非小細胞肺癌患者與健康人群血清及尿液特異性差異多肽及肺鱗癌患者血清多肽與化療療效相關(guān)性的探索性研究
發(fā)布時間：2018-09-01 13:33
【摘要】：背景:肺癌(lung cancer)是目前最常見的惡性腫瘤。在全世界范圍內(nèi),肺癌的發(fā)病率和死亡率均居各種惡性腫瘤的榜首。在我國,肺癌為男性腫瘤發(fā)病率及死亡率的第一位,女性腫瘤發(fā)病率的第二位,死亡率的第一位。肺癌主要分為小細胞肺癌(small cell lung cancer,SCLC)和非小細胞肺癌(non-small cell lung cancer,NSCLC)兩大類。NSCLC是肺癌最常見的病理類型,占新發(fā)肺癌病例的80%以上,其病理類型主要包括:肺鱗癌(squamous cell carcinoma of lung)和肺腺癌(adenocarcinoma of lung)。目前NSCLC患者的5年生存率只有10%-15%,然而早期診斷,早期手術(shù)的I期NSCLC患者,術(shù)后的10年生存率可達92%。但是由于起病隱匿,且缺乏靈敏度和特異性均能滿足臨床需求的早期診斷手段,致使初診時已有70%以上的NSCLC患者失去了手術(shù)機會。因此早發(fā)現(xiàn),早診斷,早治療才是延長患者生存期,提高患者生活質(zhì)量的關(guān)鍵所在。目前常用的診斷肺癌的方法,如病理學,影像學及肺癌標記物。穿刺活檢是明確病理的主要手段。但其為有創(chuàng)檢查,且部分患者不易通過穿刺取到活檢;影像學診斷是目前初步診斷肺癌最常用的手段,其價格較高,有射線損害,并且早期肺癌患者體內(nèi)癌變的細胞遠遠小于影像學技術(shù)可測量的最小閾值;腫瘤標志物作為目前最常用的肺癌初篩手段,其敏感度及特異性均不夠臨床需求,這三種常規(guī)診斷肺癌的方法均難以實現(xiàn)肺癌的早期診斷。因此亟需一種可以方便開展的用于NSCLC早期診斷的手段。盡管抗血管生成治療、免疫治療、靶向治療等已被FDA批準為晚期肺鱗癌患者的二線或多線治療手段。晚期肺鱗癌的臨床治療仍停留在以傳統(tǒng)化療為主的階段,鉑二聯(lián)方案化療依然是晚期肺鱗癌患者主要的一線治療手段。而在臨床觀察中發(fā)現(xiàn),由于缺乏有效手段能在化療前預測患者化療療效,致使部分肺鱗癌患者承受化療帶來的毒性作用卻未能從化療中獲益。單個基因標志物如ERCC1、RRM1、TUBB3及XRCC1,因細胞毒藥物應答與單個基因標志物的相關(guān)性較弱,在進一步擴大樣本驗證的臨床試驗中均未能有效預測療效。因此尋找有效的化療療效預測手段,對肺鱗癌患者進行化療療效預測,對于使肺鱗癌患者更好的從化療中獲益,減少化療帶來的毒性作用至關(guān)重要。如今蛋白質(zhì)組學已廣泛應用于血液、尿液等各種體液及組織的研究,成為應用于各種腫瘤研究的蛋白質(zhì)組學工具。本研究從NSCLC患者的診斷及治療兩個方面進行蛋白質(zhì)組學研究,以期對NSCLC患者進行更好地診斷及治療,實現(xiàn)對于患者的全程管理。第一部分運用MALDI-TOF質(zhì)譜儀檢測非小細胞肺癌患者與健康人群血清及尿液特異性差異多肽目的:本研究應用基質(zhì)輔助激光解析電離飛行時間質(zhì)譜(matrix-assisted laser desorption/ionization-time of flight-mass spectrometry,MALDI-TOF-MS)檢測NSCLC患者與健康者血清及尿液樣本之間的多肽差異,建立NSCLC患者的診斷模型及病理分類模型,為進行NSCLC患者的早期診斷,明確病理奠定基礎(chǔ)。內(nèi)容:收集2014年10月-2016年4月期間于我院肺部腫瘤內(nèi)科就診的經(jīng)組織病理學或細胞學診斷為NSCLC患者的血液及尿液樣本。收集肺鱗癌患者血清樣本82例,尿液樣本41例,肺腺癌患者血清樣本82例,尿液樣本41例。健康者的血清及尿液樣本均取自自愿者,其中血清樣本115例,尿液樣本74例。經(jīng)血清及尿液樣本預處理,MALDI-TOF-MS質(zhì)譜檢測,CPT軟件處理分析,得到NSCLC患者的診斷模型及病理分類模型。所建立模型可用于NSCLC患者的早期篩查,明確診斷及明確病理。方法:將一般情況相匹配的NSCLC患者血清及尿液樣本,健康人群血清及尿液樣本,按照3:1的比例隨機分為訓練組和驗證組:訓練組用于建立NSCLC患者診斷及病理分類模型;驗證組用以驗證所建立的診斷及病理分類模型。采用銅離子鰲合納米磁珠(MB-IMAC-Cu2+)提取血清及尿液樣本中的多肽,運用MALDI-TOF-MS檢測,并通過CPT軟件分析處理得到訓練組NSCLC患者與健康者的血清及尿液多肽指紋圖譜。分別應用Clin Pro Tools(CPT)軟件自帶的3種不同的生物學算法:快速分類法(Quickclassifier,QC算法)、遺傳算法(genetic algorithm,GA算法)和監(jiān)督神經(jīng)網(wǎng)絡算法(supervised neural network,SNN算法)建立診斷模型。選取最優(yōu)算法所建立的NSCLC患者血清和尿液診斷模型。應用驗證組對所建立診斷模型進行盲樣驗證。對肺鱗癌及肺腺癌兩種不同病理分型的血清及尿液樣本所得到多肽指紋圖譜進一步分類,應用CPT軟件內(nèi)置的3種不同的生物學算法建立病理分類模型,選取最優(yōu)算法所建立的NSCLC患者的血清及尿液病理分類模型,應用驗證組進行盲樣驗證。結(jié)果:將一般情況相匹配的164例NSCLC患者血清(鱗癌腺癌各82例),82例NSCLC患者尿液(鱗癌腺癌各41例)及115例健康人群血清,74例健康人群尿液按照3:1的比例隨機分成訓練組和驗證組:訓練組由124例NSCLC患者血清(鱗癌腺癌各62例),62例NSCLC患者尿液(鱗癌腺癌各31例)及85例健康人群血清,54例健康人群尿液組成,用于建立NSCLC患者診斷及病理分類模型;驗證組由40例NSCLC患者血清(鱗癌腺癌各20例),20例NSCLC患者尿液(鱗癌腺癌各10例)及30例健康人群血清,20例健康人群尿液組成,用以驗證所建立的診斷及病理分類模型。訓練組在800-10000Da范圍內(nèi)尋找差異多肽,并找到具有統(tǒng)計學意義的差異多肽(p0.001)。NSCLC患者vs健康人,血清樣本中發(fā)現(xiàn)有107個差異多肽,具有統(tǒng)計學意義的差異多肽有52個。其診斷模型最優(yōu)算法為GA算法,所建立的NSCLC診斷模型由5個多肽(2105.93Da,867.25Da,4093.08Da,7651.25Da,5341.39Da)組成,模型的識別率為96.77%,交叉驗證率為89.86%。應用驗證組樣本對所建立模型進行盲樣驗證,該模型準確率為92.9%(65/70),靈敏度為95.0%(38/40),特異性為90%(27/30);尿液樣本中發(fā)現(xiàn)有131個差異多肽,具有統(tǒng)計學意義的差異多肽有19個,其診斷模型最優(yōu)算法為SNN算法,所建立診斷模型由9個多肽(1718.26Da,2193.17Da,1378.24Da,812.56Da,2376.22Da,5954.38Da,5810.98Da,2812.4Da,2438.33Da)組成,模型的識別率為98.25%,交叉驗證率為91.74%。應用驗證組樣本對所建立模型進行盲樣驗證,該模型準確率為90%(36/40),靈敏度為95%(19/20),特異性為85.0%(17/20);從NSCLC患者與健康人群血清及尿液樣本差異多肽中,查見1個相同的具有統(tǒng)計學意義的差異多肽:3242Da,經(jīng)鑒定為纖維蛋白原α。