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Extraction of Aqueous Metabolites from Cultured Adherent Cells for Metabolomic Analysis by Capillary Electrophoresis-Mass Spectrometry

Metabolomic analysis is a promising omics approach to not only understand the specific metabolic regulation in cancer cells compared to normal cells but ...

National Cancer Center

To understand the heterogeneous behaviors of individual cancer cells, it is essential to investigate gene expression levels as well as their divergence between different individual cells. Recent advances in next-generation sequencing-related technologies have enabled us to conduct a single-cell RNA-Seq analysis of a series of lung adenocarcinoma cell lines.

Single-cell analysis of lung adenocarcinoma cell lines reveals diverse expression patterns of individual cells invoked by a molecular target drug treatment.

EGFR-mutated lung cancers account for a significant subgroup of non-small cell lung cancers overall. Third-generation EGFR tyrosine kinase inhibitors (TKI) are mutation-selective inhibitors with minimal effects on wild-type EGFR. Acquired resistance develops to these agents, however, the mechanisms are as yet uncharacterized. In this study, we report that the Src-AKT pathway contributes to acquired resistance to these TKI. In addition, amplification of EGFR wild-type alleles but not mutant alleles was sufficient to confer acquired resistance. These findings underscore the importance of signals from wild-type EGFR alleles in acquiring resistance to mutant-selective EGFR-TKI. Our data provide evidence of wild-type allele-mediated resistance, a novel concept of acquired resistance in response to mutation-selective inhibitor therapy in cancer treatment. .

Amplification of EGFR Wild-Type Alleles in Non-Small Cell Lung Cancer Cells Confers Acquired Resistance to Mutation-Selective EGFR Tyrosine Kinase Inhibitors.

Next generation sequencing (NGS)-based tumor profiling identified an overwhelming number of uncharacterized somatic mutations, also known as variants of unknown significance (VUS). The therapeutic significance of  mutations outside mutational hotspots, consisting of >50 types, in nonsmall cell lung carcinoma (NSCLC) is largely unknown. In fact, our pan-nation screening of NSCLC without hotspot  mutations ( = 3,779) revealed that the majority (>90%) of cases with rare  mutations, accounting for 5.5% of the cohort subjects, did not receive EGFR-tyrosine kinase inhibitors (TKIs) as a first-line treatment. To tackle this problem, we applied a molecular dynamics simulation-based model to predict the sensitivity of rare EGFR mutants to EGFR-TKIs. The model successfully predicted the diverse in vitro and in vivo sensitivities of exon 20 insertion mutants, including a singleton, to osimertinib, a third-generation EGFR-TKI ( = 0.72,  = 0.0037). Additionally, our model showed a higher consistency with experimentally obtained sensitivity data than other prediction approaches, indicating its robustness in analyzing complex cancer mutations. Thus, the in silico prediction model will be a powerful tool in precision medicine for NSCLC patients carrying rare  mutations in the clinical setting. Here, we propose an insight to overcome mutation diversity in lung cancer.

Katsuya Tsuchihara

Division of Translational Informatics,
Exploratory Oncology Research and Clinical Trial Center,
Division of Translational Informatics, Exploratory Oncology Research and Clinical Trial Center

National Cancer Center

Extraction of Aqueous Metabolites from Cultured Adherent Cells for Metabolomic Analysis by Capillary Electrophoresis-Mass Spectrometry

Katsuya Tsuchihara

.css-o250i3{font-size:18px;font-family:Open Sans,sans-serif;line-height:21.6px;}Division of Translational Informatics,Exploratory Oncology Research and Clinical Trial Center,Division of Translational Informatics, Exploratory Oncology Research and Clinical Trial Center

National Cancer Center

Extraction of Aqueous Metabolites from Cultured Adherent Cells for Metabolomic Analysis by Capillary Electrophoresis-Mass Spectrometry

Division of Translational Informatics,
Exploratory Oncology Research and Clinical Trial Center,
Division of Translational Informatics, Exploratory Oncology Research and Clinical Trial Center