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组 (COG) 多中心试验中患者的标本来鉴定可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应用研究 (TARGET) 计划的一部分，我们正在利用一系列高通量技术分析 150 个儿科 AML 样本，以识别基因组改变、表观遗传变化、mRNA 和 miRNA 丰度特征，这些特征可作为预后生物标志物。由于细胞生理学的关键方面可以通过其蛋白质补体更好地捕获，因此我们建议使用我们实验室开发的一种新方法来关联许多样本中的蛋白质丰度，对同一患者群体进行蛋白质组学分析。 目前大多数可用的蛋白质组分析技术（例如 2-D 凝胶、ICAT、SILAC）均存在局限性，无法用于分析大量样本。我们开发了一个无凝胶和同位素标记的平台，用于分析质谱数据，能够分析大量临床样本。我们的初步结果表明，我们的方法可以识别和量化数百个样品中蛋白质组的很大一部分，而无需采用分级分离。此外，我们已经证明该方法可以利用蛋白质表达模式区分急性髓系白血病和急性淋巴细胞白血病。本研究中 TARGET 队列的蛋白质组分析将使我们能够识别儿童 AML 中的预后蛋白衍生特征。通过将蛋白质水平测量与其他分子特征（例如通过 TARGET 程序导出的综合样本注释获得的突变、染色体和表观遗传变化）相结合来获得多分析物特征。蛋白质组和基因组特征的整合将提高我们对儿童 AML 亚型进行分层并改善预后和治疗结果评估的能力。

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.