Abstract 5124: The effect of lenvatinib in combination with chemotherapy plus immune checkpoint inhibitors on tumor microenvironments in the mouse syngeneic tumor model
Bibliographic record
Abstract
Abstract Introduction: Lenvatinib (LEN) is an oral, multiple-receptor, tyrosine kinase inhibitor that mainly targets vascular endothelial growth factor and fibroblast growth factor receptors. Combination therapy with LEN and everolimus is approved for people with advanced renal cell carcinoma (RCC). Combination therapy with LEN and pembrolizumab, an anti human-programmed-cell-death-1 (PD-1) antibody, is also approved for the treatment of people with advanced endometrial cancer and advanced RCC. In this study, we investigated the antitumor activity of LEN in combination with chemotherapy and immune checkpoint inhibitors (ICIs), and explored the mechanism of action underlying these effects, in the mouse lung cancer syngeneic model. Methods: In the LL/2 mouse Lewis lung carcinoma model, LEN was dosed at either 3 or 10 mg/kg (orally; once daily for 2 weeks) with or without cisplatin 4 mg/kg (intravenously; 3 or 4 times, twice weekly) and with or without anti-PD-1 200 ug/head + anti cytotoxic T-lymphocyte antigen 4 (CTLA4) antibodies 10mg/kg (intraperitoneally; twice weekly). Functional blood vessels were evaluated in tumor microenvironments by injections of Hoechst 33342 (Hoechst) into the tail veins of mice. The Hoechst-stained area and expressions of angiogenesis/immune-cell-related molecules were analyzed by immunohistochemistry (IHC) analysis. Results: In this model, the combination treatment of LEN 3 mg/kg + cisplatin showed significant tumor growth inhibitory activity compared with each single agent without any marked body-weight loss. LEN 10 mg/kg + cisplatin did not show enhanced antitumor activity vs LEN 10 mg/kg as a single agent. IHC analysis using anti-CD31 antibody showed that LEN 3 mg/kg daily decreased microvessel density in tumors after 4 days. In addition, LEN 3 mg/kg daily for 4 days showed a trend toward an increase of the number of functional blood vessels stained with Hoechst in the tumor microenvironment compared with untreated controls. The combination of LEN 3 mg/kg + cisplatin + ICIs (anti-PD-1+anti-CTLA4) significantly inhibited tumor growth compared with combinations of cisplatin + ICIs. Conclusion: Our findings show that LEN 3 mg/kg enhances the antitumor activity of cisplatin in the LL/2 mouse Lewis lung carcinoma tumor model; and LEN 3 mg/kg + cisplatin + ICIs shows greater antitumor activity compared with cisplatin + ICIs. The results of the IHC analysis suggest that LEN 3 mg/kg inhibits angiogenesis. Conversely, LEN 3 mg/kg increases functional vessels in tumor microenvironments. This situation could enhance combination antitumor activity of chemotherapy, and also chemotherapy + ICIs combination treatment. Further analysis of the mechanism of action of LEN 3 mg/kg in combination with chemotherapy + ICIs is warranted. Citation Format: Yu Kato, Yoichi Ozawa, Keito Adachi, Masahiko Kume, Yusuke Adachi, Yudai Narita, Megumi Kuronishi, Junji Matsui, Akira Yokoi, Yasuhiro Funahashi. The effect of lenvatinib in combination with chemotherapy plus immune checkpoint inhibitors on tumor microenvironments in the mouse syngeneic tumor model. [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 5124.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".