Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA.
Stat Med. 2013 Apr 30;32(9):1524-35. doi: 10.1002/sim.5641. Epub 2012 Oct 16.
Statistical methods for variable selection, prediction, and classification have proven extremely useful in moving personalized genomics medicine forward, in particular, leading to a number of genomic-based assays now in clinical use for predicting cancer recurrence. Although invaluable in individual cases, the information provided by these assays is limited. Most often, a patient is classified into one of very few groups (e.g., recur or not), limiting the potential for truly personalized treatment. Furthermore, although these assays provide information on which individuals are at most risk (e.g., those for which recurrence is predicted), they provide no information on the aberrant biological pathways that give rise to the increased risk. We have developed an approach to address these limitations. The approach models a time-to-event outcome as a function of known biological pathways, identifies important genomic aberrations, and provides pathway-based patient-specific assessments of risk. As we demonstrate in a study of ovarian cancer from The Cancer Genome Atlas project, the patient-specific risk profiles are powerful and efficient characterizations useful in addressing a number of questions related to identifying informative patient subtypes and predicting survival.
统计方法在变量选择、预测和分类方面非常有用，尤其有助于推动个性化基因组医学的发展，目前已有许多基于基因组的检测方法用于预测癌症复发。虽然这些检测方法在个别病例中非常有价值，但提供的信息有限。通常，患者被分为少数几个非常明确的组别（例如复发或不复发），这限制了真正个性化治疗的可能性。此外，尽管这些检测方法提供了哪些人处于最高风险的信息（例如，预测复发的人），但它们没有提供导致风险增加的异常生物学途径的信息。我们已经开发了一种方法来解决这些局限性。该方法将事件时间结果建模为已知生物学途径的函数，识别重要的基因组异常，并提供基于途径的患者特定风险评估。正如我们在癌症基因组图谱项目的卵巢癌研究中所证明的那样，患者特异性风险特征是强大而有效的特征，可以用于解决与识别有意义的患者亚群和预测生存相关的许多问题。
