Protein Biomarkers in Childhood Acute Myeloid Leukemia

描述(由申请人提供)：我们计划的目标是利用参加儿童肿瘤学小组(COG)多中心试验的患者的样本，确定在儿童急性髓系白血病(AML)中作为预后和治疗反应生物标记物的蛋白质。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。这种未得到满足的需求在儿童急性髓系白血病中尤为严重。作为产生有效治疗的治疗应用研究(TARGET)计划的一部分，我们正在使用一系列高通量技术分析150个儿科AML样本，以识别可能作为预后生物标志物的基因组改变、表观遗传学改变、mRNA和miRNA丰度特征。由于细胞生理的关键方面可能更好地被其蛋白质互补捕获，我们建议使用一种新的方法对同一患者队列进行蛋白质组分析，该方法可以关联许多样本中的蛋白质丰度，这是我们实验室开发的。目前大多数可用的蛋白质组谱分析技术(如2-D凝胶、ICAT、SILAC)都有限制，使其无法用于对大量样本的谱分析。我们已经开发了一种无需凝胶和同位素标记的平台，用于分析质谱学数据，能够对大量临床样本进行分析。我们的初步结果表明，我们的方法可以在数百个样本中识别和量化蛋白质组的重要部分，而不需要求助于分级。此外，我们已经证明，这种方法可以通过蛋白质表达模式来区分急性髓系白血病和急性淋巴细胞性白血病。这项研究中对目标队列的蛋白质组分析将使我们能够识别儿童AML的预后蛋白衍生特征。多分析物签名将通过将蛋白质水平测量与其他分子签名相结合来获得，例如突变、染色体和表观遗传学变化，这些变化是通过TARGET计划获得的全面样本注释获得的。蛋白质组和基因组特征的整合将提高我们对儿童AML亚型进行分层的能力，并改善预后和治疗结果评估。

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.