Identification of nine mutant genes and establishment of three prediction models of organ tropism metastases of non‐small cell lung cancer

• Published in: Cancer medicine (Malden, MA) Vol. 12; no. 3; pp. 3089 - 3100
• Main Authors: Chen, Shuchen, Huang, Wanyi, Liu, Zhenzhen, Jin, Meizi, Li, Jielin, Meng, Lihui, Li, Ting, Diao, Yuzhu, Gao, Hong, Hong, Chengyu, Zheng, Jian, Li, Fei, Zhang, Yue, Bi, Dan, Teng, Lin, Li, Xiaoling
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.02.2023; John Wiley and Sons Inc; Wiley
• Subjects: Age; Carcinoma, Non-Small-Cell Lung - pathology; China; Epidermal growth factor receptors; ErbB-2 protein; gene mutations; Genes; Genomes; Genomics; Humans; Janus kinase 2; Kinases; Liver; Lung cancer; Lung Neoplasms - pathology; Metastases; Metastasis; Mutants; Mutation; Myc protein; Next-generation sequencing; NGS; Non-small cell lung carcinoma; NSCLC; organ tropism metastases; p53 Protein; Patients; Pleural effusion; prediction model; Prediction models; Smad4 protein; Small cell lung carcinoma; Smoking; targeted therapy; Tropism; Tumors; China; targeted therapy; gene mutations; prediction model; NGS; NSCLC; organ tropism metastases
• ISSN: 2045-7634; 2045-7634
• DOI: 10.1002/cam4.5233

Background Most Non‐small cell lung cancer (NSCLC) patients tend to have metastases at the initial diagnosis. However, limited knowledge has been established regarding which factors, are associated with its metastases. This study aims to identify more biomarkers associated with its organ tropism metastasis and to establish models for prediction of its metastatic organs. Methods We performed targeted next‐generation sequencing (NGS) to detect genes related to lung cancer in 272 patients with primary advanced NSCLC from Northeast China. We adopted Fisher test, multivariate logistic regression analysis to identify metastasis‐related gene mutations and to establish prediction models. Results Mutations of EGFR (p = 0.0003, OR = 2.554) (especially EGFR L858R [p = 0.02, OR = 2.009]), ATM (p = 0.008, OR = 11.032), and JAK2 (p = 0.009, OR = Inf) were positively and of TP53 exon4mut (p = 0.001, OR = 0.173) was negatively correlated with lung metastasis, and those of CSF1R (p = 0.01, OR = Inf), KIT (p = 0.03, OR = 4.746), MYC (p = 0.05, OR = 7.938), and ERBB2 (p = 0.02, OR = 2.666) were positively correlated with pleural dissemination; those of TP53 (p = 0.01, OR = 0.417) was negatively, while of SMAD4 (p = 0.03, OR = 4.957) was positively correlated with brain metastasis of NSCLC. Additionally, smoking history (p = 0.004, OR = 0.004) was negatively correlated with pleural dissemination of NSCLC. Furthermore, models for prediction of lung metastasis (AUC = 0.706), pleural dissemination (AUC = 0.651), and brane metastasis (AUC = 0.629) were established. Conclusion Taken together, this study revealed nine mutant genes and smoking history associated with organ tropism metastases of NSCLC and provided three models for the prediction of metastatic organs. This study enables us to predict the organs to which non‐small cell lung cancer metastasizes before it does develop. This study identified ten factors including nine gene mutations and smoking history as predictors for the organ tropism metastasis of NSCLC through analyses of NGS data and other clinical factors. Then three models were established to predict lung metastasis, brain metastasis, and pleura dissemination of NSCLC using these factors.