Statistical Methods for Cancer Detection Using Biomarkers

项目摘要/摘要 在癌症研究中，精确医学取决于开发有效的生物标志物，用于癌症诊断、疾病预后和对特定治疗干预措施的反应预测。在分子生物学科学知识和高通量组学技术快速发展的推动下，各种癌症的大量候选生物标志物已经或正在被确定。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，对于连续的生物标志物，大多数现有的方法针对的是由数学方便性而不是临床实用性决定的诊断准确度。特别是，在许多临床环境中的筛查或诊断试验需要保持高的灵敏度(或特异度)，因此控制灵敏度水平的特异度(或控制特异度水平的灵敏度)是临床所需的准确性指标。然而，这一指标的统计和计算方法大多缺乏，或者即使在有限的情况下也是次优的。为了满足这一迫切的分析需求，这个拟议的项目将开发新的和有效的统计和计算方法，专门针对这种临床上感兴趣的准确性指标。当考虑单个生物标记物或将其与另一个生物标记物进行比较时，目标1和2将为推断和协变量调整提供统计工具。另一方面，谨慎结合多个生物标志物的多重预测规则有望实现更高的诊断准确性，因为许多癌症是异质性的。为了制定最佳的多重规则，目标3和4将开发计算算法和统计推理方法，既有线性组合，也有通常出于生物和临床动机的逻辑组合。这些提出的分析方法将通过严格的渐近研究和广泛的模拟得到彻底的研究。它们将被应用于我们的前列腺癌生物标记物研究，这推动了这个项目，来自早期疾病研究网络(EDRN)。将向研究界提供用户友好的计算机软件。这些建议的方法将促进更有效的癌症和其他疾病的生物标记物研究。

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.