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: 9891028
• 负责人: YIJIAN HUANG
• 金额: $ 27.8万
• 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 2024-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9891028
• 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; Names; Patient Care; Patients; Peat; Performance; Play; Population Control; Procedures; Property; Prostate; Race; Research; Role; Specificity; Statistical Methods; Technology; Testing; Therapeutic Intervention; Translating; Universities; Washington; analytical method; anticancer research; base; biomarker evaluation; cancer biomarkers; cancer care; cancer diagnosis; cancer therapy; candidate marker; case control; clinically relevant; cost; density; diagnostic accuracy; experience; improved; interest; novel; outcome forecast; 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, dis- ease prognosis, and prediction of response to specific therapeutic interventions. Fueled by the rapid recent ad- vances in the scientific knowledge of molecular biology and high-throughput omics technologies, a large num- ber 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 con- venience 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 ur- gent 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 ana- lytic 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 re- search 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的有效生物标志物， 缓解预后，并预测对特定治疗干预的反应。在最近快速的广告宣传的推动下， 在分子生物学和高通量组学技术的科学知识的进步，大量的， 各种癌症的候选生物标志物已经或正在被鉴定艾德。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的 预测规则，以最终改善和推进癌症治疗和患者管理。但大多数 对于连续生物标志物，现有方法的目标诊断准确性测量由数学约束决定， 而不是临床实用性。特别地，在许多临床环境中的筛查或诊断测试需要 保持高灵敏度（或特异性），从而使特异性处于受控的灵敏度水平（或灵敏度处于 受控规范水平）是临床上理想的准确度指标。然而，统计和计算方法， 即使在有限的情况下可用，该度量也大多是缺乏的或次优的。为了解决这个问题- 根据分析需要，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度指标。当考虑单一生物标志物时，或 与另一种生物标志物相比，目的1和2将为推断和协变量提供统计工具 加强结构性改革另一方面，谨慎地联合收割机组合多种生物标志物的多重预测规则保持了 由于许多癌症是异质性的，因此有望提高诊断的准确性。为了实现最佳复用 规则制定，目标3和4将开发计算算法和统计推断方法， 线性组合，以及通常是生物学和临床动机的逻辑组合。这些建议的安- 裂解方法将通过严格的渐近研究和广泛的模拟进行彻底研究。 它们将被应用于我们的一些前列腺癌生物标志物研究，这些研究激发了这个项目，从 早期疾病研究网络（EDRN）此外，亦会提供方便使用的电脑软件， 搜索社区这些提出的方法也将促进更有效的癌症生物标志物研究 和其他疾病一样。