Publication-only abstracts (abstract number preceded by an "e"), published in conjunction with the 2019 ASCO Annual Meeting but not presented at the Meeting, can be found online only.
Prediction of EGFR-TKI efficacy in non-small cell lung cancer patients by metabolomics and genomics.
Metastatic Non-Small Cell Lung Cancer
Lung Cancer—Non-Small Cell Metastatic
2019 ASCO Annual Meeting
J Clin Oncol 37, 2019 (suppl; abstr e20627)
Author(s): Lei Zhang, Rongrong Luo, Lin Wang, Jiarui Yao, Di Wu, Zhishang Zhang, Xiaohong Han, Yuankai Shi; Department of Medical Oncology, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China; Department of Clinical Laboratory, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China; National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, China
Background: Metabolites and somatic mutations involved in EGFR-TKI efficacy remains unclear in non-small cell lung cancer (NSCLC) patients with EGFR sensitizing mutation (EGFRsm+). Here we performed a joint analysis of metabolomics and genomics data to identify metabolites and somatic mutations as biomarkers for EGFR-TKI efficacy. Methods: Metabolomic profiling of plasma samples (n = 43) from NSCLC patients with EGFRsm+, consisting of cohort A (n = 30) and B (n = 13), was conducted using UPLC or rapid separation LC-MS/MS. The 13 matched FFPE samples in cohort B were also used in the targeted sequencing below. FFPE samples (n = 18) from NSCLC patients with EGFRsm+ were subjected to targeted sequencing. According to progression free survival (PFS), all patients were assigned a status of poor (PFS≤42 weeks) and good responders (PFS > 42 weeks). A joint analysis of metabolomics and genomics data was adopted to identify biomarkers for EGFR-TKI efficacy. Results: The partial least squares discrimination analysis mothod was performed to establish a prediction model responsible for separation of good and poor responders in cohort A, comprising 27 metabolites with variable importance in projection score (VIP) > 1.5. Based on the prediction model, the ROC analysis demonstrated the sensitivity of 0.8, the specificity of 0.75, and the area under the ROC curve (AUC) of 0.7 in cohort B. The Welch’s t test method identified 15 significant metabolites (P< 0.05) in cohort A. With the criteria of VIP > 1.5 and P< 0.05, four metabolites, 3-Methyl-L-Histidine, LysoPE(18:2(9Z,12Z)/0:0), Histamine, and SM(d18:1/16:0), were detected as potential biomarkers. To further validate them, associations of these metabolites and somatic mutations were explored in 13 patients with both metabolomics and genomics data available using the Welch’s t test. The results revealed patients with either CTCF R415X or PTK2B G491X had significantly lower Histamine level compared with those without either mutation (both P< 0.05), and significantly increased level of SM(d18:1/16:0) was observed in patients with either GATA2 P250A or MAGI1 S763X (both P< 0.05). Intriguingly, worse PFS was showed in patients with any mutation of GATA2 P250A (P = 0.02), CTCF R415X (P = 0.002), PTK2B G491X (P = 0.002), and MAGI1 S763X (P = 0.0007). Conclusions: Our joint analysis identified two plasma metabolites and four somatic mutations as biomarkers for EGFR-TKI efficacy. The present findings may provide insights into molecular mechanisms of EGFR-TKI efficacy. Further validation in prospective studies was warranted.