Dataset Information

Optimization and comprehensive characterization of a faithful tissue culture model of the benign and malignant human prostate.

ABSTRACT: Few preclinical models accurately depict normal human prostate tissue or primary prostate cancer (PCa). In vitro systems typically lack complex cellular interactions among structured prostatic epithelia and a stromal microenvironment, and genetic and molecular fidelity are concerns in both in vitro and in vivo models. 'Tissue slice cultures' (TSCs) provide realistic preclinical models of diverse tissues and organs, but have not been fully developed or widely utilized for prostate studies. Problems encountered include degeneration of differentiated secretory cells, basal cell hyperplasia, and poor survival of PCa. Here, we optimized, characterized, and applied a TSC model of primary human PCa and benign prostate tissue that overcomes many deficiencies of current in vitro models. Tissue cores from fresh prostatectomy specimens were precision-cut at 300??m and incubated in a rotary culture apparatus. The ability of varied culture conditions to faithfully maintain benign and cancer cell and tissue structure and function over time was evaluated by immunohistological and biochemical assays. After optimization of the culture system, molecular and cellular responses to androgen ablation and to piperlongumine (PL), purported to specifically reduce androgen signaling in PCa, were investigated. Optimized culture conditions successfully maintained the structural and functional fidelity of both benign and PCa TSCs for 5 days. TSCs exhibited androgen dependence, appropriately undergoing ductal degeneration, reduced proliferation, and decreased prostate-specific antigen expression upon androgen ablation. Further, TSCs revealed cancer-specific reduction of androgen receptor and increased apoptosis upon treatment with PL, validating data from cell lines. We demonstrate a TSC model that authentically recapitulates the structural, cellular, and genetic characteristics of the benign and malignant human prostate, androgen dependence of the native tissue, and cancer-specific response to a potentially new therapeutic for PCa. The work described herein provides a basis for advancing the experimental utility of the TSC model.

SUBMITTER: Maund SL

PROVIDER: S-EPMC3946793 | biostudies-literature | 2014 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Optimization and comprehensive characterization of a faithful tissue culture model of the benign and malignant human prostate.

Maund Sophia Lisette SL Nolley Rosalie R Peehl Donna Mae DM

Laboratory investigation; a journal of technical methods and pathology 20131202 2

Few preclinical models accurately depict normal human prostate tissue or primary prostate cancer (PCa). In vitro systems typically lack complex cellular interactions among structured prostatic epithelia and a stromal microenvironment, and genetic and molecular fidelity are concerns in both in vitro and in vivo models. 'Tissue slice cultures' (TSCs) provide realistic preclinical models of diverse tissues and organs, but have not been fully developed or widely utilized for prostate studies. Proble ...[more]

PMID: 24296879

Similar Datasets

Project description:BackgroundTo study the expression pattern, localisation and potential clinical significance of aquaporin water channels (AQP) both in prostate cancer (PC) cell lines and in benign and malignant human prostate tissue.MethodsThe AQP transcript and protein expression of HPrEC, LNCaP, DU-145 and PC3 cell lines was investigated using reverse transcriptase polymerase chain reaction (RT-PCR) and immunofluorescence (IF) microscopy labelling. Immunohistochemistry (IHC) was performed to assess AQP protein expression in surgical specimens of benign prostatic hyperplasia as well as in PC. Tissue mRNA expression of AQPs was quantified by single-step reverse transcriptase quantitative polymerase chain reaction (qPCR). Relative gene expression was determined using the 40-?CT method and correlated to clinicopathological parameters.ResultsTranscripts of AQP 1, 3, 4, 7, 8, 10 and 11 were expressed in all four cell lines, while AQP 9 transcripts were not detected in malignant cell lines. IF microscopy confirmed AQP 3, 4, 5, 7 and 9 protein expression. IHC revealed highly heterogeneous AQP 3 protein expression in PC specimens, with a marked decrease in expression in tumours of increasing malignancy. Loss of AQP 9 was shown in PC specimens. mRNA expression of AQP3 was found to be negatively correlated to PSA levels (??=?-?0.354; p =?0.013), D'Amico risk stratification (??=?-?0.336; p =?0.012), ISUP grade (??=?-?0.321; p =?0.017) and Gleason score (??=?-?0.342; p =?0.011).ConclusionsThis is the first study to systematically characterize human prostate cell lines, benign prostatic hyperplasia and PC in relation to all 13 members of the AQP family. Our results indicate the differential expression of several AQPs in benign and malignant prostate tissue. A significant correlation was observed between AQP 3 expression and tumour grade, with progressive loss in more malignant tumours. Taken together, AQPs may play a role in the progression of PC and AQP expression patterns may serve as a prognostic marker.

