abstract Combination therapies consisting of immune checkpoint inhibitors plus anti-VEGF therapy show enhanced antitumor activity and are approved treatments for patients with renal cell carcinoma (RCC). The immunosuppressive roles of VEGF in the tumor microenvironment are well studied, but those of FGF/FGFR signaling remain largely unknown. Lenvatinib is a receptor tyrosine kinase inhibitor that targets both VEGFR and FGFR. Here, we examine the antitumor activity of anti-PD-1 mAb combined with either lenvatinib or axitinib, a VEGFR-selective inhibitor, in RCC. Both combination treatments showed greater antitumor activity and longer survival in mouse models versus either single agent treatment, whereas anti-PD-1 mAb plus lenvatinib had enhanced antitumor activity compared with anti-PD-1 mAb plus axitinib. Flow cytometry analysis showed that lenvatinib decreased the population of tumor-associated macrophages and increased that of IFNγ-positive CD8+ T cells. Activation of FGFR signaling inhibited the IFNγ-stimulated JAK/STAT signaling pathway and decreased expression of its target genes, including B2M, CXCL10, and PD-L1. Furthermore, inhibition of FGFR signaling by lenvatinib restored the tumor response to IFNγ stimulation in mouse and human RCC cell lines. These preclinical results reveal novel roles of tumor FGFR signaling in the regulation of cancer immunity through inhibition of the IFNγ pathway, and the inhibitory activity of lenvatinib against FGFRs likely contributes to the enhanced antitumor activity of combination treatment comprising lenvatinib plus anti-PD-1 mAb. Significance: FGFR pathway activation inhibits IFNγ signaling in tumor cells, and FGFR inhibition with lenvatinib enhances antitumor immunity and the activity of anti-PD-1 antibodies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it