J 2022

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

POP-BICA, Cecilia, Cristina Alexandra CIOCAN, Cornelia BRAICU, Antonia HARANGUS, Marioara SIMON et. al.

Basic information

Original name

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

Authors

POP-BICA, Cecilia, Cristina Alexandra CIOCAN, Cornelia BRAICU, Antonia HARANGUS, Marioara SIMON, Andreea NUTU, Laura Ancuta POP, Ondřej SLABÝ (203 Czech Republic, guarantor, belonging to the institution), Atanas G ATANASOV, Radu PIRLOG, Al Hajjar NADIM and Ioana BERINDAN-NEAGOE

Edition

Journal of Personalized Medicine, Basel, MDPI, 2022, 2075-4426

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

30218 General and internal medicine

Country of publisher

Switzerland

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

Impact factor

Impact factor: 3.508 in 2021

RIV identification code

RIV/00216224:14740/22:00128637

Organization unit

Central European Institute of Technology

UT WoS

000776366700001

Keywords in English

non-small-cell lung cancer; small-cell lung cancer; targeted sequencing; patients

Tags

International impact, Reviewed
Změněno: 14/2/2023 15:22, Mgr. Pavla Foltynová, Ph.D.

Abstract

V originále

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.