Application of Laser Microdissection and Reverse-Phase Protein Microarrays to the Molecular Profiling of Cancer Signal Pathway Networks in the Tissue Microenvironment

Lactate-An Integrative Mirror of Cancer Metabolism

The technique of induced metabolic bioluminescence imaging (imBI) has been developed to obtain a "snapshot" of the momentary metabolic status of biological tissues. Using cryosections of snap-frozen tissue specimens, imBI combines highly specific and sensitive in situ detection of metabolites with a spatial resolution on a microscopic level and with metabolic imaging in relation to tissue histology. Here, we present the application of imBI in human colorectal cancer. Comparing the metabolic information of one biopsy with that of 2 or 3 biopsies per individual cancer, the classification into high versus low lactate tumors, reflecting different glycolytic activities, based on a single biopsy was in agreement with the result from multiple biopsies in 83 % of all cases. We further demonstrate that the metabolic status of tumor tissue can be preserved at least over 10 years by storage in liquid nitrogen, but not by storage at -80 °C. This means that tissue banking with long-term preservation of the metabolic status is possible at -180 °C, which may be relevant for studies on long-term survival of cancer patients. As with other tumor entities, tissue lactate concentration was shown to be correlated with tumor development and progression in colorectal cancer. At first-time diagnosis, lactate values were low in rectal normal tissue and adenomas, were significantly elevated to intermediate levels in non-metastatic adenocarcinomas, and were very high in carcinomas with distant metastasis. There was an inverse behavior of tissue glucose concentration under corresponding conditions. The expression level of monocarboxylate transporter-4 (MCT4) was positively correlated with the tumor lactate concentration and may thus contribute to high lactate tumors being associated with a high degree of malignancy.

Reverse phase protein arrays: mapping the path towards personalized medicine

Reverse phase protein array (RPPA) technology evolved from the advent of miniaturized immunoassays and gene microarray technology. Reverse phase protein arrays provide either a low throughput or high throughput methodology for quantifying proteins and their post-translationally modified forms in both cellular and non-cellular samples. As the demand for patient tailored therapies increases so does the need for precise and sensitive technology to accurately profile the molecular circuitry driving an individual patient's disease. RPPAs are currently utilized in clinical trials for profiling and comparing the functional state of protein signaling pathways, either temporally within tumors, between patients, or within the same patients before/after treatment. RPPAs are generally employed for quantifying large numbers of samples on one array, under identical experimental conditions. However, the goal of personalized cancer medicine is to design therapies based on the molecular portrait of a patient's tumor, which in turn result in more efficacious treatments with less toxicity. Therefore, RPPAs are also being validated for low throughput assays of individual patient samples. This review explores RPPA technology in the cancer research field, concentrating on its role as a fundamental tool for deciphering protein signaling networks and its emerging role in personalized medicine.

Modeling human carcinomas: physiologically relevant 3D models to improve anti-cancer drug development

Anti-cancer drug development is inefficient, mostly due to lack of efficacy in human patients. The high fail rate is partly due to the lack of predictive models or the inadequate use of existing preclinical test systems. However, progress has been made and preclinical models were improved or newly developed, which all account for basic features of solid cancers, three-dimensionality and heterotypic cell interaction. Here we give an overview of available in vivo and in vitro models of cancer, which meet the criteria of being 3D and mirroring human tumor-stroma interactions. We only focus on drug response models without touching models for pharmacokinetic and dynamic, toxicity or delivery aspects.

Spatial normalization of reverse phase protein array data

Reverse phase protein arrays (RPPA) are an efficient, high-throughput, cost-effective method for the quantification of specific proteins in complex biological samples. The quality of RPPA data may be affected by various sources of error. One of these, spatial variation, is caused by uneven exposure of different parts of an RPPA slide to the reagents used in protein detection. We present a method for the determination and correction of systematic spatial variation in RPPA slides using positive control spots printed on each slide. The method uses a simple bi-linear interpolation technique to obtain a surface representing the spatial variation occurring across the dimensions of a slide. This surface is used to calculate correction factors that can normalize the relative protein concentrations of the samples on each slide. The adoption of the method results in increased agreement between technical and biological replicates of various tumor and cell-line derived samples. Further, in data from a study of the melanoma cell-line SKMEL-133, several slides that had previously been rejected because they had a coefficient of variation (CV) greater than 15%, are rescued by reduction of CV below this threshold in each case. The method is implemented in the R statistical programing language. It is compatible with MicroVigene and SuperCurve, packages commonly used in RPPA data analysis. The method is made available, along with suggestions for implementation, at http://bitbucket.org/rppa_preprocess/rppa_preprocess/src.

