Genome-Scale Discovery of DNA-Methylation Biomarkers for Blood-Based Detection of Colorectal Cancer

Background

There is an increasing demand for accurate biomarkers for early non-invasive colorectal cancer detection. We employed a genome-scale marker discovery method to identify and verify candidate DNA methylation biomarkers for blood-based detection of colorectal cancer.

Methodology/Principal Findings

We used DNA methylation data from 711 colorectal tumors, 53 matched adjacent-normal colonic tissue samples, 286 healthy blood samples and 4,201 tumor samples of 15 different cancer types. DNA methylation data were generated by the Illumina Infinium HumanMethylation27 and the HumanMethylation450 platforms, which determine the methylation status of 27,578 and 482,421 CpG sites respectively. We first performed a multistep marker selection to identify candidate markers with high methylation across all colorectal tumors while harboring low methylation in healthy samples and other cancer types. We then used pre-therapeutic plasma and serum samples from 107 colorectal cancer patients and 98 controls without colorectal cancer, confirmed by colonoscopy, to verify candidate markers. We selected two markers for further evaluation: methylated THBD (THBD-M) and methylated C9orf50 (C9orf50-M). When tested on clinical plasma and serum samples these markers outperformed carcinoembryonic antigen (CEA) serum measurement and resulted in a high sensitive and specific test performance for early colorectal cancer detection.

Conclusions/Significance

Our systematic marker discovery and verification study for blood-based DNA methylation markers resulted in two novel colorectal cancer biomarkers, THBD-M and C9orf50-M. THBD-M in particular showed promising performance in clinical samples, justifying its further optimization and clinical testing.     

Selection of Aptamers for Mature White Adipocytes by Cell SELEX Using Flow Cytometry

Background

Adipose tissue, mainly composed of adipocytes, plays an important role in metabolism by regulating energy homeostasis. Obesity is primarily caused by an abundance of adipose tissue. Therefore, specific targeting of adipose tissue is critical during the treatment of obesity, and plays a major role in overcoming it. However, the knowledge of cell-surface markers specific to adipocytes is limited.

Methods and Results

We applied the CELL SELEX (Systematic Evolution of Ligands by EXponential enrichment) method using flow cytometry to isolate molecular probes for specific recognition of adipocytes. The aptamer library, a mixture of FITC-tagged single-stranded random DNAs, is used as a source for acquiring molecular probes. With the increasing number of selection cycles, there was a steady increase in the fluorescence intensity toward mature adipocytes. Through 12 rounds of SELEX, enriched aptamers showing specific recognition toward mature 3T3-L1 adipocyte cells were isolated. Among these, two aptamers (MA-33 and 91) were able to selectively bind to mature adipocytes with an equilibrium dissociation constant (Kd) in the nanomolar range. These aptamers did not bind to preadipocytes or other cell lines (such as HeLa, HEK-293, or C2C12 cells). Additionally, it was confirmed that MA-33 and 91 can distinguish between mature primary white and primary brown adipocytes.

Conclusions

These selected aptamers have the potential to be applied as markers for detecting mature white adipocytes and monitoring adipogenesis, and could emerge as an important tool in the treatment of obesity.     

Mucosa-Associated Bacterial Diversity in Necrotizing Enterocolitis

Background

Previous studies of infant fecal samples have failed to clarify the role of gut bacteria in the pathogenesis of NEC. We sought to characterize bacterial communities within intestinal tissue resected from infants with and without NEC.

Methods

26 intestinal samples were resected from 19 infants, including 16 NEC samples and 10 non-NEC samples. Bacterial 16S rRNA gene sequences were amplified and sequenced. Analysis allowed for taxonomic identification, and quantitative PCR was used to quantify the bacterial load within samples.

Results

NEC samples generally contained an increased total burden of bacteria. NEC and non-NEC sample sets were both marked by high inter-individual variability and an abundance of opportunistic pathogens. There was no statistically significant distinction between the composition of NEC and non-NEC microbial communities. K-means clustering enabled us to identify several stable clusters, including clusters of NEC and midgut volvulus samples enriched with Clostridium and Bacteroides. Another cluster containing both NEC and non-NEC samples was marked by an abundance of Enterobacteriaceae and decreased diversity among NEC samples.

