论文部分内容阅读
背景与目的晚期肺鳞癌(squamous cell carcinoma of lung,SCC)一线治疗以化疗为主,其标准铂二联方案化疗只能给患者带来有限的获益。并且不同的患者对于化疗药物的获益不同。所以实现化疗药物最优选择达到个体化预见性治疗尤为重要。本研究应用基质辅助激光解析电离飞行时间质谱(matrix-assisted laser desorption/ionization-time of flight-mass spectrometry,MALDI-TOF-MS)检测初治晚期SCC患者接受紫杉醇类联合铂类化疗前血清多肽,并分析其与化疗疗效的相关性。方法初治晚期SCC患者接受紫杉醇类联合铂类方案化疗,每两周期进行疗效评价。评效为完全缓解(complete response,CR)或部分缓解(partial response,PR)患者定义为化疗敏感组,疾病进展(progressive disease,PD)患者定义为耐药组。留取SCC患者化疗前血清样本,81例患者按照3:1的比例随机分为训练组(敏感组I与耐药组I)和验证组(敏感组II与耐药组II),预处理训练组血清样本并进行MALDI-TOFMS检测,得到血清多肽指纹图谱。经Clin Pro Tools软件系统分析处理,得到敏感组I与耐药组I的差异多肽。应用软件内置的3种不同的生物学算法分别建立疗效预测模型,选取最优算法建立疗效预测模型。运用验证组进行盲样验证。结果训练组共纳入30例敏感组患者,31例耐药组患者;验证组共纳入敏感与耐药组患者各10例。训练组在敏感与耐药组有96个差异多肽,其中具有统计学意义的多肽有16个(P<0.001)。由5个多肽(1,897.75 Da,2,023.93 Da,3,683.36 Da,4,269.56 Da,5,341.29 Da)建立疗效预测模型。该模型对化疗敏感组患者的识别率为95.11%,交叉验证率为89.18%。经验证组进行盲样验证,其模型的准确率为85%,灵敏度为90.0%,特异性为80.0%。敏感组I中位无进展生存期(progress free survival,PFS)为7.2个月(95%CI:4.4-14.5);耐药组I中位PFS为1.8个月(95%CI:0.7-3.5)。结果发现:4,232.04 Da、4,269.56 Da的差异多肽与SCC患者PFS存在相关性(P<0.001)。结论应用MALDI-TOF-MS技术可检测到化疗敏感组及耐药组患者的血清多肽存在差异,初步建立的疗效预测模型可用于预测紫杉醇类联合铂类方案化疗疗效。但需进一步扩大样本量完善及验证模型。
BACKGROUND & OBJECTIVE: Chemotherapy is the first-line treatment of advanced squamous cell carcinoma of lung (SCC). The standard platinum regimen chemotherapy can only bring limited benefit to patients. And different patients benefit from chemotherapy drugs differently. Therefore, the optimal choice of chemotherapy drugs to achieve individualized predictive treatment is particularly important. In this study, we used matrix-assisted laser desorption / ionization-time of flight-mass spectrometry (MALDI-TOF-MS) to detect the levels of serum polypeptide in patients with newly diagnosed advanced SCC receiving paclitaxel combined with platinum- And analyze its correlation with the curative effect of chemotherapy. Methods The patients with late-onset SCC received paclitaxel plus platinum regimen, and the efficacy was evaluated every two cycles. Patients with complete response (CR) or partial response (PR) were defined as chemotherapy-sensitive and patients with progressive disease (PD) as drug-resistant. Pre-chemotherapy serum samples from SCC patients were collected and 81 patients were randomly divided into training group (sensitive group I and drug-resistant group I) and verification group (sensitive group II and drug-resistant group II) according to a 3: 1 ratio, pretreatment training Serum samples were collected and tested by MALDI-TOFMS to obtain serum polypeptide fingerprinting. The Clin Pro Tools software system analysis and processing, sensitive group I and resistant group I difference polypeptide. The three different biological algorithms built in the application software respectively establish the efficacy prediction model and select the optimal algorithm to establish the efficacy prediction model. Use verification group for blind sample verification. Results In the training group, 30 patients in the sensitive group and 31 patients in the drug-resistant group were enrolled. In the validation group, 10 patients were included in the sensitive and resistant groups. The training group had 96 differential polypeptides in the sensitive and resistant groups, of which 16 were statistically significant (P <0.001). The efficacy prediction model was established by five polypeptides (1,897.75 Da, 2,023.93 Da, 3,683.36 Da, 4,269.56 Da, 5,341.29 Da). The model of chemotherapy-sensitive patients with a recognition rate of 95.11%, the cross-validation rate was 89.18%. The validation group was blind-like verification, the accuracy of the model was 85%, the sensitivity was 90.0% and the specificity was 80.0%. The median progression-free survival (PFS) in the sensitive group I was 7.2 months (95% CI: 4.4-14.5); median PFS in the resistant group I was 1.8 months (95% CI: 0.7-3.5) . The results showed that 4,232.04 Da, 4,269.56 Da difference in the polypeptide and SCC patients with PFS correlation (P <0.001). Conclusions There are differences in the serum polypeptides detected by MALDI-TOF-MS in chemosensitive and drug resistant patients. The preliminary prediction model of efficacy can be used to predict the efficacy of paclitaxel combined with platinum regimen. However, we need to further expand the sample size and verify the model.