Next-Generation Sequencing in Lung Cancer Patients: A Comparative Approach in NSCLC and SCLC Mutational Landscapes.

Marioara Simon,Nadim Al Hajjar,Radu Pirlog,Laura Ancuta Pop,Antonia Haranguș,Cecilia Pop-Bica,Cristina Alexandra Ciocan,Ioana Berindan-Neagoe,Ondrej Slaby,Andreea Nutu,Cornelia Braicu,Atanas G. Atanasov

doi:10.3390/jpm12030453

Abstract

Background: Lung cancer remains one of the most diagnosed malignancies, being the second most diagnosed cancer, while still being the leading cause of cancer-related deaths. Late diagnosis remains a problem, alongside the high mutational burden encountered in lung cancer. Methods: We assessed the genetic profile of cancer genes in lung cancer using The Cancer Genome Atlas (TCGA) datasets for mutations and validated the results in a separate cohort of 32 lung cancer patients using tumor tissue and whole blood samples for next-generation sequencing (NGS) experiments. Another separate cohort of 32 patients was analyzed to validate some of the molecular alterations depicted in the NGS experiment. Results: In the TCGA analysis, we identified the most commonly mutated genes in each lung cancer dataset, with differences among the three histotypes analyzed. NGS analysis revealed TP53, CSF1R, PIK3CA, FLT3, ERBB4, and KDR as being the genes most frequently mutated. We validated the c.1621A>C mutation in KIT. The correlation analysis indicated negative correlation between adenocarcinoma and altered PIK3CA (r = −0.50918; p = 0.0029). TCGA survival analysis indicated that NRAS and IDH2 (LUAD), STK11 and TP53 (LUSC), and T53 (SCLC) alterations are correlated with the survival of patients. Conclusions: The study revealed differences in the mutational landscape of lung cancer histotypes.

Highlights

GLOBOCAN 2020 indicates lung cancer as being the second most diagnosed cancer after breast cancer, while still being the leading cause of cancer-related deaths [1], 4.0/).despite the decline recorded in the last decade regarding lung cancer incidence and mortality rates
The The Cancer Genome Atlas (TCGA) data analysis began with downloading the mutation and can file for each dataset of lung cancer type: lung adenocarcinoma (LUAD), lung squamous squamous cell cell carcinoma carcinoma (LUSC), and small-cell carcinoma (SCLC)
We aimed to identify the genetic alterations in cancer genes using three different lung cancer datasets (LUAD, LUSC, and SCLC), to identify common mutational patterns specific for each histotype, to address the differences between men and women, and to compare the results in the TCGA datasets with the genetic alterations observed in an next-generation sequencing (NGS) experiment performed on a cohort of 32 lung cancer patients

Summary

Introduction

GLOBOCAN 2020 indicates lung cancer as being the second most diagnosed cancer after breast cancer, while still being the leading cause of cancer-related deaths [1], 4.0/).despite the decline recorded in the last decade regarding lung cancer incidence and mortality rates. The late diagnosis is not the only cause for high mortality in lung cancer Another cause is the fact that lung cancer treatments appear to be less effective than treatments for other types of cancer (i.e., breast cancer) [3]. The mutational burden of lung tumors appears to be the most critical aspect that influences response to therapy and, survival rates of lung cancer patients. Most cancer patients display a high mutational burden that appears during a process of accumulation of driver mutations that confer to the tumor cell a selective advantage. This way, the targeted therapies available in clinical practice for oncologists to treat lung cancer are restricted to a few options [2]. Late diagnosis remains a problem, alongside the high mutational burden encountered in lung cancer

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