Characterisation of lung cancer cell lines classifiers

Abstract

Lung cancer poses a significant global health challenge due to its high fatality rates and late-stage detection. Detection and treatment response are complicated by the interpatient and intratumor heterogeneity of lung cancer. Resultantly, effective screening methods for early diagnosis remain elusive. Copy number variations (CNVs) are implicated in lung carcinogenesis and can exert a profound influence on gene expression and patient prognosis. Specifically, gene amplifications can drive oncogene overexpression and play a mediating role in various cancer-associated processes. This project explores the potential of gene amplifications as biomarkers for early detection. The Cancer Genome Atlas (TCGA) data was accessed through cBioPortal to identify highly amplified chromosomal regions in lung adenocarcinoma (LUAD) samples. The dataset was also used to correlate copy number states with gene expression levels in six candidate genes (CCND3, CDX2, TPX2, MYC, MET, and ERBB2), and compare the data to the results from in vitro testing. Quantitative PCR (qPCR) and digital droplet PCR (ddPCR) were performed to assess gene expression and CNV status (respectively) in three cultured non-small cell lung cancer (NSCLC) cell lines (A549, H1975, and H460). Copy number gain (CNG) was detected in several of the target genes, but only MYC exhibited high-level amplification. Although MYC was the only investigated gene that showed initial promise as a biomarker, the results warrant further research into the feasibility of a CNV-driven multi-gene panel for early lung cancer detection. Additionally, this study highlighted the efficacy of ddPCR as a method for detecting CNVs, with potential applicability to liquid biopsy-based approaches.