Yu Namhee, Hwang Mihwa, Ahn Beung Chul, Lee Youngjoo, Hong Sehwa, Sim Hanna, Song Bo Ram, Kim Sunshin, Park Charny, Han Ji-Youn
Research Institute, National Cancer Center, Goyang-si, Republic of Korea.
Exp Mol Med. 2025 Jul;57(7):1567-1578. doi: 10.1038/s12276-025-01493-2. Epub 2025 Jul 4.
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.
患者间纵向肿瘤进化的动态特性给有效药物的设计带来了挑战。在此，我们旨在确定肿瘤进化的耐药机制，并探索针对特定肿瘤进化类型的候选药物。我们对国家癌症中心难治性肺癌队列（n = 199）中34例患者的73个样本数据集进行了纵向药物基因组分析。通过确定基因组图谱来识别每位患者的进化树，并根据主要的主干突变、TP53和表皮生长因子受体将其分类为肿瘤进化组。根据这两个克隆的状态，这些组被分为持续组、灭绝组和扩增组。药物基因组图谱分析表明，XAV - 939对表现出上皮-间质转化激活耐药性的表皮生长因子受体-灭绝组有效。此外，在整个进化过程中，MYC亚克隆与耐药残留细胞的维持情况相似。而且，MYC肺腺癌预后较差，转化为小细胞肺癌的风险更高。此外，上皮-间质转化激活和MYC亚克隆与表皮生长因子受体-酪氨酸激酶抑制剂的同时耐药有关。最后，我们的药物筛选确定极光激酶抑制剂巴瑞替尼为与表皮生长因子受体-酪氨酸激酶抑制剂和XAV - 939联合用于MYC细胞的三联组合候选药物。这项研究证明了纵向药物基因组分析在根据个体肿瘤进化类型制定治疗策略方面的实用性。该研究强调了在临床实践中整合基因组和药物基因组分析以根据肿瘤进化类型定制治疗的重要性。
