Li Lexin
Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA.
Bioinformatics. 2006 Feb 15;22(4):466-71. doi: 10.1093/bioinformatics/bti824. Epub 2005 Dec 8.
It is important to predict the outcome of patients with diffuse large-B-cell lymphoma after chemotherapy, since the survival rate after treatment of this common lymphoma disease is <50%. Both clinically based outcome predictors and the gene expression-based molecular factors have been proposed independently in disease prognosis. However combining the high-dimensional genomic data and the clinically relevant information to predict disease outcome is challenging.
We describe an integrated clinicogenomic modeling approach that combines gene expression profiles and the clinically based International Prognostic Index (IPI) for personalized prediction in disease outcome. Dimension reduction methods are proposed to produce linear combinations of gene expressions, while taking into account clinical IPI information. The extracted summary measures capture all the regression information of the censored survival phenotype given both genomic and clinical data, and are employed as covariates in the subsequent survival model formulation. A case study of diffuse large-B-cell lymphoma data, as well as Monte Carlo simulations, both demonstrate that the proposed integrative modeling improves the prediction accuracy, delivering predictions more accurate than those achieved by using either clinical data or molecular predictors alone.
预测弥漫性大B细胞淋巴瘤患者化疗后的结果很重要，因为这种常见淋巴瘤疾病治疗后的生存率低于50%。基于临床的结果预测指标和基于基因表达的分子因素已分别被用于疾病预后评估。然而，将高维基因组数据与临床相关信息相结合以预测疾病结果具有挑战性。
我们描述了一种综合临床基因组建模方法，该方法结合基因表达谱和基于临床的国际预后指数（IPI），用于个性化预测疾病结果。提出了降维方法以生成基因表达的线性组合，同时考虑临床IPI信息。提取的汇总指标捕获了给定基因组和临床数据的删失生存表型的所有回归信息，并在随后的生存模型构建中用作协变量。弥漫性大B细胞淋巴瘤数据的案例研究以及蒙特卡罗模拟均表明，所提出的综合建模提高了预测准确性，其预测比仅使用临床数据或分子预测指标更准确。
