Proteomic analysis of tissue samples in translational breast cancer research

In the last decade, many proteomic technologies have been applied, with varying success, to the study of tissue samples of breast carcinoma for protein expression profiling in order to discover protein biomarkers/signatures suitable for: characterization and subtyping of tumors; early diagnosis, and both prognosis and prediction of outcome of chemotherapy. The purpose of this review is to critically appraise what has been achieved to date using proteomic technologies and to bring forward novel strategies – based on the analysis of clinically relevant samples – that promise to accelerate the translation of basic discoveries into the daily breast cancer clinical practice. In particular, we address major issues in experimental design by reviewing the strengths and weaknesses of current proteomic strategies in the context of the analysis of human breast tissue specimens.     

Analysis of protein expression in cell microarrays: a tool for antibody-based proteomics.

Tissue microarray (TMA) technology provides a possibility to explore protein expression patterns in a multitude of normal and disease tissues in a high-throughput setting. Although TMAs have been used for analysis of tissue samples, robust methods for studying in vitro cultured cell lines and cell aspirates in a TMA format have been lacking. We have adopted a technique to homogeneously distribute cells in an agarose gel matrix, creating an artificial tissue. This enables simultaneous profiling of protein expression in suspension- and adherent-grown cell samples assembled in a microarray. In addition, the present study provides an optimized strategy for the basic laboratory steps to efficiently produce TMAs. Presented modifications resulted in an improved quality of specimens and a higher section yield compared with standard TMA production protocols. Sections from the generated cell TMAs were tested for immunohistochemical staining properties using 20 well-characterized antibodies. Comparison of immunoreactivity in cultured dispersed cells and corresponding cells in tissue samples showed congruent results for all tested antibodies. We conclude that a modified TMA technique, including cell samples, provides a valuable tool for high-throughput analysis of protein expression, and that this technique can be used for global approaches to explore the human proteome.     

A global view of protein expression in human cells,tissues, and organs

Defining the protein profiles of tissues and organs is critical to understanding the unique characteristics of the various cell types in the human body. In this study, we report on an anatomically comprehensive analysis of 4842 protein profiles in 48 human tissues and 45 human cell lines. A detailed analysis of over 2 million manually annotated, high‐resolution, immunohistochemistry‐based images showed a high fraction (>65%) of expressed proteins in most cells and tissues, with very few proteins (<2%) detected in any single cell type. Similarly, confocal microscopy in three human cell lines detected expression of more than 70% of the analyzed proteins. Despite this ubiquitous expression, hierarchical clustering analysis, based on global protein expression patterns, shows that the analyzed cells can be still subdivided into groups according to the current concepts of histology and cellular differentiation. This study suggests that tissue specificity is achieved by precise regulation of protein levels in space and time, and that different tissues in the body acquire their unique characteristics by controlling not which proteins are expressed but how much of each is produced.     

The efficiency of mapping of quantitative trait loci using cofactor analysis in half-sib design

This simulation study was designed to study the power and type I error rate in QTL mapping using cofactor analysis in half-sib designs. A number of scenarios were simulated with different power to identify QTL by varying family size, heritability, QTL effect and map density, and three threshold levels for cofactor were considered. Generally cofactor analysis did not increase the power of QTL mapping in a half-sib design, but increased the type I error rate. The exception was with small family size where the number of correctly identified QTL increased by 13% when heritability was high and 21% when heritability was low. However, in the same scenarios the number of false positives increased by 49% and 45% respectively. With a liberal threshold level of 10% for cofactor combined with a low heritability, the number of correctly identified QTL increased by 14% but there was a 41% increase in the number of false positives. Also, the power of QTL mapping did not increase with cofactor analysis in scenarios with unequal QTL effect, sparse marker density and large QTL effect (25% of the genetic variance), but the type I error rate tended to increase. A priori, cofactor analysis was expected to have higher power than individual chromosome analysis especially in experiments with lower power to detect QTL. Our study shows that cofactor analysis increased the number of false positives in all scenarios with low heritability and the increase was up to 50% in low power experiments and with lower thresholds for cofactors.     

Targeted therapy in the treatment of solid tumors: Practice contradicts theory

The basic principle of targeted therapy formulated about ten years ago consists in the design and application of drugs specifically directed against well-defined targets that are critical for tumor survival and not compromising for normal organs and tissues. The past decade has been marked by the appearance of an immense diversity of novel antitumor agents with claimed targeted action. Unfortunately, despite indisputable progress in clinical settings, some popular drugs against solid tumors (e.g. bevacizumab, trastuzumab, erlotinib, gefitinib) nominally assigned to targeted-action drugs, cannot actually be classified with this group being nonconforming to a priori stated goals of targeted therapy. The state-of-the-art and current problems in targeted therapy of solid tumors are reviewed.     

An array of possibilities in cancer research using cytokine antibody arrays

Protein arrays have shown potential applications in cancer research. After several decades of research, it has become evident that many cytokines are central to the development of cancer and its treatment. Cytokine antibody arrays that have been designed to simultaneously detect multiple cytokines are not only available, but show a diversity of applications in the study of many diseases in addition to cancer. This review will focus on the implementation of cytokine antibody arrays in many aspects of cancer research, such as biomarker discovery, molecular mechanisms of cancer development, preclinical studies and the effects of cancer compounds.     

