CN110780070A - Plasma protein molecule for detecting cancer chemotherapy sensitivity, application and kit - Google Patents
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Abstract
Translated fromChinese
Description
Translated fromChinese技术领域technical field
本发明涉及生物技术检测技术领域，特别是涉及一种用于检测癌症化疗敏感性的血浆蛋白分子、应用及试剂盒。The invention relates to the technical field of biotechnology detection, in particular to a plasma protein molecule, an application and a kit for detecting the sensitivity of cancer chemotherapy.
背景技术Background technique
肿瘤是威胁人类生命健康的重要疾病之一。胰腺癌是恶性程度最高的肿瘤，素有“万癌之王”之称。其发病隐匿，使得很多患者往往在诊断时已失去了外科手术的机会，而不佳的化疗响应率，更是极大的限制了胰腺癌患者的预后。Tumor is one of the important diseases that threaten human life and health. Pancreatic cancer is the most malignant tumor and is known as the "king of all cancers". Due to its insidious onset, many patients often lose the opportunity for surgery at the time of diagnosis, and the poor response rate to chemotherapy greatly limits the prognosis of pancreatic cancer patients.
近年随着多方研究的探索，FOLFIRINOX、纳米白蛋白紫杉醇联合吉西他滨等一些化疗方案逐渐脱颖而出，成为治疗胰腺癌的主要化疗策略。然而不幸的是，这些方案在胰腺癌人群中有效率仍然十分有限。因此，寻找有效的肿瘤标记物帮助准确识别化疗获益人群，对于提升胰腺癌患者预后有着重要意义。CA19-9是临床上最为常用的肿瘤标记物之一，其对于胰腺癌诊断有着良好的灵敏性和特异性。基于CA19-9的表达模式，研究发现对于CA19-9下降超过20％的患者，FOLFIRINOX方案可获得更高的客观缓释率(ORR，44.0％vs.22.9％)。然而，CA19-9并非表达于所有胰腺癌患者，该策略也无法在化疗前对患者的响应情况作出预测。为解决这个问题，一些研究在肿瘤循环DNA和血清蛋白标记物上作出了探索，发现ctDNA(KRAS)、CEA和sCD40L等生物标记物均可在一定程度上预测化疗药物的敏感性。然而，这些生物标记物，不论是单用还是联用，其预测的灵敏性和特异性都难以满足临床的需求。所以，开发新的肿瘤标记物，在化疗前对药物敏感性作出预测具有良好的应用前景。In recent years, with the exploration of various researches, some chemotherapy regimens such as FOLFIRINOX, nano-albumin-paclitaxel combined with gemcitabine have gradually come to the fore and become the main chemotherapy strategies for the treatment of pancreatic cancer. Unfortunately, however, the effectiveness of these regimens in the pancreatic cancer population remains limited. Therefore, finding effective tumor markers to help accurately identify the benefit group from chemotherapy is of great significance for improving the prognosis of pancreatic cancer patients. CA19-9 is one of the most commonly used tumor markers in clinic, and it has good sensitivity and specificity for the diagnosis of pancreatic cancer. Based on the expression pattern of CA19-9, the study found that for patients whose CA19-9 decreased by more than 20%, the FOLFIRINOX regimen could achieve a higher objective sustained release rate (ORR, 44.0% vs. 22.9%). However, CA19-9 is not expressed in all pancreatic cancer patients, and this strategy cannot predict patient response before chemotherapy. To solve this problem, some studies have explored tumor circulating DNA and serum protein markers, and found that biomarkers such as ctDNA (KRAS), CEA, and sCD40L can predict chemotherapeutic drug sensitivity to a certain extent. However, the predictive sensitivity and specificity of these biomarkers, whether used alone or in combination, are difficult to meet clinical needs. Therefore, the development of new tumor markers to predict drug sensitivity before chemotherapy has a good application prospect.
随着技术的发展与成熟，基于质谱的蛋白质组学策略逐渐在临床检测和基础研究中大展身手。其高通量、高灵敏度、高准确性的技术特点，为生物医学领域带来了巨大的便利，使得大规模筛选生物标记物成为了可能。因此，利用组学大数据的策略筛选新的标记物，并构建多标记物的预测模型，或能将化疗预测的准确性提升到新的水平。With the development and maturity of technology, mass spectrometry-based proteomics strategies have gradually shown their potential in clinical detection and basic research. Its high-throughput, high-sensitivity, and high-accuracy technical characteristics have brought great convenience to the biomedical field, making it possible to screen biomarkers on a large scale. Therefore, using the strategy of omics big data to screen new markers and build a multi-marker prediction model may improve the accuracy of chemotherapy prediction to a new level.