Implications of functional proteomics in breast cancer

Breast cancer is one of the major public health problems of the Western world. Recent advances in genomics and gene expression-profiling approaches have enriched our understanding of this heterogeneous disease. However, progress in functional proteomics in breast cancer research has been relatively slow. Allied with genomics, the functional proteomics approach will be important in improving diagnosis through better classification of breast cancer and in predicting prognosis and response to different therapies, including chemotherapy, hormonal therapy, and targeted therapy. In this review, we will present functional proteomic approaches with a focus on the recent clinical implications of utilizing the reverse-phase protein array platform in breast cancer research.

Application of molecular technologies for phosphoproteomic analysis of clinical samples

The integration of small kinase inhibitors and monoclonal antibodies into oncological practice has opened a new paradigm for treating cancer patients. As proteins are the direct targets of the new generations of targeted therapeutics, many of which are kinase/enzymatic inhibitors, there is an increasing interest in developing technologies capable of monitoring post-translational changes of the human proteome for the identification of new predictive, prognostic and therapeutic biomarkers. It is well known that the vast majority of the activation/deactivation of these drug targets is driven by phosphorylation. This review provides a description of the main proteomic platforms (planar and bead array, reverse phase protein microarray, phosphoflow, AQUA and mass spectrometry) that have successfully been used for measuring changes in phosphorylation level of drug targets and downstream substrates using clinical specimens. Major emphasis was given to the strengths and weaknesses of the different platforms and to the major barriers that are associated with the analysis of the phosphoproteome. Finally, a number of examples of application of the above-mentioned technologies in the clinical setting are reported.

Stratification of clear cell renal cell carcinoma by signaling pathway analysis

Investigation of cell signaling pathways in 16 clear cell renal cell carcinomas to identify groups based on commonly shared phosphorylation-driven signaling networks. Using laser capture microdissection and reverse-phase protein arrays, we profiled 75 key nodes spanning signaling pathways important in tumorigenesis. Analysis revealed significantly different (P < 0.05) signaling levels for 27 nodes between two groups of samples, designated A (4 samples; high EGFR, RET, and RASGFR1 levels, converging to activate AKT/mTOR) and B (12 samples; high ERK1/2 and STAT phosphorylation). Group B was further partitioned into groups C (7 samples; elevated expression of LC3B) and D (5 samples; activation of Src and STAT). Network analysis indicated that group A was characterized by signaling pathways related to cell cycle and proliferation, and group B by pathways related to cell death and survival. Homogeneous clear cell renal cell carcinomas could be stratified into at least two major functional groups.

Preparation and use of reverse protein microarrays

Reverse-phase protein array (RPPA) is a multiplex, high-throughput proteomic technique for profiling the activation status of signal transduction pathways involved in cancer survival and progression, potentially allowing for identification of new biomarkers and drug targets. On RPPA, the entire patient proteome is immobilized on a spot and single proteins can be quantified across a set of samples, spotted on the same array, with high specificity and sensitivity. Array immunostaining and signal amplification systems are used to generate a signal proportional to the concentration of the analyte. Dedicated scanners and software are used to detect spots, measure intensity, subtract background, normalize signal, and generate a numeric value as output. The generated output file is then analyzed using several different bioinformatic and biostatistical tools. In this unit, the RPPA procedure is described in depth, from sample handling and preparation to data analysis, with particular emphasis on tissue sample analysis.

Feasibility of whole RNA sequencing from single-cell mRNA amplification

Single-cell sampling with RNA-seq analysis plays an important role in reference laboratory; cytogenomic diagnosis for specimens on glass-slides or rare cells in circulating blood for tumor and genetic diseases; measurement of sensitivity and specificity in tumor-tissue genomic analysis with mixed-cells; mechanism analysis of differentiation and proliferation of cancer stem cell for academic purpose. Our single- cell RNA-seq technique shows that fragments were 250-450 bp after fragmentation, amplification, and adapter addition. There were 11.6 million reads mapped in raw sequencing reads (19.6 million). The numbers of mapped genes, mapped transcripts, and mapped exons were 31,332, 41,210, and 85,786, respectively. All QC results demonstrated that RNA-seq techniques could be used for single-cell genomic performance. Analysis of the mapped genes showed that the number of genes mapped by RNA-seq (6767 genes) was much higher than that of differential display (288 libraries) among similar specimens which we have developed and published. The single-cell RNA-seq can detect gene splicing using different subtype TGF-beta analysis. The results from using Q-rtPCR tests demonstrated that sensitivity is 76% and specificity is 55% from single-cell RNA-seq technique with some gene expression missing (2/8 genes). However, it will be feasible to use RNA-seq techniques to contribute to genomic medicine at single-cell level.

