Integrating Constitutive Gene Expression and Chemoactivity: Mining the NCI60 Anticancer Screen

Studies into the genetic origins of tumor cell chemoactivity pose significant challenges to bioinformatic mining efforts. Connections between measures of gene expression and chemoactivity have the potential to identify clinical biomarkers of compound response, cellular pathways important to efficacy and potential toxicities; all vital to anticancer drug development. An investigation has been conducted that jointly explores tumor-cell constitutive NCI60 gene expression profiles and small-molecule NCI60 growth inhibition chemoactivity profiles, viewed from novel applications of self-organizing maps (SOMs) and pathway-centric analyses of gene expressions, to identify subsets of over- and under-expressed pathway genes that discriminate chemo-sensitive and chemo-insensitive tumor cell types. Linear Discriminant Analysis (LDA) is used to quantify the accuracy of discriminating genes to predict tumor cell chemoactivity. LDA results find 15% higher prediction accuracies, using ∼30% fewer genes, for pathway-derived discriminating genes when compared to genes derived using conventional gene expression-chemoactivity correlations. The proposed pathway-centric data mining procedure was used to derive discriminating genes for ten well-known compounds. Discriminating genes were further evaluated using gene set enrichment analysis (GSEA) to reveal a cellular genetic landscape, comprised of small numbers of key over and under expressed on- and off-target pathway genes, as important for a compound’s tumor cell chemoactivity. Literature-based validations are provided as support for chemo-important pathways derived from this procedure. Qualitatively similar results are found when using gene expression measurements derived from different microarray platforms. The data used in this analysis is available at http://pubchem.ncbi.nlm.nih.gov/andhttp://www.ncbi.nlm.nih.gov/projects/geo (GPL96, GSE32474).

关于肿瘤细胞化疗活性遗传起源的研究，给生物信息学挖掘工作带来了显著挑战。基因表达水平与化疗活性之间的关联，有望识别出化合物响应的临床生物标志物、与药效及潜在毒性密切相关的细胞通路——这些均对抗癌药物研发至关重要。本研究联合分析了肿瘤细胞组成型NCI60基因表达谱与小分子NCI60生长抑制化疗活性谱，并创新性地应用自组织映射（Self-Organizing Maps, SOMs）以及以通路为中心的基因表达分析方法，旨在筛选出可区分化疗敏感与化疗耐受肿瘤细胞类型的通路基因过表达与低表达子集。本研究采用线性判别分析（Linear Discriminant Analysis, LDA）量化判别基因预测肿瘤细胞化疗活性的准确率。线性判别分析结果显示，相较于基于传统基因表达-化疗活性关联筛选得到的基因，基于通路筛选得到的判别基因可将预测准确率提升15%，同时所需基因数量减少约30%。本研究提出的以通路为中心的数据挖掘流程，被用于筛选10种常见化合物的判别基因。研究进一步通过基因集富集分析（Gene Set Enrichment Analysis, GSEA）对判别基因进行评估，揭示了由少量关键过表达与低表达的靶点及脱靶点通路基因构成的细胞遗传图谱，该图谱与化合物的肿瘤细胞化疗活性密切相关。本研究通过文献验证，为基于该流程筛选得到的化疗相关通路提供了支持依据。采用不同微阵列平台获取的基因表达数据进行分析时，可得到定性一致的研究结果。本分析所用的数据集可在http://pubchem.ncbi.nlm.nih.gov/ 及 http://www.ncbi.nlm.nih.gov/projects/geo（GPL96、GSE32474）获取。