Cross-Study Projections of Genomic Biomarkers: An Evaluation in Cancer Genomics

Human disease studies using DNA microarrays in both clinical/observational and experimental/controlled studies are having increasing impact on our understanding of the complexity of human diseases. A fundamental concept is the use of gene expression as a “common currency” that links the results of in vitro controlled experiments to in vivo observational human studies. Many studies – in cancer and other diseases – have shown promise in using in vitro cell manipulations to improve understanding of in vivo biology, but experiments often simply fail to reflect the enormous phenotypic variation seen in human diseases. We address this with a framework and methods to dissect, enhance and extend the in vivo utility of in vitro derived gene expression signatures. From an experimentally defined gene expression signature we use statistical factor analysis to generate multiple quantitative factors in human cancer gene expression data. These factors retain their relationship to the original, one-dimensional in vitro signature but better describe the diversity of in vivo biology. In a breast cancer analysis, we show that factors can reflect fundamentally different biological processes linked to molecular and clinical features of human cancers, and that in combination they can improve prediction of clinical outcomes.

在针对人类疾病的临床/观察性与实验/控制性研究中，DNA微阵列（DNA microarrays）技术的应用正日益加深我们对人类疾病复杂性的认知。其中一项核心概念，是将基因表达作为一种“通用媒介”，用以衔接体外（in vitro）控制性实验与体内（in vivo）观察性人类研究的结果。诸多针对癌症及其他疾病的研究已证实，借助体外细胞操作可加深对体内生物学过程的理解，但此类实验往往无法反映人类疾病中存在的海量表型变异。为此，我们提出一套分析框架与研究方法，用以解析、优化并拓展体外来源基因表达特征（gene expression signatures）的体内应用价值。我们从实验确定的基因表达特征出发，借助统计因子分析（statistical factor analysis）在人类癌症基因表达数据中生成多个定量因子。这些因子保留了与原始一维体外特征的关联，却能更精准地刻画体内生物学过程的多样性。在乳腺癌（breast cancer）分析中，我们证实这些因子可反映与人类癌症分子及临床特征相关的截然不同的生物学过程，且通过因子组合能够提升临床结局的预测性能。