血清多肽图用于急性白血病诊断及疗效评价的研究
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摘要
急性白血病是一类造血干细胞异常的血液系统恶性肿瘤,严重威胁人类的生命。由于造血干细胞在发育过程中一系列基因的改变,从而使造血干细胞不能正常分化,对增殖调控因子失去反应能力,造成大量原始的造血干细胞积聚在骨髓、外周血和其他组织器官中,抑制正常造血细胞生长,进而产生一系列与此相关的临床表现,如贫血、出血、感染、肝脾及淋巴结肿大等表现。目前临床上通过骨髓细胞形态学、免疫学、细胞遗传学和分子生物学方法的联合应用对急性白血病做出诊断。治疗后,通过骨髓细胞形态学或其他分子生物学方法进行早期疗效评价,指导下一步治疗和做出预后评价。骨髓来源的检测分析需要在局部麻醉的条件下抽取病人骨髓,创伤性大、耗时长,而且存在一定的操作风险,目前尚缺乏一种无创、省时、灵敏度和特异性高的血清学诊断和评价方法。
血清/血浆检验具有创伤性小、花费少、易获得,处理简便的特点,而且在各种生理或病理状态下,血清/血浆中的蛋白质常有质或量的变化,因此广泛用于多种疾病诊断标志物的研究。血清多肽图(简称血肽图)方法是一种全新的诊断方法,以质谱作为主要检测手段,可检测到大量的血清多肽,根据谱图模式的差异做出诊断。血清多肽谱图技术用于肿瘤诊断研究之初就取得了令人振奋的结果。2002年发表在Lancet上的一篇题为“血清多肽组质谱扫描技术成功预警早期卵巢癌”的论文阐述了血清多肽谱用于肿瘤的诊断。此论文采用生物质谱技术比较了疾病组和对照组的血清多肽谱图模式,根据两组之间的差异特征分别对疾病组和对照组进行诊断。结果显示,50例卵巢癌患者全部被正确检出,而其中18例为Ⅰ期卵巢癌患者。该方法检测卵巢癌分别获得了100%的敏感性、95%的特异性和94%的阳性预测值,不仅优于传统的CA125检测,而且对于早期卵巢癌的诊断也十分有效。随后,国际上陆续发表了血清多肽图技术用于肿瘤诊断的论文。如,2006年初来至于Memorial Sloan-Kettering肿瘤中心的Villanueva等在The Journal of Clinical Investigation杂志发表了多肽分离技术与MALDI-TOF-MS联合诊断膀胱癌、乳腺癌和前列腺癌。2006年底Nature杂志上发表了对该文章的一篇评论,评论中对此方法给予了肯定。同时,Villanueva等再次在Molecular & Cellular Proteomics杂志上发表了利用上述技术诊断转移性甲状腺癌的论文。此外,相关论文也在Clinical Cancer Research, Journal of Clinical Oncology以及Clinical Chemistry等杂志发表。值得一提的是这一方法不仅用于肿瘤的诊断,也可用于其它疾病的诊断。2006年Lancet上发表了一篇题为“血清蛋白质组谱图法检测结核诊断标志物”的论文,与传统的诊断方法相比,结核病诊断的特异性和灵敏度均超过95%。这些研究成果表明人的血清多肽,尤其是低分子量的多肽中存在大量与肿瘤诊断相关的重要信息,因此研究血清多肽谱图比传统研究单个肿瘤诊断标记物更具有优势和前景。
血清蛋白和多肽的提取是蛋白质组学用于疾病诊断研究的关键技术环节。在蛋白质组学研究中,双向凝胶电泳是蛋白质分离技术的典型代表。该技术充分利用了蛋白质的等电点和分子量两大物理特性将样品中的蛋白质复合物分离。但是它需要的样品量大、可检测的蛋白范围有限、重复性差以及耗时费力,并不适于临床诊断研究。大量血清多肽研究采用蛋白质芯片分离技术,该技术不需要对样品做预处理,样品点靶后可直接进行质谱检测分析,但是检测后的样品无法洗脱进行后续的鉴定分析,而且芯片制备工艺复杂、价格昂贵,限制了蛋白芯片的应用。随着高分辨质谱MALDI-TOF-MS应用于肿瘤诊断研究,多种血清蛋白或多肽分离方法可以与其联用,弥补了蛋白质芯片的不足。而超高分辨率和灵敏度的FT-ICR-MS质谱仪的出现,使血清多肽的鉴定成为可能。磁珠分离体系是近年兴起并广泛用于血清多肽制备的方法。它的组成通常包括磁性内核和表面的高分子外壳。其表面的大分子决定了结合蛋白质/多肽的性质和种类。由于磁珠表面积较大,结合能力高,因此具有较高的提取效率,而且操作简便,重复性较好,已用于疾病诊断的蛋白质组学研究。
在本研究中,我们选用金属螯合磁珠和高分辨质谱MALDI-TOF-MS作为急性白血病血清多肽谱图研究的技术体系。我们对磁珠分离和质谱检测的稳定性和重复性进行了实验评价,评价结果显示上述体系稳定,达到了国际要求。应用这一技术体系对147例急性白血病和107例对照血清进行检测,获得了两组的血清多肽谱图,通过Biosun_MS生物信息学软件中的支持向量机(support vector machine, SVM)算法和t检验分析,初步建立了急性白血病的血清多肽图诊断模型,该诊断模型的诊断特异性和敏感性分别为100%和97%。进一步分析55个特征多肽(P<0.001)的峰强度在初治急性白血病、急性白血病血液学完全缓解、急性白血病分子缓解和健康对照组的分布趋势发现,两个血请多肽m/z 1865.13和1778.05的峰强度随着急性白血病治疗缓解程度提高有所降低,尤其在分子缓解时二者强度明显减低。上述趋势也同样存在于M3型急性白血病经治疗达分子缓解时,两个多肽的峰强度明显减低接近于健康水平,提示这两个多肽具有微小残留病监测的潜力。FT-ICR-MS质谱测序结果证明这两个多肽为补体C3f片段。此外,该技术体系还用于急性白血病的疗效评价研究,研究中通过检测分析30例初治急性白血病及其治疗两个疗程后达血液学完全缓解的血清,获得了初治与血液学缓解后的血清多肽谱图,通过Biosun_MS生物信息学软件中的SVM算法和检验分析,初步建立了急性白血病疗效评价的血清多肽谱图模型,该诊断模型的诊断特异性和敏感性分别为90%和100%。
上述研究结果表明血清多肽谱图模式能够反映急性白血病的病理状态,为我们进一步的研究提供了依据。综合我们的研究结果,本研究的主要结论如下：①本研究初步建立了急性白血病血清多肽谱图诊断模型和疗效评价模型,同时发现了与微小残留病监测相关的血清多肽。②测序分析所获得的7个特征血清多肽序列为急性白血病机制的探讨提供了线索。本研究的创新之处在于将血清多肽谱图模式诊断技术引入急性白血病研究,发现并探讨了其在急性白血病诊断、微小残留病监测和疗效评价方面的应用潜力。
Acute leukemia (AL) is a malignant blood disease with abnormal hematopoietic stem cells, which can cause severe health problems, even death. The hematopoietic cells with gene defects in AL patients lost the ability of normal differentiation and response to normal regulators of proliferation. Finally, many primary hematopoietic stem cells cumulate in the bone marrow, peripheral blood and other organs, which inhibit normal hematopoietic cells growth and cause a series of clinical manifestation, such as anemia, haemorrhage, infection, hepatosplenomegaly and lymphadenectasis. Now the diagnosis of AL can be made on the bone marrow analysis, including morphology, immunology, cytogenetics and molecular genetics analysis. After treatment, the diagnostic methods mentioned above also can be used for the evaluation of early therapeutic efficacy that is essential for further treatment and prognostic assessment. However, AL patients should undergo bone marrow aspiration under local anesthesia before analysis, which is invasive, time-consuming and risky, so a minimally invasive, fast and sensitive approach for serum diagnosis and evaluation is necessary.