肺鱗癌vs肺腺癌,血液樣本中發(fā)現(xiàn)有96個差異多肽,具有統(tǒng)計學意義的差異多肽有20個,其分類模型最優(yōu)算法為GA算法,所建立病理分類模型由5個多肽(9312.15Da,3242.37Da,4213.52Da,5297.33Da,4645.83Da)組成,模型的識別率為90.1%,交叉驗證率為75.65%,應用驗證組樣本對所建立模型進行盲樣驗證,該模型準確率為82.5%(33/40),靈敏度為85.0%(17/20),特異性為80.0%(16/20);尿液樣本中發(fā)現(xiàn)有119個差異多肽,具有統(tǒng)計學意義的差異多肽有0個,未能建立病理分類模型。結(jié)論:本研究表明NSCLC患者與健康者,肺鱗癌與肺腺癌患者的血清及尿液多肽存在差異。運用MALDI-TOF-MS技術(shù)建立NSCLC患者診斷模型及病理分類模型,具有較高的敏感度和特異性。所建立模型可用于NSCLC患者早期診斷及明確病理的補充手段。但需進一步擴大樣本量完善及驗證預測模型。第二部分應用MALDI-TOF-MS檢測肺鱗癌患者血清多肽并分析與其化療療效相關(guān)性目的:本研究應用基質(zhì)輔助激光解析電離飛行時間質(zhì)譜(MALDI-TOF-MS)檢測初治晚期肺鱗癌患者接受紫杉醇類聯(lián)合鉑類化療前血清多肽,并分析其與化療療效的相關(guān)性。建立晚期肺鱗癌患者化療療效預測模型,為進行化療療效的個體化預測,指導肺鱗癌患者進行個體化化療奠定基礎(chǔ)。內(nèi)容:本研究共入組2014年10月-2016年4月期間就診于我院肺部腫瘤內(nèi)科經(jīng)組織病理學或細胞學診斷為肺鱗癌患者81例。一線接受紫杉醇類聯(lián)合鉑類方案化療,并每2周期進行療效評價,分為肺鱗癌化療敏感組及化療耐藥組。經(jīng)血清樣本預處理,MALDI-TOF-MS質(zhì)譜檢測,CPT軟件處理分析,得到晚期肺鱗癌患者化療療效預測模型。所建立模型可用于預測紫杉醇類聯(lián)合鉑類方案化療療效。方法:收集治療前晚期肺鱗癌患者的血清樣本,一線行紫杉醇類聯(lián)合鉑類方案化療,并每兩周期進行療效評價。按照實體瘤療效評價標準(Response Evaluation Criteria in Solid Tumors RECIST1.1)評價治療療效。評效為CR或PR的肺鱗癌患者歸為化療敏感組,評效為PD的肺鱗癌患者歸為化療耐藥組。將入組標本按照3:1的比例隨機分為訓練組(敏感組I與耐藥組I)和驗證組(敏感組II與耐藥組II)。采用MB-IMAC-Cu2+進行血清預處理,分離得到血清樣本中的多肽,MALDI-TOF-MS檢測訓練組血清多肽并得到血清多肽指紋圖譜。CPT軟件系統(tǒng)分析處理,得到兩組樣本間差異多肽,并應用CPT軟件內(nèi)置的3種不同的生物學算法(SNN,GA,QC算法)建立療效預測模型。選取最優(yōu)算法所建立的肺鱗癌化療療效預測模型,運用驗證組對所建立模型進行盲樣驗證,得到模型的準確率,靈敏度,特異性。統(tǒng)計入組患者治療療效,通過統(tǒng)計學方法分析,得到血清差異多肽與兩組患者PFS之間的相關(guān)性。結(jié)果:入組的81例一線接受紫杉醇類聯(lián)合鉑類方案化療的初治晚期肺鱗癌患者。其中CR為0例,PR為40例(49.4%,40/81),PD為41例(50.6%,41/81)。訓練組共納入30例敏感患者(敏感組I),31例耐藥患者(耐藥組I);驗證組共納入敏感(敏感組II)與耐藥(耐藥組II)患者各10例。敏感組I中位PFS為7.2個月(95%CI:4.4-14.5);耐藥組I中位PFS為1.8個月(95%CI:0.7-3.5)。敏感組I與耐藥組I患者血清樣本中發(fā)現(xiàn)有96個差異多肽,其中具有統(tǒng)計學意義的差異多肽有16個(p0.001)。療效預測模型最優(yōu)算法為GA算法,所建立模型由5個多肽(1897.75Da,2023.93Da,3683.36Da,4269.56Da,5341.29Da)組成。該模型對敏感組患者的識別率為95.11%,交叉驗證率為89.18%。應用驗證組患者樣本進行驗證,該模型總的準確率85%(17/20),靈敏度90%(9/10),特異性80%(8/10)。敏感組I中位PFS為7.2個月(95%CI:4.4-14.5);耐藥組I中位PFS為1.8個月(95%CI:0.7-3.5)。結(jié)合臨床預后參數(shù)PFS運用雙變量相關(guān)分析,得到各個多肽峰與PFS間的相關(guān)系數(shù)。結(jié)果發(fā)現(xiàn):4232.04Da,4269.56Da的差異多肽與肺鱗癌患者PFS存在相關(guān)性(p0.01)。其中4269.56Da多肽同樣用于建立化療療效預測模型,進一步說明了其多肽與化療療效的密切關(guān)系。進一步進行多肽峰的鑒定,得到2個在化療耐藥組患者血清中表達上調(diào)的多肽峰。其質(zhì)荷比為:1897Da,2023Da,經(jīng)鑒定為補體C4a,補體C3f。結(jié)論:本研究表明化療敏感組及化療耐藥組患者的血清多肽存在差異,運用MALDI-TOF-MS技術(shù)建立療效預測模型,可用于預測紫杉醇類聯(lián)合鉑類方案化療療效,并且具有較高的敏感度和特異性。但需進一步擴大樣本量完善及驗證預測模型。
[Abstract]:BACKGROUND: Lung cancer is the most common malignant tumor at present. In the world, the incidence and mortality of lung cancer rank first among all kinds of malignant tumors. Small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) are two major types. NSCLC is the most common pathological type of lung cancer, accounting for more than 80% of new lung cancer cases. The pathological types include squamous cell carcinoma of lung and adenocarcinoma of lung. The annual survival rate is only 10%-15%. However, the 10-year survival rate of early stage I NSCLC patients is 92%. However, due to the concealed onset, lack of sensitivity and specificity can meet the clinical needs of early diagnosis, more than 70% of the initial diagnosis of NSCLC patients have lost the opportunity for surgery. Puncture biopsy is the main method of defining pathology, but it is invasive, and it is difficult for some patients to get biopsy through puncture. The most commonly used method of step-by-step diagnosis of lung cancer is high price, with radiation damage, and the cancer cells in early lung cancer patients are far less than the minimum threshold that can be measured by imaging technology; tumor markers as the most commonly used