Breast tumor metastasis: analysis via proteomic profiling

The ability to predict the metastatic behavior of a patient's cancer, as well as to detect and eradicate such recurrences, remain major clinical challenges in oncology. While many potential molecular biomarkers have been identified and tested previously, none have greatly improved the accuracy of specimen evaluation over routine histopathological criteria and, to date, they predict individual outcomes poorly. The ongoing development of high-throughput proteomic profiling technologies is opening new avenues for the investigation of cancer and, through application in tissue-based studies and animal models, will facilitate the identification of molecular signatures that are associated with breast tumor cell phenotype. The appropriate use of these approaches has the potential to provide efficient biomarkers, and to improve our knowledge of tumor biology. This, in turn, will enable the development of targeted therapeutics aimed at ameliorating the lethal dissemination of breast cancer. In this review, we focus on the accumulating proteomic signatures of breast tumor progression, particularly those that correlate with the occurrence of distant metastases, and discuss some of the expected future developments in the field.     

Breast tumor metastasis: analysis via proteomic profiling

The ability to predict the metastatic behavior of a patient’s cancer, as well as to detect and eradicate such recurrences, remain major clinical challenges in oncology. While many potential molecular biomarkers have been identified and tested previously, none have greatly improved the accuracy of specimen evaluation over routine histopathological criteria and, to date, they predict individual outcomes poorly. The ongoing development of high-throughput proteomic profiling technologies is opening new avenues for the investigation of cancer and, through application in tissue-based studies and animal models, will facilitate the identification of molecular signatures that are associated with breast tumor cell phenotype. The appropriate use of these approaches has the potential to provide efficient biomarkers, and to improve our knowledge of tumor biology. This, in turn, will enable the development of targeted therapeutics aimed at ameliorating the lethal dissemination of breast cancer. In this review, we focus on the accumulating proteomic signatures of breast tumor progression, particularly those that correlate with the occurrence of distant metastases, and discuss some of the expected future developments in the field.     

miRNA在肝细胞癌中的研究进展和展望

微小RNA(microRNA, miRNA)是一类长度为二十几个核苷酸的内源性非编码调控RNA,通过序列特异性翻译抑制或mRNA裂解来调控基因表达,参与细胞发育、增殖、分化、凋亡等一系列重要生物学进程。近期的研究发现,miRNA具有癌基因和抑癌基因的作用,在肿瘤的发生和发展中起着重要的作用。已发现若干miRNA直接参与肝细胞癌的发生和发展,miRNA表达谱与肝细胞癌的诊断、分期、进展和预后等相关。作为一类新的分子靶标,miRNA应用于肝细胞癌的诊断和生物治疗具有广阔的前景。     

Metabolomic profiling reveals a role for androgen in activating amino acid metabolism and methylation in prostate cancer cells

Prostate cancer is the second leading cause of cancer related death in American men. Development and progression of clinically localized prostate cancer is highly dependent on androgen signaling. Metastatic tumors are initially responsive to anti-androgen therapy, however become resistant to this regimen upon progression. Genomic and proteomic studies have implicated a role for androgen in regulating metabolic processes in prostate cancer. However, there have been no metabolomic profiling studies conducted thus far that have examined androgen-regulated biochemical processes in prostate cancer. Here, we have used unbiased metabolomic profiling coupled with enrichment-based bioprocess mapping to obtain insights into the biochemical alterations mediated by androgen in prostate cancer cell lines. Our findings indicate that androgen exposure results in elevation of amino acid metabolism and alteration of methylation potential in prostate cancer cells. Further, metabolic phenotyping studies confirm higher flux through pathways associated with amino acid metabolism in prostate cancer cells treated with androgen. These findings provide insight into the potential biochemical processes regulated by androgen signaling in prostate cancer. Clinically, if validated, these pathways could be exploited to develop therapeutic strategies that supplement current androgen ablative treatments while the observed androgen-regulated metabolic signatures could be employed as biomarkers that presage the development of castrate-resistant prostate cancer.     