The characteristics of serum examination are minimally invasive, low cost, easy acquiring and processing. Also the serum protein level under disease state is different from the level in healthy state. Thus, serum is a good specimen to be generally used in disease marker research. Serum peptide pattern is a novel diagnostic method, in which mass spectrometry is used to generate serum peptide profiles, and the diagnosis will be given based on the peptide pattern difference by bioinformatics software. The researchers have obtained a lot of exciting results when this method was applied for the investigation in tumor diagnosis. In 2002, a paper named "Use of proteomic patterns in serum to identify ovarian cancer" was published in Lancet journal. In this paper, the serum peptide pattern was used for ovarian cancer diagnosis. They analyzed the peptide pattern of healthy control group and disease group to diagnose ovarian cancer by mass spectrometry and bioinformatics software. The results showed that all 55 ovarian cancer patients were correctly diagnosis, and 18 of 55 cases were stage I ovarian cancer patients. The sensitivity, specificity and positive predictive value is 100%, 95%,94%, respectively. It suggested that this method was better than traditional detection of CA125 and was also very effective in the diagnosis of early stage ovarian cancer. Later, several papers about serum peptide pattern for tumor diagnosis were published, such as 2006, Villanueva et al published their research in bladder cancer, breast cancer, and prostate cancer diagnosis on The Journal of Clinical Investigation. At the end of 2006, a comment on this paper was given on Nature. In the comment, the author shows a positive attitude toward the development and application of this serum peptide technique in future. Also, there is another paper about serum peptide technique used for metastatic thyroid cancer diagnosis published by Villanueva et al on the Molecular & Cellular Proteomics journal. Additionally, serum peptide technique related papers were also published on the Clinical Cancer Research, Journal of Clinical Oncology and Clinical Chemistry. It is worth mentioning that this technique not only can be used in tumor diagnosis, but also used in other disease research, such as tuberculosis. In 2006 Lancet, a paper "Identification of diagnostic markers for tuberculosis by proteomic fingerprinting of serum" was published. The results revealed that serum peptide technique obtained more than 95% specificity and sensitivity in diagnosis of tuberculosis, which is better than the traditional diagnostic method. All of these results indicate that human serum peptides, especially low molecular weight peptides contain important information for tumor diagnosis. It is thus more useful and helpful to do research in serum peptide patterns than in traditional single marker
Serum protein and peptide isolation is the key point in proteomics research. 2-DE is a typical protein isolation technique in proteomics research. It can isolate intact protein from complex according to their physical properties, eg, isoelectric point and molecular weight. But it requires large sample quantities, has a limited dynamic range for protein detection, is poorly reproducible and labor intensive, thus it is not fit for clinical diagnosis. Recently, protein chip has been applied in serum peptide research. The sample without preparation can be used for detection directly. However, this advantage is also the disadvantage of this method. Because the samples can not be further identified. Also, protein chip is so sophisticated and high cost that limits its application. Given high-resolution MALDI-TOF-MS has been developed for tumor research, a variety of protein/peptide isolation methods can be combined to overcome the problems of protein chip. Moreover, the emergence of high resolution and sensitivity FT-ICR-MS makes the serum peptide identification to be possible. Magnetic beads based technique is newly developed for serum peptide preparation. This method uses different chemical chromatographic surfaces on the out layer of magnetic beads to selective purify certain subsets of proteins/peptides. Since magnetic beads have the characteristics of large surface area, high binding capacity, high efficiency, easy processing and good reproducibility, it has been combined with MALDI-TOF-MS detection and utilized in disease proteomic research.
In this study, magnetic beads and high-resolution MALDI-TOF-MS were used as the main techniques for the research in serum peptide pattern of acute leukemia. The evaluation about stability and reproducibility of this technique system was performed at beginning. The results showed this system was stable and accord with the standard. We further used this system to analyze AL group and healthy control group sera, and obtained the serum peptide profiles. After analysis by bioinformatics software Biosun_MS (SVM algorithm and t-test), a diagnostic model based on serum peptide pattern was set up for diagnosing acute leukemia. This model achieved 100% specificity and 97% sensitivity. Further intensity analysis of 55 features (P<0.001) in primary AL, AL with hematologic complete remission, AL with molecular remission and healthy controls, two peptides m/z 1865.13 and 1778.05 were found decreased in their relative intensity with the increase of remission degree. In M3-AL, their relative intensities also decreased and were close to normal state after molecular remission. With FT-ICR-MS detection, both the peptides were identified as fragments of complement C3f. Furthermore, this system was also applied for the assessment of treatment efficacy in acute leukemia. Through analyzing primary AL and their hematologic complete remission sera, we obtained the serum peptide profiles. After analysis by bioinformatics software Biosun_MS (SVM algorithm and t-test), a diagnostic model based on serum peptide pattern was set up for the assessment of treatment efficacy in acute leukemia, and got 100% sensitivity and 90% specificity.
These results manifested that serum peptide pattern could reflect the pathological state of disease, which will be the evidence for our further study. The conclusions of our investigation were as following:(1) A model for acute leukemia diagnosis and a model for treatment efficacy evaluation in acute leukemia have been built, respectively. Moreover, two peptides were found to be related with minimal residual disease monitoring. (2) 7 feature peptide sequences we got in this research could be helpful for further discussion about mechanisms of acute leukemia. The novelty of this study is the application of serum peptide techniques in acute leukemia research as well as investigating and discussing the potential role of serum peptide technique in minimal residual disease monitoring and therapeutic evaluation.