发明内容SUMMARY OF THE INVENTION
本发明研究发现人外周血提取的游离蛋白GSN、APOA4、IGHG1、Immunoglobulin muheavy chain(IgM heavy chain，IgM抗体的重链)、FCN2，其血浆蛋白浓度在化疗响应不同的胰腺癌患者中呈现出显著差异，进一步分析可以发现血清GSN、APOA4、Immunoglobulinmu heavy chain、FCN2的高表达及IGHG1的低表达与肿瘤对FOLFIRINOX方案的敏感性相关。以该蛋白分子构建胰腺癌化疗响应预测模型，其受试者工作特征(receiver operatingcharacteristic，ROC)曲线下面积可达0.88，联合患者年龄预测胰腺癌化疗响应的ROC曲线下面积可达0.915。所以，血浆蛋白标志物分子联合患者临床信息构建的预测模型可作为预测肿瘤化疗响应的肿瘤标记物，进而帮助临床筛选化疗敏感人群。The present invention finds that the plasma protein concentrations of free proteins GSN, APOA4, IGHG1, Immunoglobulin muheavy chain (IgM heavy chain, heavy chain of IgM antibody) and FCN2 extracted from human peripheral blood show significant differences in pancreatic cancer patients with different chemotherapy responses. Further analysis showed that the high expression of serum GSN, APOA4, Immunoglobulinmu heavy chain, FCN2 and low expression of IGHG1 were related to the sensitivity of the tumor to the FOLFIRINOX regimen. Using this protein molecule to build a prediction model of pancreatic cancer chemotherapy response, the area under the receiver operating characteristic (ROC) curve can reach 0.88, and the area under the ROC curve combined with patient age to predict pancreatic cancer chemotherapy response can reach 0.915. Therefore, the prediction model constructed by combining plasma protein marker molecules with clinical information of patients can be used as tumor markers to predict the response of tumor chemotherapy, and then help clinical screening of chemotherapy-sensitive populations.
一种用于检测癌症化疗敏感性的血浆蛋白分子，包括人血浆蛋白GSN、APOA4、IGHG1、Immunoglobulin mu heavy chain和FCN2中的至少一种。A plasma protein molecule for detecting cancer chemotherapy sensitivity, comprising at least one of human plasma proteins GSN, APOA4, IGHG1, Immunoglobulin mu heavy chain and FCN2.
优选的，所述的血浆蛋白分子，包括以下蛋白组合中的一种：GAI、GAF、GAM、GIF、GIM、GFM、AIF、AFM、IFM、AIM，其中G：GSN、A：APOA4、I：IGHG1、M：Immunoglobulin mu heavychain、F：FCN2。Preferably, the plasma protein molecule includes one of the following protein combinations: GAI, GAF, GAM, GIF, GIM, GFM, AIF, AFM, IFM, AIM, wherein G: GSN, A: APOA4, I: IGHG1, M: Immunoglobulin mu heavychain, F: FCN2.
更优选的，所述的血浆蛋白分子，包括人血浆蛋白GSN、APOA4、IGHG1、Immunoglobulin mu heavy chain和FCN2。五种人血浆蛋白组合在一起使用，结果更加准确。More preferably, the plasma protein molecules include human plasma proteins GSN, APOA4, IGHG1, Immunoglobulin mu heavy chain and FCN2. The five human plasma proteins were used in combination and the results were more accurate.
本发明又提供了所述的血浆蛋白分子在作为检测癌症化疗敏感性的靶点中的应用。所述的应用，癌症种类为胰腺癌。所述的应用，化疗方案为FOLFIRINOX方案。The present invention further provides the application of the plasma protein molecule as a target for detecting the sensitivity of cancer chemotherapy. In the application, the type of cancer is pancreatic cancer. In the application, the chemotherapy regimen is the FOLFIRINOX regimen.
所述的应用，包括检测血浆中人血浆蛋白GSN、APOA4、IGHG1、Immunoglobulin muheavy chain和FCN2的浓度，以及将上述5种人血浆蛋白浓度数据输入到预生成的随机森林模型中对患者的化疗响应进行预测评估。The application includes detecting the concentrations of human plasma proteins GSN, APOA4, IGHG1, Immunoglobulin muheavy chain and FCN2 in plasma, and inputting the above five human plasma protein concentration data into a pre-generated random forest model to respond to chemotherapy in patients Conduct predictive evaluations.
