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

用于乳腺癌和卵巢癌个性化风险评估的血液生物标志物

• 批准号: 10721949
• 负责人: Johannes F Fahrmann
• 金额: $ 67.23万
• 依托单位: UNIVERSITY OF TX MD ANDERSON CAN CTR
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2028-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10721949
• 关键词: Algorithms; Antigens; Autoantibodies; Biological Markers; Blood Tests; Breast Cancer Detection; Breast Cancer Prevention; Breast Cancer Risk Assessment Tool; Characteristics; Clinical; Colorectal; Data; Development; Diagnosis; Diagnostic; Disease; Early Detection Research Network; Early Diagnosis; Exhibits; Female; Immunoassay; Individual; Lung; Malignant Neoplasms; Malignant neoplasm of ovary; Modeling; Ovarian; Participant; Patients; Pattern; Performance; Phase; Plasma; Predictive Value; Principal Investigator; Probability; Prostate; Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial; Proteins; Random Allocation; Resources; Risk; Risk Assessment; Sampling; Series; Specificity; Specimen; Standardization; TP53 gene; Technology; Testing; Time; Tumor Antigens; Validation; biomarker discovery; biomarker panel; biomarker validation; blood-based biomarker; cancer risk; candidate marker; cohort; follow-up; improved; long short term memory; malignant breast neoplasm; multidisciplinary; novel; pre-clinical; recurrent neural network; risk prediction; risk prediction model; screening; screening program; success; tool; tumor; validation studies

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.

项目概要 仍然需要开发生物标志物测试来对乳腺癌和卵巢进行个性化风险评估 此类测试不会取代筛查计划，而是可以作为一种基本工具。 根据受试者的特征与其他风险模型相结合，以个性化患癌症的风险 并告知是否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血液的多分析物 基于循环蛋白和针对肿瘤抗原的自身抗体的生物标志物组，可告知 申请人团队的研究得出了受试者患有乳腺癌或卵巢癌的可能性。 鉴定一组与癌症相关的循环蛋白以及针对肿瘤蛋白的自身抗体， 包括 TP53 和新型瓜氨酸抗原，用于检测乳腺癌和卵巢癌。 是进一步推进候选生物标志物测试并建立组合的绝佳资源 结合受试者特征对乳腺癌和卵巢癌进行个体化风险评估的规则 优化筛查和监测，以便及早发现这些疾病。 在具体目标 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 病例以及未发育的女性 PLCO 参与者的非病例血浆数量的四倍 在研究随访期间，我们将评估癌症的时间依赖性（例如 0-1 年、1-2 年等）预测 性能（AUC、敏感性、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将是优先候选生物标志物。 先进的模型与相关患者特征一起建立（例如乳腺癌的盖尔模型） 对于 BrCa 和 OvCa 的 1 年风险预测，我们将坚持可预测性、可计算性和可计算性。 整个 PLCO 样本集将分为开发集和预留测试集。 超参数的建模和调整以及初始验证将在开发中进行。 将选择开发集中具有最佳预测性能（AUC）的模型。 在特定目标 2 中，我们将利用从预留测试集中采购的系列样本进行后续测试。 诊断为 BrCa 或 OvCa 之前的病例以及非病例的系列样本，我们将进行测试 生物标志物组评分的纵向轨迹是否可以改善风险预测。 代表了癌症相关蛋白和自身抗体生物标志物的验证，并且很有可能 开发一种可在临床环境中实施的血液检测，以进行个体化的乳腺风险评估 和卵巢癌。