Development of Novel Ovarian Cancer Biomarkers for Early Detection Algorithms

摘要 卵巢癌(OC)是一种致命的疾病，但通常是沉默的，直到发展到晚期才会显示出特定的症状 各阶段。晚期OC的5年生存率仅为50%，因为大多数肿瘤最终对 治疗：1、2细胞减少性手术和联合化疗的进展提高了5年生存率 在上皮性OC患者中，但治愈率在过去20年中没有提高。计算机模型 提示在早期(I-II)检测OC可以显著提高治愈率，但患病率较低 一般绝经后人群中OC的发病率限制了早期发现的努力。明确的诊断需要 手术干预，但共识是不应进行超过10次的手术来诊断 单一OC(&gt；10%阳性预测值，PPV)。根据目前的要求，一线生物标志物 筛查试验必须达到至少75%的敏感性(SN)和98%的特异性(SP)，然后才能 通过增加二线筛查手段，如经阴道超声，进一步提高到99.6% (电视)。1，3-6，因为现有的筛查测试仍然不足以值得广泛实施，根据我们的 提出的项目旨在开发一种新颖的、可广泛翻译的、经济的 可以降低OC死亡率的可行测试。目前，美国制定的唯一有希望的战略是 王国OC筛查合作试验(UKCTOCS)，是对血清中标志物CA125的序贯分析 随着时间的推移(OC算法的风险，ROCA)，其次是电视。UKCTOCS在#年仅小幅下降了20% 死亡率，不足以促使美国预防服务工作组改变其对 以人群为基础的OC筛查。1 CA125将死亡率适度降低的最有可能的原因 措施是不充分的准备时间(在基于症状的诊断之前估计的检测间隔)。生物- 数学模型表明，OC在症状出现前一年以上进展到晚期， CA125水平仅提供有限诊断能力的时间范围。因此，为了改善目前的临床实践， 需要新的筛选算法，允许更长的提前期。基于我们强有力的初步调查 我们的目标是开发和验证一种双管齐下的方法，即一线多生物标记物测试 识别具有高SN(&gt；80%)和中等SP(&gt；80%)的OC，然后是基于速度的二线生物标记物 对第一次检测呈阳性的女性进行测试，合并后的SP为98%。支持这一点 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)，基于 一次性测量多个生物标志物浓度，可识别80%SN-70%SP女性 将在1-7年后发展为OC。我们进一步确定了几个生物标记物，它们显示了强大的时间动力学 (速度)与1-7YTD间隔内OC的发展有关。因此我们假设我们可以产生 一种两步算法，通过将我们的新型TBA与基于速度的 算法(VBA)。在这种方法中，与ROCA类似，TBA的积极结果将引发频繁的后续行动-- 使用VBA进行UP筛选。与UKCTOCS的ROCA相比，我们提出的算法的关键优势是我们的新算法 组合算法将识别OC超过1YTD，增加了早期发现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的几个 有希望的诊断前OC生物标记物，包括自身抗体(AABS)。我们的长期目标是 开发一种稳健、准确、可广泛翻译的OC风险早期筛查算法。我们的 近期目标是增强我们基于生物标记物的诊断前样本分类器，该分类器是在 初步研究，通过添加我们已经确定的新的有希望的候选生物标记物，并在 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，提前期为1.5-7年。我们将评估新的候选人是否 生物标记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 在诊断前PLCO样本中的优化算法。2.生成并验证生物标记物时间 基于动力学(速度)的算法。我们将验证基于速度的有前景的候选生物标记物 在UKCTOCS和NROSS前瞻性研究的诊断前系列样本中发现了AIM 1 生成一种基于速度的分类算法来检测OC，以补充和增强截止点- 基于目标1.3开发的算法(S)。确定两步(阈值+速度)的性能- 基于连续样本提前期为1.5-7年的OC筛选算法。我们将确定 包括在目标1中开发的基于阈值的算法的顺序算法的累积性能， 然后是在AIM 2中开发的基于速度的算法，用于在1.5-7 YTD间隔内连续进行OC筛查 UKCTOCS样本。总之，我们预计我们的结果将产生第一个的开发和验证 基于血液生物标记物的算法和所需的&gt；75%SN、&gt；98%SP，用于临床前OC的可靠分类 样本采集时间为1.5-7年。这些算法将在前瞻性筛查临床试验中进行验证。 评价早期发现对卵巢癌患者生存的影响。这项提议得到了广泛的初步支持 数据，并将由一支高素质的多学科研究团队进行。

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.