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.

抽象的卵巢癌（OC）是一种致命但静音疾病，直到达到高级才显示出任何特定迹象阶段。高级OC的5年生存率仅为50％，因为大多数肿瘤最终都能抵抗 1,2细胞减少手术和联合化疗的进展提高了5年生存率在上皮OC的患者中，但是在过去的二十年中，治愈率没有提高。计算机型号表明在早期（I-II）中检测OC可以大大提高治疗率，但患病率低绝经后人口中的OC限制了早期发现工作。确定的诊断要求手术干预措施，但达成共识是，应进行不超过10次操作以诊断单个OC（> 10％的阳性预测值，PPV）。根据当前的要求，基于一线生物标志物筛选测试必须达到至少75％的灵敏度（SN），而特异性（SP）为98％，然后可以是通过添加二线筛选方式（例如经阴道超声处理）进一步增加到99.6％（电视）。 1,3-6，因为根据我们的强有力的初步发现该拟议项目旨在开发一部小说，可广泛翻译和经济。可行的测试可以降低OC死亡率。目前，联合国制定的唯一承诺策略 OC筛查的王国协作试验（UKCTOC）是对串行中标记CA125的顺序分析随着时间的流逝（OC算法的风险，ROCA），然后是电视。 UKCTOC仅减少20％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 CA125降低这种适度死亡率的最可能原因措施是他们的交货时间不足（在基于症状的诊断之前进行检测的估计间隔）。生物数学建模表明，OC在症状发作之前超过1年的后期阶段 CA125水平仅提供有限的诊断能力时的时间范围。因此，为了改善当前的临床实践，新颖的筛选算法需要大大较长的铅时间。基于我们强大的初步调查结果，我们旨在开发和验证一种两管齐下的方法，从而进行一线多生物标志物测试识别高SN（> 80％）和适度SP（> 80％）的OC，其次是二线生物标志物速度在第一次测试中测试阳性的女性的测试，然后产生98％的合并SP。支持这个方法，我们基于多个生物标志物浓度的一次性测量，该浓度识别为80％SN-70％SP女性 1 - 7年后将开发OC。我们进一步确定了几种显示强大临时动力学的生物标志物（速度）与1-7 YTD间隔中OC开发相关的（速度）。因此，我们假设我们可以生成通过将我们的新颖的TBA与基于速度的基于速度相结合的2步算法，可在> 98％的SP下提供> 75％的SN 算法（VBA）。在这种方法中，类似于Roca，TBA的积极结果将经常触发 - 用VBA进行筛选。我们提出的算法与UKCTOC的ROCA的关键优势是我们的小说组合算法将识别OC超过1 YTD，从而增加了在早期检测OC的可能性，治疗响应阶段。我们已经发现，并将优先级集成到测试中，有几个有希望的候选诊断前OC生物标志物，包括自身抗体（AABS）。我们的长期目标是为OC风险开发出强大，准确且广泛可翻译的早期筛选算法。我们的直接的目标是增强我们基于生物标志物的分类器，用于诊断前样本，开发初步研究，通过添加我们已经确定的新承诺候选生物标志物，并验证它们独立的诊断前样品。具体目的是：1。生成并验证优化的第一线基于阈值的分类算法，交货时间为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善我们在初步研究中开发的算法，然后验证诊断前PLCO样品中优化的算法。 2。生成并验证生物标志物临时基于动力学（速度）算法。我们将验证基于候选速度的承诺候选生物标志物在AIM 1中确定的诊断前串行样本中的UKCTOC和NROSS前瞻性研究和生成基于速度的分类算法，用于检测OC，以补充和增强临界值 - 基于AIM1。3中开发的算法（S）。确定2步（阈值+速度）的性能 - 基于串行样品的OC筛查算法为1。5 - 7年。我们将确定顺序算法的累积性能，包括在AIM 1中开发的基于阈值的算法其次是AIM 2中开发的基于速度的算法，以在1.5-7 ytd间隔中进行OC筛选 UKCTOC样品。总而言之，我们预计我们的结果将产生第一个的开发和验证基于血液生物标志物的算法，所需的> 75％SN，> 98％SP，用于可靠的临床前分类OC 样品收集了1.5-7 ytd。这些算法将准备好在前瞻性筛查临床试验中验证评估早期检测对OC存活的影响。该提案得到了广泛的初步支持数据并将由高度合格的多学科研究团队进行。