means of lung cancer screening, their sensitivity and specificity are not enough for clinical needs, these three routine diagnosis of lung cancer. Although anti-angiogenesis therapy, immunotherapy and targeted therapy have been approved by FDA as second-line or multi-line therapies for patients with advanced lung squamous cell carcinoma, the clinical treatment of advanced lung squamous cell carcinoma remains in the traditional chemotherapy. In the primary stage, platinum-based chemotherapy is still the main first-line treatment for advanced lung squamous cell carcinoma. In clinical observation, the lack of effective means to predict the efficacy of chemotherapy before chemotherapy, resulting in some lung squamous cell carcinoma patients withstanding the toxic effects of chemotherapy but not benefit from chemotherapy. ERCC1, RRM1, TUBB3 and XRCC1, because of the weak correlation between cytotoxic drug response and single gene markers, can not effectively predict the efficacy in further clinical trials to verify the expanded sample. Proteomics has been widely used in the study of various body fluids and tissues, such as blood, urine, and so on. It has become a proteomics tool for the study of various tumors. The first part is the detection of serum and urine specific polypeptides by MALDI-TOF mass spectrometry in patients with non-small cell lung cancer and healthy people. The purpose of this study was to apply matrix-assisted laser desorption/ionization mass spectrometry (MALDI-TOF mass spectrometry). - time of flight-mass spectrometry, MALDI-TOF-MS) was used to detect the polypeptide differences between the serum and urine samples of patients with NSCLC and healthy subjects, to establish the diagnosis model and pathological classification model of NSCLC patients, and to lay the foundation for early diagnosis and pathological diagnosis of NSCLC patients. Blood and urine samples from 82 patients with squamous cell carcinoma of the lung, 41 with urine, 82 with adenocarcinoma of the lung and 41 with urine were collected. Serum and urine samples from healthy volunteers were collected, including 115 serum samples and 74 urine samples. The diagnosis model and pathological classification model of NSCLC patients were obtained by pretreatment of serum and urine samples, detection of MALDI-TOF-MS mass spectrometry and analysis of CPT software. The established model can be used for early screening of NSCLC patients, definite diagnosis and definite pathology. Liquid samples were randomly divided into training group and validation group according to the ratio of 3:1: training group was used to establish the diagnosis and pathological classification model of NSCLC patients; validation group was used to validate the established diagnosis and pathological classification model. Serum and urine polypeptide fingerprints of NSCLC patients and healthy subjects in training group were obtained by CPT software. Three different biological algorithms, Quick Classifier (QC algorithm), Genetic Algorithm (GA algorithm) and Supervised Neural Network (supervised neural network) were used respectively. The serum and urine diagnostic models of NSCLC patients established by the optimal algorithm were selected. The diagnostic models were blindly validated by the validation group. The serum and urine pathological classification models of NSCLC patients were established by three different biological algorithms