权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Development of Novel Ovarian Cancer Biomarkers for Early Detection Algorithms

开发用于早期检测算法的新型卵巢癌生物标志物

基本信息

批准号：
10670063
负责人：
ROBERT C BAST
金额：
$ 72.43万
依托单位：
UNIVERSITY OF PITTSBURGH AT PITTSBURGH
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-01 至 2025-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10670063
关键词：
Algorithms Autoantibodies Biological Markers CA-125 Antigen Classification Clinical Trials Combination Drug Therapy Complement Computer Models Consensus Data Detection Development Diagnosis Diagnostic Disease Early Diagnosis Epithelial ovarian cancer Goals Interdisciplinary Study Intervention Lead Low Prevalence Malignant neoplasm of ovary Measurement Measures Modality Modeling Nurses&apos Health Study Patient Care Patients Performance Population Postmenopause Predictive Value Probability Prospective Studies Qualifying Recommendation Resistance Risk Sampling Screening for Ovarian Cancer Serum Specificity Survival Rate Symptoms Testing Time Tumor Debulking Ultrasonography United Kingdom United States Preventative Services Task Force Vagina Validation Woman biomarker identification biomathematics blood-based biomarker cancer biomarkers cancer survival candidate marker care costs classification algorithm clinical diagnosis clinical practice cohort collaborative trial diagnostic value early detection biomarkers feasibility testing follow-up improved mortality novel operation population based pre-clinical prospective screening tumor tumor progression

项目摘要

ABSTRACT Ovarian cancer (OC) is a deadly but often silent disease, showing no specific signs until it reaches advanced stages. The 5-year survival rate for advanced OC is only 50%, as most tumors ultimately become resistant to treatment.1,2 Advances in cytoreductive surgery and combination chemotherapy have improved 5-year survival in patients with epithelial OC, but the rate of cure has not improved over the last two decades. Computer models suggest that detection of OC in early stages (I-II) could substantially improve cure rates, but the low prevalence of OC in the general postmenopausal population restricts early detection efforts. Definitive diagnosis requires operative intervention, but a consensus is that no more than 10 operations should be performed to diagnose a single OC (>10% positive predictive value, PPV). According to current requirements, a first-line biomarker-based screening test must achieve a sensitivity (SN) of at least 75% and a specificity (SP) of 98%, which can then be further increased to 99.6% by adding a second-line screening modality such as transvaginal sonography (TVS). 1,3-6 Because available screening tests remain inadequate to merit wide implementation, based on our strong preliminary findings the proposed project aims to develop a novel, widely translatable, and economically feasible test that can reduce OC mortality rates. Currently, the only promising strategy developed in the United Kingdom Collaborative Trial for OC screening (UKCTOCS), is sequential analysis of the marker CA125 in serum over time (Risk of OC Algorithm, ROCA), followed by TVS. UKCTOCS yielded only a modest 20% decrease in mortality, insufficient to prompt the US Preventive Services Task Force to change its recommendation against population-based OC screening. 1 The most likely reason for such modest mortality reduction by CA125 measures is their insufficient lead-time (estimated interval for detection prior to symptoms-based diagnosis). Bio- mathematical modeling suggests that OC progresses to late stages more than 1 year before symptoms onset, a time range when CA125 levels offer only limited diagnostic power. Therefore, to improve current clinical practice, novel screening algorithms allowing substantially longer lead-times are needed. Based on our strong preliminary findings, we aim to develop and validate a 2-pronged approach, whereby a first-line multi-biomarker test recognizes OC with high SN (>80%) and modest SP (>80%), followed by a second-line biomarker velocity-based test in women who tested positive in the first test, that then yields a combined SP of 98%. Supporting this approach, we have generated a preliminary classification algorithm (threshold-based algorithm, TBA) based on one-time measurement of multiple biomarker concentrations, that identifies with 80%SN-70%SP women who will develop OC 1-7 years later. We further identified several biomarkers that display robust temporal dynamics (velocity) associated with OC development in the 1-7 YTD interval. We thus hypothesize that we can generate a 2-step algorithm that provides >75%SN at >98%SP, by combining our novel TBA with a velocity-based algorithm (VBA). In this approach, similar to ROCA, the positive results of the TBA would trigger frequent follow- up screening with VBA. The crucial advantage of our proposed algorithm vs. UKCTOCS' ROCA is that our novel combined algorithm will recognize OC more than 1 YTD, increasing the probability of detecting OC at early, treatment-responsive stages. We have discovered, and will prioritize for integration into the tests, several promising candidate pre-diagnostic OC biomarkers, including autoantibodies (AAbs). Our long-term goal is to develop a robust, accurate and widely translatable early-stage screening algorithm for risk of OC. Our immediate objectives are to enhance our biomarker-based classifiers for pre-diagnostic samples, developed in preliminary studies, by adding new promising candidate biomarkers we have identified, and validate them in independent pre-diagnostic samples. The Specific Aims are: 1. Generate and validate an optimized first-line threshold-based classification algorithm with 1.5-7 years lead-time. We will assess whether new candidate biomarkers can further improve the algorithm we developed in preliminary studies, and then validate the optimized algorithms in pre-diagnostic PLCO samples. 2. Generate and validate a biomarker temporal dynamics (velocity)-based algorithm. We will validate the promising candidate velocity-based biomarkers identified in Aim 1 in pre-diagnostic serial samples from UKCTOCS and NROSS prospective studies and generate a velocity-based classification algorithm for detecting OC, to complement and enhance the cut-off- based algorithm(s) developed in Aim 1. 3. Determine the performance of a 2-step (threshold+velocity)– based OC screening algorithm with 1.5-7 years lead-time in serial samples. We will determine the cumulative performance of sequential algorithms including the threshold-based algorithm developed in Aim 1, followed by the velocity-based algorithm developed in Aim 2, for OC screening in the 1.5-7 YTD interval, in serial UKCTOCS samples. In summary, we anticipate our results will yield development and validation of the first blood biomarker-based algorithms with the required >75% SN, >98% SP, for reliably classifying OC in preclinical samples collected 1.5-7 YTD. These algorithms will be ready for validation in prospective screening clinical trials to evaluate the effect of early detection upon OC survival. The proposal is supported by extensive preliminary data and will be carried out by a highly qualified, multi-disciplinary research team.