Conclusions

The results indicate that NEC is a disease without a uniform pattern of microbial colonization, but that NEC is associated with an abundance of strict anaerobes and a decrease in community diversity.     

Relationship between body composition, inflammation and lung function in overweight and obese asthma

Background

The obese-asthma phenotype is not well defined. The aim of this study was to examine both mechanical and inflammatory influences, by comparing lung function with body composition and airway inflammation in overweight and obese asthma.

Methods

Overweight and obese (BMI 28-40 kg/m²) adults with asthma (n = 44) completed lung function assessment and underwent full-body dual energy x-ray absorptiometry. Venous blood samples and induced sputum were analysed for inflammatory markers.

Results

In females, android and thoracic fat tissue and total body lean tissue were inversely correlated with expiratory reserve volume (ERV). Conversely in males, fat tissue was not correlated with lung function, however there was a positive association between android and thoracic lean tissue and ERV. Lower body (gynoid and leg) lean tissue was positively associated with sputum %neutrophils in females, while leptin was positively associated with android and thoracic fat tissue in males.

Conclusions

This study suggests that both body composition and inflammation independently affect lung function, with distinct differences between males and females. Lean tissue exacerbates the obese-asthma phenotype in females and the mechanism responsible for this finding warrants further investigation.     

An Investigation into the Protein Composition of the Teneral Glossina morsitans morsitans Peritrophic Matrix

Background

Tsetse flies serve as biological vectors for several species of African trypanosomes. In order to survive, proliferate and establish a midgut infection, trypanosomes must cross the tsetse fly peritrophic matrix (PM), which is an acellular gut lining surrounding the blood meal. Crossing of this multi-layered structure occurs at least twice during parasite migration and development, but the mechanism of how trypanosomes do so is not understood. In order to better comprehend the molecular events surrounding trypanosome penetration of the tsetse PM, a mass spectrometry-based approach was applied to investigate the PM protein composition using Glossina morsitans morsitans as a model organism.

Methods

PMs from male teneral (young, unfed) flies were dissected, solubilised in urea/SDS buffer and the proteins precipitated with cold acetone/TCA. The PM proteins were either subjected to an in-solution tryptic digestion or fractionated on 1D SDS-PAGE, and the resulting bands digested using trypsin. The tryptic fragments from both preparations were purified and analysed by LC-MS/MS.

Results

Overall, nearly 300 proteins were identified from both analyses, several of those containing signature Chitin Binding Domains (CBD), including novel peritrophins and peritrophin-like glycoproteins, which are essential in maintaining PM architecture and may act as trypanosome adhesins. Furthermore, 27 proteins from the tsetse secondary endosymbiont, Sodalis glossinidius, were also identified, suggesting this bacterium is probably in close association with the tsetse PM.

Conclusion

To our knowledge this is the first report on the protein composition of teneral G. m. morsitans, an important vector of African trypanosomes. Further functional analyses of these proteins will lead to a better understanding of the tsetse physiology and may help identify potential molecular targets to block trypanosome development within the tsetse.     

Global analysis of circulating immune cells by matrix-assisted laser desorption ionization time-of-flight mass spectrometry

Background

MALDI-TOF mass spectrometry is currently used in microbiological diagnosis to characterize bacterial populations. Our aim was to determine whether this technique could be applied to intact eukaryotic cells, and in particular, to cells involved in the immune response.

Methodology/Principal Findings

A comparison of frozen monocytes, T lymphocytes and polymorphonuclear leukocytes revealed specific peak profiles. We also found that twenty cell types had specific profiles, permitting the establishment of a cell database. The circulating immune cells, namely monocytes, T lymphocytes and polymorphonuclear cells, were distinct from tissue immune cells such as monocyte-derived macrophages and dendritic cells. In addition, MALDI-TOF mass spectrometry was valuable to easily identify the signatures of monocytes and T lymphocytes in peripheral mononuclear cells.

Conclusions/Significance

This method was rapid and easy to perform, and unlike flow cytometry, it did not require any additional components such as specific antibodies. The MALDI-TOF mass spectrometry approach could be extended to analyze the cell composition of tissues and the activation state of immune cells.     

