title: 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.
authors.affiliation: Biological Chemistry, Faculty of Natural Sciences, Imperial College, London, South Kensington, London SW7 2AZ, United Kingdom.
authors.forename: Mattias
authors.initials: M
authors.lastname: Rantalainen
authors.forename: Olivier
authors.initials: O
authors.lastname: Cloarec
authors.forename: Olaf
authors.initials: O
authors.lastname: Beckonert
authors.forename: I D
authors.initials: ID
authors.lastname: Wilson
authors.forename: David
authors.initials: D
authors.lastname: Jackson
authors.forename: Robert
authors.initials: R
authors.lastname: Tonge
authors.forename: Rachel
authors.initials: R
authors.lastname: Rowlinson
authors.forename: Steve
authors.initials: S
authors.lastname: Rayner
authors.forename: Janice
authors.initials: J
authors.lastname: Nickson
authors.forename: Robert W
authors.initials: RW
authors.lastname: Wilkinson
authors.forename: Jonathan D
authors.initials: JD
authors.lastname: Mills
authors.forename: Johan
authors.initials: J
authors.lastname: Trygg
authors.forename: Jeremy K
authors.initials: JK
authors.lastname: Nicholson
authors.forename: Elaine
authors.initials: E
authors.lastname: Holmes
mesh_terms: D000818:Animals; D014408:Biomarkers, Tumor / Q000097:blood*; D001798:Blood Proteins / Q000032:analysis*; D045744:Cell Line, Tumor; D004195:Disease Models, Animal; D015180:Electrophoresis, Gel, Two-Dimensional; D018260:Gelsolin / Q000097:blood; D006801:Humans; D009682:Magnetic Resonance Spectroscopy; D008297:Male; D051379:Mice; D008810:Mice, Inbred C57BL; D011471:Prostatic Neoplasms / Q000097:blood* / Q000378:metabolism*; D040901:Proteomics / Q000379:methods*; D014183:Transplantation, Heterologous; D014443:Tyrosine / Q000097:blood
chemical_list: D014408:Biomarkers, Tumor; D001798:Blood Proteins; D018260:Gelsolin; D014443:Tyrosine