Longitudinal pharmacogenomic analysis of refractory lung cancer to identify therapeutic candidates for epidermal growth factor receptor–tyrosine kinase inhibitor resistance subclones
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The dynamic nature of longitudinal tumor evolution across patients presents challenges in designing effective drugs. Here we aimed to determine tumor evolution resistance mechanisms and explore candidate drugs for specific tumor evolution types. We conducted longitudinal pharmacogenomic analysis of datasets of 73 samples in 34 patients among a National Cancer Center refractory lung cancer cohort (n = 199). Genomic profiles were determined to identify evolutionary trees in each patient, which were classified into tumor evolution groups according to the predominant truncal mutations, TP53 and epidermal growth factor receptor. These groups were categorized into persistence, extinction and expansion groups according to the status of these two clones. Pharmacogenomic profile analysis identified that XAV-939 was effective for the epidermal growth factor receptor-extinction group exhibiting epithelial-to-mesenchymal transition-activated resistance. In addition, MYC+ subclones were maintained similarly to drug-tolerant residual cells throughout the evolution period. Moreover, MYC+ lung adenocarcinoma showed a poor outcome and had higher risk of transformation to small-cell lung cancer. Furthermore, the epithelial-to-mesenchymal transition-activated and MYC+ subclones were implicated in concurrent epidermal growth factor receptor–tyrosine kinase inhibitor resistance. Finally, our drug screening identified barasertib, an aurora kinase inhibitor, as a triple-combination candidate with epidermal growth factor receptor–tyrosine kinase inhibitors and XAV-939 for MYC+ cells. This study demonstrates the utility of longitudinal pharmacogenomic analysis to develop treatment strategies according to individual tumor evolution type. The study underscores the importance of integrating genomic and pharmacogenomic profiling in clinical practice to tailor treatments according to tumor evolution type.
Abstract
The dynamic nature of longitudinal tumor evolution across patients presents challenges in designing effective drugs. Here we aimed to determine tumor evolution resistance mechanisms and explore candidate drugs for specific tumor evolution types. We conducted longitudinal pharmacogenomic analysis of datasets of 73 samples in 34 patients among a National Cancer Center refractory lung cancer cohort (n = 199). Genomic profiles were determined to identify evolutionary trees in each patient, which were classified into tumor evolution groups according to the predominant truncal mutations, TP53 and epidermal growth factor receptor. These groups were categorized into persistence, extinction and expansion groups according to the status of these two clones. Pharmacogenomic profile analysis identified that XAV-939 was effective for the epidermal growth factor receptor-extinction group exhibiting epithelial-to-mesenchymal transition-activated resistance. In addition, MYC+ subclones were maintained similarly to drug-tolerant residual cells throughout the evolution period. Moreover, MYC+ lung adenocarcinoma showed a poor outcome and had higher risk of transformation to small-cell lung cancer. Furthermore, the epithelial-to-mesenchymal transition-activated and MYC+ subclones were implicated in concurrent epidermal growth factor receptor–tyrosine kinase inhibitor resistance. Finally, our drug screening identified barasertib, an aurora kinase inhibitor, as a triple-combination candidate with epidermal growth factor receptor–tyrosine kinase inhibitors and XAV-939 for MYC+ cells. This study demonstrates the utility of longitudinal pharmacogenomic analysis to develop treatment strategies according to individual tumor evolution type. The study underscores the importance of integrating genomic and pharmacogenomic profiling in clinical practice to tailor treatments according to tumor evolution type.