Critical comparison of sample preparation strategies for shotgun proteomic analysis of formalin-fixed,paraffin-embedded samples: insights from liver tissue

Background

The growing field of formalin-fixed paraffin-embedded (FFPE) tissue proteomics holds promise for improving translational research. Direct tissue trypsinization (DT) and protein extraction followed by in solution digestion (ISD) or filter-aided sample preparation (FASP) are the most common workflows for shotgun analysis of FFPE samples, but a critical comparison of the different methods is currently lacking.

Experimental design

DT, FASP and ISD workflows were compared by subjecting to the same label-free quantitative approach three independent technical replicates of each method applied to FFPE liver tissue. Data were evaluated in terms of method reproducibility and protein/peptide distribution according to localization, MW, pI and hydrophobicity.

Results

DT showed lower reproducibility, good preservation of high-MW proteins, a general bias towards hydrophilic and acidic proteins, much lower keratin contamination, as well as higher abundance of non-tryptic peptides. Conversely, FASP and ISD proteomes were depleted in high-MW proteins and enriched in hydrophobic and membrane proteins; FASP provided higher identification yields, while ISD exhibited higher reproducibility.

Conclusions

These results highlight that diverse sample preparation strategies provide significantly different proteomic information, and present typical biases that should be taken into account when dealing with FFPE samples. When a sufficient amount of tissue is available, the complementary use of different methods is suggested to increase proteome coverage and depth.     

Methylcap-seq reveals novel DNA methylation markers for the diagnosis and recurrence prediction of bladder cancer in a Chinese population

Purpose

There is a need to supplement or supplant the conventional diagnostic tools, namely, cystoscopy and B-type ultrasound, for bladder cancer (BC). We aimed to identify novel DNA methylation markers for BC through genome-wide profiling of BC cell lines and subsequent methylation-specific PCR (MSP) screening of clinical urine samples.

Experimental Design

The methyl-DNA binding domain (MBD) capture technique, methylCap/seq, was performed to screen for specific hypermethylated CpG islands in two BC cell lines (5637 and T24). The top one hundred hypermethylated targets were sequentially screened by MSP in urine samples to gradually narrow the target number and optimize the composition of the diagnostic panel. The diagnostic performance of the obtained panel was evaluated in different clinical scenarios.

Results

A total of 1,627 hypermethylated promoter targets in the BC cell lines was identified by Illumina sequencing. The top 104 hypermethylated targets were reduced to eight genes (VAX1, KCNV1, ECEL1, TMEM26, TAL1, PROX1, SLC6A20, and LMX1A) after the urine DNA screening in a small sample size of 8 normal control and 18 BC subjects. Validation in an independent sample of 212 BC patients enabled the optimization of five methylation targets, including VAX1, KCNV1, TAL1, PPOX1, and CFTR, which was obtained in our previous study, for BC diagnosis with a sensitivity and specificity of 88.68% and 87.25%, respectively. In addition, the methylation of VAX1 and LMX1A was found to be associated with BC recurrence.

Conclusions

We identified a promising diagnostic marker panel for early non-invasive detection and subsequent BC surveillance.     

Quantification of Normal Cell Fraction and Copy Number Neutral LOH in Clinical Lung Cancer Samples Using SNP Array Data

Background

Technologies based on DNA microarrays have the potential to provide detailed information on genomic aberrations in tumor cells. In practice a major obstacle for quantitative detection of aberrations is the heterogeneity of clinical tumor tissue. Since tumor tissue invariably contains genetically normal stromal cells, this may lead to a failure to detect aberrations in the tumor cells.

Principal Finding

Using SNP array data from 44 non-small cell lung cancer samples we have developed a bioinformatic algorithm that accurately models the fractions of normal and tumor cells in clinical tumor samples. The proportion of normal cells in combination with SNP array data can be used to detect and quantify copy number neutral loss-of-heterozygosity (CNNLOH) in the tumor cells both in crude tumor tissue and in samples enriched for tumor cells by laser capture microdissection.

