The most recent version of the European Society for Medical Oncology (ESMO) Clinical Practice Guidelines for the diagnosis, treatment and follow-up of metastatic non-small-cell lung cancer (NSCLC) was published in 2016. At the ESMO Asia Meeting in November 2017 it was decided by both ESMO and the Chinese Society of Clinical Oncology (CSCO) to convene a special guidelines meeting immediately after the Chinese Thoracic Oncology Group Annual Meeting 2018, in Guangzhou, China. The aim was to adapt the ESMO 2016 guidelines to take into account the ethnic differences associated with the treatment of metastatic NSCLC cancer in Asian patients. These guidelines represent the consensus opinions reached by experts in the treatment of patients with metastatic NSCLC representing the oncological societies of China (CSCO), Japan (JSMO), Korea (KSMO), Malaysia (MOS), Singapore (SSO) and Taiwan (TOS). The voting was based on scientific evidence, and was independent of both the current treatment practices and the drug availability and reimbursement situations in the six participating Asian countries. During the review process, the updated ESMO 2018 Clinical Practice Guidelines for metastatic NSCLC were released and were also considered, during the final stages of the development of the Pan-Asian adapted Clinical Practice Guidelines.

Erlotinib improves QoL compared with standard chemotherapy in the first-line treatment of patients with EGFR mutation-positive advanced NSCLC.

Long non-coding RNAs (lncRNAs) have proved to act as crucial biomarkers in tumors. Novel biomarkers in non-small cell lung cancer (NSCLC) need to be investigated badly. To identify the differentially expressed lncRNAs between NSCLC tissue and adjacent tissue, microarray analysis was performed. lncRNA SLC16A1-AS1 was significantly less expressed in NSCLC tissue than that in adjacent tissue. Gain-of-function experiments was performed to determine the biological functions of SLC16A1-AS. In situhybridization and survival analysis were applied in lung cancer tissue samples to determine the prognostic role of SLC16A1-AS1. It was showed that SLC16A1-AS1 was remarkably downregulated in NSCLC tissues and cell lines. Functionally, SLC16A1-AS1 overexpression could inhibit the viability and proliferation of lung cancer cell, block the cell cycle and promote cell apoptosis in vitro which may result from reduced phosphorylation of rat sarcoma (RAS)/ proto-oncogene serine/threonine-protein kinase (RAF)/ mitogen-activated protein kinase kinase (MEK)/ extracellular regulated protein kinases (ERK) pathway caused by elevated expression of SLC16A1-AS1. Clinical sample analysis showed that SLC16A1-AS1 had a favorable impact on the overall survival and progression-free survival of patients with NSCLC. Our results suggested that SLC16A1-AS1 may act as a potential biomarker for patients with NSCLC.

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PurposeThe present study was designed to explore the prognostic value of preoperative inflammatory and nutritional biomarkers in stage III gastric cancer (GC) patients with adjuvant chemotherapy and to develop a novel scoring system called the inflammatory-nutritional prognostic score (INPS).MethodsA total of 513 patients with pathological stage III GC undergoing radical gastrectomy followed by adjuvant chemotherapy from 2010 to 2017 were enrolled in the study. Clinicopathological characteristics and blood test parameters of individual patients were collected. The least absolute shrinkage and selection operator (LASSO) Cox regression model was used for feature selection to construct INPS. Survival curves were generated using the Kaplan-Meier method with log-rank tests. The nomogram was generated based on the result of the multivariate analysis using Cox’s proportional hazards model. The model was assessed by the concordance index (C-index) and was internally validated by bootstraps.ResultsAccording to the results of Lasso Cox regression and K-M survival curves, INPS was determined as follows: a low body mass index (BMI) (<23 kg/m2), a low prealbumin (<180 mg/L), a high neutrophil-lymphocyte ratio (NLR) (≥2.7), a high platelet-lymphocyte ratio (PLR) (≥209.4), a low lymphocyte-monocyte ratio (LMR) (<2.8), and a low prognostic nutritional index (PNI) (<45.1); each were scored as 1, and the remaining values were scored as 0. The individual scores were then summed up to construct the INPS and further divided into 4 groups: Low Risk (INPS 0); Low-medium Risk (INPS 1); High-medium Risk (INPS 2-4); and High Risk (INPS 5-6). In multivariate analysis, INPS was an independent predictor of overall survival (OS) in stage III GC, with the 5-year OS rates of 70.8%, 57.4%, 41.5%, and 30.6%, respectively. The nomogram based on INPS and other independent predictors (gender, pT stage, pN stage, lymphovascular invasion, and CEA level) showed good predicting performance with a C-index of 0.707, which was superior to the TNM stage alone (C-index 0.645, p=0.008) and was internally validated with the corrected C-index of 0.693.ConclusionPreoperative INPS was an independent prognostic factor of stage III GC patients with radical surgery followed by adjuvant chemotherapy. The nomogram based on INPS may serve as a simple and potential model in risk stratification and guiding treatment strategies in clinical practice.

Purpose To test the reliability and validity of the Chinese version of the Cancer Stigma Scale (CASS). Methods After translation, back-translation and cross-cultural adaptation of the CASS into Chinese (C-CASS), a random online survey of the general population in China was conducted. Reliability was analyzed by internal consistency (Cronbach’s α) and construct validity was analyzed by confirmatory factor analysis. The C-CASS was evaluated in a sample of 382 non-cancer patients through online format. Results The study found that the C-CASS had satisfactory internal reliability (Cronbach’s α of the overall scale and six components was 0.88 and 0.70–0.89, respectively). Confirmatory factor analysis confirmed the six-factor structure (χ2/df = 2.2, GFI = 0.91, CFI = 0.94, RMSEA = 0.056, SRMR = 0.065). Younger individuals and those who had less knowledge of cancer showed more negative attitudes towards cancer. Conclusion The C-CASS had adequate internal consistency, reliability and indices of model fit, allowing its feasible use to assess levels of cancer stigma in Chinese populations.

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ObjectivePostoperative complications adversely affected the prognosis in patients with gastric cancer. This study intends to investigate the feasibility of using machine-learning model to predict surgical outcomes in patients undergoing gastrectomy.MethodsIn this study, cancer patients who underwent gastrectomy at Shanghai Rui Jin Hospital in 2017 were randomly assigned to a development or validation cohort in a 9:1 ratio. A support vector classification (SVC) model to predict surgical outcomes in patients undergoing gastrectomy was developed and further validated.ResultsA total of 321 patients with 32 features were collected. The positive and negative outcomes of postoperative complication after gastrectomy appeared in 100 (31.2%) and 221 (68.8%) patients, respectively. The SVC model was constructed to predict surgical outcomes in patients undergoing gastrectomy. The accuracy of 10-fold cross validation and external verification was 78.17% and 78.12%, respectively. Further, an online web server has been developed to share the SVC model for machine-learning-assisted prediction of surgical outcomes in patients undergoing gastrectomy in the future procedures, which is accessible at the web address: http://47.100.47.97:5005/r_model_prediction.ConclusionsThe SVC model was a useful predictor for measuring the risk of postoperative complications after gastrectomy, which may help stratify patients with different overall status for choice of surgical procedure or other treatments. It can be expected that machine-learning models in cancer informatics research are possibly shareable and accessible via web address all over the world.