Comprehensive genomic profile of Chinese lung cancer patients and mutation characteristics of individuals resistant to icotinib/gefitinib

Abstract

Lung cancer is the leading causes of cancer-related death worldwide. Precise treatment based on next-generation sequencing technology has shown advantages in the diagnosis and treatment of lung cancer. This cohort study included 371 lung cancer patients. The lung cancer subtype was related to the smoking status and sex of the patients. The most common mutated genes were TP53 (62%), EGFR (55%), and KRAS (11%). The mutation frequencies of EGFR, TP53, PIK3CA, NFE2L2, KMT2D, FGFR1, CCND1, and CDKN2A were significantly different between lung adenocarcinoma and lung squamous cell carcinoma. We identified the age-associated mutations in ALK, ERBB2, KMT2D, RBM10, NRAS, NF1, PIK3CA, MET, PBRM1, LRP2, and CDKN2B; smoking-associated mutations in CDKN2A, FAT1, FGFR1, NFE2L2, CCNE1, CCND1, SMARCA4, KEAP1, KMT2C, and STK11; tumor stage-associated mutations in ARFRP1, AURKA, and CBFB; and sex-associated mutations in EGFR. Tumor mutational burden (TMB) is associated with tumor subtype, age, sex, and smoking status. TMB-associated mutations included CDKN2A, LRP1B, LRP2, TP53, and EGFR. EGFR amplification was commonly detected in patients with acquired lcotinib/gefitinib resistance. DNMT3A and NOTCH4 mutations may be associated with the benefit of icotinib/gefitinib treatment.

Protein co-expression network-based profiles revealed from laser-microdissected cancerous cells of lung squamous-cell carcinomas

“…Shang et al . reported a mutational landscape of Chinese lung SqCC patients, in which mutation frequencies of PIK3CA , NFE2L2 , and KMT2D were most significant 56 , which was quite similar to our TCGA-based study, although most of their cohorts were at advanced tumor stages. In our TCGA-based genomic alterations, PIK3CA , SOX2 , and TP63 co-occurred with high significances of q -value < 0.001, and NOTCH2 and DDR2 co-occurred with q = 0.002.…”

Protein Co-expression Network-based Profiles Revealed from Laser-microdissected Cancerous Cells of Lung Squamous-Cell Carcinomas

“…Our IPA analysis of the WM26 module annotated the proliferation of SqCC, in which the key regulator HIF1A was important in the adaptive response to hypoxia and angiogenesis. The ROS-responsive transcription factor NRF2 encoded by NEF2L2 can bind and transactivate an antioxidant response element upstream of HIF1A, through which the expression of HIF1α encoded by HIF1A is regulated directly by NRF2 [57]. Moreover, the accumulation of HIF1α directly upregulates SLC2A1 (known as GLUT1, glucose transporter-1), as we indeed observed in the co-expression networks of WM26, most likely suggesting hypoxia-induced metabolic changes to aerobic glycolysis (the Warburg effect).…”

Multi-Omics Analysis of the Effects of Smoking on Human Tumors

“…We noted that non-smokers were substantially more sensitive to multiple chemotherapeutic drugs than smokers. Similarly, previous studies have found that smoking can induce mutations in multiple genes, which in turn can induce patients to become resistant to chemotherapy drugs (Alexandrov et al, 2016;Shang et al, 2020). For instance, TP53-mutations type can interact with BCAR1 to promote tumor cell invasion, leading to poor prognosis (Guo et al, 2020).…”

“…A study by Yu et al (2019) analyzed somatic mutations in 100 cases of NSCLC, whose results revealed that a variety of gene mutations such as TTN, CSMD3, RYR2, USH2A, and ZFHX4 were different in patients with different smoking histories, and thus the mutation incidence was higher in smokers than that in non-smokers. Likewise, Shang et al found that the mutation frequency of CDKN2A, FAT1, FGFR1, NFE2L1, CCNE1, CCND1, SMARCA4, KEAP1, KMT2C, and STK11 was higher in smokers compared with non-smokers (Shang et al, 2020). According to the literature, chromosome instability can lead to CNV and genetic heterogeneity, which may trigger the occurrence of cancer (Myllykangas et al, 2008).…”