血清/血浆检验具有创伤性小、花费少、易获得,处理简便的特点,而且在各种生理或病理状态下,血清/血浆中的蛋白质常有质或量的变化,因此广泛用于多种疾病诊断标志物的研究。血清多肽图(简称血肽图)方法是一种全新的诊断方法,以质谱作为主要检测手段,可检测到大量的血清多肽,根据谱图模式的差异做出诊断。血清多肽谱图技术用于肿瘤诊断研究之初就取得了令人振奋的结果。2002年发表在Lancet上的一篇题为“血清多肽组质谱扫描技术成功预警早期卵巢癌”的论文阐述了血清多肽谱用于肿瘤的诊断。此论文采用生物质谱技术比较了疾病组和对照组的血清多肽谱图模式,根据两组之间的差异特征分别对疾病组和对照组进行诊断。结果显示,50例卵巢癌患者全部被正确检出,而其中18例为Ⅰ期卵巢癌患者。该方法检测卵巢癌分别获得了100%的敏感性、95%的特异性和94%的阳性预测值,不仅优于传统的CA125检测,而且对于早期卵巢癌的诊断也十分有效。随后,国际上陆续发表了血清多肽图技术用于肿瘤诊断的论文。如,2006年初来至于Memorial Sloan-Kettering肿瘤中心的Villanueva等在The Journal of Clinical Investigation杂志发表了多肽分离技术与MALDI-TOF-MS联合诊断膀胱癌、乳腺癌和前列腺癌。2006年底Nature杂志上发表了对该文章的一篇评论,评论中对此方法给予了肯定。同时,Villanueva等再次在Molecular & Cellular Proteomics杂志上发表了利用上述技术诊断转移性甲状腺癌的论文。此外,相关论文也在Clinical Cancer Research, Journal of Clinical Oncology以及Clinical Chemistry等杂志发表。值得一提的是这一方法不仅用于肿瘤的诊断,也可用于其它疾病的诊断。2006年Lancet上发表了一篇题为“血清蛋白质组谱图法检测结核诊断标志物”的论文,与传统的诊断方法相比,结核病诊断的特异性和灵敏度均超过95%。这些研究成果表明人的血清多肽,尤其是低分子量的多肽中存在大量与肿瘤诊断相关的重要信息,因此研究血清多肽谱图比传统研究单个肿瘤诊断标记物更具有优势和前景。
血清蛋白和多肽的提取是蛋白质组学用于疾病诊断研究的关键技术环节。在蛋白质组学研究中,双向凝胶电泳是蛋白质分离技术的典型代表。该技术充分利用了蛋白质的等电点和分子量两大物理特性将样品中的蛋白质复合物分离。但是它需要的样品量大、可检测的蛋白范围有限、重复性差以及耗时费力,并不适于临床诊断研究。大量血清多肽研究采用蛋白质芯片分离技术,该技术不需要对样品做预处理,样品点靶后可直接进行质谱检测分析,但是检测后的样品无法洗脱进行后续的鉴定分析,而且芯片制备工艺复杂、价格昂贵,限制了蛋白芯片的应用。随着高分辨质谱MALDI-TOF-MS应用于肿瘤诊断研究,多种血清蛋白或多肽分离方法可以与其联用,弥补了蛋白质芯片的不足。而超高分辨率和灵敏度的FT-ICR-MS质谱仪的出现,使血清多肽的鉴定成为可能。磁珠分离体系是近年兴起并广泛用于血清多肽制备的方法。它的组成通常包括磁性内核和表面的高分子外壳。其表面的大分子决定了结合蛋白质/多肽的性质和种类。由于磁珠表面积较大,结合能力高,因此具有较高的提取效率,而且操作简便,重复性较好,已用于疾病诊断的蛋白质组学研究。
在本研究中,我们选用金属螯合磁珠和高分辨质谱MALDI-TOF-MS作为急性白血病血清多肽谱图研究的技术体系。我们对磁珠分离和质谱检测的稳定性和重复性进行了实验评价,评价结果显示上述体系稳定,达到了国际要求。应用这一技术体系对147例急性白血病和107例对照血清进行检测,获得了两组的血清多肽谱图,通过Biosun_MS生物信息学软件中的支持向量机(support vector machine, SVM)算法和t检验分析,初步建立了急性白血病的血清多肽图诊断模型,该诊断模型的诊断特异性和敏感性分别为100%和97%。进一步分析55个特征多肽(P<0.001)的峰强度在初治急性白血病、急性白血病血液学完全缓解、急性白血病分子缓解和健康对照组的分布趋势发现,两个血请多肽m/z 1865.13和1778.05的峰强度随着急性白血病治疗缓解程度提高有所降低,尤其在分子缓解时二者强度明显减低。上述趋势也同样存在于M3型急性白血病经治疗达分子缓解时,两个多肽的峰强度明显减低接近于健康水平,提示这两个多肽具有微小残留病监测的潜力。FT-ICR-MS质谱测序结果证明这两个多肽为补体C3f片段。此外,该技术体系还用于急性白血病的疗效评价研究,研究中通过检测分析30例初治急性白血病及其治疗两个疗程后达血液学完全缓解的血清,获得了初治与血液学缓解后的血清多肽谱图,通过Biosun_MS生物信息学软件中的SVM算法和检验分析,初步建立了急性白血病疗效评价的血清多肽谱图模型,该诊断模型的诊断特异性和敏感性分别为90%和100%。
上述研究结果表明血清多肽谱图模式能够反映急性白血病的病理状态,为我们进一步的研究提供了依据。综合我们的研究结果,本研究的主要结论如下：①本研究初步建立了急性白血病血清多肽谱图诊断模型和疗效评价模型,同时发现了与微小残留病监测相关的血清多肽。②测序分析所获得的7个特征血清多肽序列为急性白血病机制的探讨提供了线索。本研究的创新之处在于将血清多肽谱图模式诊断技术引入急性白血病研究,发现并探讨了其在急性白血病诊断、微小残留病监测和疗效评价方面的应用潜力。
Acute leukemia (AL) is a malignant blood disease with abnormal hematopoietic stem cells, which can cause severe health problems, even death. The hematopoietic cells with gene defects in AL patients lost the ability of normal differentiation and response to normal regulators of proliferation. Finally, many primary hematopoietic stem cells cumulate in the bone marrow, peripheral blood and other organs, which inhibit normal hematopoietic cells growth and cause a series of clinical manifestation, such as anemia, haemorrhage, infection, hepatosplenomegaly and lymphadenectasis. Now the diagnosis of AL can be made on the bone marrow analysis, including morphology, immunology, cytogenetics and molecular genetics analysis. After treatment, the diagnostic methods mentioned above also can be used for the evaluation of early therapeutic efficacy that is essential for further treatment and prognostic assessment. However, AL patients should undergo bone marrow aspiration under local anesthesia before analysis, which is invasive, time-consuming and risky, so a minimally invasive, fast and sensitive approach for serum diagnosis and evaluation is necessary.
