Improving prostate cancer screening by integration of SNPs with blood biomarkers

描述（由申请人提供）：每年，对前列腺特异性抗原（PSA）水平升高但活检时未显示癌症证据的男性进行超过1，000，000次不必要的前列腺活检。无论人们是否认为广泛的PSA检测会导致惰性癌症的过度治疗，或者是降低前列腺癌死亡率的重要工具，前列腺癌筛查都不太可能消失，很明显，减少这些假阳性结果将大大减少不必要的医疗程序及其伴随的不良后果。该研究项目的长期目标是开发改进的筛查测试，以确定男性患前列腺癌的风险，特别是潜在的 致死性疾病，具有高灵敏度和特异性。本申请的总体目标是确定考虑个体的遗传组成是否可以提高基于PSA和其他前列腺产生的生物标志物的筛查测试的准确性。本申请的核心假设是，考虑与前列腺癌生物标志物水平相关的SNP及其在预测模型中的相互作用将改善模型性能。该项目背后的基本原理是，如果SNP确实影响独立于疾病状态的生物标志物水平，则需要考虑SNP基因型的个性化生物标志物评估，以最大限度地提高测试的准确性。这种前列腺癌筛查的方法将通过以下方式开发：1）在没有前列腺癌的男性中鉴定与PSA和其他生物标志物水平相关的SNP。将测试先前与前列腺癌风险和/或前列腺分泌的生物标志物水平相关的一组SNP与健康年轻男性和健康老年男性中前列腺分泌蛋白的一组五种亚型的相关性。2)确定SNP提高基于PSA的预测模型的准确性的能力。使用巢式病例对照研究，将与目标1中的生物标志物水平相关的那些SNP以及它们与生物标志物水平的相互作用沿着纳入前列腺增生的预测模型中。 癌3)确定这些模型在不同人群中的普遍性。目标2中的最佳模型将在来自美国和瑞典的三项独立研究中进行外部验证，这些研究代表了各种种族、巢式病例对照设计和活检队列。这项提议是创新的，因为它调查了SNP与健康年轻男性中前列腺生物标志物水平的相关性，这些男性可以被认为是无癌症的;因为它建议根据SNP基因型解释生物标志物，而不是简单地结合生物标志物和SNP信息;并且因为它不限于具有单一祖先的人群。这项研究的预期成果是前列腺癌的预测模型，该模型将遗传变异与前列腺分泌蛋白生物标志物相结合。这种模型的积极影响将是减少不必要的活检，同时仍然促进前列腺癌的早期检测。

DESCRIPTION (provided by applicant): Each year, over 1,000,000 needless prostate biopsies are performed on men who present with elevated levels of prostate specific antigen (PSA) but show no evidence of cancer upon biopsy. Whether one believes that widespread PSA testing leads to overtreatment of indolent cancer or is an important tool to reduce prostate cancer mortality, screening for prostate cancer is unlikely to go away and it is clear that reducin these false positive findings will greatly reduce unnecessary medical procedures and their attendant adverse consequences. The long-term goal of this research project is to develop improved screening tests that will identify men at risk for prostate cancer, especially potentially lethal disease, with high sensitivity and specificity. The overall objective of this application isto determine if consideration of an individual's genetic makeup can improve the accuracy of screening tests based on PSA and other prostate-produced biomarkers. The central hypothesis underlying this application is that consideration of SNPs that are associated with levels of prostate cancer biomarkers and their interaction in predictive models will improve model performance. The rationale behind this project is that if SNPs do influence biomarker levels independent of disease status, then personalized biomarker evaluation that takes into account SNP genotype is necessary to maximize accuracy of the test. This approach to prostate cancer screening will be developed by: 1) Identifying SNPs associated with levels of PSA and other biomarkers in men without prostate cancer. A panel of SNPs previously associated with prostate cancer risk and/or levels of prostate secreted biomarkers will be tested for association with a panel of five isoforms of prostate secreted proteins in healthy young men and healthy older men. 2) Determining the ability of SNPs to improve accuracy of PSA-based predictive models. Using a nested case- control study, those SNPs associated with biomarker levels in aim 1 will be included, along with their interaction with biomarker levels, in a predictive model for prostate cancer. 3) Determining the generalizability of such models in diverse populations. The best model from aim 2 will be externally validated in three separate studies from the US and Sweden representing a variety of ethnicities and both nested case-control designs and a biopsy cohort. This proposal is innovative because it investigates the association of SNPs with levels of prostate biomarkers in healthy young men who can be presumed to be cancer-free; because it suggests interpretation of biomarkers in light of SNP genotype rather than simply combining biomarker and SNP information; and because it is not restricted to a population with a single ancestry. The expected outcome of this research is a predictive model for prostate cancer that integrates genetic variation with prostate secreted protein biomarkers. The positive impact of such a model will be the reduction of unnecessary biopsies while still facilitating the early detection of prostate cancer.