More than 95% of EGFR lung cancer patients have developed anticancer drug resistance mutation exploration technology
Nov 25, 2024
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A research team led by Professor Kim Hyung-beom of Yonsei University Medical School, Professor Oh Hyung-chul and Professor Lee Seung-ho of Surgery, and Professor Kim Young-kwang of Catholic University Medical School have developed mutagenesis and detection technology by applying Prime Editor, a next-generation genetic correction technology, which can confirm drug susceptibility in more than 95% of all mutations.
The results of this study were published in the latest issue of the international journal 'Nature Biotechnology (IF 33.1)'.
In lung cancer patients, epidermal growth factor receptor (EGFR) mutations are found in 30% of all lung cancer patients. EGFR tumor mutations are known as an important criterion for determining the use of targeted therapeutic agents such as 'tyrosine kinase inhibitors (TKI)'. Targeted treatments respond well at the beginning of treatment, but the therapeutic effect is often reduced or recurred due to drug resistance arising from the acquisition of new mutations 1 to 2 years after drug use.
Previously, to study the relationship between drug resistance and mutations, a method of collecting tumor tissue from patients exposed to drugs and discovering resistant genetic mutations was used. However, it was difficult to evaluate drug resistance at the level of a single mutation because mutations were different from patient to patient and it was difficult to secure sufficient cases.
The research team developed an artificial intelligence-based mutation detection technology 'PEER-seq (prime editing and endogenous region sequencing)' by applying Prime Editor, a next-generation gene correction technology that can induce all forms of mutations in the tyrosine kinase domain where most mutations in EGFR genes are observed.
The mutation detection technology 'PEER-seq' introduced all possible forms of single nucleotide variation (SNV) through an artificial intelligence-based optimized gene-editing library. In order to accurately detect additional introduced mutations, we additionally introduced synonymous mutations that do not change the protein amino acid sequence even when gene mutations occur.
This allows PEER-seq technology to detect more than 95% of all tumor mutations through algorithm-based prime editing in the tyrosine kinase domain, where most mutations in the EGFR gene are observed, and to evaluate the extent to which the mutation affects anticancer drug resistance. Unlike the existing method of indirectly tracking changes in the number of cells through gene scissors, functional evaluation with higher accuracy can be conducted by checking the degree of drug response of mutations directly within the gene-edited genome.
The research team evaluated whether 2476 mutations were resistant to anticancer drugs after administering apatinib and osimertinib to cell lines where mutations were found using PEER-seq technology. We also analyzed the effect of 'complex mutation' combination, in which other mutations were induced in the presence of T790M mutation, which occurs most frequently in EGFR genes and is known to activate lung cancer cells, on anticancer drug resistance. Through this, it was found that the resistance of known mutations to anticancer drugs only in the presence of T790M varies considerably in the absence of T790M.
As a result of the analysis, we newly discovered the resistance of 46 combinations of 2476 mutations and 3 drug administration combinations, which have not been identified for resistance to anticancer drugs, and confirmed the drug sensitivity of 4,270 mutations. In particular, new mutations showing drug resistance, such as K754Q, G930R, and E931K, which were not identified with conventional diagnostic kits, were also discovered in the tyrosine kinase domain.
In addition, the research team verified whether the mutations identified through this study show sensitivity and resistance to anticancer drugs are consistent with previous reports. As a result, we confirmed the results consistent with existing reports in a combination of approximately 59 of 63 drug-variant combinations following two drugs, afatinib and osimertinib, and T790M mutation, demonstrating the high accuracy of PEER-seq techniques.
Professor Kim Hyung-beom said, `The PEER-seq technology developed by this study has enabled us to analyze drug resistance of a large number of mutations at once with high accuracy.'This technology can be used not only to treat lung cancer but also to evaluate various anticancer drugs and combinations of mutations in other carcinomas, which can be used in various fields of precision medicine, such as developing new drugs and establishing a customized treatment platform for patients.'
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