Protein Biomarkers in Childhood Acute Myeloid Leukemia

描述（由申请人提供）：我们提案的目标是使用儿童肿瘤学组（COG）多中心试验中招募的患者标本，鉴定作为儿童急性髓性白血病（AML）预后和治疗反应生物标志物的蛋白质。我们更有效地治疗癌症的能力关键取决于改进和完善肿瘤分类。这种未满足的需求在儿童AML中尤为严重。作为产生有效治疗的治疗适用研究（TARGET）计划的一部分，我们正在使用一系列高通量技术分析150例儿童AML样本，以确定可能作为预后生物标志物的基因组改变，表观遗传变化，mRNA和miRNA丰度特征。由于细胞生理学的关键方面可能更好地被其蛋白质补体捕获，我们建议使用一种新方法对同一患者队列进行蛋白质组学分析，该方法用于在我们实验室开发的许多样品中关联蛋白质丰度。 大多数目前可用的蛋白质组分析技术（如2-D凝胶，ICAT，SILAC）有局限性，阻止其用于分析大量的标本。我们已经开发了一种用于分析质谱数据的无凝胶和同位素标记的平台，该平台能够分析大量临床样品。我们的初步结果表明，我们的方法可以识别和定量的蛋白质组的重要组成部分，在几百个样品，而不诉诸分馏。此外，我们已经证明，这种方法可以区分急性髓系和急性淋巴细胞白血病蛋白表达模式。在这项研究中，TARGET队列的蛋白质组分析将使我们能够识别儿童AML中的预后蛋白质来源的特征。将通过整合蛋白质水平测量与其他分子特征（如突变、染色体和表观遗传变化）获得多分析物特征，这些分子特征通过TARGET程序获得的综合样本注释获得。蛋白质组学和基因组特征的整合将提高我们对儿童AML亚型进行分层的能力，并改善预后和治疗结果评估。 公共卫生相关性：急性髓细胞白血病是一组异质性血液癌症，可以单独使用化疗或化疗后进行干细胞移植治愈。目前，我们确定哪些患者仅用化疗治愈以及哪些患者需要干细胞移植的能力不足，这可能导致在不需要的患者中应用毒性治疗或对需要积极治疗的患者治疗不足。在这项提案中，我们使用一种新的质谱技术来测量来自急性白血病儿童的大量诊断样本中约1000种蛋白质的丰度，以鉴定可以预测患者预后的蛋白质和肽，从而帮助医生确定个体患者的最佳治疗方案。

DESCRIPTION (provided by applicant): The goal of our proposal is to identify proteins that serve as prognostic and treatment response biomarkers in childhood acute myeloid leukemia (AML) using specimens from patients enrolled in the Children's Oncology Group (COG) multicenter trials. Our ability to treat cancer more effectively depends critically on improving and refining tumor classifications. This unmet need is particularly acute in childhood AML. As part of the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) Initiative, we are analyzing 150 pediatric AML samples with a battery of high throughput technologies in order to identify genomic alterations, epigenetic changes, mRNA and miRNA abundance signatures that may serve as prognostic biomarkers. Because critical aspects of a cell's physiology may be better captured by its protein complement, we propose to carry out proteomic analysis of the same patient cohort using a new method for correlating protein abundance across many samples that was developed in our laboratory. Most currently available proteome profiling technologies (e.g. 2-D gels, ICAT, SILAC) have limitations that prevent their use for profiling large numbers of specimens. We have developed a gel- and isotopic label- free platform for analysis of mass spectrometric data that is capable of profiling large numbers of clinical samples. Our preliminary results demonstrate that our method can identify and quantify a significant portion of the proteome across several hundred samples without resorting to fractionation. Furthermore, we have already demonstrated that this method can differentiate between acute myeloid and acute lymphoid leukemias using protein expression patterns. The proteome analysis of the TARGET cohort in this study will enable us to identify prognostic protein-derived signatures in childhood AML. Multi-analyte signatures will be obtained by integrating protein level measurements with other molecular signatures such as mutational, chromosomal and epigenetic changes obtained by comprehensive sample annotations derived through the TARGET program. Integration of proteomic and genomic signatures will improve our ability to stratify childhood AML sub-types and improve prognostic and therapeutic outcome assessment. PUBLIC HEALTH RELEVANCE: Acute myeloid leukemia is a heterogenous group of blood cancers that can be cured with chemotherapy alone or with chemotherapy followed by stem cell transplant. At the present time our ability to determine which patient will be cured with chemotherapy alone and which patient needs stem cell transplant is inadequate which may lead to application of toxic treatments in patients that do not need it or insufficient treatment for patients that need aggressive therapy. In this proposal we use a novel mass spectrometric technology to measure the abundance of approximately 1000 proteins across large number of diagnostic samples from children with acute leukemia in order to identify proteins and peptides that can predict patients' prognosis and thus assist physicians in determining the best treatment option for individual patients.