Mass spectrometry-based expression profiling of clinical prostate cancer

The maturation of MS technologies has provided a rich opportunity to interrogate protein expression patterns in normal and disease states by applying expression protein profiling methods. Major goals of this research strategy include the identification of protein biomarkers that demarcate normal and disease populations, and the identification of therapeutic biomarkers for the treatment of diseases such as cancer (Celis, J. E., and Gromov, P. (2003) Proteomics in translational cancer research: Toward an integrated approach. Cancer Cell 3, 9-151). Prostate cancer is one disease that would greatly benefit from implementing MS-based expression profiling methods because of the need to stratify the disease based on molecular markers. In this review, we will summarize the current MS-based methods to identify and validate biomarkers in human prostate cancer. Lastly, we propose a reverse proteomic approach implementing a quantitative MS research strategy to identify and quantify biomarkers implicated in prostate cancer development. With this approach, the absolute levels of prostate cancer biomarkers will be identified and quantified in normal and diseased samples by measuring the levels of native peptide biomarkers in relation to a chemically identical but isotopically labeled reference peptide. Ultimately, a centralized prostate cancer peptide biomarker expression database could function as a repository for the identification, quantification, and validation of protein biomarker(s) during prostate cancer progression in men.     

Heterotypic communication as the promoter of phenotypic plasticity of cancer cells: The role of cancer secretomes

Cellular communication relies on signaling circuits whose statuses are mainly modulated by soluble biomolecules such as carbohydrates, lipids, proteins, and metabolites as well as extracellular vesicles (EVs). Therefore, the active secretion of such biomolecules is critical for both cell homeostasis and proper pathophysiological responses in a timely fashion. In this context, proteins are among the main modulators of such biological responses. Hence, profiling cell line secretomes may be an opportunity for the identification of “signatures” of specific cell types (i.e., stromal or metastatic cells) with important prognostic/therapeutic value. This review will focus on the biological implications of cell secretomes in the context of cancer, as well as their functional roles in shaping the tumoral microenvironment (TME) and communication status of participating cells.     

Genomic signatures associated with the development, progression, and outcome of prostate cancer

Prostate cancer remains a common cause of cancer death in men. Applications of new genomic technologies to the recent development of high-quality prostate cancer models in multiple contexts have added great molecular insight into the development of and progression to metastasis. Genomic analysis of DNA, RNA, and protein alterations allows for the global assessment of this disease and provides the molecular framework to improve risk classification, outcome prediction, and development of targeted therapies. The creation of expression profiles and signatures will allow the evaluation of cancer phenotypes and give insight into determining those with increased risk of cancer, identification of critical pathways involved in the development of cancer, prediction of disease outcome, and assessment of the response of cancer to established and novel therapies.This review focuses on highlighting recent work in genomics and on its role in evaluating potential genetic modifiers of prostate cancer and novel biomarkers that may help with prostate cancer diagnosis, its potential to provide a better understanding of prostate cancer behavior and transition to metastatic disease, and its role in current and new therapies in prostate cancer. This framework has the exciting potential to be predictive and provide personalized and individual treatment to the large number of men diagnosed with prostate cancer each year.     

Urinary proteomic profiling for diagnostic bladder cancer biomarkers

The ability to detect and monitor bladder cancer in noninvasively obtained urine samples is a major goal. While a number of protein biomarkers have been identified and commercially developed, none have greatly improved the accuracy of sample evaluation over invasive cystoscopy. The ongoing development of high-throughput proteomic profiling technologies will facilitate the identification of molecular signatures that are associated with bladder disease. The appropriate use of these approaches has the potential to provide efficient biomarkers for the early detection and monitoring of recurrent bladder cancer. Identification of disease-associated proteins will also advance our knowledge of tumor biology, which, in turn, will enable development of targeted therapeutics aimed at reducing morbidity from bladder cancer. In this article, we focus on the accumulating proteomic signatures of urine in health and disease, and discuss expected future developments in this field of research.     

