Table_4_The Comprehensive Analyses of Genomic Variations and Assessment of TMB and PD-L1 Expression in Chinese Lung Adenosquamous Carcinoma.XLSX
The poor prognosis and fewer treatment option is a current clinical challenge for patients with lung adenosquamous carcinoma (ASC). The previous studies reported that tumor mutational burden (TMB, numbers of mutation per Megabase) is a predictor of clinical response in trials of multiple cancer types, while fewer studies assessed the relationship between TMB level and clinical features and outcomes of lung ASC. Herein, the present study enrolled Chinese patients with lung ASC. DNA was extracted from formalin-fixed paraffin-embedded tumor samples and subjected to next generation sequencing (NGS), and the 457 cancer related genes were evaluated. The results demonstrated that 95 unique genes with somatic variations were identified in the enrolled patients. The top three of high frequency gene mutations were TP53, EGFR, PIK3CA with rates of 62% (13 cases), 48% (10 cases), and 14% (3 cases), respectively. We identified TMB value was significantly correlated with pathological stages (p < 0.05) and invasion of lymph node (p < 0.05). However, TMB value was not significantly correlated to other clinicopathologic indexes, for examples, age, sex, smoking history, tumor size, as well as TP53 and EGFR mutations in lung ASC. Moreover, TMB value was associated with the overall survival (p < 0.01), but not with the relapse-free survival (p = 0.23). In conclusion, this study indicated that lung ASC with high TMB might be associated with the invasion of lymph node and short overall survival. Immunotherapy might be a promising treatment option for lung ASC patients with high TMB.
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- Gene and Molecular Therapy
- Biomarkers
- Genetics
- Genetically Modified Animals
- Developmental Genetics (incl. Sex Determination)
- Epigenetics (incl. Genome Methylation and Epigenomics)
- Gene Expression (incl. Microarray and other genome-wide approaches)
- Livestock Cloning
- Genome Structure and Regulation
- Genetic Engineering
- Genomics