built-in. The validation group was used to verify the pathological classification models. Results: The serum samples of 164 NSCLC patients (82 squamous cell carcinomas, 82 adenocarcinomas, 41 adenocarcinomas) and 82 urine samples of NSCLC patients (41 squamous cell carcinomas, 41 adenocarcinomas, 41 adenocarcinomas, respectively) were matched. The training group consisted of 124 NSCLC patients'serum (62 squamous cell carcinoma patients each), 62 NSCLC patients' urine (31 squamous cell carcinoma patients each) and 85 healthy people's serum, 54 healthy people's urine was used to establish the diagnosis and diagnosis of NSCLC. The validation group consisted of 40 NSCLC patients'serum (20 squamous cell carcinoma, 20 adenocarcinoma), 20 NSCLC patients' urine (10 squamous cell carcinoma, 10 adenocarcinoma) and 30 healthy people's serum, and 20 healthy people's urine to validate the established diagnosis and pathological classification model. There were 107 different polypeptides in the serum samples of healthy people with NS CLC (p0.001). There were 52 different polypeptides with statistical significance. The optimal algorithm of diagnosis model was GA algorithm. The diagnosis model of NSCLC was composed of five polypeptides (2105.93 Da, 867.25 Da, 4093.08 Da, 7651.25 Da, 5341.39 Da). The accuracy of the model was 92.9% (65/70), the sensitivity was 95.0% (38/40) and the specificity was 90% (27/30). 131 differential peptides were found in urine samples, 19 of which were statistically significant. The optimal algorithm of the diagnosis model was SNN algorithm, the established diagnosis model is composed of nine peptides (1718.26Da, 2193.17Da, 1378.24Da, 812.56Da, 2376.22Da, 5954.38Da, 5810.98Da, 2812.4Da, 2438.33Da). The recognition rate of the model is 98.25%, the cross validation rate is 91.74%. The validation of the model by the validation group of blind samples, the accuracy rate of the model is 90% (36/40), the sensitivity is 95% (95%). 19/20, specificity 85.0% (17/20); from the serum and urine samples of patients with NSCLC and healthy people, we found one of the same statistically significant polypeptides: 3242Da, identified as fibrinogen alpha. lung squamous cell carcinoma vs lung adenocarcinoma, 96 differential polypeptides were found in blood samples, 20 of which were statistically significant. The optimal algorithm of the classification model is GA algorithm. The pathological classification model is composed of five polypeptides (9312.15Da, 3242.37Da, 4213.52Da, 5297.33Da, 4645.83Da). The recognition rate of the model is 90.1%, the cross validation rate is 75.65%. The accuracy and sensitivity of the model are 82.5% (33/40) and 85.0% (85.0%) respectively. 17/20, specificity 80.0% (16/20); urine samples found 119 different polypeptides, statistically significant difference polypeptides 0, can not establish a pathological classification model. Conclusion: This study shows that NSCLC patients and healthy people, lung squamous cell carcinoma and lung adenocarcinoma patients serum and urine polypeptides are different. The established model can be used for early diagnosis of NSCLC patients and as a supplementary means for defining pathology. However, further enlargement of the sample size is needed to improve and validate the prediction model. The second part uses MALDI-TOF-MS to detect serum polypeptides in patients with squamous cell carcinoma of the lung and to analyze their relationship