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Analysis of normal-tumour tissue interaction in tumours: prediction of prostate cancer features from the molecular profile of adjacent normal cells.

ABSTRACT: Statistical modelling, in combination with genome-wide expression profiling techniques, has demonstrated that the molecular state of the tumour is sufficient to infer its pathological state. These studies have been extremely important in diagnostics and have contributed to improving our understanding of tumour biology. However, their importance in in-depth understanding of cancer patho-physiology may be limited since they do not explicitly take into consideration the fundamental role of the tissue microenvironment in specifying tumour physiology. Because of the importance of normal cells in shaping the tissue microenvironment we formulate the hypothesis that molecular components of the profile of normal epithelial cells adjacent the tumour are predictive of tumour physiology. We addressed this hypothesis by developing statistical models that link gene expression profiles representing the molecular state of adjacent normal epithelial cells to tumour features in prostate cancer. Furthermore, network analysis showed that predictive genes are linked to the activity of important secreted factors, which have the potential to influence tumor biology, such as IL1, IGF1, PDGF BB, AGT, and TGF?.

SUBMITTER: Trevino V

PROVIDER: S-EPMC3068146 | biostudies-literature | 2011 Mar

REPOSITORIES: biostudies-literature

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Analysis of normal-tumour tissue interaction in tumours: prediction of prostate cancer features from the molecular profile of adjacent normal cells.

Trevino Victor V Tadesse Mahlet G MG Vannucci Marina M Al-Shahrour Fatima F Antczak Philipp P Durant Sarah S Bikfalvi Andreas A Dopazo Joaquin J Campbell Moray J MJ Falciani Francesco F

PloS one 20110330 3

Statistical modelling, in combination with genome-wide expression profiling techniques, has demonstrated that the molecular state of the tumour is sufficient to infer its pathological state. These studies have been extremely important in diagnostics and have contributed to improving our understanding of tumour biology. However, their importance in in-depth understanding of cancer patho-physiology may be limited since they do not explicitly take into consideration the fundamental role of the tiss ...[more]

PMID: 21479216

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Project description:BackgroundTypical high throughput microarrays experiments compare gene expression across two specimen classes - an experimental class and baseline (or comparison) class. The choice of specimen classes is a major factor in the differential gene expression patterns revealed by these experiments. In most studies of prostate cancer, histologically malignant tissue is chosen as the experimental class while normal appearing prostate tissue adjacent to the tumor (adjacent normal) is chosen as the baseline against which comparison is made. However, normal appearing prostate tissue from tumor free organ donors represents an alterative source of baseline tissue for differential expression studies.MethodsTo examine the effect of using donor normal tissue as opposed to adjacent normal tissue as a baseline for prostate cancer expression studies, we compared, using oligonucleotide microarrays, the expression profiles of primary prostate cancer (tumor), adjacent normal tissue and normal tissue from tumor free donors.ResultsStatistical analysis using Significance Analysis of Microarrays (SAM) demonstrates the presence of unique gene expression profiles for each of these specimen classes. The tumor v donor expression profile was more extensive that the tumor v adjacent normal profile. The differentially expressed gene lists from tumor v donor, tumor v adjacent normal and adjacent normal v donor comparisons were examined to identify regulated genes. When donors were used as the baseline, similar genes are highly regulated in both tumor and adjacent normal tissue. Significantly, both tumor and adjacent normal tissue exhibit significant up regulation of proliferation related genes including transcription factors, signal transducers and growth regulators compared to donor tissue. These genes were not picked up in a direct comparison of tumor and adjacent normal tissues.ConclusionsThe up-regulation of these gene types in both tissue types is an unexpected finding and suggests that normal appearing prostate tissue can undergo genetic changes in response to or in expectation of morphologic cancer. A possible field effect surrounding prostate cancers and the implications of these findings for characterizing gene expression changes in prostate tumors are discussed.

Project description:Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers worldwide; it develops in a relatively symptom-free manner, leading to rapid disease progression and metastasis, leading to a 5-year survival rate of less than 5%. A lack of dependable diagnostic markers and rapid development of resistance to conventional therapies are among the problems associated with management of the disease. A better understanding of pancreatic tumour biology and discovery of new potential therapeutic targets are important goals in pancreatic cancer research. This study describes the comparative quantitative LC-MS/MS proteomic analysis of the membrane-enriched proteome of 10 human pancreatic ductal adenocarcinomas, 9 matched adjacent-normal pancreas and patient-derived xenografts (PDXs) in mice (10 at F1 generation and 10 F2). Quantitative label-free LC-MS/MS data analysis identified 129 proteins upregulated, and 109 downregulated, in PDAC, compared to adjacent-normal tissue. In this study, analysing peptide MS/MS data from the xenografts, great care was taken to distinguish species-specific peptides definitively derived from human sequences, or from mice, which could not be distinguished. The human-only peptides from the PDXs are of particular value, since only human tumour cells survive, and stromal cells are replaced during engraftment in the mouse; this list is, therefore, enriched in tumour-associated proteins, some of which might be potential therapeutic or diagnostic targets. Using human-specific sequences, 32 proteins were found to be upregulated, and 113 downregulated in PDX F1 tumours, compared to primary PDAC. Differential expression of CD55 between PDAC and normal pancreas, and expression across PDX generations, was confirmed by Western blotting. These data indicate the value of using PDX models in PDAC research. This study is the first comparative proteomic analysis of PDAC which employs PDX models to identify patient tumour cell-associated proteins, in an effort to find robust targets for therapeutic treatment of PDAC.

Project description:Prostate cancer is characterized by heterogeneity in the clinical course that often does not to correlate with morphologic features of the tumor. Metastasis reflects the most adverse outcome of prostate cancer, and to date there are no reliable morphologic features or serum biomarkers that can reliably predict which patients are at higher risk of developing metastatic disease. Understanding the differences in the biology of metastatic and organ confined primary tumors is essential for developing new prognostic markers and therapeutic targets. Using Affymetrix oligonucleotide arrays, we analyzed gene expression profiles of 24 androgen-ablation resistant metastatic samples obtained from 4 patients and a previously published dataset of 64 primary prostate tumor samples. Differential gene expression was analyzed after removing potentially uninformative stromal genes, addressing the differences in cellular content between primary and metastatic tumors. The metastatic samples are highly heterogeneous in expression; however, differential expression analysis shows that 415 genes are upregulated and 364 genes are downregulated at least 2 fold in every patient with metastasis. The expression profile of metastatic samples reveals changes in expression of a unique set of genes representing both the androgen ablation related pathways and other metastasis related gene networks such as cell adhesion, bone remodeling and cell cycle. The differentially expressed genes include metabolic enzymes, transcription factors such as Forkhead Box M1 (FoxM1) and cell adhesion molecules such as Osteopontin (SPP1). We hypothesize that these genes have a role in the biology of metastatic disease and that they represent potential therapeutic targets for prostate cancer. Experiment Overall Design: Using Affymetrix oligonucleotide arrays, we analyzed gene expression profiles of 24 androgen-ablation resistant metastatic samples obtained from 4 patients and a previously published dataset of 64 primary prostate tumor samples. Differential gene expression was analyzed after removing potentially uninformative stromal genes, addressing the differences in cellular content between primary and metastatic tumors.

Dataset Information

Analysis of normal-tumour tissue interaction in tumours: prediction of prostate cancer features from the molecular profile of adjacent normal cells.

Publications

Analysis of normal-tumour tissue interaction in tumours: prediction of prostate cancer features from the molecular profile of adjacent normal cells.

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