Glioblastoma cell enrichment is critical for analysis of phosphorylated drug targets and proteomic-genomic correlations

The quality of cancer genomic and proteomic data relies upon the quality of the clinical specimens examined. Here, we show that data derived from non-microdissected glioblastoma multiforme tumor tissue is either masked or not accurate, producing correlations between genomic and proteomic data that lead to false classifications for therapeutic stratification. We analyzed the level of 133 key signaling proteins and phosphoproteins in laser capture microdissected (LCM) primary tumors from a study set of tissues used for the Cancer Genome Atlas (TCGA) profiling efforts, comparing the results to tissue-matched, nontumor cell-enriched lysates from adjacent sections. Among the analytes, 44%, including targets for clinically important inhibitors, such as phosphorylated mTOR, AKT, STAT1, VEGFR2, or BCL2, differed between matched tumor cell-enriched and nonenriched specimens (even in tumor sections with 90% tumor cell content). While total EGFR protein levels were higher in tumors with EGFR mutations, regardless of tumor cell enrichment, EGFR phosphorylation was increased only in LCM-enriched tumor specimens carrying EGFR mutations. Phosphorylated and total PTEN, which is highly expressed in normal brain, was reduced only in LCM-enriched tumor specimens with either PTEN mutation or loss in PTEN copy number, with no differences observed in non-microdissected samples. These results were confirmed in an independent, non-microdissected, publicly available protein data set from the TCGA database. Our findings highlight the necessity for careful upfront cellular enrichment in biospecimens that form the basis for targeted therapy selection and for molecular characterization efforts such as TCGA.

Laser capture microdissection for the investigative pathologist

An important step in translational research is the validation of molecular findings from in vitro experiments using tissue specimens. However, tissue specimens are complex and contain a multitude of diverse cell populations that interfere with the molecular profiling data of a specific cell type. Laser capture microdissection (LCM) alleviates this issue by providing a valuable tool for the enrichment of a specific cell type within complex tissue samples. However, LCM and molecular analysis from tissue specimens can be complex and challenging due to numerous issues related with the tissue processing and its impact on the integrity of biomolecules in the specimen. The intricate nature of this application highlights the essential role a pathologist plays in translational research by contributing an expertise in histopathology, tissue handling, tissue analysis techniques, and clinical correlation of biological findings. The present review examines key practical aspects in tissue handling, specimen selection, quality control, and sample preparation for LCM and downstream molecular analyses that are a primary objective of the investigative pathologist.

Laser microdissection and saturation labeling DIGE method for the analysis of human arteries

Laser microdissection (LMD) is a novel methodology for noncontact isolation of tissue regions or cells for subsequent molecular analysis. Although it is an upcoming field, its combination with proteomics for differential analysis remains not very well explored, since amount of protein obtained after LMD is scarce. We have combined LMD arterial layer isolation with saturation labeling DIGE, successfully achieving differential analysis of healthy and pathological intima and media layers. Identification of differential spots could be performed in whole tissue extract as reference proteome, since studied regions are subproteomes of the aforementioned.

Integrated proteomic, transcriptomic, and biological network analysis of breast carcinoma reveals molecular features of tumorigenesis and clinical relapse

Gene and protein expression changes observed with tumorigenesis are often interpreted independently of each other and out of context of biological networks. To address these limitations, this study examined several approaches to integrate transcriptomic and proteomic data with known protein-protein and signaling interactions in estrogen receptor positive (ER+) breast cancer tumors. An approach that built networks from differentially expressed proteins and identified among them networks enriched in differentially expressed genes yielded the greatest success. This method identified a set of genes and proteins linking pathways of cellular stress response, cancer metabolism, and tumor microenvironment. The proposed network underscores several biologically intriguing events not previously studied in the context of ER+ breast cancer, including the overexpression of p38 mitogen-activated protein kinase and the overexpression of poly(ADP-ribose) polymerase 1. A gene-based expression signature biomarker built from this network was significantly predictive of clinical relapse in multiple independent cohorts of ER+ breast cancer patients, even after correcting for standard clinicopathological variables. The results of this study demonstrate the utility and power of an integrated quantitative proteomic, transcriptomic, and network analysis approach to discover robust and clinically meaningful molecular changes in tumors.