The characteristics of serum examination are minimally invasive, low cost, easy acquiring and processing. Also the serum protein level under disease state is different from the level in healthy state. Thus, serum is a good specimen to be generally used in disease marker research. Serum peptide pattern is a novel diagnostic method, in which mass spectrometry is used to generate serum peptide profiles, and the diagnosis will be given based on the peptide pattern difference by bioinformatics software. The researchers have obtained a lot of exciting results when this method was applied for the investigation in tumor diagnosis. In 2002, a paper named "Use of proteomic patterns in serum to identify ovarian cancer" was published in Lancet journal. In this paper, the serum peptide pattern was used for ovarian cancer diagnosis. They analyzed the peptide pattern of healthy control group and disease group to diagnose ovarian cancer by mass spectrometry and bioinformatics software. The results showed that all 55 ovarian cancer patients were correctly diagnosis, and 18 of 55 cases were stage I ovarian cancer patients. The sensitivity, specificity and positive predictive value is 100%, 95%,94%, respectively. It suggested that this method was better than traditional detection of CA125 and was also very effective in the diagnosis of early stage ovarian cancer. Later, several papers about serum peptide pattern for tumor diagnosis were published, such as 2006, Villanueva et al published their research in bladder cancer, breast cancer, and prostate cancer diagnosis on The Journal of Clinical Investigation. At the end of 2006, a comment on this paper was given on Nature. In the comment, the author shows a positive attitude toward the development and application of this serum peptide technique in future. Also, there is another paper about serum peptide technique used for metastatic thyroid cancer diagnosis published by Villanueva et al on the Molecular & Cellular Proteomics journal. Additionally, serum peptide technique related papers were also published on the Clinical Cancer Research, Journal of Clinical Oncology and Clinical Chemistry. It is worth mentioning that this technique not only can be used in tumor diagnosis, but also used in other disease research, such as tuberculosis. In 2006 Lancet, a paper "Identification of diagnostic markers for tuberculosis by proteomic fingerprinting of serum" was published. The results revealed that serum peptide technique obtained more than 95% specificity and sensitivity in diagnosis of tuberculosis, which is better than the traditional diagnostic method. All of these results indicate that human serum peptides, especially low molecular weight peptides contain important information for tumor diagnosis. It is thus more useful and helpful to do research in serum peptide patterns than in traditional single marker
Serum protein and peptide isolation is the key point in proteomics research. 2-DE is a typical protein isolation technique in proteomics research. It can isolate intact protein from complex according to their physical properties, eg, isoelectric point and molecular weight. But it requires large sample quantities, has a limited dynamic range for protein detection, is poorly reproducible and labor intensive, thus it is not fit for clinical diagnosis. Recently, protein chip has been applied in serum peptide research. The sample without preparation can be used for detection directly. However, this advantage is also the disadvantage of this method. Because the samples can not be further identified. Also, protein chip is so sophisticated and high cost that limits its application. Given high-resolution MALDI-TOF-MS has been developed for tumor research, a variety of protein/peptide isolation methods can be combined to overcome the problems of protein chip. Moreover, the emergence of high resolution and sensitivity FT-ICR-MS makes the serum peptide identification to be possible. Magnetic beads based technique is newly developed for serum peptide preparation. This method uses different chemical chromatographic surfaces on the out layer of magnetic beads to selective purify certain subsets of proteins/peptides. Since magnetic beads have the characteristics of large surface area, high binding capacity, high efficiency, easy processing and good reproducibility, it has been combined with MALDI-TOF-MS detection and utilized in disease proteomic research.
In this study, magnetic beads and high-resolution MALDI-TOF-MS were used as the main techniques for the research in serum peptide pattern of acute leukemia. The evaluation about stability and reproducibility of this technique system was performed at beginning. The results showed this system was stable and accord with the standard. We further used this system to analyze AL group and healthy control group sera, and obtained the serum peptide profiles. After analysis by bioinformatics software Biosun_MS (SVM algorithm and t-test), a diagnostic model based on serum peptide pattern was set up for diagnosing acute leukemia. This model achieved 100% specificity and 97% sensitivity. Further intensity analysis of 55 features (P<0.001) in primary AL, AL with hematologic complete remission, AL with molecular remission and healthy controls, two peptides m/z 1865.13 and 1778.05 were found decreased in their relative intensity with the increase of remission degree. In M3-AL, their relative intensities also decreased and were close to normal state after molecular remission. With FT-ICR-MS detection, both the peptides were identified as fragments of complement C3f. Furthermore, this system was also applied for the assessment of treatment efficacy in acute leukemia. Through analyzing primary AL and their hematologic complete remission sera, we obtained the serum peptide profiles. After analysis by bioinformatics software Biosun_MS (SVM algorithm and t-test), a diagnostic model based on serum peptide pattern was set up for the assessment of treatment efficacy in acute leukemia, and got 100% sensitivity and 90% specificity.
These results manifested that serum peptide pattern could reflect the pathological state of disease, which will be the evidence for our further study. The conclusions of our investigation were as following:(1) A model for acute leukemia diagnosis and a model for treatment efficacy evaluation in acute leukemia have been built, respectively. Moreover, two peptides were found to be related with minimal residual disease monitoring. (2) 7 feature peptide sequences we got in this research could be helpful for further discussion about mechanisms of acute leukemia. The novelty of this study is the application of serum peptide techniques in acute leukemia research as well as investigating and discussing the potential role of serum peptide technique in minimal residual disease monitoring and therapeutic evaluation.
引文
[1]Lander ES, Linton LM, Birren B, et al. Initial sequencing and analysis of the human genome [J]. Nature,2001,409 (6822):860-921.
[2]Venter JC, Adams MD, Myers EW, et al. The sequence of the human genome [J]. Science,2001,291(5507):1304-1351.
[3]Wasinger VC, Cordwell SJ, Cerpa-Poljak A, et al. Progress with gene-product mapping of the mollicutes:mycoplasma genitalium [J]. Electrophoresis,1995, 16:1090-1094.
[4]Pandey A, Mann M. Proteomics to study genes and genomes [J]. Nature,2000, 405:837-846.
[5]O'Donovan C, Apweiler, Bairoch A. The human proteomics initiative (HPI) [J]. Trends in Biotech,2001,19:178-181.
[6]Sewald N, Jakubke HD. Peptides:Chemistry and biology [M]. Weinheim: WILEY-VCH,2009.
[7]Anderson L, Scihamer J. A comparison of selected mRNA and protein abundances in human liver [J]. Electrophoresis,1997,18:533-537.
[8]Gysi S, Roehon Y, Franza BR, et al. Correlation between Protein and mRNA abundance in yeast [J]. Mol Cell Biol,1999,19:1720-1730.
[9]Arne T. A new apparatus for electrophoretic analysis of colloidal mixtures [J]. Transactions of the Faraday Society,1937,33:524.
[10]O'Farrel PH. High resolution two-dimensional electroporesis of Proteins [J]. J Biol Chem,1975:4007-4021.
[11]Chahed K, Kabbage M, Ehret Sabatier L, et al. Expression of fibrinogen E-fragment and fibrin E-fragment is inhibited in the human infiltrating ductal carcinoma of the breast:the two-dimensional electrophoresis and MALDI-TOF mass spectrometry analyses [J]. Int J Oncol,2005,27: 1425-1431.
[12]Rabilloud, T. Two-dimensional gel electrophoresis in proteomics:old, old fashioned, but it still climbs up the mountains [J]. Proteomics,2002,2:3-10.
[13]Issaq HJ, Veenstra TD, Conrads TP, et al. The SELDI-TOF-MS approach to proteomics:protein profiling and biomarker identification [J]. Biochem Biophys Res Commun,2002,292 (3):587-592.
[14]Bouamrani A, Ternier J, Ratel D, et al. Direct-tissue SELDI-TOF mass spectrometry analysis:a new application for clinical proteomics [J]. Clin Chem,2006,52 (11):2103-2106.
[15]Chapman K. The proteinchip biomarker system from ciphergen biosystems: a novel proteomies platform for rapid biomarker discovery and validation [J]. Biochem Soc Trans,2002,30(2):82-87.
