title: Combining magnetic resonance spectroscopy and molecular genomics offers better accuracy in brain tumor typing and prediction of survival than either methodology alone.
abstract: Recent advents in magnetic resonance spectroscopy (MRS) techniques permit subsequent microarray analysis over the entire human transcriptome in the same tissue biopsies. However, extracting information from such immense quantities of data is limited by difficulties in recognizing and evaluating the relevant patterns of apparent gene expression in the context of the existing knowledge of phenotypes by histopathology. Using a quantitative approach derived from a knowledge base of pathology findings, we present a novel methodology used to process genome-wide transcription and MRS data. This methodology was tested to examine metabolite and genome-wide profiles in MRS and RNA in 55 biopsies from human subjects with brain tumors with ~100% certainty. With the guidance of histopathology and clinical outcome, 15 genes with the assistance of 15 MRS metabolites were able to be distinguished by tumor categories and the prediction of survival was better than when either method was used alone. This new method, combining MRS, genomics, statistics and biological content, improves the typing and understanding of the complexity of human brain tumors, and assists in the search for novel tumor biomarkers. It is an important step for novel drug development, it generates testable hypotheses regarding neoplasia and promises to guide human brain tumor therapy provided improved in vivo methods for monitoring response to therapy are developed.
authors.affiliation: NMR Surgical Laboratory, Department of Surgery, Massachusetts General Hospital and Shriners Burn Institute, Harvard Medical School, Boston, MA 02114, USA.
authors.forename: Loukas
authors.initials: L
authors.lastname: Astrakas
authors.forename: Konstantinos D
authors.initials: KD
authors.lastname: Blekas
authors.forename: Caterina
authors.initials: C
authors.lastname: Constantinou
authors.forename: Ovidiu C
authors.initials: OC
authors.lastname: Andronesi
authors.forename: Michael N
authors.initials: MN
authors.lastname: Mindrinos
authors.forename: Aristidis C
authors.initials: AC
authors.lastname: Likas
authors.forename: Laurence G
authors.initials: LG
authors.lastname: Rahme
authors.forename: Peter M
authors.initials: PM
authors.lastname: Black
authors.forename: Karen J
authors.initials: KJ
authors.lastname: Marcus
authors.forename: A Aria
authors.initials: AA
authors.lastname: Tzika
mesh_terms: D001185:Artificial Intelligence; D014408:Biomarkers, Tumor / Q000378:metabolism; D001932:Brain Neoplasms / Q000175:diagnosis* / Q000473:pathology; D003198:Computer Simulation; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D016015:Logistic Models; D009682:Magnetic Resonance Spectroscopy / Q000379:methods*; D008297:Male; D055442:Metabolome; D011379:Prognosis; D016019:Survival Analysis
chemical_list: D014408:Biomarkers, Tumor