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 �C in cancer and other diseases �C 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.

在临床/观察性研究与实验/对照研究中采用DNA微阵列（DNA microarray）开展的人类疾病相关研究，正不断加深我们对人类疾病复杂性的认知。其核心理念在于将基因表达作为通用中介，用以衔接体外（in vitro）对照实验与体内（in vivo）人类观察性研究的结果。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我们对体内生物学过程的理解，但此类实验往往无法如实反映人类疾病中存在的显著表型变异（phenotypic variation）。为此，我们提出一套研究框架与分析方法，用以解析、优化并拓展体外来源的基因表达特征（gene expression signature）在体内研究中的应用价值。我们基于实验确定的基因表达特征，通过统计因子分析（statistical factor analysis）在人类癌症基因表达数据中生成多个量化因子。这些因子既保留了与原始一维体外特征的关联，又能更精准地刻画体内生物学过程的多样性。在乳腺癌分析案例中，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多种本质迥异的生物学过程，且联合使用这些因子可提升临床结局的预测效能。