Statistically Integrated Metabonomic−Proteomic Studies on a Human Prostate Cancer Xenograft Model in Mice

Abstract

A novel statistically integrated proteometabonomic method has been developed and applied to a human tumor xenograft mouse model of prostate cancer. Parallel 2D-DIGE proteomic and 1H NMR metabolic profile data were collected on blood plasma from mice implanted with a prostate cancer (PC-3) xenograft and from matched control animals. To interpret the xenograft-induced differences in plasma profiles, multivariate statistical algorithms including orthogonal projection to latent structure (OPLS) were applied to generate models characterizing the disease profile. Two approaches to integrating metabonomic data matrices are presented based on OPLS algorithms to provide a framework for generating models relating to the specific and common sources of variation in the metabolite concentrations and protein abundances that can be directly related to the disease model. Multiple correlations between metabolites and proteins were found, including associations between serotransferrin precursor and both tyrosine and 3-D-hydroxybutyrate. Additionally, a correlation between decreased concentration of tyrosine and increased presence of gelsolin was also observed. This approach can provide enhanced recovery of combination candidate biomarkers across multi-omic platforms, thus, enhancing understanding of in vivo model systems studied by multiple omic technologies.

Chemometrics in Metabonomics

“…Statistical evaluation and interpretation were performed separately and provided an insight into the underlying biochemistry. Rantalainen et al 89 demonstrated a novel approach using the OPLS method to integrate 2D-DIGE proteomic and NMR metabolic data from a human tumor xenograft mouse model of prostate cancer.…”

Perspectives for metabolomics in testosterone replacement therapy

“…This integration of multiple 'omics' data has been initially performed in small-scale animal studies integrating metabolic profiles with quantitative trait locus data in a diabetic rat model (Dumas et al 2007) and in combined metabolic and proteomic data of a mouse model of prostate cancer (Rantalainen et al 2006). With the availability of increasingly powerful high-throughput technologies, computational tools and integrated knowledge bases, multiple 'omics' integration is now being applied to large-scale human clinical and population studies.…”

Analysis of DIGE data using a linear mixed model allowing for protein‐specific dye effects

“…The common reference sample also serves the purpose of facilitating the spot matching between gels by having the same sample on all gels. Since its inception, the DIGE method has been used to quantify protein changes between distinct biological groups including cancer cells [13][14][15]. Several studies on statistical aspects of the DIGE technique have been published as well [16][17][18][19][20].…”