Project description:BackgroundProstate cancer is a major health concern in aging men. Paralleling an aging society, prostate cancer prevalence increases emphasizing the need for efficient diagnostic algorithms.MethodsRetrospectively, 106 prostate tissue samples from 48 patients (mean age, [Formula: see text] years) were included in the study. Patients suffered from prostate cancer (n = 38) or benign prostatic hyperplasia (n = 10) and were treated with radical prostatectomy or Holmium laser enucleation of the prostate, respectively. We constructed tissue microarrays (TMAs) comprising representative malignant (n = 38) and benign (n = 68) tissue cores. TMAs were processed to histological slides, stained, digitized and assessed for the applicability of machine learning strategies and open-source tools in diagnosis of prostate cancer. We applied the software QuPath to extract features for shape, stain intensity, and texture of TMA cores for three stainings, H&E, ERG, and PIN-4. Three machine learning algorithms, neural network (NN), support vector machines (SVM), and random forest (RF), were trained and cross-validated with 100 Monte Carlo random splits into 70% training set and 30% test set. We determined AUC values for single color channels, with and without optimization of hyperparameters by exhaustive grid search. We applied recursive feature elimination to feature sets of multiple color transforms.ResultsMean AUC was above 0.80. PIN-4 stainings yielded higher AUC than H&E and ERG. For PIN-4 with the color transform saturation, NN, RF, and SVM revealed AUC of [Formula: see text], [Formula: see text], and [Formula: see text], respectively. Optimization of hyperparameters improved the AUC only slightly by 0.01. For H&E, feature selection resulted in no increase of AUC but to an increase of 0.02-0.06 for ERG and PIN-4.ConclusionsAutomated pipelines may be able to discriminate with high accuracy between malignant and benign tissue. We found PIN-4 staining best suited for classification. Further bioinformatic analysis of larger data sets would be crucial to evaluate the reliability of automated classification methods for clinical practice and to evaluate potential discrimination of aggressiveness of cancer to pave the way to automatic precision medicine.

Project description:Intra-tumor heterogeneity (ITH) has been studied at the morphologic, genomic, and transcriptomic level, but not proteomic level. Recent advances in mass spectrometric (MS) proteome quantification techniques, exemplified by SWATH-MS, a massively parallel targeting method, now also support precise quantitative proteomic comparisons across multiple samples, thus identifying molecular and implied functional differences. Here we used SWATH-MS to analyze the proteome profiles of a set of fresh-frozen prostate tissue samples derived from radical prostatectomy specimens. A high confidence set of 1,906 proteins were consistently quantified across 60 biopsy-level tissue samples from three prostatectomy patients, each consisting of 1.0 mm punch biopsies from histologically malignant (acinar and ductal adenocarcinoma) and matching benign prostatic hyperplasia tissues. The quantitative protein profiles allowed independent quantification of the degree of intra-tumor heterogeneity for each protein in benign and malignant tissues. We found that while majority of the proteins showed comparably low intra-tumor variability, 122 proteins were highly variable in malignant and/or matching benign tissues. We observed that proteins that varied between patients or tissue types also tended to be highly variable within prostate tissues, suggesting that these variability patterns will be a critical selection criterion in future protein biomarker studies. The data also permitted investigation of the heterogeneity of multiple biochemical pathways. The high variability of several of the pathways, including Glypican-1 network, alpha-linolenic acid metabolism and celecoxib pathway, explained contradictory results regarding them in the literature. In conclusion, we demonstrated a methodology for investigating proteomic intra-tumor heterogeneity from biopsy-level tissue samples, and quantified the degree of intra-tumor heterogeneity of 1,906 proteins in prostate tumors. The method and data presented here have advanced our understanding of tumor biology and offered critical insights for future biomarker development.

Dataset Information

Optimization and comprehensive characterization of a faithful tissue culture model of the benign and malignant human prostate.

Publications

Optimization and comprehensive characterization of a faithful tissue culture model of the benign and malignant human prostate.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets