The clinical significance and functional role of adhesion G protein-coupled receptors (aGPCRs) in breast cancer remain largely unknown. We aimed to comprehensively analyze the prognostic potential of aGPCRs in breast cancer, identify the key prognostic GPCRs, and comprehend their functional roles. A total of 1593 breast patients were analyzed using RNA-seq and clinical data from four independent public datasets, including 1064 patients from TCGA, 198 from GSE7390, 327 from GSE20685, and 104 from GSE42568. We also analyzed the mutated landscape based on exome and copy number alteration data from TCGA. In addition, single-cell RNA-seq (scRNA-seq) data from three breast cancer patients from GSE198745, and data on mammary gland tissue from the Tabula Muris, were used to measure the variation of the candidate GPCRs across diverse cell types. The CellMiner database was used to examine drug sensitivity. Among the 33 aGPCRs, GPR56 was the only significant prognostic factor in all four datasets. High GPR56 expression was significantly lined to worse relapse-free survival and overall survival. GPR56 expression was higher in younger, high-grade, and triple-negative breast cancers. GPR56-related genes were involved in several pathways, including cell division, DNA replication, cell cycle, protein binding, and others. GPR56 also participated in the activation of multiple cancer signaling pathways. High GPR56 expression was correlated with higher levels of several immune cells, immune checkpoints, and immune-related scores. scRNA-seq analysis revealed that GPR56 was mainly expressed in tumor cells, tumor stem cells and endothelial cells. In normal breast tissues, GPR56 expression was higher in basal cell subsets than in luminal epithelial cell subsets, suggesting that GPR56 may interact with breast stem cells. In addition, there were some differences in the mutant landscape between the high and low GPR56 expression groups. Furthermore, GPR56 was significantly associated with sensitivity to multiple antitumor drugs. GPR56 was significantly linked to the molecular pathology and prognosis of breast cancer. GPR56 may serve as a promising novel therapeutic target in breast cancer.

AIM: To develop and evaluate a radiomics composite model for predicting disease-free survival (DFS) in stage I solid lung adenocarcinoma, and compare it to a simple radiomics model. MATERIALS AND METHODS: Patients of pathological stage I solid lung adenocarcinoma treated with lobectomy (n = 119) were enrolled retrospectively. Three hundred and ninety-seven radiomics features per lesion were extracted from enhanced chest computed tomography (CT) imaging. Spearman's correlation coefficient and the LASSO (least absolute shrinkage and selection operator) regression model were used to reduce the dimension and select radiomics features. Univariate or multivariate logistic regression was used to build prediction models. A survival curve based on the radiomics composite model was plotted with Kaplan-Meier survival analysis to stratify the risk of recurrence. The confusion matrix, receiver operating characteristic (ROC) curve, and decision curve analysis were used to evaluate the performance of the prediction models. RESULTS: Recurrence occurred in 22.6% of patients. The survival curve of the radiomics composite model could accurately differentiate high-risk from low-risk patients. In the validation sets, the areas under the ROC curves (AUCs) of the pathological TNM stage (8th IASLC), clinicopathological model, radiomics model, and radiomics composite model were 0.587 (95% confidence interval [CI] 0.502-0.650), 0.629 (95% CI 0.558-0.682), 0.726 (95% CI 0.681-0.770), and 0.849 (95% CI 0.783-0.898), respectively. CONCLUSION: The prognosis of stage I solid lung adenocarcinoma predicted by an individualised radiomics composite model was more accurate than that of the simple radiomics model. (C) 2020 The Royal College of Radiologists. Published by Elsevier Ltd.