Conclusion

Genome-wide quantitative analysis of CNNLOH using the CNNLOH Quantifier method can help to identify recurrent aberrations contributing to tumor development in clinical tumor samples. In addition, SNP-array based analysis of CNNLOH may become important for detection of aberrations that can be used for diagnostic and prognostic purposes.     

Methylome analysis identifies a Wilms tumor epigenetic biomarker detectable in blood

Background

Wilms tumor is the most common pediatric renal malignancy and there is a clinical need for a molecular biomarker to assess treatment response and predict relapse. The known mutated genes in this tumor type show low mutation frequencies, whereas aberrant methylation at 11p15 is by far the most common aberration. We therefore analyzed the epigenome, rather than the genome, to identify ubiquitous tumor-specific biomarkers.

Results

Methylome analysis of matched normal kidney and Wilms tumor identifies 309 preliminary methylation variable positions which we translate into three differentially methylated regions (DMRs) for use as tumor-specific biomarkers. Using two novel algorithms we show that these three DMRs are not confounded by cell type composition. We further show that these DMRs are not methylated in embryonic blastema but are intermediately methylated in Wilms tumor precursor lesions. We validate the biomarker DMRs using two independent sample sets of normal kidney and Wilms tumor and seven Wilms tumor histological subtypes, achieving 100% and 98% correct classification, respectively. As proof-of-principle for clinical utility, we successfully use biomarker DMR-2 in a pilot analysis of cell-free circulating DNA to monitor tumor response during treatment in ten patients.

Conclusions

These findings define the most common methylated regions in Wilms tumor known to date which are not associated with their embryonic origin or precursor stage. We show that this tumor-specific methylated DNA is released into the blood circulation where it can be detected non-invasively showing potential for clinical utility.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-014-0434-y) contains supplementary material, which is available to authorized users.     

4D super-resolution microscopy with conventional fluorophores and single wavelength excitation in optically thick cells and tissues

Background

Optical super-resolution imaging of fluorescently stained biological samples is rapidly becoming an important tool to investigate protein distribution at the molecular scale. It is therefore important to develop practical super-resolution methods that allow capturing the full three-dimensional nature of biological systems and also can visualize multiple protein species in the same sample.

Methodology/Principal Findings

We show that the use of a combination of conventional near-infrared dyes, such as Alexa 647, Alexa 680 and Alexa 750, all excited with a 671 nm diode laser, enables 3D multi-colour super-resolution imaging of complex biological samples. Optically thick samples, including human tissue sections, cardiac rat myocytes and densely grown neuronal cultures were imaged with lateral resolutions of ∼15 nm (std. dev.) while reducing marker cross-talk to <1%. Using astigmatism an axial resolution of ∼65 nm (std. dev.) was routinely achieved. The number of marker species that can be distinguished depends on the mean photon number of single molecule events. With the typical photon yields from Alexa 680 of ∼2000 up to 5 markers may in principle be resolved with <2% crosstalk.

Conclusions/Significance

Our approach is based entirely on the use of conventional, commercially available markers and requires only a single laser. It provides a very straightforward way to investigate biological samples at the nanometre scale and should help establish practical 4D super-resolution microscopy as a routine research tool in many laboratories.     

Increasing the Number of Thyroid Lesions Classes in Microarray Analysis Improves the Relevance of Diagnostic Markers

Background

Genetic markers for thyroid cancers identified by microarray analysis have offered limited predictive accuracy so far because of the few classes of thyroid lesions usually taken into account. To improve diagnostic relevance, we have simultaneously analyzed microarray data from six public datasets covering a total of 347 thyroid tissue samples representing 12 histological classes of follicular lesions and normal thyroid tissue. Our own dataset, containing about half the thyroid tissue samples, included all categories of thyroid lesions.

