Expression and genomic profiling of colorectal cancer

Colorectal cancer still represents a paradigm for the elucidation of the cellular, genetic and molecular mechanisms that underly solid tumor initiation, progression to malignancy, and metastasis to distal organ sites. The relative ease with which pathological specimens can be obtained by either surgery or endoscopy from different stages of tumor progression has facilitated the application of omics technologies to allow the genome-wide analysis both at the RNA (gene expression) and DNA (aneuploidy) levels. Here, we have reviewed the multiplicity of studies appeared to date in the scientific literature on the expression and genomic analysis of colorectal cancer, and attempted an integration of the profiling data generated and made available in the public domain. This approach is likely to pinpoint specific chromosomal loci and the corresponding genes which (i) play rate-limiting roles in colorectal cancer, (ii) represent putative diagnostic and prognostic markers for the accurate prediction of clinical outcome and response to treatment, and (iii) encompass potential therapeutic targets. Moreover, cross-species data mining and integration of the human colorectal cancer profiles with those obtained from mouse models of intestinal tumorigenesis will even more contribute to the elucidation of highly conserved pathways and cellular functions underlying malignancy in the GI tract. Notwithstanding the above promises, tumor heterogeneity, limited cohort sizes, and methodological differences among experimental and bioinformatic approaches still poses main obstacles towards the optimal utilization and integration of omics profiles.     

IRX5 prompts genomic instability in colorectal cancer cells

The Iroquois homeobox gene 5 (IRX5), one of the members of the Iroquois homeobox family, has been identified to correlate with worse prognosis in many cancers, including colorectal cancer (CRC). In this study, upregulation of IRX5 revealed a great reduction in the proliferation of CRC colorectal cancer cell line SW480 and DLD-1, which was accompanied by G1/S arrest, increased expression in cyclin E1, P21, and P53 and a decrease in cyclin A2, B1, and D1. Furthermore, IRX5-mediated an increase expression of RH2A protein, the biomarker of DNA damage. Consequently, the SA-β-gal level is higher in IRX5-overexpression cells compared to control ones, which showed elevated DNA damage triggered cellular senescence. Recapitulating the above findings, IRX5 exhibited higher levels of genomic instability. IRX5 may be a perspective target for cancer therapy and it deserves further investigation.     

Single-cell genomic profile-based analysis of tissue differentiation in colorectal cancer

Science China Life Sciences - Colorectal cancer (CRC) progression is associated with cancer cell dedifferentiation and sternness acquisition. Several methods have been developed to identify...     

Integrative genomic data mining for discovery of potential blood-borne biomarkers for early diagnosis of cancer

Background

With the arrival of the postgenomic era, there is increasing interest in the discovery of biomarkers for the accurate diagnosis, prognosis, and early detection of cancer. Blood-borne cancer markers are favored by clinicians, because blood samples can be obtained and analyzed with relative ease. We have used a combined mining strategy based on an integrated cancer microarray platform, Oncomine, and the biomarker module of the Ingenuity Pathways Analysis (IPA) program to identify potential blood-based markers for six common human cancer types.

Methodology/Principal Findings

In the Oncomine platform, the genes overexpressed in cancer tissues relative to their corresponding normal tissues were filtered by Gene Ontology keywords, with the extracellular environment stipulated and a corrected Q value (false discovery rate) cut-off implemented. The identified genes were imported to the IPA biomarker module to separate out those genes encoding putative secreted or cell-surface proteins as blood-borne (blood/serum/plasma) cancer markers. The filtered potential indicators were ranked and prioritized according to normalized absolute Student t values. The retrieval of numerous marker genes that are already clinically useful or under active investigation confirmed the effectiveness of our mining strategy. To identify the biomarkers that are unique for each cancer type, the upregulated marker genes that are in common between each two tumor types across the six human tumors were also analyzed by the IPA biomarker comparison function.