Asia-Pacific region is a highly endemic area of Hepatitis B virus (HBV) infection, especially in China. Accordingly, some HBV infected individuals further developed various kind of tumor. PD 1/PD-L1 blockade immunotherapy has been shown to be a promising option to advanced cancer patients. However, immunotherapy related hepatitis had raised the question whether it is safe for the patient with HBV infection and advanced cancer to be treated with anti-PD 1/PD-L1 monoclonal antibody. This was a multicenter, retrospective, propensity-matched study by enrolling and matching PD 1/PD-L1 blockade treated cancer patients with or without HBV infection between November 2016 through December 2019. All patients had received PD 1 and/or PD-L1 monoclonal antibody immunotherapy. Exclusion criteria were as follows: liver cancer, deficiency of liver function, PS≥2. Safety of the immunotherapy was evaluated according to the Common Terminology Criteria Adverse Events (CTCAE) V5.0. Immunotherapy safety would be evaluated until the treatment was completed. Tumor size was measured by computed tomography or magnetic resonance imaging according to Response Evaluation Criteria in Solid Tumors (RECIST) 1.1. The propensity-matching approach was employed to analyze the change of liver function and HBV DNA copy numbers. 89 HBV infected participants (62 men and 27 women; median [range] age, 55 [28-84] years) were enrolled and matched to 267 HBV negative patients from November 2016 to December 2019. Most treatment-emergent adverse events were grade 0 or 1 (n = 72), and only 1 was grade 3. Prior to and after immunotherapy, the titer of Hepatitis B surface antigen and e antigen antibody were consistent. Moreover, there is no significant change of the liver function for all patients, including Alanine aminotransferase (ALT) and Aspartate aminotransferase (AST) as well as HBV DNA copy numbers. For patients with HBV infection and advanced nonliver tumor, anti-PD 1/PD-L1 monoclonal antibody immunotherapy is safe and tolerable.

Neoadjuvant chemoradiotherapy (nCRT) is a standard treatment for locally advanced rectal cancer (LRAC), and tumor regression grading (TRG) system is used to evaluate the therapeutic response, which is essential for formulating treatment and forecasting survival. However, the prediction of TRG is still a large challenge. Here, we aimed to establish a radiomics-based intelligent model to predict TRG pattern for LRAC patients by integrating images of pretreatment MRI and biopsy. All enrolled 784 clinicopathologically confirmed LARC patients (481 in the training cohort and 303 in the validation cohort) were treated with nCRT and surgical resection between Aug 2007 and Nov 2017. The image of MRI and digital biopsy prior to treatment and clinical characteristics of patients were collected. Regions of interest including tumor and peritumor (5-pixel-circum surrounding tumor region) were segmented in anatomical T2-weighted MRI, diffusion-weighted MRI (DWI) and dynamic contrast-enhanced (DCE) MRI using ITK-SNAP software, whereas neoplastic areas in scanned biopsy image were delineated. More than two thousand radiomics features and one thousand cancer cell features were extracted for quantitatively characterizing rectal tumor. Features of utmost significance were selected to establish a prediction model in the training cohort. The prediction model was further examined in an independent validation cohort and the predictive performance was evaluated by receiver operating characteristic (ROC) curves. Utilizing binary classification (TRG0 vs TRG1-3, and TRG0-1 vs TRG2-3), our models yielded an AUC of 0.85 (95% confidence intervals [CI]: 0.80-0.88) and 0.82 (95% CI: 0.79-0.85) in the validation set respectively, both superior to result of MRI or pathology image only. The two models were further integrated into a three-category prediction system classifying patients into TRG0, TRG1, TRG2-3 subgroup with accuracy of 88.2%, 86.5% and 84.7% respectively. By using pretreatment MRI and biopsy, we developed a system of great accuracy in predicting therapeutic response to neoadjuvant chemoradiotherapy of rectal cancer patients, which may be used as an auxiliary method for treatment decision and precision healthcare in clinic.

Aim. To examine the survival of patients with non-small cell lung cancer (NSCLC) and solitary adrenal metastasis that underwent adrenalectomy (ADX), in comparison with the survival of those treated nonoperatively. To investigate potential prognostic factors for survival among these patients.Methods. Retrospective review of inpatient records from 2001 to 2010 were identified 26 patients with NSCLC and solitary adrenal metastasis. Log-rank tests were used to compare the overall survival of patients who underwent both primary resection and ADX and that of patients who were treated conservatively. Clinical, therapeutic, pathologic, primary, and metastatic characteristics were evaluated as potential prognostic factors using univariate and multivariate analyses.Results. Among patients with NSCLC patients and solitary adrenal metastasis, the median overall survival was 11 months (95% confidence interval [ CI], 9.4-12.6 months), and the 1-year survival rate was 51.4% (95% CI, 29.6-73.2%). No significant survival difference was observed between patients who underwent primary and metastasis resection (N.=10) and those treated conservatively (N.=12; P=0.209). Univariate analyses identified Eastern Cooperative Oncology Group performance status (ECOG PS) as the only risk factor for death (P=0.024). A stepwise multivariate Cox analysis retained only ECOG PS (P=0.007; RR=3.57) and pathologic type (P=0.069) in the final model.Conclusion. ECOG PS and histology may be the principal prognostic factors for NSCLC with solitary adrenal metastasis. Primary and metastatic radical resection did not extend the survival of patients with ECOG PS of 3 or N2 disease.