Methodology/Principal Findings

Classifier predictions were strongly affected by similarities between classes and by the number of classes in the training sets. In each dataset, sample prediction was improved by separating the samples into three groups according to class similarities. The cross-validation of differential genes revealed four clusters with functional enrichments. The analysis of six of these genes (APOD, APOE, CLGN, CRABP1, SDHA and TIMP1) in 49 new samples showed consistent gene and protein profiles with the class similarities observed. Focusing on four subclasses of follicular tumor, we explored the diagnostic potential of 12 selected markers (CASP10, CDH16, CLGN, CRABP1, HMGB2, ALPL2, ADAMTS2, CABIN1, ALDH1A3, USP13, NR2F2, KRTHB5) by real-time quantitative RT-PCR on 32 other new samples. The gene expression profiles of follicular tumors were examined with reference to the mutational status of the Pax8-PPARγ, TSHR, GNAS and NRAS genes.

Conclusion/Significance

We show that diagnostic tools defined on the basis of microarray data are more relevant when a large number of samples and tissue classes are used. Taking into account the relationships between the thyroid tumor pathologies, together with the main biological functions and pathways involved, improved the diagnostic accuracy of the samples. Our approach was particularly relevant for the classification of microfollicular adenomas.     

MALDI Profiling of Human Lung Cancer Subtypes

Background

Proteomics is expected to play a key role in cancer biomarker discovery. Although it has become feasible to rapidly analyze proteins from crude cell extracts using mass spectrometry, complex sample composition hampers this type of measurement. Therefore, for effective proteome analysis, it becomes critical to enrich samples for the analytes of interest. Despite that one-third of the proteins in eukaryotic cells are thought to be phosphorylated at some point in their life cycle, only a low percentage of intracellular proteins is phosphorylated at a given time.

Methodology/Principal Findings

In this work, we have applied chromatographic phosphopeptide enrichment techniques to reduce the complexity of human clinical samples. A novel method for high-throughput peptide profiling of human tumor samples, using Parallel IMAC and MALDI-TOF MS, is described. We have applied this methodology to analyze human normal and cancer lung samples in the search for new biomarkers. Using a highly reproducible spectral processing algorithm to produce peptide mass profiles with minimal variability across the samples, lineal discriminant-based and decision tree–based classification models were generated. These models can distinguish normal from tumor samples, as well as differentiate the various non–small cell lung cancer histological subtypes.

Conclusions/Significance

A novel, optimized sample preparation method and a careful data acquisition strategy is described for high-throughput peptide profiling of small amounts of human normal lung and lung cancer samples. We show that the appropriate combination of peptide expression values is able to discriminate normal lung from non-small cell lung cancer samples and among different histological subtypes. Our study does emphasize the great potential of proteomics in the molecular characterization of cancer.     

Messina: A Novel Analysis Tool to Identify Biologically Relevant Molecules in Disease

Background

Morphologically similar cancers display heterogeneous patterns of molecular aberrations and follow substantially different clinical courses. This diversity has become the basis for the definition of molecular phenotypes, with significant implications for therapy. Microarray or proteomic expression profiling is conventionally employed to identify disease-associated genes, however, traditional approaches for the analysis of profiling experiments may miss molecular aberrations which define biologically relevant subtypes.

Methodology/Principal Findings

Here we present Messina, a method that can identify those genes that only sometimes show aberrant expression in cancer. We demonstrate with simulated data that Messina is highly sensitive and specific when used to identify genes which are aberrantly expressed in only a proportion of cancers, and compare Messina to contemporary analysis techniques. We illustrate Messina by using it to detect the aberrant expression of a gene that may play an important role in pancreatic cancer.

Conclusions/Significance

Messina allows the detection of genes with profiles typical of markers of molecular subtype, and complements existing methods to assist the identification of such markers. Messina is applicable to any global expression profiling data, and to allow its easy application has been packaged into a freely-available stand-alone software package.     

Gene Expression Profiling Unravels Cancer-Related Hepatic Molecular Signatures in Steatohepatitis but Not in Steatosis

Background

Pathogenesis and factors for determining progression of alcoholic and non-alcoholic steatosis to steatohepatitis with risk of further progression to liver cirrhosis and cancer are poorly understood. In the present study, we aimed to identify potential molecular signatures for discrimination of steatohepatitis from steatosis.