所述预生成的随机森林模型构建方法为：The pre-generated random forest model construction method is:
取已知FOLFIRINOX方案敏感及耐药的患者血浆中人血浆蛋白GSN、APOA4、IGHG1、Immunoglobulin mu heavy chain和FCN2的浓度数据，The concentration data of human plasma proteins GSN, APOA4, IGHG1, Immunoglobulin mu heavy chain and FCN2 in the plasma of patients known to be sensitive and resistant to the FOLFIRINOX regimen were obtained.
设定种子数为2019，以2∶1比例进行随机分组构建训练集与验证集，导入5种蛋白的浓度矩阵、治疗响应情况和相应的临床信息：性别、年龄、肿瘤标记物、肿瘤大小及位置，利用随机森林算法构建模型，并重复以上运算100次获得模型平均预测的ROC工作曲线，设定树的数量为1000，末梢节点最小观察资料数为1∶5，利用ranger函数拟合得到最终模型。The seed number was set to 2019, and the training set and the validation set were randomly grouped at a ratio of 2:1 to construct a training set and a validation set, and the concentration matrix of the five proteins, the treatment response and the corresponding clinical information were imported: gender, age, tumor markers, tumor size and Location, use the random forest algorithm to build the model, and repeat the
本发明还提供了一种用于检测癌症化疗敏感性的试剂盒，包括：The present invention also provides a kit for detecting cancer chemotherapy sensitivity, comprising:
(1)用于从血浆中提取蛋白的试剂；(1) Reagents for extracting proteins from plasma;
(2)用于检测人血浆蛋白GSN、APOA4、IGHG1、Immunoglobulin mu heavy chain和FCN2浓度的试剂。(2) Reagents for detecting the concentrations of human plasma proteins GSN, APOA4, IGHG1, Immunoglobulin mu heavy chain and FCN2.
本发明研究发现人血浆蛋白GSN、APOA4、IGHG1、Immunoglobulin mu heavychain、FCN2浓度在不同化疗响应的胰腺癌患者中存在显著差异，作为肿瘤标记物单独使用时预测胰腺癌化疗响应的ROC曲线下面积可达到0.5550～0.7275；将这五种人血浆蛋白浓度组合作为肿瘤标记物联合患者年龄预测胰腺癌化疗响应的ROC曲线下面积可达0.915。所以，基于血浆蛋白分子GSN、APOA4、IGHG1、Immunoglobulin mu heavy chain、FCN2(包含以上五种蛋白的任意组合及其中各部分的任意组合)联合患者临床信息，可作为预测肿瘤化疗敏感性的肿瘤标记物，辅以前期构建的随机森林模型，实现化疗敏感人群的有效筛选，大大提升临床获益。The present study found that the concentrations of human plasma proteins GSN, APOA4, IGHG1, Immunoglobulin mu heavychain and FCN2 were significantly different in pancreatic cancer patients with different chemotherapy responses. When used alone as tumor markers, the area under the ROC curve for predicting pancreatic cancer chemotherapy response could It reached 0.5550-0.7275; the area under the ROC curve of predicting pancreatic cancer chemotherapy response by combining these five human plasma protein concentrations as tumor markers and patient age could reach 0.915. Therefore, based on the plasma protein molecules GSN, APOA4, IGHG1, Immunoglobulin mu heavy chain, FCN2 (including any combination of the above five proteins and any combination of their parts) combined with clinical information of patients, they can be used as tumor markers for predicting tumor chemotherapy sensitivity With the help of the random forest model constructed in the previous stage, the effective screening of chemotherapy-sensitive populations can be achieved, and the clinical benefit can be greatly improved.
附图说明Description of drawings
图1为化疗耐药和敏感胰腺癌患者的血浆蛋白浓度对比结果图。Figure 1 shows the comparison results of plasma protein concentrations in patients with chemotherapy-resistant and sensitive pancreatic cancer.
图2为实施例3中模型构建时抽取的其中一个模型决策树结果图。FIG. 2 is a result diagram of one of the model decision trees extracted when the model is constructed in Example 3. FIG.
图3为血浆蛋白组合在胰腺癌化疗响应预测中的敏感性和特异性分析结果图。Figure 3 is a graph showing the results of sensitivity and specificity analysis of plasma protein combinations in predicting pancreatic cancer chemotherapy response.
