Clinical trials frequently include multiple end points that mature at different times. The initial report, typically based on the primary end point, may be published when key planned co-primary or secondary analyses are not yet available. Clinical Trial Updates provide an opportunity to disseminate additional results from studies, published in JCO or elsewhere, for which the primary end point has already been reported. The randomized, open-label, phase II EVAN study investigated the efficacy (disease-free survival [DFS] and 5-year overall survival [OS]) and safety of erlotinib versus vinorelbine/cisplatin as adjuvant chemotherapy after complete resection (R0) for stage III epidermal growth factor receptor ( EGFR) mutation+ non–small-cell lung cancer. We describe the updated results at the 43-month follow-up. In EVAN, patients were randomly assigned (1:1) to erlotinib (n = 51) or vinorelbine/cisplatin (n = 51). The median follow-up was 54.8 and 63.9 months in the erlotinib and chemotherapy arms, respectively. With erlotinib, the respective 5-year DFS by Kaplan-Meier analysis was 48.2% (95% CI, 29.4 to 64.7) and 46.2% (95% CI, 27.6 to 62.9) in the intention-to-treat and per-protocol populations. The median OS was 84.2 months with erlotinib versus 61.1 months with chemotherapy (hazard ratio, 0.318; 95% CI, 0.151 to 0.670). The 5-year survival rates were 84.8% and 51.1% with erlotinib and chemotherapy, respectively. In whole-exome sequencing analysis, frequent genes with variants co-occurring at baseline were TP53, MUC16, FAM104B, KMT5A, and DNAH9. With erlotinib, a single-nucleotide polymorphism mutation in UBXN11 was associated with significantly worse DFS ( P = .01). To our knowledge, this study is the first to demonstrate clinically meaningful OS improvement with adjuvant erlotinib compared with chemotherapy in R0 stage III EGFR+ non–small-cell lung cancer.

Clinical trials frequently include multiple end points that mature at different times. The initial report, typically based on the primary end point, may be published when key planned co-primary or secondary analyses are not yet available. Clinical Trial Updates provide an opportunity to disseminate additional results from studies, published in JCO or elsewhere, for which the primary end point has already been reported. We previously reported superior symptom control of electronic patient-reported outcome (ePRO)–based symptom management after lung cancer surgery for up to 1 month postdischarge. Here, we present the long-term results (1-12 months) of this multicenter, randomized trial, where patients were assigned 1:1 to receive postoperative ePRO-based symptom management or usual care daily postsurgery, twice weekly postdischarge until 1 month, and at 3, 6, 9, and 12 months postdischarge. Long-term patient-reported outcomes were assessed with MD Anderson Symptom Inventory-Lung Cancer module. Per-protocol analyses were performed with 55 patients in the ePRO group and 57 in the usual care group. At 12 months postdischarge, the ePRO group reported significantly fewer symptom threshold events (any of the five target symptom scored ≥4; median [IQR], 0 [0-0] v 0 [0-1]; P = .040) than the usual care group. From 1 to 12 months postdischarge, the ePRO group consistently reported significantly lower composite scores for physical interference (estimate, –0.86 [95% CI, –1.32 to –0.39]) and affective interference (estimate, –0.70 [95% CI, –1.14 to –0.26]). Early intensive ePRO-based symptom management after lung cancer surgery reduced symptom burden and improved functional status for up to 1 year postdischarge, supporting its integration into standard care.

Accurate classification of adenocarcinoma (AC) and squamous cell carcinoma (SCC) in lung cancer is critical to physicians’ clinical decision-making. Exhaled breath analysis provides a tremendous potential approach in non-invasive diagnosis of lung cancer but was rarely reported for lung cancer subtypes classification. In this paper, we firstly proposed a combined method, integrating K-nearest neighbor classifier (KNN), borderline2-synthetic minority over-sampling technique (borderlin2-SMOTE), and feature reduction methods, to investigate the ability of exhaled breath to distinguish AC from SCC patients. The classification performance of the proposed method was compared with the results of four classification algorithms under different combinations of borderline2-SMOTE and feature reduction methods. The result indicated that the KNN classifier combining borderline2-SMOTE and feature reduction methods was the most promising method to discriminate AC from SCC patients and obtained the highest mean area under the receiver operating characteristic curve (0.63) and mean geometric mean (58.50) when compared to others classifiers. The result revealed that the combined algorithm could improve the classification performance of lung cancer subtypes in breathomics and suggested that combining non-invasive exhaled breath analysis with multivariate analysis is a promising screening method for informing treatment options and facilitating individualized treatment of lung cancer subtypes patients.

Introduction Lung cancer is the most common cancer and the leading cause of cancer death in China, as well as in the world. Late diagnosis is the main obstacle to improving survival. Currently, early detection methods for lung cancer have many limitations, for example, low specificity, risk of radiation exposure and overdiagnosis. Exhaled breath analysis is one of the most promising non-invasive techniques for early detection of lung cancer. The aim of this study is to identify volatile organic compound (VOC) biomarkers in lung cancer and to construct a predictive model for lung cancer based on exhaled breath analysis. Methods and analysis The study will recruit 389 lung cancer patients in one cancer centre and 389 healthy subjects in two lung cancer screening centres. Bio-VOC breath sampler and Tedlar bag will be used to collect breath samples. Gas chromatography-mass spectrometry coupled with solid phase microextraction technique will be used to analyse VOCs in exhaled breath. VOC biomarkers with statistical significance and showing abilities to discriminate lung cancer patients from healthy subjects will be selected for the construction of predictive model for lung cancer. Ethics and dissemination The study was approved by the Ethics Committee of Sichuan Cancer Hospital on 6 April 2017 (No. SCCHEC-02-2017-011). The results of this study will be disseminated in presentations at academic conferences, publications in peer-reviewed journals and the news media. Trial registration number ChiCTR-DOD-17011134; Pre-results.