Multiple biomarker tissue microarrays: bioinformatics and practical approaches

The Application of Prussian Blue Nanoparticles in Tumor Diagnosis and Treatment

Prussian blue nanoparticles (PBNPs) have attracted increasing research interest in immunosensors, bioimaging, drug delivery, and application as therapeutic agents due to their large internal pore volume, tunable size, easy synthesis and surface modification, good thermal stability, and favorable biocompatibility. This review first outlines the effect of tumor markers using PBNPs-based immunosensors which have a sandwich-type architecture and competitive-type structure. Metal ion doped PBNPs which were used as T1-weight magnetic resonance and photoacoustic imaging agents to improve image quality and surface modified PBNPs which were used as drug carriers to decrease side effects via passive or active targeting to tumor sites are also summarized. Moreover, the PBNPs with high photothermal efficiency and excellent catalase-like activity were promising for photothermal therapy and O2 self-supplied photodynamic therapy of tumors. Hence, PBNPs-based multimodal imaging-guided combinational tumor therapies (such as chemo, photothermal, and photodynamic therapies) were finally reviewed. This review aims to inspire broad interest in the rational design and application of PBNPs for detecting and treating tumors in clinical research.

Polyelectrolyte-protected Dual-color-quantum-dot Assembled Silica Nanoparticles and Their Application in Simultaneous Fluorescence Determination of e Antigen and Surface Antigen of Hepatitis B

Cationic poly-diallyldimethylammonium (PDADMAC), green CdTe quantum dots (QDs) or red CdS coated CdTe QDs, and anionic polyacrylic acid (PAA) were respectively assembled on the nano-carrier SiO₂ to prepare green fluorescence composite nanoparticles (GF-QDs) and red ones (RF-QDs) with the structure SiO₂/PDADMAC/QD/PDADMAC/PAA. The sandwich structure "PDADMAC/QD/PDADMAC" on the nano-carrier not only realized the protection to fluorescence of QDs but also avoided the fluorescence shielding of silica shell for the assembled QDs. In 7 days, the diluent solutions of GF-QD and RF-QD all have a very stable fluorescence. On the contrary, the fluorescence of diluent solutions of red and green QDs reduced by 75.99 and 94.35%, respectively. Indeed, they have not fluorescent shielding and have a very slight fluorescent enhancement. Based on GF-QD and RF-QD, the simultaneous determination of Hepatitis B e antigen and surface antigen has been established. Their determination in buffer and plasma all showed good precision and accuracy.

Identification of Differentially Expressed Proteins in the Serum of Colorectal Cancer Patients Using 2D-DIGE Proteomics Analysis

Early detection of colorectal cancer (CRC) is vital for the improvement of disease prognosis. However to date there are no blood-based biomarkers sensitive and specific enough for early diagnosis. We analysed the differences in serum protein expression of early stage CRC (Dukes’ A and B) and late stage CRC (Dukes’ C and D) against normal controls using 2D Fluorescence Difference Gel Electrophoresis (2D-DIGE). Analysis of the 2D maps showed that 23 proteins were differentially expressed between groups (p ≤ 0.05) and these proteins were identified with LC-MS/MS. Eight proteins were up-regulated and 2 down-regulated in patients with early CRC, whereas 14 proteins were up-regulated and 4 down-regulated in those with late CRC compared to normal controls (p ≤ 0.05). Five proteins, namely apolipoprotein A1 (APOA1), apolipoprotein E (APOE), complement factor H (CFH), galectin-7 (GAL7) and synaptojanin-2 (SYNJ2) were validated using ELISA and only APOA1 and GAL-7 showed consistent findings. Further validation using immunohistochemistry showed negative immunoreactivity for GAL-7 in CRC tissues, suggesting that GAL-7 detected in the serum did not originate from the CRC tumour. APOA1 showed positive immunoreactivity but its expression did not correlate with Dukes’ staging (p = 0.314), tumour grading (p = 0.880) and lymph node involvement (p = 0.108). Differences in APOA1 isoforms and/or conformation between serum and tissue samples as well as tumour heterogeneity may explain for the discrepancies between DIGE and ELISA when compared to immunohistochemistry. Structural and functional studies of APOA1 in future would best describe the role of APOA1 in CRC.