Normalization of microRNA expression levels in quantitative RT-PCR assays: identification of suitable reference RNA targets in normal and cancerous human solid tissues

Proper normalization is a critical but often an underappreciated aspect of quantitative gene expression analysis. This study describes the identification and characterization of appropriate reference RNA targets for the normalization of microRNA (miRNA) quantitative RT-PCR data. miRNA microarray data from dozens of normal and disease human tissues revealed ubiquitous and stably expressed normalization candidates for evaluation by qRT-PCR. miR-191 and miR-103, among others, were found to be highly consistent in their expression across 13 normal tissues and five pair of distinct tumor/normal adjacent tissues. These miRNAs were statistically superior to the most commonly used reference RNAs used in miRNA qRT-PCR experiments, such as 5S rRNA, U6 snRNA, or total RNA. The most stable normalizers were also highly conserved across flash-frozen and formalin-fixed paraffin-embedded lung cancer tumor/NAT sample sets, resulting in the confirmation of one well-documented oncomir (let-7a), as well as the identification of novel oncomirs. These findings constitute the first report describing the rigorous normalization of miRNA qRT-PCR data and have important implications for proper experimental design and accurate data interpretation.     

Tumor profiling using protein biomarker panels in malignant melanoma: application of tissue microarrays and beyond

In the past decade, analysis of the urinary proteome (urinary proteomics) has intensified in response to the need for novel biomarkers that support early diagnosis of kidney diseases. In particular, this also applies to acute kidney injury, which is a heterogeneous complex syndrome with a still-increasing incidence at the intensive care unit. Unfortunately, this major need remains largely unmet to date. The current report aims to explain why attempts to implement urinary proteomic-discovered acute kidney injury diagnostic candidates in the intensive care unit setting have not yet led to success. Subsequently, some key notes are provided that should enhance the chance of translating selected urinary proteomic candidates to valuable tools for the nephrologist and intensivist in the near future.     

Efficiency of population structures for mapping of Mendelian and imprinted quantitative trait loci in outbred pigs using variance component methods

In a simulation study different designs for a pure line pig population were compared for efficiency of mapping QTL using the variance component method. Phenotypes affected by a Mendelian QTL, a paternally expressed QTL, a maternally expressed QTL or by a QTL without an effect were simulated. In all alternative designs 960 progeny were phenotyped. Given the limited number of animals there is an optimum between the number of families and the family size. Estimation of Mendelian and parentally expressed QTL is more efficient in a design with large family sizes. Too small a number of sires should be avoided to minimize chances of sires to be non-segregating. When a large number of families is used, the number of haplotypes increases which reduces the accuracy of estimating the QTL effect and thereby reduces the power to show a significant QTL and to correctly position the QTL. Dense maps allow for smaller family size due to exploitation of LD-information. Given the different possible modes of inheritance of the QTL using 8 to16 boars, two litters per dam was optimal with respect to determining significance and correct location of the QTL for a data set consisting of 960 progeny. The variance component method combining linkage disequilibrium and linkage analysis seems to be an appropriate choice to analyze data sets which vary in marker density and which contain complex family structures.     

Current progress in protein profiling of complex samples

Accurate cancer biomarkers are needed for early detection, disease classification, prediction of therapeutic response and monitoring treatment. While there appears to be no shortage of candidate biomarker proteins, a major bottleneck in the biomarker pipeline continues to be their verification by enzyme linked immunosorbent assays. Multiple reaction monitoring (MRM), also known as selected reaction monitoring, is a targeted mass spectrometry approach to protein quantitation and is emerging to bridge the gap between biomarker discovery and clinical validation. Highly multiplexed MRM assays are readily configured and enable simultaneous verification of large numbers of candidates facilitating the development of biomarker panels which can increase specificity. This review focuses on recent applications of MRM to the analysis of plasma and serum from cancer patients for biomarker verification. The current status of this approach is discussed along with future directions for targeted mass spectrometry in clinical biomarker validation.     

The clinical impact of recent advances in LC-MS for cancer biomarker discovery and verification

Mass spectrometry (MS) -based proteomics has become an indispensable tool with broad applications in systems biology and biomedical research. With recent advances in liquid chromatography (LC) and MS instrumentation, LC–MS is making increasingly significant contributions to clinical applications, especially in the area of cancer biomarker discovery and verification. To overcome challenges associated with analyses of clinical samples (for example, a wide dynamic range of protein concentrations in bodily fluids and the need to perform high throughput and accurate quantification of candidate biomarker proteins), significant efforts have been devoted to improve the overall performance of LC–MS-based clinical proteomics platforms. Reviewed here are the recent advances in LC–MS and its applications in cancer biomarker discovery and quantification, along with the potentials, limitations and future perspectives.     

