Prostate Cancer Detection by Serum Proteomic Profiling

通过血清蛋白质组分析检测前列腺癌

• 批准号: 6783863
• 负责人: DAVID K ORNSTEIN
• 金额: $ 7.58万
• 依托单位: UNIVERSITY OF CALIFORNIA-IRVINE
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-01 至 2006-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6783863
• 关键词: artificial intelligence; clinical research; computer program /software; diagnosis design /evaluation; human subject; male; mass screening; mass spectrometry; microarray technology; neoplasm /cancer diagnosis; prostate neoplasms; prostate specific antigen; protein quantitation /detection; proteomics; serum

DESCRIPTION (provided by applicant): Our hypothesis is that the patterns of low molecular weight proteins and peptide fragments in serum are altered by the presence of prostate cancer even when the cancer is still contained within the prostate. We propose to use an artificial intelligence-based pattern recognition algorithm to identify expression patterns of serum protein and peptide fragments that discriminate men with prostate cancer from those men with benign prostates. The goal of this study is to develop a serum proteomic based detection method that will help determine the need for prostate biopsy for men with serum PSA levels between 2.5 and 10.0 ng/ml. Aim 1: Determine the protein chip surface and mass spectrometer instrument that generates the serum proteomic profile that best discriminates men with prostate cancer from those with benign prostates. Aim 2: Determine the sensitivity, specificity and positive predictive value for biopsy-detected prostate cancer of the optimized serum proteomic algorithm among men with serum total PSA levels of 2.5 to 10.0 ng/ml. Aim 3: Compare the performance of serum proteomic profiling to PSA density and percent free PSA measurements for men with serum total PSA levels of 2.5 to 10.0 ng/ml. Aim 4: Determine the biological variability of serum proteomic patterns by analyzing serum samples collected from the same patient on 3 consecutive days.

描述(由申请人提供)： 我们的假设是，即使前列腺癌仍在前列腺内，血清中低分子蛋白质和多肽片段的模式也会因前列腺癌的存在而改变。我们建议使用基于人工智能的模式识别算法来识别血清蛋白质和肽片段的表达模式，以区分前列腺癌男性和良性前列腺癌男性。这项研究的目标是开发一种基于血清蛋白质组学的检测方法，该方法将有助于确定血清PSA水平在2.5至10.0 ng/ml之间的男性是否需要进行前列腺活检。 目的1：确定蛋白质芯片表面和质谱仪，以产生最好地区分前列腺癌和良性前列腺癌患者的血清蛋白质组图谱。 目的：在血清总PSA水平为2.5~10.0 ng/ml的男性中，确定优化的血清蛋白质组学算法对前列腺癌活检诊断的敏感性、特异性和阳性预测值。 目的3：比较血清总PSA水平在2.5~10.0 ng/ml的男性血清蛋白质组图谱与PSA密度和游离PSA百分比的差异。 目的4：通过对同一患者连续3天采集的血清标本进行分析，确定血清蛋白质组模式的生物变异性。