[16]Li JN, Zhang Z, Rosenzweig J, et al. Proteomics and bioinformatics approaches for identification of serum biomarkers to detect breast cancer [J]. Clin Chem,2002,48:1296-1304
[17]Koopmann J, Zhang Z, White N, et al. Serum diagnosis of pancreatic adenocarcinoma using surface-enhanced laser desorption and ionization mass spectrometry [J]. Clin Cancer Res,2004,10:860-868.
[18]Papadopoulos MC, Abel PM, Agranoff D, et al. A novel and accurate diagnostic test for human African trypanosomiasis [J]. Lancet,2004,363: 1358-1363.
[19]Hayashida Y, Honda K, Osaka Y, et al. Possible prediction of chemoradiosensitivity of esophageal cancer by serum protein profiling [J]. Clin Cancer Res,2005,11(22):8042-8047.
[20]Menard C, Johann D, Lowenthal M, et al. Discovering clinical biomarkers of ionizing radiation exposure with serum proteomic analysis [J]. Cancer Res,2006,66:(3):1844-1850.
[21]Ward DG, Suggett N, Cheng Y, et al. Identification of serum biomarkers for colon cancer by proteomic analysis [J]. Br J Cancer,2006,94:1898-1905.
[22]Cui JF, Kang XN, Dai Z et al. Prediction of chronic hepatitis B, liver cirrhosis and hepatocellular carcinoma by SELDI-based serum decision tree classification [J]. J Cancer Res Clin Oncol,2007,133:825-834.
[23]Hendriks MM, Smit S, Akkermans WL, et al. How to distinguish healthy from diseased? Classification strategy for mass spectrometry-based clinical proteomics [J]. Proteomics,2007,7:3672-3680.
[24]Tiss A, Smith C, Camuzeaux S, et al. Serum peptide profiling using MALDI mass spectrometry:avoiding the pitfalls of coated magnetic beads using well-established ZipTip technology [J]. Proteomics,2007,7 (Suppl 1):77-89.
[25]Zhang XY, Leung SM, Morris CR, et al. Evaluation of a novel, integrated approach using functionalized magnetic beads, bench-top MALDI-TOF-MS with prestructured sample supports, and pattern recognition software for profiling potential biomarkers in human plasma [J]. J Biomol Tech,2004,15: 167-175.
[26]Solassol J, Jacot W, Lhermitte L, et al. Clinical proteomics and mass spectrometry profiling for cancer detection [J]. Expert Rev Proteomics,2006, 3(3):311-320.
[27]Aebersold R, Mann M. Mass spectrometry-based proteomics [J]. Nature, 2003,422 (6928):198-207.
[28]Fenn JB, Mann M, Meng CK, et al. Electrospray ionization for mass spectrometry of large biomolecules [J]. Sicence,1989,246:64-71.
[29]Kalas M, Hillenkamp F. Laser desorption ionization of proteins with molecular masses exceeding 10,000 daltons [J]. Anal Chem,1988,60: 2299-2301.
[30]Titulaer MK, Siccama I, Dekker LJ, et al. A database application for pre-processing, storage and comparison of mass spectra derived from patients and controls [J]. BMC Bioinformatics,2006,7:403.
[31]Villanueva J, Philip J, Chaparro CA, et al. Correcting common errors in identifying cancer specific serum peptide signatures [J]. J Proteome Res, 2005,4:1060-1072.
[32]Leptos KC, Sarracino DA, Jaffe JD, et al. MapQuant:opensource software for large-scale protein quantification [J]. Proteomics,2006,6:1770-1782.
[33]Hartler J, Thallinger GG, Stocker G, et al. MASPECTRAS:a platform for management and analysis of proteomics LC-MS/MS data [J]. BMC Bioinformatics,2007,8:197.
[34]Li XJ, Yi EC, Kemp CJ, et al. A software suite for the generation and comparison of peptide arrays from sets of data collected by liquid chromatography-mass spectrometry [J]. Mol Cell Proteomics,2005,4: 1328-1340.
[35]Bellew M, Coram M, Fitzgibbon M, et al. A suite of algorithms for the comprehensive analysis of complex protein mixtures using high-resolution LC-MS [J]. Bioinformatics,2006,22:1902-1909.
[36]Katajamaa M, Miettinen J, Oresic M. MZmine:toolbox for processing and visualization of mass spectrometry based molecular profile data [J]. BMC Bioinformatics,2006,22:634-636.
[37]Liang SL, Chan DW. Enzymes and related proteins as cancer biomarkers:a proteomic approach [J]. Clin Chim Acta,2007,381:93-97.
[38]Srinivas PR, Kramer BS, Srivastava S. Trends in biomarker research for cancer detection [J]. Lancet Oncol,2001,2:698-704.
[39]Tarnaris A, Watkins LD, Kitchen ND. Biomarkers in chronic adult hydrocephalus [J]. Cerebrospinal Fluid Res,2006,3:11.
[40]Adkins JN, Varnum SM, Auberry KJ, et al. Toward a human blood serum proteome [J]. Mol Cell Proteomics,2002,1:947-955.
[41]Villar-Garea A, Griese M, Imhof A. Biomarker discovery from body fluids using mass spectrometry [J]. J Chromatogr B Analyt Technol Biomed Life Sci,2007,849:105-114.
[42]Anderson NL, Anderson NG. The human plasma proteome:history, character and diagnostic prospects [J]. Mol Cell Proteomics,2002,1: 845-867.
[43]Schrader M, Knappe PS. Peptidomics technologies for human body fluids [J]. Trends Biotechnol,2001,19:S55-60.
[44]Tirumalai RS, Chan KC, Prieto DA, et al. Characterization of the low molecular weight human serum proteome [J]. Mol Cell Proteomics,2003,2: 1096-1103.
[45]Petricoin III EF, Ardekani AM, Hitt BA, et al. Use of proteomic patterns in serum to identify ovarian cancer [J]. Lancet,2002,359:572-577.
[46]Villanueva J, Shaffer DR, Philip J, et al. Differential exoprotease activities confer tumor-specific serum peptidome patterns [J]. J Clin Invest,2006,116: 271-284.
[47]Novak K. Biomarkers:Taking out the trash [J]. Nature,2006,6:92.
[48]Villanueva J, Martorella AJ, Lawlor K, et al. Serum peptidome patterns that distinguish metastatic thyroid carcinoma from cancer-free controls are unbiased by gender and age [J]. Mol Cell Proteomics,2006,5:1840-1852.
[49]Koopmann J, Zhang Z, White N, et al. Serum diagnosis of pancreatic adenocarcinoma using surface-enhanced laser desorption and ionization mass spectrometry [J]. Clin Cancer Res,2004,10:860-868.
[50]Zinkin NT, Grall F, Bhaskar K, et al. Serum proteomics and biomarkers in hepatocellular carcinoma and chronic liver disease [J]. Clin Cancer Res,2008, 14(2):470-477.
[51]Mian S, Ugurel S, Parkinson E, et al. Serum proteomic fingerprinting discriminates between clinical stages and predicts disease progression in melanoma patients [J]. J Clin Oncol,2005,23:5088-5093.