with chemotherapy. Objective: To detect serum polypeptides in patients with advanced lung squamous cell carcinoma before chemotherapy with paclitaxel and platinum by matrix-assisted laser-resolved ionization time-of-flight mass spectrometry (MALDI-TOF-MS) and to analyze the correlation between serum polypeptides and chemotherapy efficacy. Content: A total of 81 patients with lung squamous cell carcinoma diagnosed by histopathology or cytology from October 2014 to April 2016 were enrolled in this study. They received paclitaxel combined with platinum chemotherapy in the first line and were evaluated every 2 cycles. After pretreatment of serum samples, detection of MALDI-TOF-MS and analysis of CPT software, a model for predicting the efficacy of chemotherapy in advanced lung squamous cell carcinoma was established. The model can be used to predict the efficacy of paclitaxel combined with platinum regimen chemotherapy. Methods: Serum samples from patients with advanced lung squamous cell carcinoma before and after treatment were collected. Samples were treated with paclitaxel plus platinum regimen chemotherapy on the first line, and the efficacy was evaluated every two cycles. Treatment efficacy was evaluated according to the Response Evaluation Criteria in Solid Tumors RECIST 1.1. Patients with CR or PR were classified as chemosensitivity group and those with PD were classified as chemosensitivity group. Group B. The samples were randomly divided into two groups according to the ratio of 3:1: training group (sensitive group I and drug resistance group I) and validation group (sensitive group II and drug resistance group II). In this paper, three different biological algorithms (SNN, GA, QC algorithm) built in CPT software were used to establish a therapeutic effect prediction model. Results: 81 patients with advanced lung squamous cell carcinoma received paclitaxel plus platinum chemotherapy in the first line, including 0 CR, 40 PR (49.4%, 40/81), 41 PD (50.6%, 41/81). The training group was included in the study. 30 sensitive patients (sensitive group I), 31 resistant patients (resistant group I); 10 sensitive patients (sensitive group II) and 10 resistant patients (resistant group II) were included in the validation group; the median PFS in sensitive group I was 7.2 months (95% CI: 4.4-14.5); and the median PFS in resistant group I was 1.8 months (95% CI: 0.7-3.5). 96 different polypeptides were found in the serum samples of sensitive group I and resistant group I. Among them, 16 were statistically significant (p0.001). The optimal algorithm for predicting the therapeutic effect was GA algorithm, and the established model consisted of five.
【學位授予單位】：中國人民解放軍軍事醫(yī)學科學院
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：R734.2
本文編號：2217361
[Abstract]:BACKGROUND: Lung cancer is the most common malignant tumor at present. In the world, the incidence and mortality of lung cancer rank first among all kinds of malignant tumors. Small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) are two major types. NSCLC is the most common pathological type of lung cancer, accounting for more than 80% of new lung cancer cases. The pathological types include squamous cell carcinoma of lung and adenocarcinoma of lung. The annual survival rate is only 10%-15%. However, the 10-year survival rate of early stage I NSCLC patients is 92%. However, due to the concealed onset, lack of sensitivity and