A knowledge-driven approach to extract disease-related biomarkers from the literature

The biomedical literature represents a rich source of biomarker information. However, both the size of literature databases and their lack of standardization hamper the automatic exploitation of the information contained in these resources. Text mining approaches have proven to be useful for the exploitation of information contained in the scientific publications. Here, we show that a knowledge-driven text mining approach can exploit a large literature database to extract a dataset of biomarkers related to diseases covering all therapeutic areas. Our methodology takes advantage of the annotation of MEDLINE publications pertaining to biomarkers with MeSH terms, narrowing the search to specific publications and, therefore, minimizing the false positive ratio. It is based on a dictionary-based named entity recognition system and a relation extraction module. The application of this methodology resulted in the identification of 131,012 disease-biomarker associations between 2,803 genes and 2,751 diseases, and represents a valuable knowledge base for those interested in disease-related biomarkers. Additionally, we present a bibliometric analysis of the journals reporting biomarker related information during the last 40 years.

Guidelines and considerations for conducting experiments using tissue microarrays

Tissue microarrays (TMAs) represent a powerful method for undertaking large-scale tissue-based biomarker studies. While TMAs offer several advantages, there are a number of issues specific to their use which need to be considered when employing this method. Given the investment in TMA-based research, guidance on design and execution of experiments will be of benefit and should help researchers new to TMA-based studies to avoid known pitfalls. Furthermore, a consensus on quality standards for TMA-based experiments should improve the robustness and reproducibility of studies, thereby increasing the likelihood of identifying clinically useful biomarkers. In order to address these issues, the National Cancer Research Institute Biomarker and Imaging Clinical Studies Group organized a 1-day TMA workshop held in Nottingham in May 2012. The document herein summarizes the conclusions from the workshop. It includes guidance and considerations on all aspects of TMA-based research, including the pre-analytical stages of experimental design, the analytical stages of data acquisition, and the postanalytical stages of data analysis. A checklist is presented which can be used both for planning a TMA experiment and interpreting the results of such an experiment. For studies of cancer biomarkers, this checklist could be used as a supplement to the REMARK guidelines.

Personalized medicine in breast cancer: a systematic review

The recent advent of "-omics" technologies have heralded a new era of personalized medicine. Personalized medicine is referred to as the ability to segment heterogeneous subsets of patients whose response to a therapeutic intervention within each subset is homogeneous. This new paradigm in healthcare is beginning to affect both research and clinical practice. The key to success in personalized medicine is to uncover molecular biomarkers that drive individual variability in clinical outcomes or drug responses. In this review, we begin with an overview of personalized medicine in breast cancer and illustrate the most encountered statistical approaches in the recent literature tailored for uncovering gene signatures.

Statistical Methods for Tissue Array Images - Algorithmic Scoring and Co-training

Recent advances in tissue microarray technology have allowed immunohistochemistry to become a powerful medium-to-high throughput analysis tool, particularly for the validation of diagnostic and prognostic biomarkers. However, as study size grows, the manual evaluation of these assays becomes a prohibitive limitation; it vastly reduces throughput and greatly increases variability and expense. We propose an algorithm-Tissue Array Co-Occurrence Matrix Analysis (TACOMA)-for quantifying cellular phenotypes based on textural regularity summarized by local inter-pixel relationships. The algorithm can be easily trained for any staining pattern, is absent of sensitive tuning parameters and has the ability to report salient pixels in an image that contribute to its score. Pathologists' input via informative training patches is an important aspect of the algorithm that allows the training for any specific marker or cell type. With co-training, the error rate of TACOMA can be reduced substantially for a very small training sample (e.g., with size 30). We give theoretical insights into the success of co-training via thinning of the feature set in a high dimensional setting when there is "sufficient" redundancy among the features. TACOMA is flexible, transparent and provides a scoring process that can be evaluated with clarity and confidence. In a study based on an estrogen receptor (ER) marker, we show that TACOMA is comparable to, or outperforms, pathologists' performance in terms of accuracy and repeatability.

