Histopathological classification of human prostate cancer (PCA) relies on the morphological assessment of tissue specimens but has limited prognostic value.
To address this deficiency, we performed comparative transcriptome analysis of human prostatic acini generated in a three-dimensional basement membrane that recapitulates the differentiated morphological characteristics and gene expression profile of a human prostate glandular epithelial tissue. We then applied an acinar morphogenesis-specific gene profile to two independent cohorts of patients with PCA (total n = 79) and found that those with tumors expressing this profile, which we designated acini-like tumors, had a significantly lower risk of postoperative relapse compared with those tumors with a lower correlation (hazard ratio, 0.078; log-rank test P = 0.009). Multivariate analyses showed superior prognostic prediction performance using this classification system compared with clinical criteria and Gleason scores. We prioritized the genes in this profile and identified programmed cell death protein 4 (PDCD4) and Kruppel-like factor 6 (KLF6) as critical regulators and surrogate markers of prostatic tissue architectures, which form a gene signature that robustly predicts clinical prognosis with a remarkable accuracy in several large series of PCA tumors (total n = 161; concordance index, 0.913 to 0.951). Thus, by exploiting the genomic program associated with prostate glandular differentiation, we identified acini-like PCA and related molecular markers that significantly enhance prognostic prediction of human PCAWritten by:
Li CR, Su JJ, Wang WY, Lee MT, Wang TY, Jiang KY, Li CF, Hsu JM, Chen CK, Chen M, Jiang SS, Weaver VM, Tsai KK Are you the author?
National Institute of Cancer Research and the Translational Center for Glandular Malignancies, the National Health Research Institutes, Tainan, Taiwan; Department of Medical Education and the School of Nursing, Chung Shan Medical University and Hospital, Taichung, Taiwan
Reference: Am J Pathol. 2013 Feb;182(2):363-74
doi: 10.1016/j.ajpath.2012.10.024
PubMed Abstract
PMID: 23219426
