Tumor microenvironments (TMEs) are closely related to tumor resistance. TMEs are divided into cellular components and acellular components. The cellular components include tumor-associated macrophages, tumor-associated fibroblasts, mesenchymal stem cells, etc., which can enhance tumor resistance through recruitment and secretion of a variety of protective cytokines; acellular components such as extracellular matrix, hypoxia and acidification, etc., can mediate drug resistance by constructing physical barriers, affecting tumor cell growth and metabolism. Studying the mechanisms of TME-mediated drug resistance and reshaping TMEs can provide new strategies for anti-tumor therapy.

Objective:To construct and validate a nomogram model based on clinical factors and PET/CT metabolic parameters of 18F-FDG for predicting epidermal growth factor receptor (EGFR) mutations in lung adenocarcinoma. Methods:From January 2014 to January 2019, 114 patients (59 males, 55 females, age (60.0±10.8) years) with lung adenocarcinoma in the First Affiliated Hospital of Harbin Medical University were retrospectively enrolled. Clinical data (smoking status, tumor location, clinical stage and carcinoembryonic antigen (CEA) level), 18F-FDG PET/CT metabolic parameters (SUV max, metabolic tumor volume (MTV) and total lesion glycolysis (TLG)) and EGFR mutation status were analyzed. Patients were divided into training group (80 cases) and validation group (34 cases). In the training group, univariate analyses (independent-sample t test, Wilcoxon rank sum test, χ2 test or Fisher′s exact probability method) were used for categorical variables. Variables that showed significant differences between EGFR mutation group and wild type group were selected. Variance inflation factors (VIF) were calculated and the collinearity variables were deleted, and a nomogram model of optimal logistic model was constructed based on Akaike information criterion (AIC). The effect of the model was evaluated by the concordance index (C-index), sensitivity, specificity, accuracy, calibration and decision curve analysis (DCA) in the training group and the validation group. Results:Among 114 patients, 56 were with EGFR mutations and 58 were with EGFR wild type. In the training group, there were significant differences in gender (male/female: 14/26 vs 25/15; χ2=6.05, P=0.014), smoking status (with/without smoking history: 4/36 vs 22/18; χ2=18.46, P<0.001) and SUV max (5.72(3.90, 8.32) vs 8.09(4.56, 12.55); W=1 045.50, P=0.018) between EGFR mutation group and wild type group. However, there were no significant differences in other factors ( t=-0.54, χ2 values: 0.20 and 0.20, W values: 921.50 and 983.00, all P>0.05). The VIF of gender, smoking status and SUV max were all less than 10, and the nomogram model with three factors showed the minimum AIC (90.06). In the training group, C-index value of the model was 0.798 (95% CI: 0.699-0.897), with the sensitivity of 85.0%(34/40), the specificity of 70.0%(28/40) and the accuracy of 77.5%(62/80). In the validation group, C-index value was 0.854(95% CI: 0.725-0.984), with the sensitivity of 13/16, the specificity of 14/18, and the accuracy of 79.4%(27/34). The calibration curve and the goodness of fit test showed good calibration, and DCA showed that the model could benefit patients clinically within a large risk threshold range (training group: 0-0.59, validation group: 0-0.65). Conclusion:The nomogram model based on gender, smoking status and SUV max can be used to easily predict EGFR mutation status in lung adenocarcinoma.

BACKGROUND: Advanced epidermal growth factor receptor (EGFR)-mutant lung adenocarcinoma had a high overall incidence of brain metastasis during the full course, and local brain radiotherapy combined with systemic targeted therapy may be a better strategy. This study aimed to identify the prognostic factors of EGFR-mutant brain-metastatic lung adenocarcinoma patients who received EGFR-tyrosine kinase inhibitors (EGFR-TKIs) in combination with gamma knife radiosurgery. METHODS: Retrospective analysis of EGFR-mutant lung adenocarcinoma patients with brain metastases which developed at initial diagnosis or during EGFR-TKIs treatment period were performed. Intracranial progression free survival (PFS) was statistically analyzed between different subgroups to find out the prognostic factors including gender, age, smoking history, extracranial metastasis, EGFR mutation type, size and number of intracranial lesions, carcino-embryonic antigen (CEA) level, lung-molGPA score and so on. RESULTS: A total of 74 EGFR-mutant brain-metastatic lung adenocarcinoma patients were enrolled in this study, with median intracranial PFS of 14.7 months. One-year intracranial-progression-free rate was 58.5%, and two-year rate was 22.2%. Univariate survival analysis showed that patients with lower CEA level at initial diagnosis (<10 ng/L)(16.9 months vs 12.6 months, P=0.012) and smaller intracranial lesions (<2 cm)(15.4 months vs 10.8 months, P=0.021) and higher lung-molGPA score (>3)(15 months vs 12.6 months, P=0.041) were prone to have a superior intracranial PFS. Multivariate analysis showed that CEA≥10 ng/mL and intracranial lesion≥2 cm were the independent risk factors of intracranial PFS. CONCLUSIONS EGFR-TKIs in combination with gamma knife radiosurgery was an efficient treatment option to control the cranial tumor lesion. CEA≥10 μg/L at initial diagnosis and intracranial lesion≥2 cm were the risk factors of EGFR-mutant brain-metastatic lung adenocarcinoma patients receiving EGFR-TKIs in combination with gamma knife radiosurgery.
Adenocarcinoma of Lung ; drug therapy ; pathology ; radiotherapy ; therapy ; Adult ; Aged ; Brain Neoplasms ; secondary ; Combined Modality Therapy ; ErbB Receptors ; antagonists & inhibitors ; genetics ; Female ; Humans ; Male ; Middle Aged ; Mutation ; Prognosis ; Protein Kinase Inhibitors ; pharmacology ; therapeutic use ; Radiosurgery ; Retrospective Studies