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Immunomodulatory activity of lenvatinib contributes to antitumor activity in the Hepa1-6 hepatocellular carcinoma model.

ABSTRACT: Angiogenesis inhibitors such as lenvatinib and sorafenib, and an immune checkpoint inhibitor (ICI), nivolumab, are used for anticancer therapies against advanced hepatocellular carcinoma (HCC). Combination treatments comprising angiogenesis inhibitors plus ICIs are promising options for improving clinical benefits in HCC patients, and clinical trials are ongoing. Here, we investigated the antitumor and immunomodulatory activities of lenvatinib (a multiple receptor tyrosine kinase inhibitor targeting vascular endothelial growth factor receptor 1-3, fibroblast growth factor receptor 1-4, platelet-derived growth factor receptor ?, KIT and RET) and the combined antitumor activity of lenvatinib plus anti-programmed cell death 1 (PD-1) antibody in the Hepa1-6 mouse HCC syngeneic model. We found that the antitumor activities of lenvatinib and sorafenib were not different in immunodeficient mice, but lenvatinib showed more potent antitumor activity than sorafenib in immunocompetent mice. The antitumor activity of lenvatinib was greater in immunocompetent mice than in immunodeficient mice and was attenuated by CD8+ T cell depletion. Treatment with lenvatinib plus anti-PD-1 antibody resulted in more tumor regression and a higher response rate compared with either treatment alone in immunocompetent mice. Single-cell RNA sequencing analysis demonstrated that treatment with lenvatinib with or without anti-PD-1 antibody decreased the proportion of monocytes and macrophages population and increased that of CD8+ T cell populations. These data suggest that lenvatinib has immunomodulatory activity that contributes to the antitumor activity of lenvatinib and enhances the antitumor activity in combination treatment with anti-PD-1 antibody. Combination treatment of lenvatinib plus anti-PD-1 antibody therefore warrants further investigation against advanced HCC.

SUBMITTER: Kimura T

PROVIDER: S-EPMC6272102 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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Immunomodulatory activity of lenvatinib contributes to antitumor activity in the Hepa1-6 hepatocellular carcinoma model.

Kimura Takayuki T Kato Yu Y Ozawa Yoichi Y Kodama Kotaro K Ito Junichi J Ichikawa Kenji K Yamada Kazuhiko K Hori Yusaku Y Tabata Kimiyo K Takase Kazuma K Matsui Junji J Funahashi Yasuhiro Y Nomoto Kenichi K

Cancer science 20181116 12

Angiogenesis inhibitors such as lenvatinib and sorafenib, and an immune checkpoint inhibitor (ICI), nivolumab, are used for anticancer therapies against advanced hepatocellular carcinoma (HCC). Combination treatments comprising angiogenesis inhibitors plus ICIs are promising options for improving clinical benefits in HCC patients, and clinical trials are ongoing. Here, we investigated the antitumor and immunomodulatory activities of lenvatinib (a multiple receptor tyrosine kinase inhibitor targe ...[more]

PMID: 30447042

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Project description:BackgroundLenvatinib is a first-line agent for advanced hepatocellular carcinoma (HCC), but individual responses to treatment are highly heterogeneous. The aim of this study was to investigate the clinical parameters that influence the efficacy of Lenvatinib and to develop a prognostic model.MethodsWe retrospectively enrolled 333 Lenvatinib-treated patients with HCC with a median age of 57 years. Two hundred nd sixty-three of these patients had BCLC (2022) stage C. The median overall survival (mOS) time within the cohort was 12.1 months, and the median progression-free survival (mPFS) time was 4.7 months. Univariate Cox regression, best subset regression, and Lasso regression were used to screen primary variables for possible contribution to OS, multivariate Cox analysis was used to fit selected models, and the final model was selected using the maximum area under the curve (AUC) and minimum AIC. Receiver operating curves (ROC), calibration curves, and decision curve analysis were plotted to assess model performance, and 5-fold cross-validation was performed for internal validation. X-tile software was used to select the best cutoff points and to divide the study cohort into 3 different risk groups.ResultsSeven variables were included in the final model: BCLC stage, prior transarterial chemoembolization and immunotherapy history, tumor number, prognostic nutritional index, log (alpha-fetoprotein), and log (platelet-to-lymphocyte ratio). We named this final model the "multivariate prognostic model for Lenvatinib" (MPML), and a nomogram was constructed to predict the probability of survival at 6, 9, and 12 months. The MPML had good discrimination, calibration, and applicability. Cross-validation showed mean AUC values of 0.7779, 0.7738, and 0.7871 at 6, 9, and 12 months, respectively. According to nomogram points, mOS time was 21.57, 8.70, and 5.37 months in the low, medium, and high-risk groups, respectively (P < .001), and these differences were also observed in the PFS survival curve (P < .001).ConclusionsThe MPML stratified patients according to baseline clinical characteristics had a strong performance in predicting Lenvatinib efficacy and has the potential for use as an auxiliary clinical tool for individualized decision-making.

Project description:BackgroundLenvatinib monotherapy and combination therapy with immune checkpoint inhibitors (ICI) were widely applied for unresectable hepatocellular carcinoma (uHCC). However, many patients failed to benefit from the treatments. A prognostic model was needed to predict the treatment outcomes and guide clinical decisions.Methods304 patients receiving lenvatinib monotherapy or lenvatinib plus ICI for uHCC were retrospectively included. The risk factors derived from the multivariate analysis were used to construct the predictive model. The C-index and area under the receiver-operating characteristic curve (AUC) were calculated to assess the predictive efficiency.ResultsMultivariate analysis revealed that protein induced by vitamin K absence or antagonist-II (PIVKA-II) (HR, 2.05; P=0.001) and metastasis (HR, 2.07; P<0.001) were independent risk factors of overall survival (OS) in the training cohort. Herein, we constructed a prognostic model called PIMET score and stratified patients into the PIMET-low group (without metastasis and PIVKA-II<600 mAU/mL), PIMET-int group (with metastasis or PIVKA-II>600 mAU/mL) and PIMET-high group (with metastasis and PIVKA-II>600 mAU/mL). The C-index of PIMET score for the survival prediction was 0.63 and 0.67 in the training and validation cohort, respectively. In the training cohort, the AUC of 12-, 18-, and 24-month OS was 0.661, 0.682, and 0.744, respectively. The prognostic performances of the model were subsequently validated. The AUC of 12-, 18-, and 24-month OS was 0.724, 0.726, and 0.762 in the validation cohort. Subgroup analyses showed consistent predictive value for patients receiving lenvatinib monotherapy and patients receiving lenvatinib plus ICI. The PIMET score could also distinguish patients with different treatment responses. Notably, the combination of lenvatinib and ICI conferred survival benefits to patients with PIMET-int or PIMET-high, instead of patients with PIMET-low.ConclusionThe PIMET score comprising metastasis and PIVKA-II could serve as a helpful prognostic model for uHCC receiving lenvatinib monotherapy or lenvatinib plus ICI. The PIMET score could guide the treatment decision and facilitate precision medicine for uHCC patients.

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Immunomodulatory activity of lenvatinib contributes to antitumor activity in the Hepa1-6 hepatocellular carcinoma model.

Publications

Immunomodulatory activity of lenvatinib contributes to antitumor activity in the Hepa1-6 hepatocellular carcinoma model.

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