[52]Cheng AJ, Chen LC, Chien KY, et al. Oral cancer plasma tumor marker identified with bead-based affinity-fractionated proteomic technology [J]. Clin Chem,2005,51(12):2236-2244.
[53]Agranoff D, Fernandez-Reyes D, Papadopoulos MC, et al. Identification of diagnostic markers for tuberculosis by proteomic fingerprinting of serum [J]. Lancet,2006,368:1012-1021.
[54]Emanuel FP, Claudio B, Robyn P, et al. The blood peptidome:a higher dimension of information content for cancer biomarker discovery [J]. Nature, 2006,6:961-967.
[55]Haferlach T, Bacher U, Kern W, et al. Diagnostic pathways in acute leukemias:a proposal for a multimodal approach [J]. Ann Hematol,2007,86: 311-327.
[56]Bennett JM, Catovsky D, Daniel MT, et al. Proposals for the classification of the acute leukemias (FAB cooperative group) [J]. Br J Haematol,1976,33: 451-458.
[57]Vardiman JW, Thiele J, Arber DA, et al. The 2008 revision of the WHO classification of myeloid neoplasms and acute leukemia:rationale and important changes [J]. Blood,2009,114:937-951.
[58]Gabert J, Beillard E, van der Velden VHJ, et al. Standardization and quality control studies of'real-time'quantitative reverse transcriptase polymerase chain reaction of fusion gene transcripts for residual disease detection in leukemia-A Europe Against Cancer Program [J]. Leukemia,2003,17: 2318-2357.
[59]Kern W, Voskova D, Schoch C, et al. Determination of relapse risk based on assessment of minimal residual disease during complete remission by multiparameter flow cytometry in unselected patients with acute myeloid leukemia [J]. Blood,2004,104:3078-3085.
[60]Cui JW, Wang J, He K, et al. Proteomic analysis of human acute leukemia cells:insight into their classification [J]. Clin Cancer Res,2004,10: 6887-6896.
[61]Cui JW, Li WH, Wang J, et al. Proteomics based identification of human acute leukemia antigens that induce humoral immune response [J]. Mol Cell Proteomics,2005,4:1718-1724.
[62]Cristea IM, Gaskell SJ, Whetton AD. Proteomics techniques and their application to hematology [J]. Blood,2004,103:3624-3634.
[63]Hu S, Loo JA, Wong DT. Human body fluid proteome analysis [J]. Proteomics,2006,6:6326-6353.
[64]Villanueva J, Lawlor K, Toledo-Crow R, et al. Automated serum peptide profiling [J]. Nat Protoc,2006,1:880-891.
[65]Estey E, DOhner H. Acute myeloid leukaemia [J]. Lancet,2006,368: 1894-1907.
[66]Santamaria C, Chillon MC, Fernandez C, et al. Using quantification of the PML-RARa transcript to stratify the risk of relapse in patients with acute promyelocytic leukemia [J]. Haematologica,2007,92:315-322.
[67]Wang N, Dong FT, Zhang XM, et al. Hydrothermal synthesis of metal-chelated magnetic nanobeads for exclusive use of peptides extraction by mass spectrum [J]. Acta Chimica Sinica,2007,65:344-348.
[68]Cao Y, Wang N, Ying XM, et al. BioSun_MS:A plug-in-based software for the management of patients information and the analysis of peptide profiles from mass spectrometry [J]. BMC Med Inform Decis Mak,2009,9:13.
[69]Baumann S, Ceglarek U, Fiedler GM, et al. Standardized approach to proteome profiling of human serum based on magnetic bead separation and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry [J]. Clin Chem,2005,51(6):973-980.
[70]van der Velden VHJ, Hochhaus A, Cazzaniga G, et al. Detection of minimal residual diseases in hematologic malignances by real-time quantitative PCR: principles, approaches, and laboratory aspects [J]. Leukemia,2003,17: 1013-1034.
[71]Maurillo L, Buccisano F, Spagnoli A, et al. Monitoring of residual disease in adult acute myeloid leukemia using peripheral blood as an alternative source to bone marrow [J]. Haematologica,2007,92:605-611.
[72]Hortin GL. The MALDI-TOF mass spectrometric view of the plasma proteome and peptidome [J]. Clin Chem,2006,52:1223-1237.
[73]Janssen BJ, Huizinga EG, Raaijmakers HC, et al. Structures of complement component C3 provide insights into the function and evolution of immunity [J]. Nature,2005,437:505-511.
[74]Ganu VS, Muller-Eberhard HJ, Hugh TE. Factor C3f is a spasmogenic fragment released from C3b by factors I and H: the heptadeca-peptide C3f was synthesized and characterized [J]. Mol Immunol,1989,26:939-948.
[75]Yang X, Toshihiro M, Kosuke M, et al. Comprehensive investigation of disease-specific short peptides in sera from patients with systemic sclerosis Complement C3f-des-arginine, detected predominantly in systemic sclerosis sera, enhances proliferation of vascular endothelial cells [J]. Arth Rheum, 2007,56:2018-2030.
[76]Carroll MC, The complement system in regulation of adaptive immunity [J]. Nat Immunol,2004,5:981-986.
[77]Ziller F, Macor P, Bulla R, et al. Controlling complement resistance in cancer by using human monoclonal antibodies that neutralize complement-regulatory proteins CD55 and CD59 [J]. Eur J Immunol,2005,35:2175-2183.
[78]Rozanov DV, Savinov AY, Golubkov VS, et al. Interference with the complement system by tumor cell membrane type-1 matrix metalloproteinase plays a significant role in promoting metastasis in mice [J]. Cancer Res,2006, 66:6258-6263.
[79]Ajona D, Castano Z, Garayoa M, et al. Expression of complement factor H by lung cancer cells:effects on the activation of the alternative pathway of complement [J]. Cancer Res,2004,64:6310-6318.
[80]Walport MJ. Complement-Second of two parts [J]. N Engl J Med,2001,344: 1140-1144.
[2]Venter JC, Adams MD, Myers EW, et al. The sequence of the human genome [J]. Science,2001,291(5507):1304-1351.
[3]Wasinger VC, Cordwell SJ, Cerpa-Poljak A, et al. Progress with gene-product mapping of the mollicutes:mycoplasma genitalium [J]. Electrophoresis,1995, 16:1090-1094.
[4]Pandey A, Mann M. Proteomics to study genes and genomes [J]. Nature,2000, 405:837-846.
[5]O'Donovan C, Apweiler, Bairoch A. The human proteomics initiative (HPI) [J]. Trends in Biotech,2001,19:178-181.
[6]Sewald N, Jakubke HD. Peptides:Chemistry and biology [M]. Weinheim: WILEY-VCH,2009.
[7]Anderson L, Scihamer J. A comparison of selected mRNA and protein abundances in human liver [J]. Electrophoresis,1997,18:533-537.
[8]Gysi S, Roehon Y, Franza BR, et al. Correlation between Protein and mRNA abundance in yeast [J]. Mol Cell Biol,1999,19:1720-1730.
[9]Arne T. A new apparatus for electrophoretic analysis of colloidal mixtures [J]. Transactions of the Faraday Society,1937,33:524.
