ABSTRACT
The identification of new biomarkers is essential in the implementation of personalized health care strategies that offer new therapeutic approaches with optimized and individualized treatment. In support of hypothesis generation and testing in the course of our biomarker research an online portal and respective function-tested reverse transcription quantitative real-time PCR assays (RT-qPCR) facilitated the selection of relevant biomarker genes. We have established workflows applicable for convenient high throughput gene expression analysis in biomarker research with cell lines (in vitro studies) and xenograft mouse models (in vivo studies) as well as formalin-fixed paraffin-embedded tissue (FFPET) sections from various human research and clinical tumor samples. Out of 92 putative biomarker candidate genes selected in silico, 35 were shown to exhibit differential expression in various tumor cell lines. These were further analysed by in vivo xenograft mouse models, which identified 13 candidate genes including potential response prediction biomarkers and a potential pharmacodynamic biomarker. Six of these candidate genes were selected for further evaluation in FFPET samples, where optimized RNA isolation, reverse transcription and qPCR assays provided reliable determination of relative expression levels as precondition for differential gene expression analysis of FFPET samples derived from projected clinical studies. Thus, we successfully applied function tested RT-qPCR assays in our biomarker research for hypothesis generation with in vitro and in vivo models as well as for hypothesis testing with human FFPET samples. Hence, appropriate function-tested RT-qPCR assays are available in biomarker research accompanying the different stages of drug development, starting from target identification up to early clinical development. The workflow presented here supports the identification and validation of new biomarkers and may lead to advances in efforts to achieve the goal of personalized health care.
Subject(s)
Biomarkers, Pharmacological/metabolism , Biomarkers, Tumor/genetics , Drug Discovery/methods , Gene Expression Profiling/methods , Animals , Biomarkers, Tumor/metabolism , Carcinoma, Non-Small-Cell Lung/drug therapy , Carcinoma, Non-Small-Cell Lung/metabolism , Cell Line, Tumor , Colorectal Neoplasms/drug therapy , Colorectal Neoplasms/metabolism , DNA, Complementary/genetics , Fixatives , Formaldehyde , Gene Expression , Humans , Lung Neoplasms/drug therapy , Lung Neoplasms/metabolism , Mice , Mice, Nude , Paraffin Embedding , Precision Medicine , RNA/genetics , RNA/isolation & purification , RNA/metabolism , Real-Time Polymerase Chain Reaction , Reverse Transcriptase Polymerase Chain Reaction , Xenograft Model Antitumor AssaysABSTRACT
Methylation of CpG islands (CGIs) plays an important role in gene silencing. For genome-wide methylation analysis of CGIs in female white blood cells and in sperm, we used four restriction enzymes and a size selection step to prepare DNA libraries enriched with CGIs. The DNA libraries were treated with sodium bisulfite and subjected to a modified 454/Roche Genome Sequencer protocol. We obtained 163 034 and 129 620 reads from blood and sperm, respectively, with an average read length of 133 bp. Bioinformatic analysis revealed that 12 358 (7.6%) blood library reads and 10 216 (7.9%) sperm library reads map to 6167 and 5796 different CGIs, respectively. In blood and sperm DNA, we identified 824 (13.7%) and 482 (8.5%) fully methylated autosomal CGIs, respectively. Differential methylation, which is characterized by the presence of methylated and unmethylated reads of the same CGI, was observed in 53 and 52 autosomal CGIs in blood and sperm DNA, respectively. Remarkably, methylation of X-chromosomal CGIs in female blood cells was most often incomplete (25-75%). Such incomplete methylation was mainly found on the X-chromosome, suggesting that it is linked to X-chromosome inactivation.