specificity can meet the clinical needs of early diagnosis, more than 70% of the initial diagnosis of NSCLC patients have lost the opportunity for surgery. Puncture biopsy is the main method of defining pathology, but it is invasive, and it is difficult for some patients to get biopsy through puncture. The most commonly used method of step-by-step diagnosis of lung cancer is high price, with radiation damage, and the cancer cells in early lung cancer patients are far less than the minimum threshold that can be measured by imaging technology; tumor markers as the most commonly used means of lung cancer screening, their sensitivity and specificity are not enough for clinical needs, these three routine diagnosis of lung cancer. Although anti-angiogenesis therapy, immunotherapy and targeted therapy have been approved by FDA as second-line or multi-line therapies for patients with advanced lung squamous cell carcinoma, the clinical treatment of advanced lung squamous cell carcinoma remains in the traditional chemotherapy. In the primary stage, platinum-based chemotherapy is still the main first-line treatment for advanced lung squamous cell carcinoma. In clinical observation, the lack of effective means to predict the efficacy of chemotherapy before chemotherapy, resulting in some lung squamous cell carcinoma patients withstanding the toxic effects of chemotherapy but not benefit from chemotherapy. ERCC1, RRM1, TUBB3 and XRCC1, because of the weak correlation between cytotoxic drug response and single gene markers, can not effectively predict the efficacy in further clinical trials to verify the expanded sample. Proteomics has been widely used in the study of various body fluids and tissues, such as blood, urine, and so on. It has become a proteomics tool for the study of various tumors. The first part is the detection of serum and urine specific polypeptides by MALDI-TOF mass spectrometry in patients with non-small cell lung cancer and healthy people. The purpose of this study was to apply matrix-assisted laser desorption/ionization mass spectrometry (MALDI-TOF mass spectrometry). - time of flight-mass spectrometry, MALDI-TOF-MS) was used to detect the polypeptide differences between the serum and urine samples of patients with NSCLC and healthy subjects, to establish the diagnosis model and pathological classification model of NSCLC patients, and to lay the foundation for early diagnosis and pathological diagnosis of NSCLC patients. Blood and urine samples from 82 patients with squamous cell carcinoma of the lung, 41 with urine, 82 with adenocarcinoma of the lung and 41 with urine were collected. Serum and urine samples from healthy volunteers were collected, including 115 serum samples and 74 urine samples. The diagnosis model and pathological classification model of NSCLC patients were obtained by pretreatment of serum and urine samples, detection of MALDI-TOF-MS mass spectrometry and analysis of CPT software. The established model can be used for early screening of NSCLC patients, definite diagnosis and definite pathology. Liquid samples were randomly divided into training group and validation group according to the ratio of 3:1: training group was used to establish the diagnosis and pathological classification model of NSCLC patients; validation group was used to validate the established diagnosis and pathological classification model. Serum and urine polypeptide fingerprints of