摘要卵巢癌（OC）是一种致命的，但往往沉默的疾病，没有具体的迹象，直到它达到先进的阶段晚期OC的5年生存率仅为50%，因为大多数肿瘤最终对化疗产生耐药性。肿瘤细胞减灭术和联合化疗的进展提高了5年生存率在上皮OC患者中，但在过去的二十年中治愈率没有提高。计算机模型提示早期发现OC（I-II）可显著提高治愈率，但低患病率一般绝经后人群中OC的存在限制了早期检测的努力。连续诊断需要手术干预，但一个共识是，不超过10个手术应执行诊断一个单一OC（>10%阳性预测值，PPV）。根据目前的要求，一个以一线生物标记为基础的筛查试验必须达到至少75%的灵敏度（SN）和98%的特异性（SP），然后可以通过增加二线筛查方式（如经阴道超声检查），进一步增加至99.6% （电视）。1，3 -6由于现有的筛查测试仍然不足以值得广泛实施，根据我们的强有力的初步调查结果，拟议的项目旨在开发一个新颖的，广泛的翻译，和经济可行的测试，可以降低OC死亡率。目前，美国唯一有希望的战略是 Kingdom Collaborative Trial for OC screening（UKCTOCS）是对血清中标志物CA 125的连续分析随着时间的推移（OC算法风险，ROCA），然后是TVS。UKCTOCS仅产生了20%的温和下降，死亡率，不足以促使美国预防服务工作组改变其建议，基于人群的OC筛查。1 CA 125导致死亡率适度降低的最可能原因这些措施的不足之处在于其前置时间（在基于诊断的诊断之前的检测估计间隔）。生物- 数学模型表明，OC在症状发作前1年以上进展到晚期， CA 125水平仅提供有限诊断能力时的时间范围。因此，为了改善当前的临床实践，需要允许显著更长的前置时间的新的筛选算法。根据我们初步的证据研究结果，我们的目标是开发和验证一种双管齐下的方法，即一线多生物标志物检测识别具有高SN（>80%）和中等SP（>80%）的OC，然后是基于速度的二线生物标志物在第一次测试中测试呈阳性的女性中进行测试，然后产生98%的综合SP。支持这一方法，我们已经生成了一个初步的分类算法（基于阈值的算法，TBA）的基础上一次性测量多种生物标志物浓度，确定80%SN-70%SP女性， 1-7年后发展为OC。我们进一步确定了几个生物标志物，显示强大的时间动态（速度）与1-7 YTD间隔的OC发展相关。因此，我们假设我们可以生成一个2步算法，提供>75%SN>98%SP，通过结合我们的新的TBA与基于速度的算法（ALSO）。在这种方法中，类似于ROCA，TBA的积极结果将引发频繁的后续行动- 用电脑进行筛查。与UKCTOCS的ROCA相比，我们提出的算法的关键优势在于，组合算法将识别超过1个YTD的OC，增加早期检测OC的概率，治疗反应阶段。我们已经发现，并将优先考虑集成到测试中，有希望的候选诊断前OC生物标志物，包括自身抗体（AAb）。我们的长期目标是开发一个强大的，准确的和广泛的翻译早期筛查算法的风险OC。我们近期目标是增强我们的基于生物标志物的诊断前样本分类器，初步研究，通过添加新的有希望的候选生物标志物，我们已经确定，并验证他们在独立的诊断前样本。具体目标是：1。生成并验证优化的第一线基于阈值的分类算法，提前1.5-7年。我们将评估新候选人是否生物标志物可以进一步改进我们在初步研究中开发的算法，然后验证在预诊断PLCO样品中的优化算法。2.生成并验证生物标志物时间基于动力学（速度）的算法。我们将验证有前途的候选速度为基础的生物标志物在UKCTOCS和NROSS前瞻性研究的诊断前系列样本中确定了目标1，生成用于检测OC的基于速度的分类算法，以补充和增强截止值，基于Aim 1中开发的算法。3.确定2步（阈值+速度）的性能- 基于OC筛选算法，在系列样本中具有1.5-7年的提前期。康贝特人将以顺序算法的累积性能，包括目标1中开发的基于阈值的算法，其次是目标2中开发的基于速度的算法，用于1.5-7 YTD间隔的OC筛查， UKCTOCS样品。总之，我们预计我们的结果将产生第一个开发和验证基于血液生物标志物的算法，要求>75% SN，>98% SP，用于临床前OC的可靠分类样本采集日期为1.5-7 YTD。这些算法将准备在前瞻性筛选临床试验中进行验证评估早期发现对OC生存的影响。该提案得到了广泛的初步支持数据，并将由一个高素质的多学科研究团队进行。