Approaching MALDI molecular imaging for clinical proteomic research: current state and fields of application

MALDI imaging mass spectrometry ('MALDI imaging') is an increasingly recognized technique for biomarker research. After years of method development in the scientific community, the technique is now increasingly applied in clinical research. In this article, we discuss the use of MALDI imaging in clinical proteomics and put it in context with classical proteomics techniques. We also highlight a number of upcoming challenges for personalized medicine, development of targeted therapies and diagnostic molecular pathology where MALDI imaging could help.

Biomarkers and surrogate endpoints for normal-tissue effects of radiation therapy: the importance of dose-volume effects

Biomarkers are of interest for predicting or monitoring normal tissue toxicity of radiation therapy. Advances in molecular radiobiology provide novel leads in the search for normal tissue biomarkers with sufficient sensitivity and specificity to become clinically useful. This article reviews examples of studies of biomarkers as predictive markers, as response markers, or as surrogate endpoints for radiation side effects. Single nucleotide polymorphisms are briefly discussed in the context of candidate gene and genomewide association studies. The importance of adjusting for radiation dose distribution in normal tissue biomarker studies is underlined. Finally, research priorities in this field are identified and discussed.

Influence of baicalin and octreotide on NF-kappaB and p-selectin expression in liver and kidney of rats with severe acute pancreatitis

To observe the influence of Baicalin and Octreotide on liver and kidney of rats with severe acute pancreatitis (SAP) and discuss the related mechanism. SAP rats were randomly divided into model control, Baicalin treated and Octreotide treated group (n = 45). The same number of normal rats were included in sham-operated group (n = 45). In all groups, the mortality rate, pathological changes as well as expression levels of NF-kappaB p65 and P-selectin protein in liver and kidney were observed at 3, 6 and 12 h after operation. The survival rate of treated group was 100% at 12 h significantly higher than that of model control group (P < 0.05). The pathological changes of liver and kidney in treated groups were alleviated to different degrees, the NF-kappaB protein expression levels and pathological severity scores in liver and kidney of treated groups were significantly lower than those of model control group (P < 0.05 or P < 0.001). The hepatic P-selectin protein expression level in Baicalin treated group was significantly lower than that of model control group at 3 h (P < 0.01), and renal P-selectin expression level in Baicalin treated group at 3 and 6 h were significantly lower than those of model control group and Octreotide treated group (P < 0.01). (1) Early treatment with Baicalin or Octreotide have obvious protecting effects on liver and kidney injuries in SAP with their mechanisms associated to inhibiting NF-kappaB and P-selectin expression of liver and kidney. (2) Comparing the pharmacologic effects of Octreotide and Baicalin, we believe Baicalin as a new drug with its protecting effects on liver and kidney of SAP rats similar to Octreotide is worth further studying. (3) The advantages of tissue microarrays in pathological examination include time and energy saving and highly efficient. But the restriction of small diameter weakens the representation of tissues to various extents, which may lead to the deviation of analysis.

Sampling strategies for tissue microarrays to evaluate biomarkers in ovarian cancer

INTRODUCTION

Tissue microarrays (TMA) enable rapid analysis of biomarkers in large-scale studies involving archival tumor specimens, however, their utility in heterogeneous tumors such as ovarian cancer is limited.

METHODS

In this study, immunohistochemical analysis was done on TMAs comprised of epithelial ovarian cancer (EOC) to estimate the prevalence of loss of expression of three mismatch repair proteins. TMAs were initially created using cores sampled from the center of donor tissue blocks from 59 EOC cases. Full sections were subsequently created and levels of expression were compared between tissues sampled from the central portion versus the periphery. Follow-up analyses were done by obtaining cores from the periphery of up to five additional donor blocks per case. A linear mixed model for each protein was used to investigate differences between results from the initial and follow-up blocks.

RESULTS

In the original TMAs created using centrally sampled cores, loss of mismatch repair expression was noted in 17 (29%) of the 59 cases. By comparison, analyses from peripherally sampled cores revealed loss of expression in only 6 of these 17 cases. For each protein, significant differences (P < 0.05) were detected between results from the initial donor block and the majority of the follow-up blocks.

CONCLUSIONS

Our investigations, based on EOC, suggest that sampling variability in protein expression may result when TMAs are used. Thus, at least for EOC, it is important to preferentially sample from the periphery of tumor blocks where exposure to tissue fixatives is optimal.