Wang Lin, Tang Chuanhao, Xu Bin, Yang Lin, Qu Lili, Li Liangliang, Li Xiaoyan, Wang Weixia, Qin Haifeng, Gao Hongjun, He Kun, Liu Xiaoqing
Department of Lung Cancer, Affiliated Hospital of Academy of Military Medical Sciences, Beijing, China.
National Center of Biomedical Analysis, Beijing, China.
PLoS One. 2017 Jun 8;12(6):e0179000. doi: 10.1371/journal.pone.0179000. eCollection 2017.
Although pemetrexed plus cis/carboplatin has become the most effective chemotherapy regimen for patients with advanced lung adenocarcinoma, predictive biomarkers are not yet available, and new tools to identify chemosensitive patients who would likely benefit from this treatment are desperately needed. In this study, we constructed and validated predictive peptide models using the serum peptidome profiles of two datasets.
One hundred eighty-three patients treated with first-line platinum-based pemetrexed treatment for advanced lung adenocarcinoma were retrospectively enrolled and randomized into the training (n = 92) or validation (n = 91) set, and pre-treatment serum samples were analyzed using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) and ClinProTools software. Serum peptidome profiles from the training set were used to identify potential predictive peptide biomarkers and construct a predictive peptide model for accurate group discrimination; which was then used to classify validation samples into "good" and "poor" outcome groups. The clinical outcomes of objective response rate (ORR), disease control rate (DCR), progression-free survival (PFS), and overall survival (OS) were analyzed based on the classification result.
Eight potential peptide biomarkers were identified. A predictive peptide model based on four distinct m/z features (2,142.12, 3,316.19, 4,281.94, and 6,624.02 Da) was developed based on the clinical outcomes of training set patients after first-line pemetrexed plus platinum treatment. In the validation set, the good group had significantly higher ORR (49.1% vs. 8.3%, P <0.001) and DCR (96.4% vs. 47.2%, P <0.001), and longer PFS (7.3 months vs. 2.7 months, P <0.001) vs. the poor group. However, the model did not predict OS (13.6 months vs. 12.7 months, P = 0.0675).
Our predictive peptide model could predict pemetrexed plus platinum treatment outcomes in patients with advanced lung adenocarcinoma and might thus facilitate appropriate patient selection. Further studies are needed to confirm these findings.
尽管培美曲塞联合顺铂/卡铂已成为晚期肺腺癌患者最有效的化疗方案，但预测性生物标志物仍未可得，因此迫切需要新的工具来识别可能从该治疗中获益的化疗敏感患者。在本研究中，我们利用两个数据集的血清肽组图谱构建并验证了预测性肽模型。
回顾性纳入183例接受一线铂类培美曲塞治疗的晚期肺腺癌患者，并随机分为训练集（n = 92）或验证集（n = 91），使用基质辅助激光解吸/电离飞行时间质谱（MALDI-TOF-MS）和ClinProTools软件分析治疗前血清样本。训练集的血清肽组图谱用于识别潜在的预测性肽生物标志物，并构建用于准确分组区分的预测性肽模型；然后将其用于将验证样本分为“良好”和“不良”预后组。根据分类结果分析客观缓解率（ORR）、疾病控制率（DCR）、无进展生存期（PFS）和总生存期（OS）的临床结局。
鉴定出8种潜在的肽生物标志物。基于一线培美曲塞联合铂类治疗后训练集患者的临床结局，开发了一种基于4个不同质荷比特征（2,142.12、3,316.19、4,281.94和6,624.02 Da）的预测性肽模型。在验证集中，良好组的ORR（49.1% 对8.3%，P <0.001）和DCR（96.4% 对47.2%，P <0.001）显著高于不良组，PFS也更长（7.3个月对2.7个月，P <0.001）。然而，该模型未预测OS（13.6个月对12.7个月，P = 0.0675）。
我们的预测性肽模型可以预测晚期肺腺癌患者接受培美曲塞联合铂类治疗的结局，从而可能有助于进行合适的患者选择。需要进一步研究来证实这些发现。
