Integration of HapMap-based SNP pattern analysis and gene expression profiling reveals common SNP profiles for cancer therapy outcome predictor genes.

Abstract

Recent completion of the initial phase of a haplotype map of human genome (www.hapmap.org) provides opportunity for integrative analysis on a genome-wide scale of microarray-based gene expression profiling and SNP variation patterns for discovery of cancer-causing genes and genetic markers of therapy outcome. Here we applied this approach for analysis of SNPs of cancer-associated genes, expression profiles of which predicts the likelihood of treatment failure and death after therapy in patients diagnosed with multiple types of cancer. Unexpectedly, this analysis reveals a common SNP pattern for a majority (60 of 74; 81%) of analyzed cancer treatment outcome predictor (CTOP) genes. Our analysis suggests that heritable germ-line genetic variations driven by geographically localized form of natural selection determining population differentiations may have a significant impact on cancer treatment outcome by influencing the individual's gene expression profile. We demonstrate a translational utility of this approach by building a highly informative CTOP algorithm combining prognostic power of multiple gene expression-based CTOP models derived from signatures of oncogenic pathways associated with activation of BMI1; Myc; Her2/neu; Ras; beta-catenin; Suz12; E2F; and CCND1 oncogenes. Application of a CTOP algorithm to large databases of early-stage breast and prostate tumors identifies cancer patients with 100% probability of a cure with existing cancer therapies as well as patients with nearly 100% likelihood of treatment failure, thus providing a clinically feasible framework essential for introduction of rational evidence-based individualized therapy selection and prescription protocols. Our analysis indicates that genetic determinants of human disease susceptibility and severity are encoded by population differentiation SNP variants. Evolution of these SNPs is driven by geographically-localized form of natural selection causing population differentiation. Recent analysis identifies a class of SNPs regulating gene expression in normal individuals and likely determining unique genome-wide expression profiles of each individual. We propose that critical disease-causing combinations of SNP variants arise from SNPs regulating mRNA levels and determining genome-wide haplotype patterns of individual's disease susceptibility.