Abstract 5071: Olverembatinib (HQP1351) enhances antitumor effects of immunotherapy in renal cell carcinoma (RCC)
Bibliographic record
Abstract
Abstract In solid tumors, resistance to checkpoint inhibitors (CPIs) is frequently observed, partially due to upregulation of vascular endothelial growth factor A (VEGFA) and programmed death-ligand 1 (PD-L1). This culminates in an immunosuppressive tumor microenvironment and immune escape. Inhibitors against VEGF and the VEGF receptor (VEGFR) foster tumor vessel normalization and immunostimulatory reprogramming, in turn promoting treatment effects of immunotherapies. In recent years, TKIs, including axitinib, lenvatinib, and cabozantinib, plus immunotherapy have been approved to treat advanced RCC. Currently under clinical development for relapsed or refractory chronic myeloid leukemia and gastrointestinal tumor, olverembatinib (HQP1351) is a new-generation multikinase inhibitor with targets including VEGFR, fibroblast growth factor receptor (FGFR), SRC, BCR-ABL1, c-KIT, and platelet-derived growth factor receptor. The aim of this study was to assess whether olverembatinib combined with immunotherapy can promote inhibitory effects on RCC. In cell-free kinase assays, olverembatinib inhibited VEGFR1, -2, and -3 with IC50 values of 4.2, 6.1, and 4.1 nM, respectively. Compared to lenvatinib, olverembatinib had more potent antiproliferative effects on human umbilical vein endothelial cells. Olverembatinib also had antiproliferative activity in murine RCC lines RANCA and RAG, with IC50 values of 141 and 53 nM, respectively. When olverembatinib was coadministered with an anti-PD-1 antibody in a RANCA-derived syngeneic model, both agents exerted synergistic effects, with tumor growth inhibition rates reaching 60.6%. Mechanistically, olverembatinib influenced cancer cell proliferation directly by inhibiting phosphorylation of FGFR and downstream proteins. Increased cleavage of caspase-3 and poly (ADP-ribose) polymerase 1 were observed, suggesting induction of apoptosis. Olverembatinib also influenced proliferation of vascular endothelial cells by inhibiting phosphorylation of VEGFR, SRC, and downstream proteins Akt and extracellular signal-regulated kinases. Olverembatinib also reduced expression of PD-L1 in RCC cells. In tumor-infiltrating lymphocyte assays, olverembatinib increased numbers of cytotoxic T cells (CTL, CD8+) and natural-killer cells (NK, CD3−/CD49B+) in RANCA tumor tissues. Combined with an anti-PD-1 antibody, olverembatinib increased CTLs, NK cells, dendritic cells (DCs, MHC-II+/CD11C+), and M1 macrophages (F4/80+/CD11B+/CD86+) in RANCA tumor tissues, indicating an immunoregulatory effect of olverembatinib. Taken together, these data suggest that combining olverembatinib with a CPI confers synergistic antitumor effects in an RCC cancer mouse model by targeting tumor growth, angiogenesis, and immune regulation. This novel combination may provide an alternative approach to enhance treatment effects with CPIs in renal cancers. Citation Format: Guangfeng Wang, Eric Liang, Ping Min, Huidan Yu, Bingxing Wu, Dajun Yang, Yifan Zhai. Olverembatinib (HQP1351) enhances antitumor effects of immunotherapy in renal cell carcinoma (RCC). [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5071.
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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) | 0.001 | 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) | 0.003 | 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".