Conclusion/Significance

The upregulated marker genes shared among the six cancer types may serve as a molecular tool to complement histopathologic examination, and the combination of the commonly upregulated and unique biomarkers may serve as differentiating markers for a specific cancer. This approach will be increasingly useful to discover diagnostic signatures as the mass of microarray data continues to grow in the ‘omics’ era.     

Mechanisms of colorectal and lung cancer prevention by vegetables: a genomic approach

Colorectal cancer (CRC) and lung cancer (LC) occur at high incidence, and both can be effectively prevented by dietary vegetable consumption. This makes these two types of cancer highly suitable for elucidating the underlying molecular mechanisms of cancer chemoprevention. Numerous studies have shown that vegetables exert their beneficial effects through various different mechanisms, but effects on the genome level remain mostly unclear. This review evaluates current knowledge on the mechanisms of CRC and LC prevention by vegetables, thereby focusing on the modulation of gene and protein expressions. The majority of the effects found in the colon are changes in the expression of genes and proteins involved in apoptosis, cell cycle, cell proliferation and intracellular defense, in favor of reduced CRC risk. Furthermore, vegetables and vegetable components changed the expression of many more genes and proteins involved in other pathways for which biologic meaning is less clear. The number of studies investigating gene and protein expression changes in the lungs is limited to only a few in vitro and animal studies. Data from these studies show that mostly genes involved in biotransformation, apoptosis and cell cycle regulation are affected. In both colon and lungs, genomewide analyses of gene and protein expression changes by new genomics and proteomics technologies, as well as the investigation of whole vegetables, are few in number. Further studies applying these 'omics' approaches are needed to provide more insights on affected genetic/biologic pathways and, thus, in molecular mechanisms by which different chemopreventive compounds can protect against carcinogenesis. Particularly studies with combinations of phytochemicals and whole vegetables are needed to establish gene expression changes in the colon, but especially in the lungs.     

Network-based cancer genomic data integration for pattern discovery

Background

Next-generation sequencing (NGS) has profoundly changed the approach to genetic/genomic research. Particularly, the clinical utility of NGS in detecting mutations associated with disease risk has contributed to the development of effective therapeutic strategies. Recently, comprehensive analysis of somatic genetic mutations by NGS has also been used as a new approach for controlling the quality of cell substrates for manufacturing biopharmaceuticals. However, the quality evaluation of cell substrates by NGS largely depends on the limit of detection (LOD) for rare somatic mutations. The purpose of this study was to develop a simple method for evaluating the ability of whole-exome sequencing (WES) by NGS to detect mutations with low allele frequency. To estimate the LOD of WES for low-frequency somatic mutations, we repeatedly and independently performed WES of a reference genomic DNA using the same NGS platform and assay design. LOD was defined as the allele frequency with a relative standard deviation (RSD) value of 30% and was estimated by a moving average curve of the relation between RSD and allele frequency.

Results

Allele frequencies of 20 mutations in the reference material that had been pre-validated by droplet digital PCR (ddPCR) were obtained from 5, 15, 30, or 40 G base pair (Gbp) sequencing data per run. There was a significant association between the allele frequencies measured by WES and those pre-validated by ddPCR, whose p-value decreased as the sequencing data size increased. By this method, the LOD of allele frequency in WES with the sequencing data of 15 Gbp or more was estimated to be between 5 and 10%.

Conclusions

For properly interpreting the WES data of somatic genetic mutations, it is necessary to have a cutoff threshold of low allele frequencies. The in-house LOD estimated by the simple method shown in this study provides a rationale for setting the cutoff.

