Mass spectrometry to classify non-small-cell lung cancer patients for clinical outcome after treatment with epidermal growth factor receptor tyrosine kinase inhibitors: A multicohort cross-institutional study
Mass spectrometry to classify non-small-cell lung cancer patients for clinical outcome after treatment with epidermal growth factor receptor tyrosine kinase inhibitors: A multicohort cross-institutional study
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DOI：
10.1093/jnci/djk195
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发表时间：
2007-06-06
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通讯作者：
Carbone, David P.
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作者：
Taguchi, Fumiko;Solomon, Benjamin;Carbone, David P.
背景部分但并非所有非小细胞肺癌（NSCLC）患者对表皮生长因子受体（EGFR）酪氨酸激酶抑制剂（TKI）治疗有反应。我们开发和测试的能力的预测算法的基础上，基质辅助激光解吸电离（MALDI）质谱（MS）分析的预处理血清，以确定患者谁是可能受益于治疗EGFR TKIs. Methods从NSCLC患者的血清收集吉非替尼或厄洛替尼治疗前进行了分析，MALDI MS。光谱获得独立的两个机构。根据来自3个队列的139例患者的训练集开发了预测EGFR TKI治疗后结局的算法。然后在分别接受吉非替尼和厄洛替尼治疗的67例和96例患者的两个独立验证队列以及未接受EGFR TKI治疗的3个对照队列中对该算法进行了测试。结果基于训练集患者EGFR TKI治疗后的结果，开发了一种基于8个不同m/z特征的算法。基于在这两个机构获得的光谱的分类有97.1%的一致性。对于两个验证队列，分类器识别了EGFR TKI治疗后结局改善的患者。在一个队列中，预测的"良好"和"不良"组患者的中位生存期分别为207天和92天（良好组与不良组的死亡风险比[HR]= 0.50，95%置信区间[CI]= 0.24至0.78）。在另一队列中，中位生存期为306天与107天（HR = 0.41，95% CI = 0.17至0.63）。结论MALDI MS算法不仅能预测NSCLC患者EGFR TKI治疗后的预后，而且能对NSCLC患者EGFR TKI治疗后的预后进行分类。因此，该算法可能有助于治疗前选择合适的NSCLC患者亚组进行EGFR TKI治疗。
Background Some but not all patients with non-small-cell lung cancer (NSCLC) respond to treatment with epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs). We developed and tested the ability of a predictive algorithm based on matrix-assisted laser desorption ionization (MALDI) mass spectrometry (MS) analysis of pretreatment serum to identify patients who are likely to benefit from treatment with EGFR TKIs.Methods Serum collected from NSCLC patients before treatment with gefitinib or erlotinib were analyzed by MALDI MS. Spectra were acquired independently at two institutions. An algorithm to predict outcomes after treatment with EGFR TKIs was developed from a training set of 139 patients from three cohorts. The algorithm was then tested in two independent validation cohorts of 67 and 96 patients who were treated with gefitinib and erlotinib, respectively, and in three control cohorts of patients who were not treated with EGFR TKIs. The clinical outcomes of survival and time to progression were analyzed.Results An algorithm based on eight distinct m/z features was developed based on outcomes after EGFR TKI therapy in training set patients. Classifications based on spectra acquired at the two institutions had a concordance of 97.1%. For both validation cohorts, the classifier identified patients who showed improved outcomes after EGFR TKI treatment. In one cohort, median survival of patients in the predicted "good" and "poor" groups was 207 and 92 days, respectively (hazard ratio [HR] of death in the good versus poor groups = 0.50, 95% confidence interval [CI] = 0.24 to 0.78). In the other cohort, median survivals were 306 versus 107 days (HR = 0.41, 95% Cl = 0.17 to 0.63). The classifier did not predict outcomes in patients who did not receive EGFR TKI treatment.Conclusion This MALDI MS algorithm was not merely prognostic but could classify NSCLC patients for good or poor outcomes after treatment with EGFR TKIs. This algorithm may thus assist in the pretreatment selection of appropriate subgroups of NSCLC patients for treatment with EGFR TKIs.