alexa Humoral response profiling reveals pathways to prostate cancer progression.
Bioinformatics & Systems Biology

Bioinformatics & Systems Biology

Metabolomics:Open Access

Author(s): Taylor BS, Pal M, Yu J, Laxman B, KalyanaSundaram S,

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Abstract There is considerable evidence for an association between prostate cancer development and inflammation, which results in autoantibody generation against tumor proteins. This immune system-driven amplification of the autoantibody response to intracellular antigens can serve as a sensitive tool to detect low abundance serum proteomic tumor markers for prostate cancer as well as provide insight into biological processes perturbed during cancer development. Here we examine serum humoral responses in a cohort of 34 patients with either benign prostatic hyperplasia or clinically localized prostate cancer (PCa). The experimental strategy couples multidimensional liquid-phase protein fractionation of localized and metastatic prostate cancer tissue lysates to protein microarrays and subsequent mass spectrometry. A supervised learning analysis of the humoral response arrays generated a parsimonious predictor having 78\% sensitivity and 75\% specificity in distinguishing PCa from benign prostatic hyperplasia in a cohort of American males with elevated prostate-specific antigen. Enrichment analysis of the PCa-specific humoral signature revealed large scale immune reprogramming mediated by STAT transcription factors and the generation of autoantibodies to enzymes involved in nitrogen metabolism. Meta-analysis of independent prostate cancer gene expression data validated the presence of STAT-induced immunomodulation. Concomitant validation of elevated levels of the nitrogen metabolism pathway was obtained by direct measurement of metabolic levels of glutamate and aspartate in prostate cancer tissues. Thus, in addition to functioning as markers in prostate cancer detection, humoral response profiles can serve as powerful tools revealing pathway dysregulation that might otherwise be suppressed by the complexity of the cancer proteome. This article was published in Mol Cell Proteomics and referenced in Metabolomics:Open Access

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