Dataset Information

Combination Foretinib and Anti-PD-1 Antibody Immunotherapy for Colorectal Carcinoma.

ABSTRACT: Immune checkpoint inhibitors have achieved unprecedented success in cancer immunotherapy. However, the overall response rate to immune checkpoint inhibitor therapy for many cancers is only between 20 and 40%, and even less for colorectal cancer (CRC) patients. Thus, there is an urgent need to develop an efficient immunotherapeutic strategy for CRC. Here, we developed a novel CRC combination therapy consisting of a multiple receptor tyrosine kinase inhibitor (Foretinib) and anti-PD-1 antibody. The combination therapy significantly inhibited tumor growth in mice, led to improved tumor regression without relapse (83% for CT26 tumors and 50% for MC38 tumors) and prolonged overall survival. Mechanistically, Foretinib caused increased levels of PD-L1 via activating the JAK2-STAT1 pathway, which could improve the effectiveness of the immune checkpoint inhibitor. Moreover, the combination therapy remodeled the tumor microenvironment and enhanced anti-tumor immunity by further increasing the infiltration and improving the function of T cells, decreasing the percentage of tumor-associated macrophages (TAMs) and inhibiting their polarization toward the M2 phenotype. Furthermore, the combination therapy inhibited the metastasis of CT26-Luc tumors to the lung in BALB/c mouse by reducing proportions of regulatory T-cells, TAMs and M2 phenotype TAMs in their lungs. This study suggests that a novel combination therapy utilizing both Foretinib and anti-PD-1 antibody could be an effective combination strategy for CRC immunotherapy.

SUBMITTER: Fu Y

PROVIDER: S-EPMC8298272 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Combination Foretinib and Anti-PD-1 Antibody Immunotherapy for Colorectal Carcinoma.

Fu Yuyin Y Peng Yujia Y Zhao Shengyan S Mou Jun J Zeng Lishi L Jiang Xiaohua X Yang Chengli C Huang Cheng C Li Yuyan Y Lu Yin Y Wu Mengdan M Yang Yanfang Y Kong Ting T Lai Qinhuai Q Wu Yangping Y Yao Yuqin Y Wang Yuxi Y Gou Lantu L Yang Jinliang J

Frontiers in cell and developmental biology 20210708

Immune checkpoint inhibitors have achieved unprecedented success in cancer immunotherapy. However, the overall response rate to immune checkpoint inhibitor therapy for many cancers is only between 20 and 40%, and even less for colorectal cancer (CRC) patients. Thus, there is an urgent need to develop an efficient immunotherapeutic strategy for CRC. Here, we developed a novel CRC combination therapy consisting of a multiple receptor tyrosine kinase inhibitor (Foretinib) and anti-PD-1 antibody. Th ...[more]

PMID: 34307367

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Project description:ObjectiveThe objective of this study is to build and verify the performance of machine learning-based ultrasomics in predicting the objective response to combination therapy involving a tyrosine kinase inhibitor (TKI) and anti-PD-1 antibody for individuals with unresectable hepatocellular carcinoma (HCC). Radiomic features can reflect the internal heterogeneity of the tumor and changes in its microenvironment. These features are closely related to pathological changes observed in histology, such as cellular necrosis and fibrosis, providing crucial non-invasive biomarkers to predict patient treatment response and prognosis.MethodsClinical, pathological, and pre-treatment ultrasound image data of 134 patients with recurrent unresectable or advanced HCC who treated with a combination of TKI and anti-PD-1 antibody therapy at Henan Provincial People's Hospital and the First Affiliated Hospital of Zhengzhou University between December 2019 and November 2023 were collected and retrospectively analyzed. Using stratified random sampling, patients from the two hospitals were assigned to training cohort (n = 93) and validation cohort (n = 41) at a 7:3 ratio. After preprocessing the ultrasound images, regions of interest (ROIs) were delineated. Ultrasomic features were extracted from the images for dimensionality reduction and feature selection. By utilizing the extreme gradient boosting (XGBoost) algorithm, three models were developed: a clinical model, an ultrasomic model, and a combined model. By analyzing the area under the receiver operating characteristic (ROC) curve (AUC), specificity, sensitivity, and accuracy, the predicted performance of the models was evaluated. In addition, we identified the optimal cutoff for the radiomic score using the Youden index and applied it to stratify patients. The Kaplan-Meier (KM) survival curves were used to examine differences in progression-free survival (PFS) between the two groups.ResultsTwenty ultrasomic features were selected for the construction of the ultrasomic model. The AUC of the ultrasomic model for the training cohort and validation cohort were 0.999 (95%CI: 0.997-1.000) and 0.828 (95%CI: 0.690-0.966), which compared significant favorably to those of the clinical model [AUC = 0.876 (95%CI: 0.815-0.936) for the training cohort, 0.766 (95%CI: 0.597-0.935) for the validation cohort]. Compared to the ultrasomic model, the combined model demonstrated comparable performance within the training cohort (AUC = 0.977, 95%CI: 0.957-0.998) but higher performance in the validation cohort (AUC = 0.881, 95%CI: 0.758-1.000). However, there was no statistically significant difference (p > 0.05). Furthermore, ultrasomic features were associated with PFS, which was significantly different between patients with radiomic scores (Rad-score) greater than 0.057 and those with Rad-score less than 0.057 in both the training (HR = 0.488, 95% CI: 0.299-0.796, p = 0.003) and validation cohorts (HR = 0.451, 95% CI: 0.229-0.887, p = 0.02).ConclusionThe ultrasomic features demonstrates excellent performance in accurately predicting the objective response to TKI in combination with anti-PD-1 antibody immunotherapy among patients with unresectable or advanced HCC.

Dataset Information

Combination Foretinib and Anti-PD-1 Antibody Immunotherapy for Colorectal Carcinoma.

Publications

Combination Foretinib and Anti-PD-1 Antibody Immunotherapy for Colorectal Carcinoma.

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