Dr. Han Ji-yeon (Department of Treatment Resistance Research), Dr. Kim Sun-shin, and Dr. Park Chan-i (Department of Targeted Therapy Research) from the National Cancer Center research team have tracked and analyzed genomic changes and drug responsiveness using cancer cells derived from patients with refractory lung cancer. Based on this, they proposed a patient-tailored treatment strategy to overcome treatment resistance.

Lung cancer is the leading cause of cancer death in South Korea. Particularly in South Korea and other East Asian countries, the frequency of epidermal growth factor receptor (EGFR) mutations is high, leading to the active use of tyrosine kinase inhibitors (TKIs) targeting these mutations. Although the initial treatment response rate is high, most patients develop drug resistance within 1 to 2 years after starting treatment, necessitating the development of new personalized treatment strategies.

The research team secured a total of 73 tumor samples collected at each recurrence point during the treatment process from 34 patients with refractory lung cancer and conducted a time-series analysis of the genetic changes in the tumors. For this purpose, they utilized a 'drug-genome platform' operated by the research team for predicting drug responsiveness.

The researchers classified tumor evolution types focusing on EGFR and TP53 mutations and identified that the mechanisms of treatment resistance and effective drug combinations vary by type. Particularly, in the patient group where resistance occurred as the EGFR mutation disappeared, resistance to existing TKIs was confirmed due to the activation of epithelial-mesenchymal transition (EMT). Furthermore, through single-cell transcriptome analysis, they clearly distinguished two types of resistant cell types, identifying the cell group that remains regardless of treatment as a recurrence risk factor and suggesting its use as a biomarker to predict lung cancer metastasis and prognosis deterioration.

The drug-genome platform using lung cancer patient cells demonstrated high similarity to actual patient tumor responses, and the research team plans to develop a big data model based on this to enhance personalized treatment strategies.

This research was supported by the National Cancer Center's public interest cancer research project and the National Research Foundation of Korea's mid-career researcher program and was recently published in 'Experimental & Molecular Medicine,' a leading life sciences journal and the official journal of the Korean Society for Biochemistry and Molecular Biology.

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