Li Zhihua, Chen Junnan, Xu Bin, Zhao Wei, Zha Haoran, Han Yalin, Shen Wennan, Dong Yuemei, Zhao Nan, Zhang Manze, He Kun, Li Zhaoxia, Liu Xiaoqing
Department of Oncology, PLA Rocket Force Characteristic Medical Center, Beijing, 100088, China.
National Center of Biomedical Analysis, Beijing, 100850, China.
Clin Proteomics. 2024 May 19;21(1):35. doi: 10.1186/s12014-024-09483-8.
Currently, no effective measures are available to predict the curative efficacy of small-cell lung cancer (SCLC) chemotherapy. We expect to develop a method for effectively predicting the SCLC chemotherapy efficacy and prognosis in clinical practice in order to offer more pertinent therapeutic protocols for individual patients.
We adopted matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) and ClinPro Tools system to detect serum samples from 154 SCLC patients with different curative efficacy of standard chemotherapy and analyze the different peptides/proteins of SCLC patients to discover predictive tumor markers related to chemotherapy efficacy. Ten peptide/protein peaks were significantly different in the two groups.
A genetic algorithm model consisting of four peptides/proteins was developed from the training group to separate patients with different chemotherapy efficacies. Among them, three peptides/proteins (m/z 3323.35, 6649.03 and 6451.08) showed high expression in the disease progression group, whereas the peptide/protein at m/z 4283.18 was highly expressed in the disease response group. The classifier exhibited an accuracy of 91.4% (53/58) in the validation group. The survival analysis showed that the median progression-free survival (PFS) of 30 SCLC patients in disease response group was 9.0 months; in 28 cases in disease progression group, the median PFS was 3.0 months, a statistically significant difference (χ = 46.98, P < 0.001). The median overall survival (OS) of the two groups was 13.0 months and 7.0 months, a statistically significant difference (χ = 40.64, P < 0.001).
These peptides/proteins may be used as potential biological markers for prediction of the curative efficacy and prognosis for SCLC patients treated with standard regimen chemotherapy.
目前,尚无有效的措施可用于预测小细胞肺癌(SCLC)化疗的疗效。我们期望开发一种方法,以在临床实践中有效预测SCLC化疗疗效及预后,从而为个体患者提供更具针对性的治疗方案。
我们采用基质辅助激光解吸/电离飞行时间质谱(MALDI-TOF-MS)和ClinPro Tools系统,检测154例接受标准化疗且疗效不同的SCLC患者的血清样本,并分析SCLC患者不同的肽/蛋白质,以发现与化疗疗效相关的预测性肿瘤标志物。两组中有10个肽/蛋白质峰存在显著差异。
从训练组中开发出一种由四种肽/蛋白质组成的遗传算法模型,以区分化疗疗效不同的患者。其中,三种肽/蛋白质(m/z 3323.35、6649.03和6451.08)在疾病进展组中高表达,而m/z 4283.18的肽/蛋白质在疾病缓解组中高表达。该分类器在验证组中的准确率为91.4%(53/58)。生存分析显示,疾病缓解组的30例SCLC患者的无进展生存期(PFS)中位数为9.0个月;疾病进展组的28例患者中,PFS中位数为3.0个月,差异具有统计学意义(χ=46.98,P<0.001)。两组的总生存期(OS)中位数分别为13.0个月和7.0个月,差异具有统计学意义(χ=40.64,P<0.001)。
这些肽/蛋白质可能用作预测接受标准方案化疗的SCLC患者疗效及预后的潜在生物标志物。