Methodology and Results

Global microarray gene expression analysis was applied to unravel differentially expressed genes between steatohepatitis compared to steatosis and control samples. For functional annotation as well as the identification of disease-relevant biological processes of the differentially expressed genes the gene ontology (GO) database was used. Selected candidate genes (n = 46) were validated in 87 human liver samples from two sample cohorts by quantitative real-time PCR (qRT-PCR). The GO analysis revealed that genes down-regulated in steatohepatitis were mainly involved in metabolic processes. Genes up-regulated in steatohepatitis samples were associated with cancer progression and proliferation. In surgical liver resection samples, 39 genes and in percutaneous liver biopsies, 30 genes were significantly up-regulated in steatohepatitis. Furthermore, immunohistochemical investigation of human liver tissue revealed a significant increase of AKR1B10 protein expression in steatohepatitis.

Conclusions

The development of steatohepatitis is characterized by distinct molecular changes. The most striking examples in this respect were KRT23 and AKR1B10, which we found to be highly differentially expressed in steatohepatitis compared to steatosis and normal liver. We propose that KRT23 and AKR1B10 may serve as future potential biomarkers for steatohepatitis as well as markers for progression to HCC.     

Identification of microRNAs as potential prognostic markers in ependymoma

Introduction

We have examined expression of microRNAs (miRNAs) in ependymomas to identify molecular markers of value for clinical management. miRNAs are non-coding RNAs that can block mRNA translation and affect mRNA stability. Changes in the expression of miRNAs have been correlated with many human cancers.

Materials and Methods

We have utilized TaqMan Low Density Arrays to evaluate the expression of 365 miRNAs in ependymomas and normal brain tissue. We first demonstrated the similarity of expression profiles of paired frozen tissue (FT) and paraffin-embedded specimens (FFPE). We compared the miRNA expression profiles of 34 FFPE ependymoma samples with 8 microdissected normal brain tissue specimens enriched for ependymal cells. miRNA expression profiles were then correlated with tumor location, histology and other clinicopathological features.

Results

We have identified miRNAs that are over-expressed in ependymomas, such as miR-135a and miR-17-5p, and down-regulated, such as miR-383 and miR-485-5p. We have also uncovered associations between expression of specific miRNAs which portend a worse prognosis. For example, we have identified a cluster of miRNAs on human chromosome 14q32 that is associated with time to relapse. We also found that miR-203 is an independent marker for relapse compared to the parameters that are currently used. Additionally, we have identified three miRNAs (let-7d, miR-596 and miR-367) that strongly correlate to overall survival.

Conclusion

We have identified miRNAs that are differentially expressed in ependymomas compared with normal ependymal tissue. We have also uncovered significant associations of miRNAs with clinical behavior. This is the first report of clinically relevant miRNAs in ependymomas.     

Effects of blood collection conditions on ovarian cancer serum markers

Background

Evaluating diagnostic and early detection biomarkers requires comparing serum protein concentrations among biosamples ascertained from subjects with and without cancer. Efforts are generally made to standardize blood processing and storage conditions for cases and controls, but blood sample collection conditions cannot be completely controlled. For example, blood samples from cases are often obtained from persons aware of their diagnoses, and collected after fasting or in surgery, whereas blood samples from some controls may be obtained in different conditions, such as a clinic visit. By measuring the effects of differences in collection conditions on three different markers, we investigated the potential of these effects to bias validation studies.

Methodology and Principle Findings

We analyzed serum concentrations of three previously studied putative ovarian cancer serum biomarkers–CA 125, Prolactin and MIF–in healthy women, women with ovarian cancer undergoing gynecologic surgery, women undergoing surgery for benign ovary pathology, and women undergoing surgery with pathologically normal ovaries. For women undergoing surgery, a blood sample was collected either in the clinic 1 to 39 days prior to surgery, or on the day of surgery after anesthesia was administered but prior to the surgical procedure, or both. We found that one marker, prolactin, was dramatically affected by collection conditions, while CA 125 and MIF were unaffected. Prolactin levels were not different between case and control groups after accounting for the conditions of sample collection, suggesting that sample ascertainment could explain some or all of the previously reported results about its potential as a biomarker for ovarian cancer.

Conclusions

Biomarker validation studies should use standardized collection conditions, use multiple control groups, and/or collect samples from cases prior to influence of diagnosis whenever feasible to detect and correct for potential biases associated with sample collection.