NSCLC patients and healthy subjects in training group were obtained by CPT software. Three different biological algorithms, Quick Classifier (QC algorithm), Genetic Algorithm (GA algorithm) and Supervised Neural Network (supervised neural network) were used respectively. The serum and urine diagnostic models of NSCLC patients established by the optimal algorithm were selected. The diagnostic models were blindly validated by the validation group. The serum and urine pathological classification models of NSCLC patients were established by three different biological algorithms built-in. The validation group was used to verify the pathological classification models. Results: The serum samples of 164 NSCLC patients (82 squamous cell carcinomas, 82 adenocarcinomas, 41 adenocarcinomas) and 82 urine samples of NSCLC patients (41 squamous cell carcinomas, 41 adenocarcinomas, 41 adenocarcinomas, respectively) were matched. The training group consisted of 124 NSCLC patients'serum (62 squamous cell carcinoma patients each), 62 NSCLC patients' urine (31 squamous cell carcinoma patients each) and 85 healthy people's serum, 54 healthy people's urine was used to establish the diagnosis and diagnosis of NSCLC. The validation group consisted of 40 NSCLC patients'serum (20 squamous cell carcinoma, 20 adenocarcinoma), 20 NSCLC patients' urine (10 squamous cell carcinoma, 10 adenocarcinoma) and 30 healthy people's serum, and 20 healthy people's urine to validate the established diagnosis and pathological classification model. There were 107 different polypeptides in the serum samples of healthy people with NS CLC (p0.001). There were 52 different polypeptides with statistical significance. The optimal algorithm of diagnosis model was GA algorithm. The diagnosis model of NSCLC was composed of five polypeptides (2105.93 Da, 867.25 Da, 4093.08 Da, 7651.25 Da, 5341.39 Da). The accuracy of the model was 92.9% (65/70), the sensitivity was 95.0% (38/40) and the specificity was 90% (27/30). 131 differential peptides were found in urine samples, 19 of which were statistically significant. The optimal algorithm of the diagnosis model was SNN algorithm, the established diagnosis model is composed of nine peptides (1718.26Da, 2193.17Da, 1378.24Da, 812.56Da, 2376.22Da, 5954.38Da, 5810.98Da, 2812.4Da, 2438.33Da). The recognition rate of the model is 98.25%, the cross validation rate is 91.74%. The validation of the model by the validation group of blind samples, the accuracy rate of the model is 90% (36/40), the sensitivity is 95% (95%). 19/20, specificity 85.0% (17/20); from the serum and urine samples of patients with NSCLC and healthy people, we found one of the same statistically significant polypeptides: 3242Da, identified as fibrinogen alpha. lung squamous cell carcinoma vs lung adenocarcinoma, 96 differential polypeptides were found in blood samples, 20 of which were statistically significant. The optimal algorithm of the classification model is GA algorithm. The pathological classification model is composed of five polypeptides (9312.15Da, 3242.37Da, 4213.52Da, 5297.33Da, 4645.83Da). The recognition rate of the model is 90.1%, the cross validation rate is 75.65%. The accuracy and sensitivity of the model are 82.5% (33/40) and 85.0% (85.0%) respectively. 