Validation of pancreatic cancer biomarkers in large prospective cohorts

在大型前瞻性队列中验证胰腺癌生物标志物

• 批准号: 8987610
• 负责人: ANNA E LOKSHIN
• 金额: $ 51.68万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-05-05 至 2018-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8987610
• 关键词: Acute; Algorithms; Benign; Bioinformatics; Biological Assay; Biological Markers; Cancer Etiology; Cessation of life; Classification; Clinical Trials; Colon; Data; Development; Diagnosis; Diagnostic; Disease; Early Diagnosis; Excision; Goals; Imaging Techniques; Individual; Inflammation; Lung; Malignant Neoplasms; Malignant neoplasm of pancreas; Operative Surgical Procedures; Ovarian; Pancreas; Pancreatic Ductal Adenocarcinoma; Patient Care; Patients; Performance; Phase; Probability; Procedures; Prostate; Proteomics; Resectable; Rewards; Risk; Sampling; Sensitivity and Specificity; Serum; Staging; Survival Rate; Symptoms; Time; United States; Validation; Women' s Health; base; case control; clinical practice; cohort; cost; differential expression; lymph nodes; mortality; outcome forecast; pre-clinical; prospective; public health relevance; resistin; screening; tumor

 DESCRIPTION (provided by applicant): Pancreatic ductal adenocarcinoma (PDAC) is the fourth leading cause of cancer death in the United States. The exceptionally poor prognosis of PDAC can be largely attributed to the difficulty of detecting the cancer at an early stage when curative surgical resection remains a viable option. The less than 10% patients who present with small, surgically resectable cancers have a realistic chance of cure and a 5-year survival rate of 20-30%. The identification of serum biomarkers associated with a high probability of PDAC could substantially change clinical practice by changing the risk-reward ratio for invasive imaging procedures or facilitating the development of less invasive procedures. At present, the overriding goal for the early detection of PDAC is the reliable identification of resectable tumors of less than 1.0 cm in size with negative lymph nodes. Presently, there are no biomarkers able to capture the disease more than 1 year prior to symptoms onset where there is a high probability of detecting PDAC at early stages. Biomarkers developed using samples obtained from patients at diagnosis do not validate well in preclinical samples indicating that different se of biomarkers characterizes early asymptomatic PDAC than late and symptomatic disease where biomarkers of inflammation and acute phase could predominate. We thus hypothesize that to generate a robust algorithm for identification of resectable tumors of less than 1.0 cm in size with negative lymph nodes, biomarker discovery should be performed in preclinical samples. We further hypothesize that biomarker velocity should be considered in order to generate optimized classification algorithm. We have generated a 5-biomarker classification algorithm that diagnoses patients 12-24 months before diagnosis with 743% SN at 98% SP and 24-35 months before diagnosis - with 64% SN at 98% SP. We have additionally accumulated promising preliminary data obtained in individual Women Health Initiative and pooled PLCO samples identifying biomarkers that are differentially expressed in preclinical cases vs. healthy controls and that demonstrate time-to-diagnoses dependent changes, thus providing support for our hypothesis that pre-clinical samples will be useful for discovery of pre-clinical biomarkers We propose to validate these candidate biomarkers in individual PLCO samples including longitudinal samples, develop an optimized classification algorithm, and validate this algorithm in preclinical samples from two other screening clinical trials. We propose following Specific Aims: 1. Optimize and validate the performance of preclinical PDAC classification algorithm in prospective sample. 2. Validate performance of a pre- diagnostic classification panel in two large prospective cohorts. 3. Construct Risk of Pancreatic Malignancy (RPM) algorithm to add the velocity component to PDAC classification. At the completion of the proposed study we expect to have identified a set of biomarkers with individual robust performance in preclinical PDAC samples, and have generated a bioinformatics algorithm based on biomarker velocities for recognition of PDAC at the resectable stages with high sensitivity and specificity.

 描述（由适用提供）：胰腺导管腺癌（PDAC）是美国癌症死亡的第四个主要原因。 PDAC的预后异常不佳，主要归因于在治愈手术切除仍然是可行的选择时，在早期阶段发现癌症的困难。不到10％的患者患有小型，可切除的癌症的患者具有现实的治愈机会，而5年的存活率为20-30％。通过更改侵入性成像程序的风险回报比或支持较少的入侵程序的开发，可以鉴定与PDAC高概率相关的血清生物标志物可以实质性地改变临床实践。目前，PDAC早期检测的重大目标是可靠的可切除肿瘤的可靠鉴定 尺寸小于1.0 cm，负淋巴结。目前，在症状开始前超过1年，没有生物标志物能够在早期阶段检测PDAC的可能性很高。使用从诊断患者获得的样品开发的生物标志物在临床前样品中不能很好地验证，表明生物标志物的不同SE表征早期不对称PDAC与晚期和症状疾病，在这种情况下，炎症和急性期的生物标志物可能占主导地位。因此，我们假设要生成一种鲁棒算法，以鉴定可切除的TUM尺寸小于1.0 cm的淋巴结淋巴结低于1.0 cm，则应在临床前样品中进行生物标志物发现。我们进一步假设应考虑生物标志物速度以生成优化的分类算法。我们已经产生了一种5个生物标志物分类算法，该算法在诊断为743％的SN诊断为98％SP和诊断前24-35个月之前诊断出患者12-24个月 - 为64％SN为98％SP。我们还积累了在个别女性健康计划中获得的承诺初步数据和汇集的PLCO样本，这些样本识别出在临床前案例与健康对照中不同表达的生物标志物，并证明了依赖性的依赖性变化，从而为我们的假设提供了对临时样本的支持，这些示例将在这些假设中提供这些示例，以使这些候选者在这些候选中有用，以使这些候选者对这些候选生物群体进行研究，从而使这些候选生物群体验证了这些候选者的发现纵向样品，开发了优化的分类算法，并从其他两项筛查临床试验中验证临床前样品中的该算法。我们提出以下特定目的：1。优化和验证临床前PDAC分类算法的性能2。验证两个大型前瞻性队列中诊断前分类面板的性能。 3。构建胰腺恶性肿瘤（RPM）算法的风险，将速度分量添加到PDAC分类中。拟议研究完成时，我们希望在临床前PDAC样品中确定一组具有稳健性能的生物标志物，并基于生物标志物速度生成了生物信息学算法，以识别具有高灵敏度和特异性的可切除阶段的PDAC。