Abstract
Simple SummaryIn this study, we comprehensively and synthetically analyzed mutations in lung cancer based on the next generation sequencing data of lung tumors surgically removed from the patients, and identified the mutation-related factors that can affect clinical outcomes. Detailed understanding of the genomic landscape of lung cancers will establish the ideal model for best surgical outcomes in the era of “precision medicine”.Findings on mutations, associated with lung cancer, have led to advancements in mutation-based precision medicine. This study aimed to comprehensively and synthetically analyze mutations in lung cancer, based on the next generation sequencing data of surgically removed lung tumors, and identify the mutation-related factors that can affect clinical outcomes. Targeted sequencing was performed on formalin-fixed paraffin-embedded surgical specimens obtained from 172 patients with lung cancer who underwent surgery in our hospital. The clinical and genomic databases of the hospital were combined to determine correlations between clinical factors and mutation profiles in lung cancer. Multivariate analyses of mutation-related factors that may affect the prognosis were also performed. Based on histology, TP53 was the driver gene in 70.0% of the cases of squamous cell carcinoma. In adenocarcinoma cases, driver mutations were detected in TP53 (26.0%), KRAS (25.0%), and epidermal growth factor receptor (EGFR) (23.1%). According to multivariate analysis, the number of pathogenic mutations (≥3), presence of a TP53 mutation, and TP53 allele fraction >60 were poor prognostic mutational factors. The TP53 allele fraction tended to be high in caudally and dorsally located tumors. Moreover, TP53-mutated lung cancers located in segments 9 and 10 were associated with significantly poorer prognosis than those located in segments 1–8. This study has identified mutation-related factors that affect the postoperative prognosis of lung cancer. To our knowledge, this is the first study to demonstrate that the TP53 mutation profile varies with the site of lung tumor, and that postoperative prognosis varies accordingly.
Highlights
Along with the technological advancement in generation sequencing (NGS), accumulated findings on mutations, associated with lung cancer, have led to the development of mutation-based precision medicine [1]
We studied surgical samples from 172 patients with lung cancer who underwent surgery at our hospital between June 2014 and June 2019
Our study showed that Progression-Free Survival (PFS) was shorter in tumors with a large number of pathogenic mutations, validating the above-mentioned hypothesis from another perspective
Summary
Along with the technological advancement in generation sequencing (NGS), accumulated findings on mutations, associated with lung cancer, have led to the development of mutation-based precision medicine [1]. Patient-based clinicogenomic datasets may significantly accelerate the advancement of clinical practice and the development of novel therapeutics. The postoperative prognosis of lung cancer has been conventionally and stochastically predicted, based on the histological classification and the tumor-node-metastasis (TNM) stage [4,5], a prognostic model applicable for each case has not been established. The criteria for adjuvant chemotherapy are not clear. Accurate criteria for adjuvant chemotherapy based on appropriate prognostic models in the future should be urgently established [6,7]
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