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Expression changes in the stroma of prostate cancer predict subsequent relapse.

ABSTRACT: Biomarkers are needed to address overtreatment that occurs for the majority of prostate cancer patients that would not die of the disease but receive radical treatment. A possible barrier to biomarker discovery may be the polyclonal/multifocal nature of prostate tumors as well as cell-type heterogeneity between patient samples. Tumor-adjacent stroma (tumor microenvironment) is less affected by genetic alteration and might therefore yield more consistent biomarkers in response to tumor aggressiveness. To this end we compared Affymetrix gene expression profiles in stroma near tumor and identified a set of 115 probe sets for which the expression levels were significantly correlated with time-to-relapse. We also compared patients that chemically relapsed shortly after prostatectomy (<1 year), and patients that did not relapse in the first four years after prostatectomy. We identified 131 differentially expressed microarray probe sets between these two categories. 19 probe sets (15 genes overlapped between the two gene lists with p<0.0001). We developed a PAM-based classifier by training on samples containing stroma near tumor: 9 rapid relapse patient samples and 9 indolent patient samples. We then tested the classifier on 47 different samples, containing 90% or more stroma. The classifier predicted the risk status of patients with an average accuracy of 87%. This is the first general tumor microenvironment-based prognostic classifier. These results indicate that the prostate cancer microenvironment exhibits reproducible changes useful for predicting outcomes for patients.

SUBMITTER: Jia Z

PROVIDER: S-EPMC3411675 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Expression changes in the stroma of prostate cancer predict subsequent relapse.

Jia Zhenyu Z Rahmatpanah Farah B FB Chen Xin X Lernhardt Waldemar W Wang Yipeng Y Xia Xiao-Qin XQ Sawyers Anne A Sutton Manuel M McClelland Michael M Mercola Dan D

PloS one 20120801 8

Biomarkers are needed to address overtreatment that occurs for the majority of prostate cancer patients that would not die of the disease but receive radical treatment. A possible barrier to biomarker discovery may be the polyclonal/multifocal nature of prostate tumors as well as cell-type heterogeneity between patient samples. Tumor-adjacent stroma (tumor microenvironment) is less affected by genetic alteration and might therefore yield more consistent biomarkers in response to tumor aggressive ...[more]

PMID: 22870216

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Project description:BackgroundQuantitative high-throughput data deposited in consortia such as International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) present opportunities and challenges for computational analyses.MethodsWe present a computational strategy to systematically rank and investigate a large number (210-220) of clinically testable gene sets, using combinatorial gene subset generation and disease-free survival (DFS) analyses. This approach integrates protein-protein interaction networks, gene expression, DNA methylation, and copy number data, in association with DFS profiles from patient clinical records.ResultsAs a case study, we applied this pipeline to systematically analyze the role of ALDH1A2 in prostate cancer (PCa). We have previously found this gene to have multiple roles in disease and homeostasis, and here we investigate the role of the associated ALDH1A2 gene/protein networks in PCa, using our methodology in combination with PCa patient clinical profiles from ICGC and TCGA databases. Relationships between gene signatures and relapse were analyzed using Kaplan-Meier (KM) log-rank analysis and multivariable Cox regression. Relative expression versus pooled mean from diploid population was used for z-statistics calculation. Gene/protein interaction network analyses generated 11 core genes associated with ALDH1A2; combinatorial ranking of the power set of these core genes identified two gene sets (out of 211 - 1 = 2,047 combinations) with significant correlation with disease relapse (KM log rank p < 0.05). For the more significant of these two sets, referred to as the optimal gene set (OGS), patients have median survival 62.7 months with OGS alterations compared to >150 months without OGS alterations (p = 0.0248, hazard ratio = 2.213, 95% confidence interval = 1.1-4.098). Two genes comprising OGS (CYP26A1 and RDH10) are strongly associated with ALDH1A2 in the retinoic acid (RA) pathways, suggesting a major role of RA signaling in early PCa progression. Our pipeline complements human expertise in the search for prognostic biomarkers in large-scale datasets.

Project description:Treatment of prostate cancer (PCa) has changed considerably in the last decade due to the introduction of novel androgen receptor (AR)-targeted agents (ARTAs) for patients progressing on androgen deprivation therapy (ADT). Preclinical research however still relies heavily on AR-negative cell line models. In order to investigate potential differences in castration-resistant PCa (CRPC) growth, we set out to create a comprehensive panel of ARTA-progressive models from 4 androgen-responsive AR wild-type PCa cell lines and analyzed its androgen response as opposed to its ADT-progressive counterparts. Parallel cultures of VCaP, DuCaP, PC346C, and LAPC4 were established in their respective culture media with steroid-stripped fetal calf serum (FCS) [dextran-coated charcoal-stripped FCS (DCC)] without androgen (ADT) or in DCC plus 1 μM of the ARTAs bicalutamide, OH-flutamide, or RD162 (an enzalutamide/apalutamide analog). Cell growth was monitored and compared to those of parental cell lines. Short-term androgen response was measured using cell proliferation 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. qRT-PCR was performed to assess the mRNA expression of markers for AR signaling, steroidogenesis, glucocorticoid receptor (GR) signaling, epithelial-mesenchymal transition (EMT), and WNT signaling. Out of 35 parallel cultures per cell line, a total of 24, 15, 34, and 16 CRPC sublines emerged for VCaP, DuCaP, PC346C, and LAPC4, respectively. The addition of bicalutamide or OH-flutamide significantly increased CRPC growth compared to ADT or RD162. VCaP, DuCaP, and PC346C CRPC clones retained an AR-responsive phenotype. The expression of AR and subsequent androgen response were completely lost in all LAPC4 CRPC lines. Markers for EMT and WNT signaling were found to be elevated in the resilient PC346C model and CRPC derivatives of VCaP, DuCaP, and LAPC4. Although the resistant phenotype is pluriform between models, it seems consistent within models, regardless of type of ARTA. These data suggest that the progression to and the phenotype of the CRPC state might already be determined early in carcinogenesis.

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Expression changes in the stroma of prostate cancer predict subsequent relapse.

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Expression changes in the stroma of prostate cancer predict subsequent relapse.

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