图4为单蛋白标记物模型在胰腺癌化疗响应预测中的敏感性和特异性分析结果图。Figure 4 is a graph showing the results of the sensitivity and specificity analysis of the single protein marker model in the prediction of pancreatic cancer chemotherapy response.
图5为基于二元Logistic回归分析建立的传统三蛋白标记物组合模型在胰腺癌化疗响应预测中的敏感性和特异性分析结果图。Figure 5 is a graph showing the sensitivity and specificity analysis results of the traditional three-protein marker combination model established based on binary logistic regression analysis in the prediction of pancreatic cancer chemotherapy response.
具体实施方式Detailed ways
样品来源：胰腺癌患者血浆样品来源于浙江大学医学院附属第一医院。Sample source: Plasma samples from patients with pancreatic cancer were obtained from the First Affiliated Hospital of Zhejiang University School of Medicine.
伦理审批：经浙江大学医学院附属第一医院科研伦理审查委员会伦理审查，批件号：(2019)科研快审第(622)号。Ethical approval: Ethical review by the Scientific Research Ethics Review Committee of the First Affiliated Hospital of Zhejiang University School of Medicine, approval number: (2019) Scientific Research Quick Review No. (622).
实施例1Example 1
外周血样品提取血浆蛋白。方法如下：Plasma proteins were extracted from peripheral blood samples. Methods as below:
使用抗凝采血管采集人外周血，离心(3000g，4℃，20分钟)去除血细胞后，上清于-80℃冰箱冻存。检测时，取出冻存上清，4℃下融解。离心(10000g，60分钟)以去除大的细胞碎片等物质。取100μl上清，加入200μl蛋白提取液(购自Biognosys公司，试剂盒产品号：Ki-3013)充分混匀后4℃静置5分钟。离心(14000g，4℃，10分钟)后取上清，得到血浆蛋白样本。Human peripheral blood was collected using an anticoagulant blood collection tube, centrifuged (3000 g, 4°C, 20 minutes) to remove blood cells, and the supernatant was frozen at -80°C. During detection, the frozen supernatant was taken out and thawed at 4°C. Centrifuge (10000g, 60 minutes) to remove material such as large cell debris. 100 μl of the supernatant was taken, 200 μl of protein extract (purchased from Biognosys, kit product number: Ki-3013) was added, and the mixture was thoroughly mixed, and then allowed to stand at 4° C. for 5 minutes. After centrifugation (14000g, 4°C, 10 minutes), the supernatant was collected to obtain a plasma protein sample.
实施例2Example 2
利用蛋白定量试剂盒(购自Thermo Fisher公司，产品号：23225)的方法步骤对血浆蛋白样本进行定量。使用蛋白酶(购自Biognosys公司，试剂盒产品号：Ki-3013)对蛋白进行酶解，获得可供上机的肽段。并依据定量结果在相同含量的多肽样本中添加等量的核素标记标准肽段(购自Biognosys公司，产品号：Ki-3019)，制成可供上机的肽段样本。Plasma protein samples were quantified using the method steps of a protein quantification kit (purchased from Thermo Fisher, product number: 23225). Use protease (purchased from Biognosys, kit product number: Ki-3013) to digest the protein to obtain peptide fragments that can be used on the machine. According to the quantitative results, an equal amount of nuclide-labeled standard peptides (purchased from Biognosys, product number: Ki-3019) were added to the peptide samples with the same content to prepare peptide samples that can be used on the machine.
实施例3Example 3
对实施例2中的样本进行质谱检测，进而获得目标蛋白的定量结果。依据绝对定量结果和上机样本体积对目标蛋白的血浆浓度进行计算，并将获得的浓度导入前期构建的随机森林模型中，进而对患者的化疗响应情况作出预测。Mass spectrometry was performed on the sample in Example 2 to obtain quantitative results of the target protein. The plasma concentration of the target protein was calculated according to the absolute quantitative results and the sample volume on the machine, and the obtained concentration was imported into the random forest model constructed in the previous stage, so as to predict the chemotherapy response of patients.
