ObjectiveTo assess the association between PD-L1 expression and driver gene mutations in patients with non-small-cell lung cancer (NSCLC).MethodWe performed a meta-analysis of 26 studies (7541 patients) which were published from 2015 to 2017. Pooled odds ratios (ORs) with 95% confidence intervals (CI) were calculated to describe the correlation. Subgroup analysis was performed based on population characteristics, types of PD-L1 antibodies and quality of individual studies.ResultsA lower frequency of PD-L1 positivity was observed in NSCLCs harboring EGFR mutation (OR: 0.64, 95% CI, 0.45–0.91, p = 0.014). A negative correlation was also found at 1% (OR: 0.35, 95% CI, 0.22–0.55, p = 0.000) and 50% (OR: 0.33, 95% CI, 0.14–0.81, p = 0.015) cutoff for PD-L1 positive, elderly age group (OR: 0.56, 95% CI, 0.35–0.89, p = 0.013), female dominant group (OR: 0.55, 95% CI, 0.29–0.94, p = 0.030) and smoker dominant group (OR: 0.52, 95% CI, 0.29–0.96, p = 0.035). No significant differences in PD-L1 expression were observed among patients with different ALK, BRAF, HER2, PIK3CA status and MET expression level. Higher level of PD-L1 was found in tumors with KRAS mutation (OR: 1.45, 95% CI, 1.18–1.80, p = 0.001). PD-L1 expression level was not significantly different between triple (EGFR/ALK/KRAS) wild type NSCLCs and those with EGFR/ALK/KRAS mutation.ConclusionsPD-L1 expression in EGFR mutated NSCLCs were lower than those in EGFR wild type NSCLCs, while tumors with KRAS mutation showed higher levels of PD-L1.

Background: More than one-third of lung cancer worldwide occurring in China. However, the clinical profiles of lung cancer patients in the mainland of China are rarely reported and largely unknown. The Results: From 2011 to 2015, aggregately 5,779 patients, including 3,719 males and 2,060 females, were diagnosed as lung cancer. The major histologic subtypes of lung cancer were adenocarcinoma (ADC, 60.0%), squamous cell carcinoma (SCC, 25.6%), small cell lung cancer (SCLC, 8.5%), large cell carcinoma (0.6%), adenosquamous carcinoma (1%), other non-small cell carcinoma (1.6%) and unclassified or rare carcinoma (2.8%). ADC proportion of female was much higher than that of male. A higher proportion of advanced stage (stage IIIB, IV) of lung cancer existed in patients who were admitted to hospital due to respiratory or cancer related symptoms (RCRS) than those without RCRS. Smoking rate in male patients reached 80.2%, while it was only 2.7% in females. EGFR mutation existed in 66% of female and 37% of male patients with ADC.Conclusions: This study demonstrates the clinicopathologic characteristics of lung cancer patients from East China, including histologic composition, staging proportion, smoking prevalence and gene mutation status. During the past 5 years, the proportion of ADC has increased gradually whereas SCC decreased.

Background Lymph node status of clinical T1 (diameter ≤ 3 cm) lung cancer largely affects the treatment strategies in the clinic. In order to assess lymph node status before operation, we aim to develop a noninvasive predictive model using preoperative clinical information. Methods We retrospectively reviewed 924 patients (development group) and 380 patients (validation group) of clinical T1 lung cancer. Univariate analysis followed by polytomous logistic regression was performed to estimate different risk factors of lymph node metastasis between N1 and N2 diseases. A predictive model of N2 metastasis was established with dichotomous logistic regression, externally validated and compared with previous models. Results Consolidation size and clinical N stage based on CT were two common independent risk factors for both N1 and N2 metastases, with different odds ratios. For N2 metastasis, we identified five independent predictors by dichotomous logistic regression: peripheral location, larger consolidation size, lymph node enlargement on CT, no smoking history, and higher levels of serum CEA. The model showed good calibration and discrimination ability in the development data, with the reasonable Hosmer-Lemeshow test (p = 0.839) and the area under the ROC being 0.931 (95% CI: 0.906-0.955). When externally validated, the model showed a great negative predictive value of 97.6% and the AUC of our model was better than other models. Conclusion In this study, we analyzed risk factors for both N1 and N2 metastases and built a predictive model to evaluate possibilities of N2 metastasis of clinical T1 lung cancers before the surgery. Our model will help to select patients with low probability of N2 metastasis and assist in clinical decision to further management.