[10]O'Farrel PH. High resolution two-dimensional electroporesis of Proteins [J]. J Biol Chem,1975:4007-4021.
[11]Chahed K, Kabbage M, Ehret Sabatier L, et al. Expression of fibrinogen E-fragment and fibrin E-fragment is inhibited in the human infiltrating ductal carcinoma of the breast:the two-dimensional electrophoresis and MALDI-TOF mass spectrometry analyses [J]. Int J Oncol,2005,27: 1425-1431.
[12]Rabilloud, T. Two-dimensional gel electrophoresis in proteomics:old, old fashioned, but it still climbs up the mountains [J]. Proteomics,2002,2:3-10.
[13]Issaq HJ, Veenstra TD, Conrads TP, et al. The SELDI-TOF-MS approach to proteomics:protein profiling and biomarker identification [J]. Biochem Biophys Res Commun,2002,292 (3):587-592.
[14]Bouamrani A, Ternier J, Ratel D, et al. Direct-tissue SELDI-TOF mass spectrometry analysis:a new application for clinical proteomics [J]. Clin Chem,2006,52 (11):2103-2106.
[15]Chapman K. The proteinchip biomarker system from ciphergen biosystems: a novel proteomies platform for rapid biomarker discovery and validation [J]. Biochem Soc Trans,2002,30(2):82-87.
[16]Li JN, Zhang Z, Rosenzweig J, et al. Proteomics and bioinformatics approaches for identification of serum biomarkers to detect breast cancer [J]. Clin Chem,2002,48:1296-1304
[17]Koopmann J, Zhang Z, White N, et al. Serum diagnosis of pancreatic adenocarcinoma using surface-enhanced laser desorption and ionization mass spectrometry [J]. Clin Cancer Res,2004,10:860-868.
[18]Papadopoulos MC, Abel PM, Agranoff D, et al. A novel and accurate diagnostic test for human African trypanosomiasis [J]. Lancet,2004,363: 1358-1363.
[19]Hayashida Y, Honda K, Osaka Y, et al. Possible prediction of chemoradiosensitivity of esophageal cancer by serum protein profiling [J]. Clin Cancer Res,2005,11(22):8042-8047.
[20]Menard C, Johann D, Lowenthal M, et al. Discovering clinical biomarkers of ionizing radiation exposure with serum proteomic analysis [J]. Cancer Res,2006,66:(3):1844-1850.
[21]Ward DG, Suggett N, Cheng Y, et al. Identification of serum biomarkers for colon cancer by proteomic analysis [J]. Br J Cancer,2006,94:1898-1905.
[22]Cui JF, Kang XN, Dai Z et al. Prediction of chronic hepatitis B, liver cirrhosis and hepatocellular carcinoma by SELDI-based serum decision tree classification [J]. J Cancer Res Clin Oncol,2007,133:825-834.
[23]Hendriks MM, Smit S, Akkermans WL, et al. How to distinguish healthy from diseased? Classification strategy for mass spectrometry-based clinical proteomics [J]. Proteomics,2007,7:3672-3680.
[24]Tiss A, Smith C, Camuzeaux S, et al. Serum peptide profiling using MALDI mass spectrometry:avoiding the pitfalls of coated magnetic beads using well-established ZipTip technology [J]. Proteomics,2007,7 (Suppl 1):77-89.
[25]Zhang XY, Leung SM, Morris CR, et al. Evaluation of a novel, integrated approach using functionalized magnetic beads, bench-top MALDI-TOF-MS with prestructured sample supports, and pattern recognition software for profiling potential biomarkers in human plasma [J]. J Biomol Tech,2004,15: 167-175.
[26]Solassol J, Jacot W, Lhermitte L, et al. Clinical proteomics and mass spectrometry profiling for cancer detection [J]. Expert Rev Proteomics,2006, 3(3):311-320.
[27]Aebersold R, Mann M. Mass spectrometry-based proteomics [J]. Nature, 2003,422 (6928):198-207.
[28]Fenn JB, Mann M, Meng CK, et al. Electrospray ionization for mass spectrometry of large biomolecules [J]. Sicence,1989,246:64-71.
[29]Kalas M, Hillenkamp F. Laser desorption ionization of proteins with molecular masses exceeding 10,000 daltons [J]. Anal Chem,1988,60: 2299-2301.
[30]Titulaer MK, Siccama I, Dekker LJ, et al. A database application for pre-processing, storage and comparison of mass spectra derived from patients and controls [J]. BMC Bioinformatics,2006,7:403.
[31]Villanueva J, Philip J, Chaparro CA, et al. Correcting common errors in identifying cancer specific serum peptide signatures [J]. J Proteome Res, 2005,4:1060-1072.
[32]Leptos KC, Sarracino DA, Jaffe JD, et al. MapQuant:opensource software for large-scale protein quantification [J]. Proteomics,2006,6:1770-1782.
[33]Hartler J, Thallinger GG, Stocker G, et al. MASPECTRAS:a platform for management and analysis of proteomics LC-MS/MS data [J]. BMC Bioinformatics,2007,8:197.
[34]Li XJ, Yi EC, Kemp CJ, et al. A software suite for the generation and comparison of peptide arrays from sets of data collected by liquid chromatography-mass spectrometry [J]. Mol Cell Proteomics,2005,4: 1328-1340.
[35]Bellew M, Coram M, Fitzgibbon M, et al. A suite of algorithms for the comprehensive analysis of complex protein mixtures using high-resolution LC-MS [J]. Bioinformatics,2006,22:1902-1909.
[36]Katajamaa M, Miettinen J, Oresic M. MZmine:toolbox for processing and visualization of mass spectrometry based molecular profile data [J]. BMC Bioinformatics,2006,22:634-636.
[37]Liang SL, Chan DW. Enzymes and related proteins as cancer biomarkers:a proteomic approach [J]. Clin Chim Acta,2007,381:93-97.
[38]Srinivas PR, Kramer BS, Srivastava S. Trends in biomarker research for cancer detection [J]. Lancet Oncol,2001,2:698-704.
[39]Tarnaris A, Watkins LD, Kitchen ND. Biomarkers in chronic adult hydrocephalus [J]. Cerebrospinal Fluid Res,2006,3:11.
[40]Adkins JN, Varnum SM, Auberry KJ, et al. Toward a human blood serum proteome [J]. Mol Cell Proteomics,2002,1:947-955.
[41]Villar-Garea A, Griese M, Imhof A. Biomarker discovery from body fluids using mass spectrometry [J]. J Chromatogr B Analyt Technol Biomed Life Sci,2007,849:105-114.
[42]Anderson NL, Anderson NG. The human plasma proteome:history, character and diagnostic prospects [J]. Mol Cell Proteomics,2002,1: 845-867.
[43]Schrader M, Knappe PS. Peptidomics technologies for human body fluids [J]. Trends Biotechnol,2001,19:S55-60.
[44]Tirumalai RS, Chan KC, Prieto DA, et al. Characterization of the low molecular weight human serum proteome [J]. Mol Cell Proteomics,2003,2: 1096-1103.
[45]Petricoin III EF, Ardekani AM, Hitt BA, et al. Use of proteomic patterns in serum to identify ovarian cancer [J]. Lancet,2002,359:572-577.
