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IMPORTANCERecommendations for adjuvant therapy after surgical resection of lung adenocarcinoma (LUAD) are based solely on TNM classification but are agnostic to genomic and high-risk clinicopathologic factors. Creation of a prediction model that integrates tumor genomic and clinicopathologic factors may better identify patients at risk for recurrence.OBJECTIVE To identify tumor genomic factors independently associated with recurrence, even in the presence of aggressive, high-risk clinicopathologic variables, in patients with completely resected stages I to III LUAD, and to develop a computational machine-learning prediction model (PRecur) to determine whether the integration of genomic and clinicopathologic features could better predict risk of recurrence, compared with the TNM system. DESIGN, SETTING, AND PARTICIPANTSThis prospective cohort study included 426 patients treated from January 1, 2008, to December 31, 2017, at a single large cancer center and selected in consecutive samples. Eligibility criteria included complete surgical resection of stages I to III LUAD, broad-panel next-generation sequencing data with matched clinicopathologic data, and no neoadjuvant therapy. External validation of the PRecur prediction model was performed using The Cancer Genome Atlas (TCGA). Data were analyzed from 2014 to 2018. MAIN OUTCOMES AND MEASURESThe study end point consisted of relapse-free survival (RFS), estimated using the Kaplan-Meier approach. Associations among clinicopathologic factors, genomic alterations, and RFS were established using Cox proportional hazards regression. The PRecur prediction model integrated genomic and clinicopathologic factors using gradient-boosting survival regression for risk group generation and prediction of RFS. A concordance probability estimate (CPE) was used to assess the predictive ability of the PRecur model. RESULTSOf the 426 patients included in the analysis (286 women [67%]; median age at surgery, 69 [interquartile range, 62-75] years), 318 (75%) had stage I cancer. Association analysis showed that alterations in SMARCA4 (clinicopathologic-adjusted hazard ratio [HR], 2.44; 95% CI, 1.03-5.77; P = .042) and TP53 (clinicopathologic-adjusted HR, 1.73; 95% CI, 1.09-2.73; P = .02) and the fraction of genome altered (clinicopathologic-adjusted HR, 1.03; 95% CI, 1.10-1.04; P = .005) were independently associated with RFS. The PRecur prediction model outperformed the TNM-based model (CPE, 0.73 vs 0.61; difference, 0.12 [95% CI, 0.05-0.19]; P < .001) for prediction of RFS. To validate the prediction model, PRecur was applied to the TCGA LUAD data set (n = 360), and a clear separation of risk groups was noted (log-rank statistic, 7.5; P = .02), confirming external validation. CONCLUSIONS AND RELEVANCEThe findings suggest that integration of tumor genomics and clinicopathologic features improves risk stratification and prediction of recurrence after surgical resection of early-stage LUAD. Improved identification of patients at risk for recurrence could enrich and enhance accrual to adjuv...

Objective: Comparative survival between neoadjuvant chemotherapy (NC) and adjuvant chemotherapy (AC) for patients with cT2-4N0-1M0 non-small cell lung cancer (NSCLC) has not been extensively studied.Methods: Patients with cT2-4N0-1M0 NSCLC who received platinum-based chemotherapy were retrospectively identified. Exclusion criteria included stage IV disease, induction radiotherapy, and targeted therapy. The primary endpoint was disease-free survival (DFS). Secondary endpoints were overall survival (OS), chemotherapy tolerance, and ability of Response Evaluation Criteria In Solid Tumors (RECIST) response to predict survival. Survival was estimated using the Kaplan-Meier method, compared using the log-rank test and Cox proportional hazards models, and stratified using matched pairs following propensity score-matching.

Purpose: The majority of broad-panel tumor genomic profiling has used a gene-centric approach, although much of that data is unused in clinical decision making. We hypothesized that a pathway-centric approach using next-generation sequencing (NGS), combined with conventional clinicopathologic features, may better predict disease-free survival (DFS) in early stage lung adenocarcinoma.Experimental Design: Utilizing our prospectively maintained database, we analyzed 492 patients with primary, untreated, completely surgically resected lung adenocarcinoma. Ten canonical pathways were analyzed using broadpanel NGS. The correlations of DFS and number (and type) of pathway (NPA) were analyzed using the Kaplan-Meier method and log-rank test. Associations between altered pathways and clinicopathologic variables, as well as identification of actionable therapeutic strategies were explored.Results: Median NPA for the cohort was two (range, 0-5). Smoking status, solid morphologic appearance on preoperative CT, maximal standardized uptake value, pathologic tumor size, aggressive histologic subtype, lymphovascular invasion, visceral pleural invasion, and positive lymph nodes were significantly associated with NPA (P < 0.05). Of 543 actionable genetic alterations identified, 455 (84%) were within the RTK/RAS pathway. A total of 86 tumors had actionable therapeutic genomic alterations in >1 pathway. On multivariable analysis, higher NPA was significantly associated with worse DFS (HR, 1.31; P ¼ 0.014).Conclusions: NPA and specific pathway alterations are associated with clinicopathologic features in patients with surgically resected lung adenocarcinoma. Cell cycle, Hippo, TGFb, and p53 pathway alterations are associated with poor DFS. Finally, NPA is an independent risk factor for poor DFS in our cohort.See related commentary by Blakely, p. 7269

In patients undergoing R0 lobectomy with pN0 lung adenocarcinoma, pT stage and lymphovascular invasion were associated with distant recurrence and decreased DFS. These observations support the inclusion of these patients in future clinical trials investigating adjuvant targeted and immunotherapies.