Multiomic Metabolic Enrichment Network Analysis Reveals Metabolite–Protein Physical Interaction Subnetworks Altered in Cancer

Metabolism is recognized as an important driver of cancer progression and other complex diseases, but global metabolite profiling remains a challenge. Protein expression profiling is often a poor proxy since existing pathway enrichment models provide an incomplete mapping between the proteome and metabolism. To overcome these gaps, we introduce multiomic metabolic enrichment network analysis (MOMENTA), an integrative multiomic data analysis framework for more accurately deducing metabolic pathway changes from proteomics data alone in a gene set analysis context by leveraging protein interaction networks to extend annotated metabolic models. We apply MOMENTA to proteomic data from diverse cancer cell lines and human tumors to demonstrate its utility at revealing variation in metabolic pathway activity across cancer types, which we verify using independent metabolomics measurements. The novel metabolic networks we uncover in breast cancer and other tumors are linked to clinical outcomes, underscoring the pathophysiological relevance of the findings.     

Stromal gene expression predicts clinical outcome in breast cancer

Although it is increasingly evident that cancer is influenced by signals emanating from tumor stroma, little is known regarding how changes in stromal gene expression affect epithelial tumor progression. We used laser capture microdissection to compare gene expression profiles of tumor stroma from 53 primary breast tumors and derived signatures strongly associated with clinical outcome. We present a new stroma-derived prognostic predictor (SDPP) that stratifies disease outcome independently of standard clinical prognostic factors and published expression-based predictors. The SDPP predicts outcome in several published whole tumor-derived expression data sets, identifies poor-outcome individuals from multiple clinical subtypes, including lymph node-negative tumors, and shows increased accuracy with respect to previously published predictors, especially for HER2-positive tumors. Prognostic power increases substantially when the predictor is combined with existing outcome predictors. Genes represented in the SDPP reveal the strong prognostic capacity of differential immune responses as well as angiogenic and hypoxic responses, highlighting the importance of stromal biology in tumor progression.     

Multiomic Metabolic Enrichment Network Analysis Reveals Metabolite–Protein Physical Interaction Subnetworks Altered in Cancer

Metabolism is recognized as an important driver of cancer progression and other complex diseases, but global metabolite profiling remains a challenge. Protein expression profiling is often a poor proxy since existing pathway enrichment models provide an incomplete mapping between the proteome and metabolism. To overcome these gaps, we introduce multiomic metabolic enrichment network analysis (MOMENTA), an integrative multiomic data analysis framework for more accurately deducing metabolic pathway changes from proteomics data alone in a gene set analysis context by leveraging protein interaction networks to extend annotated metabolic models. We apply MOMENTA to proteomic data from diverse cancer cell lines and human tumors to demonstrate its utility at revealing variation in metabolic pathway activity across cancer types, which we verify using independent metabolomics measurements. The novel metabolic networks we uncover in breast cancer and other tumors are linked to clinical outcomes, underscoring the pathophysiological relevance of the findings.     

What can proteomic analyses contribute to understanding the molecular biology and clinical behavior of prostate cancer?

Identifying the proteins and their complex interactions that promote and/or sustain the aggressive malignant phenotype is essential for understanding key effectors of the molecular biology of prostate cancer. This is also essential for development of new clinical applications. A variety of proteomic techniques, ranging from mass spectrometry to new methods of multiplexing protein identification, have great potential for rapidly achieving these goals. However, in order to obtain meaningful results, these techniques must be applied within the context of our knowledge of the heterogeneity of prostate tissues and tumors, the impact of specimen processing on both the quality and quantity of proteins detected and a thorough understanding of prostate cell biology. Collaboration between the protein chemist and the prostate cell biologist will expedite progress in this important field.     

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

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 TGFβ.