[46]Villanueva J, Shaffer DR, Philip J, et al. Differential exoprotease activities confer tumor-specific serum peptidome patterns [J]. J Clin Invest,2006,116: 271-284.
[47]Novak K. Biomarkers:Taking out the trash [J]. Nature,2006,6:92.
[48]Villanueva J, Martorella AJ, Lawlor K, et al. Serum peptidome patterns that distinguish metastatic thyroid carcinoma from cancer-free controls are unbiased by gender and age [J]. Mol Cell Proteomics,2006,5:1840-1852.
[49]Koopmann J, Zhang Z, White N, et al. Serum diagnosis of pancreatic adenocarcinoma using surface-enhanced laser desorption and ionization mass spectrometry [J]. Clin Cancer Res,2004,10:860-868.
[50]Zinkin NT, Grall F, Bhaskar K, et al. Serum proteomics and biomarkers in hepatocellular carcinoma and chronic liver disease [J]. Clin Cancer Res,2008, 14(2):470-477.
[51]Mian S, Ugurel S, Parkinson E, et al. Serum proteomic fingerprinting discriminates between clinical stages and predicts disease progression in melanoma patients [J]. J Clin Oncol,2005,23:5088-5093.
[52]Cheng AJ, Chen LC, Chien KY, et al. Oral cancer plasma tumor marker identified with bead-based affinity-fractionated proteomic technology [J]. Clin Chem,2005,51(12):2236-2244.
[53]Agranoff D, Fernandez-Reyes D, Papadopoulos MC, et al. Identification of diagnostic markers for tuberculosis by proteomic fingerprinting of serum [J]. Lancet,2006,368:1012-1021.
[54]Emanuel FP, Claudio B, Robyn P, et al. The blood peptidome:a higher dimension of information content for cancer biomarker discovery [J]. Nature, 2006,6:961-967.
[55]Haferlach T, Bacher U, Kern W, et al. Diagnostic pathways in acute leukemias:a proposal for a multimodal approach [J]. Ann Hematol,2007,86: 311-327.
[56]Bennett JM, Catovsky D, Daniel MT, et al. Proposals for the classification of the acute leukemias (FAB cooperative group) [J]. Br J Haematol,1976,33: 451-458.
[57]Vardiman JW, Thiele J, Arber DA, et al. The 2008 revision of the WHO classification of myeloid neoplasms and acute leukemia:rationale and important changes [J]. Blood,2009,114:937-951.
[58]Gabert J, Beillard E, van der Velden VHJ, et al. Standardization and quality control studies of'real-time'quantitative reverse transcriptase polymerase chain reaction of fusion gene transcripts for residual disease detection in leukemia-A Europe Against Cancer Program [J]. Leukemia,2003,17: 2318-2357.
[59]Kern W, Voskova D, Schoch C, et al. Determination of relapse risk based on assessment of minimal residual disease during complete remission by multiparameter flow cytometry in unselected patients with acute myeloid leukemia [J]. Blood,2004,104:3078-3085.
[60]Cui JW, Wang J, He K, et al. Proteomic analysis of human acute leukemia cells:insight into their classification [J]. Clin Cancer Res,2004,10: 6887-6896.
[61]Cui JW, Li WH, Wang J, et al. Proteomics based identification of human acute leukemia antigens that induce humoral immune response [J]. Mol Cell Proteomics,2005,4:1718-1724.
[62]Cristea IM, Gaskell SJ, Whetton AD. Proteomics techniques and their application to hematology [J]. Blood,2004,103:3624-3634.
[63]Hu S, Loo JA, Wong DT. Human body fluid proteome analysis [J]. Proteomics,2006,6:6326-6353.
[64]Villanueva J, Lawlor K, Toledo-Crow R, et al. Automated serum peptide profiling [J]. Nat Protoc,2006,1:880-891.
[65]Estey E, DOhner H. Acute myeloid leukaemia [J]. Lancet,2006,368: 1894-1907.
[66]Santamaria C, Chillon MC, Fernandez C, et al. Using quantification of the PML-RARa transcript to stratify the risk of relapse in patients with acute promyelocytic leukemia [J]. Haematologica,2007,92:315-322.
[67]Wang N, Dong FT, Zhang XM, et al. Hydrothermal synthesis of metal-chelated magnetic nanobeads for exclusive use of peptides extraction by mass spectrum [J]. Acta Chimica Sinica,2007,65:344-348.
[68]Cao Y, Wang N, Ying XM, et al. BioSun_MS:A plug-in-based software for the management of patients information and the analysis of peptide profiles from mass spectrometry [J]. BMC Med Inform Decis Mak,2009,9:13.
[69]Baumann S, Ceglarek U, Fiedler GM, et al. Standardized approach to proteome profiling of human serum based on magnetic bead separation and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry [J]. Clin Chem,2005,51(6):973-980.
[70]van der Velden VHJ, Hochhaus A, Cazzaniga G, et al. Detection of minimal residual diseases in hematologic malignances by real-time quantitative PCR: principles, approaches, and laboratory aspects [J]. Leukemia,2003,17: 1013-1034.
[71]Maurillo L, Buccisano F, Spagnoli A, et al. Monitoring of residual disease in adult acute myeloid leukemia using peripheral blood as an alternative source to bone marrow [J]. Haematologica,2007,92:605-611.
[72]Hortin GL. The MALDI-TOF mass spectrometric view of the plasma proteome and peptidome [J]. Clin Chem,2006,52:1223-1237.
[73]Janssen BJ, Huizinga EG, Raaijmakers HC, et al. Structures of complement component C3 provide insights into the function and evolution of immunity [J]. Nature,2005,437:505-511.
[74]Ganu VS, Muller-Eberhard HJ, Hugh TE. Factor C3f is a spasmogenic fragment released from C3b by factors I and H: the heptadeca-peptide C3f was synthesized and characterized [J]. Mol Immunol,1989,26:939-948.
[75]Yang X, Toshihiro M, Kosuke M, et al. Comprehensive investigation of disease-specific short peptides in sera from patients with systemic sclerosis Complement C3f-des-arginine, detected predominantly in systemic sclerosis sera, enhances proliferation of vascular endothelial cells [J]. Arth Rheum, 2007,56:2018-2030.
[76]Carroll MC, The complement system in regulation of adaptive immunity [J]. Nat Immunol,2004,5:981-986.
[77]Ziller F, Macor P, Bulla R, et al. Controlling complement resistance in cancer by using human monoclonal antibodies that neutralize complement-regulatory proteins CD55 and CD59 [J]. Eur J Immunol,2005,35:2175-2183.
[78]Rozanov DV, Savinov AY, Golubkov VS, et al. Interference with the complement system by tumor cell membrane type-1 matrix metalloproteinase plays a significant role in promoting metastasis in mice [J]. Cancer Res,2006, 66:6258-6263.
[79]Ajona D, Castano Z, Garayoa M, et al. Expression of complement factor H by lung cancer cells:effects on the activation of the alternative pathway of complement [J]. Cancer Res,2004,64:6310-6318.
[80]Walport MJ. Complement-Second of two parts [J]. N Engl J Med,2001,344: 1140-1144.