Enhancement of MS signal processing toward improved cancer biomarker discovery

增强 MS 信号处理以改善癌症生物标志物的发现

• 批准号: 7291560
• 负责人: Dariya I. Malyarenko
• 金额: $ 46.66万
• 依托单位: COLLEGE OF WILLIAM AND MARY
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-29 至 2009-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7291560
• 关键词: Address; Algorithms; Applied Research; Biochemical; Bioinformatics; Biological Markers; Biomedical Research; Body Fluids; Cancer Detection; Charge; Clinical; Collaborations; Computational algorithm; Data; Data Analyses; Detection; Development; Diagnosis; Diagnostic; Diagnostic Neoplasm Staging; Effectiveness; Facility Construction Funding Category; Goals; Health; Heating; Human; Imagery; Ions; Label; Least-Squares Analysis; Liquid Chromatography; Location; Malignant Neoplasms; Maps; Mass Spectrum Analysis; Measures; Molecular; Molecular Profiling; Noise; Parents; Patients; Pattern; Physics; Positioning Attribute; Protein Databases; Proteins; Proteomics; Range; Research; Research Personnel; Resolution; Sampling; Scanning; Scientist; Screening procedure; Signal Transduction; Site; Software Engineering; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization; Standards of Weights and Measures; Statistical sensitivity; Surveys; Survival Rate; Techniques; Technology; Testing; Time; Translating; Uncertainty; Validation; Virginia; Work; adduct; base; college; comparative; computerized data processing; computerized tools; cost; improved; ionization; leukemia; medical schools; outcome forecast; programs; protein purification; reconstruction; research study; time use; tool; tumor molecular fingerprint

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.

描述（由申请人提供）： 临床蛋白质组样本的全面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学技术，并且在改善人类健康方面具有巨大潜力，早期癌症诊断患者的生存率提高就凸显了这一点。为此，我们将开发计算工具，旨在提高从无标记 MALDI-TOF（基质辅助激光解吸/电离飞行时间）质谱中发现癌症生物标志物的有效性，以进行验证和识别。计算算法和工具将使分子生物标志物筛选的灵敏度和选择性提高一个数量级以上。具体来说，我们建议：(i) 优化信号处理，使灵敏度至少提高 4 倍（通过信噪比测量）、选择性（分辨率）提高 2 倍、灵敏度提高 10 倍。质量准确度（目标 1）； (ii) 自动检测电离卫星，然后进行质量重新校准（目标 2），从而使选择性和质量精度提高三倍； (iii) 将卫星离子的强度分布解卷积为母蛋白峰（目标 3），从而使增强分子图谱中生物标志物的统计检测和实验鉴定的灵敏度提高三倍（目标 4）。宽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 TOP MS 平台提供基础，并推动表征癌症分子基础以实现更好的预后和治疗策略的努力。