| Literature DB >> 31754213 |
Ying Jin1,2,3, Hua Bao4, Xiuning Le5, Xiaojun Fan4, Ming Tang5, Xun Shi1,2, Jun Zhao1,2, Junrong Yan6, Yang Xu4, Kelly Quek5, Yasir Y Elamin5, Jianhua Zhang7, P Andrew Futreal7, Ignacio I Wistuba8, John V Heymach5, Guangyuan Lou1,2, Lan Shao1,2, Qiong He1,2, Chen Lin1,2, Xue Wu4, Yang W Shao6, Xiaonan Wang6, Jiachen He6, Yamei Chen1,3, Justin Stebbing9, Ming Chen10,11, Jianjun Zhang12,13, Xinmin Yu14,15.
Abstract
EGFR-mutant non-small-cell lung cancer (NSCLC) patients inevitably develop drug resistance when treated with EGFR tyrosine kinase inhibitors (TKIs). Systematic genetic analysis is important to understand drug-resistant mechanisms; however, the clinical significance of co-occurring genetic alterations at baseline, co-acquired mutations at progressive disease (PD), and the clonal evolution remain underinvestigated. We performed targeted sequencing of pre-treatment and PD tumor samples from 54 EGFR-mutant NSCLC patients. Ten additional patients were sequenced using whole-exome sequencing to infer the clonal evolution patterns. We observed a domain-dependent effect of PIK3CA mutation at baseline on patient progression-free survival (PFS). In addition, at baseline, 9q34.3/19p13.3 (NOTCH1/STK11/GNA11) showed a co-deletion pattern, which was associated with a significantly worse PFS (p = 0.00079). T790M-postive patients with other concurrent acquired oncogenic mutations had a significantly shorter PFS (p = 0.005). Besides acquired T790M mutation, chromosomal instability (CIN) related genes, including AURKA and TP53 alterations, were the most frequently acquired events. CIN significantly increased during TKI treatment in T790M-negative patients and is a candidate resistance mechanism to the first-generation TKIs. Clonal evolution analyses suggest that the composition and relationship among resistant subclones, particularly relationship with T790M subclone, affect patients' outcomes. Overall, our findings of novel co-occurring alterations and clonal evolution patterns can be served as predictive biomarkers to stratify patients and help to better understand the drug-resistant mechanism to TKIs.Entities:
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Year: 2019 PMID: 31754213 DOI: 10.1038/s41388-019-1104-z
Source DB: PubMed Journal: Oncogene ISSN: 0950-9232 Impact factor: 9.867