Metabolite Analysis and Histology on the Exact Same Tissue: Comprehensive Metabolomic Profiling and Metabolic Classification of Prostate Cancer

• Published in: Scientific reports Vol. 6; no. 1; p. 32272
• Main Authors: Huan, Tao, Troyer, Dean A., Li, Liang
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 31.08.2016; Nature Publishing Group
• Subjects: 639/638/11/296; 692/53/2421; Aged; Area Under Curve; Biomarkers, Tumor - metabolism; Biopsy; Classification; Dansylation; Histology; Histopathology; Humanities and Social Sciences; Humans; Isotope Labeling; Liquid chromatography; Male; Mass spectrometry; Mass spectroscopy; Metabolites; Metabolome; Metabolomics; Methanol; Middle Aged; multidisciplinary; Phenols; Preservation; Prostate; Prostate - metabolism; Prostate - pathology; Prostate cancer; Prostatic Neoplasms - diagnosis; Prostatic Neoplasms - metabolism; ROC Curve; Science; Tissue Fixation; Tissues
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/srep32272

We report a method of metabolomic profiling of intact tissue based on molecular preservation by extraction and fixation (mPREF) and high-performance chemical isotope labeling (CIL) liquid chromatography mass spectrometry (LC-MS). mPREF extracts metabolites by aqueous methanol from tissue biopsies without altering tissue architecture and thus conventional histology can be performed on the same tissue. In a proof-of-principle study, we applied dansylation LC-MS to profile the amine/phenol submetabolome of prostate needle biopsies from 25 patient samples derived from 16 subjects. 2900 metabolites were consistently detected in more than 50% of the samples. This unprecedented coverage allowed us to identify significant metabolites for differentiating tumor and normal tissues. The panel of significant metabolites was refined using 36 additional samples from 18 subjects. Receiver Operating Characteristic (ROC) analysis showed area-under-the-curve (AUC) of 0.896 with sensitivity of 84.6% and specificity of 83.3% using 7 metabolites. A blind study of 24 additional validation samples gave a specificity of 90.9% at the same sensitivity of 84.6%. The mPREF extraction can be readily implemented into the existing clinical workflow. Our method of combining mPREF with CIL LC-MS offers a powerful and convenient means of performing histopathology and discovering or detecting metabolite biomarkers in the same tissue biopsy.