Enhancement of MS signal processing toward improved cancer biomarker discovery

描述(由申请人提供)： 临床蛋白质组样本的全面和定量分析是生物医学研究中的一个突出挑战。迫切需要用于癌症检测的新蛋白质组技术，并具有改善人类健康的巨大潜力，癌症早期诊断患者存活率的提高突显了这一点。为此，我们将开发旨在提高从无标记MALDI-TOF(基质辅助激光解吸/电离飞行时间)质谱学中发现癌症生物标记物的有效性的计算工具，用于验证和鉴定。计算算法和工具将导致分子生物标志物筛选的灵敏度和选择性增加一个数量级以上。具体地说，我们建议：(1)优化信号处理，使灵敏度至少提高4倍(以信噪比衡量)，选择性(分辨率)提高2倍，质量精度提高10倍(目标1)；(2)自动检测电离卫星，然后进行质量重新校准(目标2)，使选择性和质量精度提高3倍；(3)将卫星离子的强度分布去卷积到母体蛋白质峰(目标3)，使从增强型分子图谱进行生物标记物的统计检测和实验识别的灵敏度提高3倍(目标4)。大质量范围筛选效率的提高将减少下游鉴定和验证实验的时间和成本。本申请中描述的研究的成功完成将为将这些计算工具扩展到其他顶级MS平台提供基础，并推动表征癌症分子基础的努力朝着更好的预后和治疗策略迈进。

DESCRIPTION (provided by applicant): The comprehensive and quantitative analysis of clinical proteomic samples is an outstanding challenge in biomedical research. New proteomic technologies for cancer detection are urgently needed and hold great potential for improving human health, as underscored by the improved survival rates of patients diagnosed in he early stages of cancer. To this end, we will develop computational tools aimed at increasing the effectiveness of cancer biomarker discovery from label-free MALDI-TOF (matrix-assisted laser- desorption/ionization time-of-flight) mass spectra for verification and identification. The computational algorithms and tools will result in more than an order of magnitude increase in both sensitivity and selectivity For molecular biomarker screening. Specifically, we propose: (i) to optimize signal processing resulting in at east a 4-fold enhancement of sensitivity (as measured by signal-to-noise), 2-fold gain in selectivity (resolution), and 10-fold increase in mass accuracy (Aim 1); (ii) to automate detection of ionization satellite ons followed by mass recalibration (Aim 2) resulting in tripling selectivity and mass accuracy; (iii) to deconvolve intensity distributions from satellite ions into parent protein peaks (Aim 3) resulting in tripling sensitivity for statistical detection and experimental identification of biomarkers from enhanced molecular maps (Aim 4). The increased efficiency of broad mass range screening will decrease the time and cost of the downstream identification and validation experiments. The successful completion of the studies described in this application will provide a basis for expanding these computational tools to other TOP MS platforms, and advance the endeavor of characterizing molecular basis for cancer toward better prognosis and treatment strategies.