检测共涉及胰腺癌化疗患者血浆样品共40例，其中FOLFIRINOX方案敏感患者25例，耐药患者15例，结果表明，不同化疗响应的患者外周血中GSN、APOA4、IGHG1、Immunoglobulin mu heavy chain、FCN2蛋白水平显著不同(图1)。A total of 40 plasma samples were involved in the detection of pancreatic cancer chemotherapy patients, including 25 patients with FOLFIRINOX regimen sensitive patients and 15 patients with drug resistance. The results showed that GSN, APOA4, IGHG1, Immunoglobulin mu heavy chain, FCN2 Protein levels were significantly different (Figure 1).
设定种子数为2019，以2∶1比例进行随机分组构建训练集与验证集，导入5种蛋白的浓度矩阵、治疗响应情况和相应的临床信息：性别、年龄、肿瘤标记物、肿瘤大小及位置，利用随机森林算法构建模型，并重复以上运算100次获得模型平均预测的ROC工作曲线。设定树的数量为1000，末梢节点最小观察资料数为1∶5，利用ranger函数拟合得到最终模型。抽取模型决策树显示基于该5个血清蛋白可对样本集的胰腺癌作出高效的预测，准确率高达95％。基于该决策树，可以发现血清GSN、APOA4、Immunoglobulin mu heavy chain、FCN2的高表达及IGHG1的低表达与肿瘤对FOLFIRINOX方案的敏感性相关(图2)。The seed number was set to 2019, and the training set and the validation set were randomly grouped at a ratio of 2:1 to construct a training set and a validation set, and the concentration matrix of the five proteins, the treatment response and the corresponding clinical information were imported: gender, age, tumor markers, tumor size and position, use the random forest algorithm to build the model, and repeat the
实施例4Example 4
基于该随机森林模型对验证队列展开分析，ROC曲线显示，基于血浆蛋白分子GSN、APOA4、IGHG1、Immunoglobulin mu heavy chain、FCN2联合患者年龄的预测模型可有效预测胰腺癌患者化疗响应情况，ROC曲线下面积可达0.915(图3)。特别地，在保证诊断特异性100％的情况下，敏感性可达80％以上。其预测效率显著优于存在单蛋白标记物模型(使用wilson/brown模型，结果如图4所示)及基于二元Logistic回归分析建立的传统三蛋白标记物组合模型(图5和表1)。而后者因利用单一队列进行训练和验证，存在较高的过拟合风险，在扩大队列中验证效果不佳。因此，血浆蛋白分子联合临床数据在胰腺癌患者化疗敏感性预测中具有极佳的优势，远超现行临床使用的标准方法。Based on the random forest model, the validation cohort was analyzed, and the ROC curve showed that the prediction model based on the combination of plasma protein molecules GSN, APOA4, IGHG1, Immunoglobulin mu heavy chain, and FCN2 combined with patient age could effectively predict the response to chemotherapy in patients with pancreatic cancer. Under the ROC curve The area can reach 0.915 (Figure 3). In particular, the sensitivity can reach more than 80% under the condition that the diagnostic specificity is guaranteed to be 100%. Its prediction efficiency was significantly better than that of the single-protein marker model (using the wilson/brown model, the results are shown in Figure 4) and the traditional three-protein marker combination model established based on binary logistic regression analysis (Figure 5 and Table 1). The latter has a high risk of overfitting due to the use of a single cohort for training and validation, and the validation effect is not good in the expanded cohort. Therefore, the combination of plasma protein molecules with clinical data has excellent advantages in the prediction of chemotherapy sensitivity in pancreatic cancer patients, far exceeding the current standard methods used in clinical practice.
表1Table 1
注：G：GSN、A：APOA4、I：IGHG1、M：IgM heavy chain、F：FCN2。Note: G: GSN, A: APOA4, I: IGHG1, M: IgM heavy chain, F: FCN2.
综上，本发明首次发现基于人血浆蛋白分子(包括GSN、APOA4、IGHG1、Immunoglobulin mu heavy chain、FCN2，包含以上五种蛋白的任意组合及其中各部分的任意组合)的化疗敏感性预测模型在胰腺癌患者中有很好的预测效果，因而可作为现有化疗响应预测策略的重要补充，效率远优于现行临床方法。To sum up, the present invention for the first time found that the chemosensitivity prediction model based on human plasma protein molecules (including GSN, APOA4, IGHG1, Immunoglobulin mu heavy chain, FCN2, including any combination of the above five proteins and any combination of parts thereof) is in Pancreatic cancer patients have a good prediction effect, so it can be used as an important supplement to existing chemotherapy response prediction strategies, and the efficiency is far superior to the current clinical methods.
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