Potential roles and targeted therapy of the CXCLs/CXCR2 axis in cancer and inflammatory diseases

The chemokine receptor CXCR2 and its ligands are implicated in the progression of tumours and various inflammatory diseases. Activation of the CXCLs/CXCR2 axis activates multiple signalling pathways, including the PI3K, p38/ERK, and JAK pathways, and regulates cell survival and migration. The CXCLs/CXCR2 axis plays a vital role in the tumour microenvironment and in recruiting neutrophils to inflammatory sites. Extensive infiltration of neutrophils during chronic inflammation is one of the most important pathogenic factors in various inflammatory diseases. Chronic inflammation is considered to be closely correlated with initiation of cancer. In addition, immunosuppressive effects of myeloid-derived suppressor cells (MDSCs) against T cells attenuate the anti-tumour effects of T cells and promote tumour invasion and metastasis. Over the last several decades, many therapeutic strategies targeting CXCR2 have shown promising results and entered clinical trials. In this review, we focus on the features and functions of the CXCLs/CXCR2 axis and highlight its role in cancer and inflammatory diseases. We also discuss its potential use in targeted therapies.     

Quantitative Biomarkers for Prediction of Epidermal Growth Factor Receptor Mutation in Non-Small Cell Lung Cancer

OBJECTIVES: To predict epidermal growth factor receptor (EGFR) mutation status using quantitative radiomic biomarkers and representative clinical variables. METHODS: The study included 180 patients diagnosed as of non-small cell lung cancer (NSCLC) with their pre-therapy computed tomography (CT) scans. Using a radiomic method, 485 features that reflect the heterogeneity and phenotype of tumors were extracted. Afterwards, these radiomic features were used for predicting epidermal growth factor receptor (EGFR) mutation status by a least absolute shrinkage and selection operator (LASSO) based on multivariable logistic regression. As a result, we found that radiomic features have prognostic ability in EGFR mutation status prediction. In addition, we used radiomic nomogram and calibration curve to test the performance of the model. RESULTS: Multivariate analysis revealed that the radiomic features had the potential to build a prediction model for EGFR mutation. The area under the receiver operating characteristic curve (AUC) for the training cohort was 0.8618, and the AUC for the validation cohort was 0.8725, which were superior to prediction model that used clinical variables alone. CONCLUSION: Radiomic features are better predictors of EGFR mutation status than conventional semantic CT image features or clinical variables to help doctors to decide who need EGFR tyrosine kinase inhibitor (TKI) treatment.     

Diagnosis of Distant Metastasis of Lung Cancer: Based on Clinical and Radiomic Features

OBJECTIVES: To analyze the distant metastasis possibility based on computed tomography (CT) radiomic features in patients with lung cancer. METHODS: This was a retrospective analysis of 348 patients with lung cancer enrolled between 2014 and February 2015. A feature set containing clinical features and 485 radiomic features was extracted from the pretherapy CT images. Feature selection via concave minimization (FSV) was used to select effective features. A support vector machine (SVM) was used to evaluate the predictive ability of each feature. RESULTS: Four radiomic features and three clinical features were obtained by FSV feature selection. Classification accuracy by the proposed SVM with SGD method was 71.02%, and the area under the curve was 72.84% with only the radiomic features extracted from CT. After the addition of clinical features, 89.09% can be achieved. CONCLUSION: The radiomic features of the pretherapy CT images may be used as predictors of distant metastasis. And it also can be used in combination with the patient's gender and tumor T and N phase information to diagnose the possibility of distant metastasis in lung cancer.