4.8 Article

Distinct co-acquired alterations and genomic evolution during TKI treatment in non-small-cell lung cancer patients with or without acquired T790M mutation

Journal

ONCOGENE
Volume 39, Issue 9, Pages 1846-1859

Publisher

NATURE PUBLISHING GROUP
DOI: 10.1038/s41388-019-1104-z

Keywords

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Funding

  1. National Natural Science Foundation of China [81702248, 81672972]
  2. Zhejiang Medical and Health Science and Technology Project [2018KY309, 2017KY239]

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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.

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