Personalized 3-Gene Panel for Prostate Cancer Target Therapy

Abstract

Many years and billions spent for research did not yet produce an effective answer to prostate cancer (PCa). Not only each human, but even each cancer nodule in the same tumor, has unique transcriptome topology. The differences go beyond the expression level to the expression control and networking of individual genes. The unrepeatable heterogeneous transcriptomic organization among men makes the quest for universal biomarkers and “fit-for-all” treatments unrealistic. We present a bioinformatics procedure to identify each patient’s unique triplet of PCa Gene Master Regulators (GMRs) and predict consequences of their experimental manipulation. The procedure is based on the Genomic Fabric Paradigm (GFP), which characterizes each individual gene by the independent expression level, expression variability and expression coordination with each other gene. GFP can identify the GMRs whose controlled alteration would selectively kill the cancer cells with little consequence on the normal tissue. The method was applied to microarray data on surgically removed prostates from two men with metastatic PCas (each with three distinct cancer nodules), and DU145 and LNCaP PCa cell lines. The applications verified that each PCa case is unique and predicted the consequences of the GMRs’ manipulation. The predictions are theoretical and need further experimental validation.

Theory and Applications of the (Cardio) Genomic Fabric Approach to Post-Ischemic and Hypoxia-Induced Heart Failure

“…In each condition, the independent characteristics AVE, REV, and COR of every gene were averaged over the valid spots probing that gene ([ 26 ], Appendix A , Equations (A1)–(A3). Our normalization procedure returns the AVE values in terms of the median gene expression level in each condition.…”

“…COR is the Pearson product-moment correlation coefficient between the (log 2 ) expressions of two genes across biological replicas ([ 26 ], Appendix A ). COR analysis identifies the ( p -value < 0.05) significantly synergistically, antagonistically, and independently expressed gene pairs, albeit it cannot determine which of the paired genes is the master.…”

“…Moreover, GFP establishes the gene hierarchy in each condition using the gene commanding height (GCH) scoring that combines the expression control and expression coordination with each and other genes [ 26 ]. The top of the hierarchy (highest GCH, termed gene master regulator, GMR) is the gene whose strongly protected expression level is the most influential on the expression and networking of other genes.…”

“…However, such absolute fold-change cut-off might be too stringent for very stably expressed genes across biological replicas and low local technical noise, while for other genes it might be too relaxed. Therefore, while maintaining the p -value < 0.05 condition, we determine the absolute fold-change cut-off for the expression ratio “ x ” separately for each quantified gene to account for both biological variability and the technical noise of the probing spot(s) ([ 26 ], Appendix A , Equation (A4)).…”

“…A more informative measure is the We ighted I ndividual (gene) R egulation (WIR) [ 32 ] that takes into account the absolute departure from the normal expression level and the statistical confidence in the expression regulation. Like the expression ratio “ x ”, WIR takes also positive values for up-regulated genes and negative values for the down-regulated ones ([ 26 ], Appendix A , Equation (A5)).…”