Antibody microarrays: analysis of cystic fibrosis

Cystic fibrosis (CF) is the most common autosomal recessive disease in the USA and Europe, whose life-limiting phenotype is manifest on epithelial cells throughout the body. The principal cause of morbidity and mortality is a massively proinflammatory condition in the lung. The mutation responsible for most cases of CF is [ΔF508]CFTR. However, the penetrance of the disease is quite variable, and adverse events leading to hospitalization cannot be easily predicted. Thus, there is a strong need for prognostic endpoints that might serve to identify impending clinical problems long before they happen. Our approach has been to search for proteomic signatures in easily accessed biological fluids that might identify the molecular basis for adverse events. We describe here a workflow that begins with patient-derived bronchial brush biopsies and progresses to analysis of serum and plasma from patients on antibody microarrays.

Sialic acid and sialyltransferase activity in serum and tissues of dogs with mammary tumors

In humans, the glycosylation pattern of serum and of membrane glycoproteins is associated with invasiveness of tumors: specifically, α2,6-sialylation and α2,3-sialylation are associated with metastasizing and nonmetastasizing tumors, respectively. In turn, the type of sialylation depends on the activity of α2,6 or α2,3 sialyltransferase (ST) enzymes. Because of the high prevalence of metastasizing tumors with biological behavior similar to the human counterpart, female dogs with metastasizing neoplasms could provide a good animal model for investigating the potential roles of sialic acid (Sia) and ST enzymes in the pathogenesis of metastatic tumors. The aims of this study were (1) to validate a solid-phase method based on lectin staining of serum and tissue homogenates to investigate sialylation and ST activity and (2) to compare the results obtained with this method and with lectin staining and to collect preliminary information on sialylation and ST activity in dogs with (n = 8) and without (n = 8) mammary tumors. The data recorded in healthy dogs revealed that serum and tissue glycoproteins are prevalently characterized by a α2,6 sialylation, but ST-α2,3 seems to be the most active enzyme in both samples. Sia-α2,3 and ST-α2,3 activity decreases in serum and tissues of dogs with tumors, especially in a dog with metastasis, suggesting that the equilibrium between ST-α2,6 and ST-α2,3 activity shifts toward the former, as reported in humans.

Machine learning methods predict locomotor response to MK-801 in mouse models of down syndrome

Down syndrome (DS), caused by trisomy of human chromosome 21 (HSA21), is a common genetic cause of cognitive impairment. This disorder results from the overexpression of HSA21 genes and the resulting perturbations in many molecular pathways and cellular processes. Knowledge-based identification of targets for pharmacotherapies will require defining the most critical protein abnormalities among these many perturbations. Here the authors show that using the Ts65Dn and Ts1Cje mouse models of DS, which are trisomic for 88 and 69 reference protein coding genes, respectively, a simple linear Naïve Bayes classifier successfully predicts behavioral outcome (level of locomotor activity) in response to treatment with the N-methyl-d-aspartate (NMDA) receptor antagonist MK-801. Input to the Naïve Bayes method were simple protein profiles generated from cortex and output was locomotor activity binned into three levels: low, medium, and high. When Feature Selection was used with the Naïve Bayes method, levels of three HSA21 and two non-HSA21 protein features were identified as making the most significant contributions to activity level. Using these five features, accuracies of up to 88% in prediction of locomotor activity were achieved. These predictions depend not only on genotype-specific differences but also on within-genotype individual variation in levels of molecular and behavioral parameters. With judicious choice of pathways and components, a similar approach may be useful in analysis of more complex behaviors, including those associated with learning and memory, and may facilitate identification of novel targets for pharmacotherapeutics.

The role of the microenvironment in tumor immune surveillance

The evidence appears compelling that the microenvironment, and associated biological cellular and molecular factors, may contribute to the progression of a variety of tumors. The effects of the microenvironment may directly influence the plasticity of T cell lineages, which was recently discussed (O'Shea & Paul, 2010 [4]). To review the putative role of the microenvironment in modulating the commitment of tumor immune surveillance, we use the model of oral premalignant lesions.