Integrating biomedical knowledge to model pathways of prostate cancer progression.
Due to pathologic, histologic, and biologic variation within prostate cancers, profiling the genetic changes associated with disease progression has been difficult. Although initial integration of data from profiling studies had been limited by platform variation, bioinformatic tools and analytic techniques have enabled integrative analysis of profiling studies and the identification of more robust and valid profiles. The identification of key transition points in the progression of prostate cancer relies on profiling precursor lesions and "pure" cell populations. Utilizing laser-capture microdissection to isolate 101 cell populations, a more specific genetic profile of progression from benign epithelium to metastatic disease was obtained. This laser-capture profile was analyzed in the context of the molecular concepts map (MCM), a compendium of over 20,000 molecular concepts including other expression profiles of prostate cancer, to obtain an integrative molecular model of progression. The conceptual connections associated with progression confirm that prostate cancer biology is largely driven by pathways related to androgen signaling and epithelial cell biology; however, further analysis of concepts associated with progression suggests stromal factors are highly associated with progression of prostate cancer. The effect of stromal signatures on the progression model suggests the impact of stromal signature downregulation may reflect both a change in the epithelia:stroma ratio within higher grade tumors and also a microenvironment influence on prostate epithelia. Analyzing complex gene expression signatures in the context of molecular concepts improves integrative models and may improve detection, prognostication, or targeted therapy.
Morris
David S
DS
Michigan Center for Translational Pathology, University of Michigan Medical School, Ann Arbor, Michigan 48109, USA.
Tomlins
Scott A
SA
Rhodes
Daniel R
DR
Mehra
Rohit
R
Shah
Rajal B
RB
Chinnaiyan
Arul M
AM
D000728:Androgens / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D018450:Disease Progression; D004847:Epithelial Cells / Q000378:metabolism; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D008297:Male; D042282:Microdissection / Q000379:methods; D008954:Models, Biological; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D011471:Prostatic Neoplasms / Q000235:genetics* / Q000378:metabolism; D015398:Signal Transduction / Q000235:genetics*
D000728:Androgens