Targeted exome sequencing identifies mutational landscape in a cohort of 1500 Chinese patients with non-small cell lung carcinoma (NSCLC)

Abstract

Background: Non-small cell lung carcinoma (NSCLC) is one of the most common human cancers, comprising approximately 80-85% of all lung carcinomas. An estimated incidence of NSCLC is approximately 2 million new cases per year worldwide.

Results: In recent decade, the treatment of NSCLC has made breakthrough progress owing to a large number of targeted therapies which were approved for clinical use. Epidemiology, genetic susceptibility, and molecular profiles in patients are likely to play an important factor in response rates and survival benefits to these targeted treatments and thus warrant further investigation on ethnic differences in NSCLC. In this study, a total number of 1500 Chinese patient samples,1000 formalin fixed paraffin-embedded (FFPE) and 500 blood samples, from patients with NSCLC were analyzed by targeted sequencing to explore mutational landscape in ethnic groups associated with China.

Conclusions: Overall, the data presented here provide a comprehensive analysis of NSCLC mutational landscape in Chinese patients and findings are discussed in the context of similar studies on different ethnic groups.

Keywords: Cancer; Chinese patients; Disease; Non-small cell lung carcinoma; Targeted exome sequencing.

Fig. 1

Overview of the genomic alterations of 1000 tissue and 500 blood samples of NSCLC patients. Distribution of tissue and blood samples with single mutation (single base variation, insertion or deletion, SM); multiple single mutations (MM); amplification (AMP), fusion (FUS) or combination of these

Fig. 2

Significantly mutated genes in NSCLC. Waterfall plot of the distribution of SNV/InDel mutations found in tissue (a) and blood (b) patient samples. The top plot show number of mutations per Mb sequenced for a cohort of 914 NSCLC samples. Left plot shows the frequency of samples mutated for the listed gene. The central plot shows the types of mutations (SNV, Insertion, Deletion) in each sample. The distribution of nonsynonymous frameshift insertions and deletions, missense mutations, Stop-gain, and other infrequent alterations (e.g. splicing) in both the tissue (c, e) and blood samples (d, f)

Fig. 3

Recurrent SNV mutations in TP53, EGFR, KRAS, CDKN2A, PTCH1, and PIK3CA. Positional distribution of SNV mutations across blood and tissue NSCLC samples. SNV mutations detected by exome sequencing are depicted on lolliplot and mapped to the structure of the corresponding gene

Fig. 4

Amplifications and gene fusion signatures identified in our NSCLC patient cohort. Structural rearrangement signatures identified in Chinese NSCLC patients. Rearrangement hotspots identified in NSCLC patients. (a) Gene amplificaitons in tissue samples; (b) Gene amplificaitons in blood samples; (c) Gene fusions in tissue samples; (d) Gene fusions in blood samples; (e) ALK fusions in tissue samples; (f) RET fusions in tissue samples

Fig. 5

An overview of significantly mutated genes. Assessment of single mutations (SNVs and InDels), multiple mutations, and amplifications across the top most frequently mutated genes, excluding TP53. Genes were depicted according to aberration frequencies