Statistical Methods for Cancer Detection Using Biomarkers

使用生物标志物检测癌症的统计方法

• 批准号: 10556352
• 负责人: YIJIAN HUANG
• 金额: $ 30.5万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-03-11 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10556352
• 关键词: Address; Advanced Malignant Neoplasm; Affect; Age; Biological; Biological Markers; Cancer Detection; Cancer Patient; Characteristics; Clinical; Communities; Computational algorithm; Computing Methodologies; Confidence Intervals; Data; Detection; Development; Diagnosis; Diagnostic tests; Disease; Evaluation; Formulation; Knowledge; Literature; Logic; Logistic Regressions; Malignant Neoplasms; Malignant neoplasm of prostate; Mathematics; Measurement; Measures; Medical; Methods; Modeling; Molecular Biology; Motivation; Patient Care; Patients; Performance; Play; Population Control; Procedures; Property; Race; Research; Role; Sampling; Specificity; Statistical Methods; Technology; Testing; Therapeutic Intervention; Translating; Universities; Washington; analytical method; anticancer research; biomarker evaluation; cancer biomarkers; cancer care; cancer diagnosis; cancer therapy; candidate marker; clinically relevant; cost; density; diagnostic accuracy; disease prognosis; experience; improved; interest; novel; patient population; precision medicine; predicting response; prostate biopsy; screening; simulation; sound; tool; user friendly software

PROJECT SUMMARY/ABSTRACT In cancer research, precision medicine hinges on the development of valid biomarkers for cancer diagnosis, disease prognosis, and prediction of response to specific therapeutic interventions. Fueled by the rapid recent advances in the scientific knowledge of molecular biology and high-throughput omics technologies, a large number of candidate biomarkers for various cancers have been or are being identified. Statistical and computational methods play a critical role in rigorously evaluating these biomarkers and further developing clinically relevant prediction rules to ultimately improve and advance cancer treatment and patient management. However, most existing methods, for continuous biomarkers, target diagnostic accuracy measures dictated by mathematical convenience rather than clinical utility. Particularly, a screening or diagnostic test in many clinical contexts needs to maintain a high sensitivity (or specificity) and thus specificity at a controlled sensitivity level (or sensitivity at a controlled specificity level) is a clinically desirable accuracy metric. Yet, statistical and computation methods for this metric are mostly lacking, or suboptimal even when available as in limited circumstances. To address this urgent analytic need, this proposed project will develop novel and efficient statistical and computational methods specifically targeting this accuracy metric of clinical interest. When a single biomarker is under consideration or compared with another biomarker, Aims 1 and 2 will provide statistical tools for the inference and for covariate adjustment. On the other hand, multiplex prediction rules that prudently combine multiple biomarkers hold the promise to achieve improved diagnostic accuracy, since many cancers are heterogeneous. For optimal multiplex rule formulation, Aims 3 and 4 will develop computation algorithms and statistical inference methods with both linear combination and, often biologically and clinically motivated, logic combinations. These proposed analytic methods will be thoroughly investigated through rigorous asymptotic studies and extensive simulations. They will be applied to a number of our prostate cancer biomarker studies, which motivated this project, from the Early Disease Research Network (EDRN). User-friendly computer software will be made available to the research community. These proposed methods will facilitate more effective biomarker research for cancer as well as other diseases.

项目总结/摘要 在癌症研究中，精准医学取决于开发用于癌症诊断、疾病预后和预测对特定治疗干预反应的有效生物标志物。在分子生物学和高通量组学技术的科学知识的快速发展的推动下，已经或正在鉴定各种癌症的大量候选生物标志物。统计和计算方法在严格评估这些生物标志物和进一步开发临床相关预测规则以最终改善和推进癌症治疗和患者管理方面发挥着关键作用。然而，对于连续生物标志物，大多数现有方法的目标是由数学便利性而不是临床实用性决定的诊断准确性测量。特别地，在许多临床背景下的筛查或诊断测试需要保持高灵敏度（或特异性），因此受控灵敏度水平下的特异性（或受控特异性水平下的灵敏度）是临床上期望的准确度度量。然而，这一指标的统计和计算方法大多缺乏，或次优，即使在有限的情况下可用。为了满足这一迫切的分析需求，该项目将开发新颖有效的统计和计算方法，专门针对临床感兴趣的准确性指标。当考虑单一生物标志物或将其与另一种生物标志物进行比较时，目的1和2将为推断和协变量调整提供统计工具。另一方面，谨慎地联合收割机组合多种生物标志物的多重预测规则有望实现提高的诊断准确性，因为许多癌症是异质性的。对于最佳的多重规则制定，目标3和4将开发计算算法和统计推断方法，同时具有线性组合和通常具有生物学和临床动机的逻辑组合。这些建议的分析方法将通过严格的渐近研究和广泛的模拟进行彻底调查。它们将被应用于我们的一些前列腺癌生物标志物研究，这些研究激发了早期疾病研究网络（EDRN）的这个项目。将向研究界提供方便用户的计算机软件。这些提出的方法将促进更有效的癌症和其他疾病的生物标志物研究。