17/20, specificity 80.0% (16/20); urine samples found 119 different polypeptides, statistically significant difference polypeptides 0, can not establish a pathological classification model. Conclusion: This study shows that NSCLC patients and healthy people, lung squamous cell carcinoma and lung adenocarcinoma patients serum and urine polypeptides are different. The established model can be used for early diagnosis of NSCLC patients and as a supplementary means for defining pathology. However, further enlargement of the sample size is needed to improve and validate the prediction model. The second part uses MALDI-TOF-MS to detect serum polypeptides in patients with squamous cell carcinoma of the lung and to analyze their relationship with chemotherapy. Objective: To detect serum polypeptides in patients with advanced lung squamous cell carcinoma before chemotherapy with paclitaxel and platinum by matrix-assisted laser-resolved ionization time-of-flight mass spectrometry (MALDI-TOF-MS) and to analyze the correlation between serum polypeptides and chemotherapy efficacy. Content: A total of 81 patients with lung squamous cell carcinoma diagnosed by histopathology or cytology from October 2014 to April 2016 were enrolled in this study. They received paclitaxel combined with platinum chemotherapy in the first line and were evaluated every 2 cycles. After pretreatment of serum samples, detection of MALDI-TOF-MS and analysis of CPT software, a model for predicting the efficacy of chemotherapy in advanced lung squamous cell carcinoma was established. The model can be used to predict the efficacy of paclitaxel combined with platinum regimen chemotherapy. Methods: Serum samples from patients with advanced lung squamous cell carcinoma before and after treatment were collected. Samples were treated with paclitaxel plus platinum regimen chemotherapy on the first line, and the efficacy was evaluated every two cycles. Treatment efficacy was evaluated according to the Response Evaluation Criteria in Solid Tumors RECIST 1.1. Patients with CR or PR were classified as chemosensitivity group and those with PD were classified as chemosensitivity group. Group B. The samples were randomly divided into two groups according to the ratio of 3:1: training group (sensitive group I and drug resistance group I) and validation group (sensitive group II and drug resistance group II). In this paper, three different biological algorithms (SNN, GA, QC algorithm) built in CPT software were used to establish a therapeutic effect prediction model. Results: 81 patients with advanced lung squamous cell carcinoma received paclitaxel plus platinum chemotherapy in the first line, including 0 CR, 40 PR (49.4%, 40/81), 41 PD (50.6%, 41/81). The training group was included in the study. 30 sensitive patients (sensitive group I), 31 resistant patients (resistant group I); 10 sensitive patients (sensitive group II) and 10 resistant patients (resistant group II) were included in the validation group; the median PFS in sensitive group I was 7.2 months (95% CI: 4.4-14.5); and the median PFS in resistant group I was 1.8 months (95% CI: 0.7-3.5). 96 different polypeptides were found in the serum samples of sensitive group I and resistant group I. Among them, 16 were statistically significant (p0.001). The optimal algorithm for predicting the therapeutic effect was GA algorithm, and the established model consisted of five.
【學位授予單位】：中國人民解放軍軍事醫(yī)學科學院
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：R734.2
【參考文獻】
相關(guān)期刊論文 前4條
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