青年和老年人结肠上皮的比较蛋白质组学研究

衰老的大肠上皮不仅多种生理功能下降,而且对大肠癌在内的多种衰老相关的肠道疾病易感性显著增加,但结肠上皮衰老及衰老的结肠上皮对癌症易感的分子机制仍然不清楚.为此,应用双向凝胶电泳(2-DE)技术分离青年人及老年人的正常结肠上皮的总蛋白质,图像分析识别差异表达的蛋白质点,基质辅助激光解吸电离飞行时间质谱(MALDI-TOF-MS)对差异表达的蛋白质点进行鉴定,免疫组化和实时定量(real-timequantitative)PCR检测部分差异蛋白,在青年人和老年人结肠上皮中的表达水平.得到了分辨率较高、重复性较好的青年人和老年人结肠上皮的2-DE图谱,质谱分析鉴定了17个结肠上皮衰老相关的蛋白质,免疫组化和real-timequantitativeRT-PCR证实了部分差异蛋白质的表达水平.研究结果提示,线粒体功能受伤、抗氧化能力下降是结肠上皮衰老的重要原因,4个差异蛋白质即guaninenucleotide-bindingproteinbetasubunit-likeprotein(Rack1)、stress-70protein、40SribosomalproteinSA和chlorideintracellularchannelprotein1可能与衰老的结肠上皮对癌症易感有关.     

应用SYBR荧光实时定量RT-PCR法检测BMSCs诱导大鼠肝星状细胞中DR5mRNA表达

目的应用SYBR荧光实时定量RT-PCR法检测骨髓间充质干细胞（BMSCs）对大鼠肝星状细胞（HSCs）的死亡受体5（DR5）mRNA表达的影响,探讨BMSCs诱导HSCs凋亡及其机制。方法采用贴壁筛选法培养、纯化SD大鼠BMSCs,传至第4代使用;大鼠原代HSCs细胞及肝纤维原细胞系冻融后传代使用。应用6孔塑料培养板,建立上下双层细胞共培养体系,常规培养。实验分为3组：（1）实验组：BMSCs与HSCs共培养;（2）空白对照组：HSCs单独培养;（3）阴性对照组：大鼠肝纤维原细胞与HSCs共培养。以上培养体系动态观察24、48、72h,应用流式细胞仪检测HSCs细胞凋亡率,采用SYBRGreenI荧光实时定量RT-PCR法检测,以β-actin基因作为内参,计算各组DR5mRNA的相对表达量。结果在共培养组中,BMSCs促进了HSCs凋亡,与其他两组比较差异有显著统计学意义（P〈O．01）,空白对照组与阴性对照组比较无统计学意义（P〉0．05）。实验组BMSCs能明显上调HSCs中DR5mRNA的表达,与空白对照组和阴性对照组比较差异有显著统计学意义（P〈O．01）;空白对照组与阴性对照组DR5mRNA的表达比较无统计学意义（P〉O．05）。结论利用SYBR荧光实时定量RT-PCR法检测BMSCs诱导大鼠肝星状细胞中DR5mRNA表达,为进一步研究BMSCs通过死亡受体途径调控HSCs凋亡以及为BMSCs用于治疗肝纤维化的机制研究提供了理论基础。     

Analysis of the expression and association of retinoblastoma binding protein 6 with the JNK signaling pathway in prostate cancers

This study aims to investigate the expression of retinoblastoma binding protein 6 (RBBP6) in prostate cancer (PCa) and its association with the c‐Jun N‐terminal kinase (JNK) pathway. Immunohistochemistry was used to detect RBBP6 and JNK1/2 expression in PCa and benign prostatic hyperplasia tissues. RBBP6 expression in PCa cells (LNCap, PC3, and DU145) and noncancerous prostate epithelial cells (RWPE‐1) was determined by quantitative real‐time polymerase chain reaction and western blot analysis. PC3 and DU145 cells were transfected with RBBP6 small interfering RNAs (siRNAs) to examine the biological characteristics. Anisomycin (a JNK activator) with/without RBBP6 siRNA was used to treat PC3 cells for further investigating the ramification of the RBBP6‐mediated JNK pathway in PCa. PCa tissues and cells showed higher RBBP6 and JNK1/2 expression. RBBP6 was positively correlated with JNK1/2 in PCa tissues. Besides, RBBP6 expression was correlated to clinical tumor stage, lymph node metastasis, Gleason grade, preoperative prostate‐specific antigen level, as well as prognosis of PCa. RBBP6 siRNA reduced cell proliferation, arrested cells at G2/M, and promoted cell apoptosis, and suppressed JNK pathway. In addition, migration and invasion decreased after the RBBP6 siRNA transfection with downregulated matrix metallopeptidase‐2 (MMP‐2) and MMP‐9. Anisomycin promoted the proliferation, invasion, and migration of PC3 cells and inhibited PC3 cell apoptosis, which could be reversed by RBBP6 siRNA. RBBP6 expression was upregulated in PCa tissues and positively correlated with expression level of JNK1/2. With inhibition of RBBP6 expression, the proliferation, invasion, and migration of PCa cells decreased dramatically, while PC3 cell apoptosis increased appreciably, accompanied by the suppression of the JNK pathway.