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

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 ge...

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Bibliographic Details
Published in:Journal of personalized medicine 2022-03, Vol.12 (3), p.453
Main Authors: Pop-Bica, Cecilia, Ciocan, Cristina Alexandra, Braicu, Cornelia, Haranguș, Antonia, Simon, Marioara, Nutu, Andreea, Pop, Laura Ancuta, Slaby, Ondrej, Atanasov, Atanas G, Pirlog, Radu, Al Hajjar, Nadim, Berindan-Neagoe, Ioana
Format: Article
Language:English
Subjects:
Adenocarcinoma
Biomarkers
Biopsy
Breast cancer
Cancer therapies
Clinical medicine
Correlation analysis
Datasets
ErbB-2 protein
Genomes
Lung cancer
Mortality
Mutation
Next-generation sequencing
Non-small cell lung carcinoma
p53 Protein
Patients
Precision medicine
Software
Survival
Tumors
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Summary: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.
ISSN:2075-4426
2075-4426
DOI:10.3390/jpm12030453