Blood-Based Biomarkers for Personalized Risk Assessment of Breast and Ovarian Cancer

项目摘要 仍然需要开发生物标志物测试，用于乳腺癌和卵巢癌的个性化风险评估。 癌的这种测试不会取代筛查程序，而是一种基本工具，可以 基于受试者的特征与其他风险模型相结合，以个性化携带癌症的风险 并告知需要进行筛查和监测，以便及早发现。 该提案的主要转化目标是开发一种基于多分析物血液的 基于循环蛋白质和针对肿瘤抗原的自身抗体的生物标志物面板， 受试者患有乳腺癌或卵巢癌的概率。申请人团队的研究导致了 鉴定一组癌症相关循环蛋白以及抗肿瘤蛋白的自身抗体， 包括TP 53和新的瓜氨酸化抗原，用于检测乳腺癌和卵巢癌。PLCO队列 是进一步推进候选生物标志物测试和建立组合的极好资源 乳腺癌和卵巢癌个体化风险评估的规则以及受试者特征， 优化筛查和监测，以便及早发现这些疾病。 在特定目标1中，利用来自969例乳腺癌病例和106例卵巢癌病例的诊断前血浆 例以及四倍的非例血浆从女性PLCO参与者，没有发展 在研究随访期间，我们将评估时间依赖性（例如0-1年、1-2年等）预测性 性能（AUC、灵敏度、特异性、阳性预测值（PPV）和阴性预测值（NPV）） 用于检测乳腺癌和卵巢癌的候选生物标志物。优先生物标志物候选物将是 进一步建立模型以及相关的患者特征（例如乳腺癌的Gail模型） 用于BrCa和OvCa的1年风险预测。对于建模，我们将坚持可预测性，可计算性和 稳定框架。整个PLCO样本集将分为开发集和搁置测试集 集超参数的建模和调整以及初始验证将在开发中进行。 集将选择开发集中具有最佳预测性能（AUC）的模型用于 在设定试验装置中进行后续试验。在具体目标2中，我们将利用从 在BrCa或OvCa诊断之前的病例和非病例的系列样本，我们将测试 生物标志物组评分的纵向轨迹是否改善了风险预测。拟定研究 代表了癌症相关蛋白和自身抗体生物标志物的验证， 开发一种可以在临床环境中实施的血液检测，用于乳腺癌的个体化风险评估 和卵巢癌

Project Summary There remains a need to develop biomarker tests for personalized risk assessment of breast and ovarian cancers. Such tests would not replace screening programs but would instead be a basic tool that can be integrated with other risk models based on a subject’s characteristics to personalize the risk of harboring cancer and inform on the need for screening and surveillance for earlier detection. The primary translational objective of this proposal is to develop a multi-analyte blood-based biomarker panel based on circulating proteins and autoantibodies against tumor antigens that inform about a subject’s probability of harboring a breast or ovarian cancer. Studies by the applicant team have led to the identification of a panel of cancer-relevant circulating proteins as well as autoantibodies against tumor proteins, including TP53 and novel citrullinated antigens, for detection of breast and ovarian cancers. The PLCO cohort is an excellent resource to further advance testing of candidate biomarkers and to also establish combination rules together with subject characteristics for individualized risk assessment of breast and ovarian cancers to optimized screening and surveillance for earlier detection of these diseases. In Specific Aim 1, leveraging pre-diagnostic plasmas from 969 breast cancer cases and 106 ovarian cancer cases as well as four times the number of non-case plasmas from female PLCO participants that did not develop cancer during study follow-up, we will assess the time-dependent (e.g. 0-1 year, 1-2 years, etc) predictive performance (AUC, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV)) of candidate biomarkers for detection of breast and ovarian cancers. Priority biomarker candidates will be advanced to establish models together with pertinent patient characteristics (e.g. Gail Model for breast cancer) for 1-year risk prediction of BrCa and OvCa. For modeling, we will adhere to the Predictability, Computability and Stability framework. The entire PLCO specimen set will be divided into a Development Set and a Set-Aside Test Set. Modeling and tuning of hyperparameters as well as initial validation will be performed in the Development Set. The model with the best predictive performance (AUC) in the Development Set will be selected for subsequent testing in the Set-Aside Test Set. In Specific Aim 2, we will leverage serial samples procured from cases preceding a diagnosis of a BrCa or OvCa and serial samples for non-cases, and we will test whether longitudinal trajectories of biomarker panel scores improve risk prediction. The proposed study represents a validation of cancer-associated protein and autoantibody biomarkers and has high probability to develop a blood test that can be implemented in the clinical setting for individualized risk assessment of breast and ovarian cancers.