     

Proteomics-based diagnosis of chronic obstructive pulmonary disease: the hunt for new markers

Chronic obstructive pulmonary disease (COPD) is an inflammatory disease characterized by the progressive deterioration of pulmonary function and irreversible airway obstruction. Investigations of the molecular pathogenesis of COPD have not yet provided complete answers to the mechanisms that determine the onset and progression of this illness. Therefore, therapeutic choices are limited and new strategies are needed to prevent, manage and treat this disorder. In particular, the application of complementary approaches, including gel- and liquid chromatography mass spectrometry-based proteomic techniques on sputum and/or bronchoalveolar lavage may provide a better understanding of the proteome differentially expressed by COPD patients in the course of the disease. The identification of appropriate and reliable biomarkers is, thus, an essential step for the diagnostics and treatment of these patients.     

Exosomal surface protein markers in diagnosis of colorectal cancer

Colorectal cancer (CRC) is one of the most common primary malignancies. Early stages of the disease are asymptomatic in the majority of cases, leading to late detection and high mortality. Available noninvasive diagnostic techniques are limited in sensitivity and specificity, and designing new ones is still a pressing problem. Exosomes are membrane-derived microvesicles secreted into human biological fluids and provide a novel way to assess the course of an oncology disease. The review describes the repertoire of exosomal surface biomarkers found in the blood of CRC patients and the prospects of employing multiplexed tests for exosomal markers in early noninvasive diagnosis of cancer.     

Development and preliminary validation of the cancer family impact scale for colorectal cancer

AIM: The aim of our study was to develop a measure of how a family history of colorectal cancer (CRC) affects families from the viewpoint of unaffected family members. METHOD: Using data from 1,285 participants (637 families) in the Johns Hopkins Colon Cancer Genetic Testing study, we developed and validated The Cancer Family Impact Scale (CFIS), an instrument for use in studies investigating relationships among family factors and CRC prevention behaviors when family history is a risk factor. RESULTS: Through exploratory factor analysis (EFA) using a 50% random sample of participants, we identified 5 latent constructs among 18 items: (1) NEGATIVE: negative effects of cancer on the family; (2) POSITIVE: positive effects of cancer on the family; (3) COMMUNICATE: how families communicate about cancer; (4) FLOW: how information about cancer is conveyed in families; and (5) NORM: how individuals react to family norms about cancer. Confirmatory factor analysis (CFA) on the same sample showed the CFIS to have a reasonably good fit (chi(2) = 389.97, degree of freedom (df ) = 122, root mean square error of approximation = 0.06 [0.05-0.07], comparative fit index = 0.90, Tucker-Lewis index = 0.88, goodness of fit index = 0.94), and findings were cross-validated on the remaining 50% of the participants. The reliability of the scale was alpha = 0.65. CONCLUSIONS: The CFIS could be used to clarify the role that family factors play in the association between CRC family history and CRC prevention behaviors, and also aid in the development and evaluation of family-based cancer prevention interventions.     

Faecal microbial biomarkers in early diagnosis of colorectal cancer

Colorectal cancer (CRC) is ranked as the second most common cause of cancer deaths and the third most common cancer globally. It has been described as a ‘silent disease’ which is often easily treatable if detected early—before progression to carcinoma. Colonoscopy, which is the gold standard for diagnosis is not only expensive but is also an invasive diagnostic procedure, thus, effective and non-invasive diagnostic methods are urgently needed. Unfortunately, the current methods are not sensitive and specific enough in detecting adenomas and early colorectal neoplasia, hampering treatment and consequently, survival rates. Studies have shown that imbalances in such a relationship which renders the gut microbiota in a dysbiotic state are implicated in the development of adenomas ultimately resulting in CRC. The differences found in the makeup and diversity of the gut microbiota of healthy individuals relative to CRC patients have in recent times gained attention as potential biomarkers in early non-invasive diagnosis of CRC, with promising sensitivity, specificity and even cost-effectiveness. This review summarizes recent studies in the application of these microbiota biomarkers in early CRC diagnosis, limitations encountered in the area of the faecal microbiota studies as biomarkers for CRC, and future research exploits that address these limitations.     

Shrunken p-values for assessing differential expression with applications to genomic data analysis

In many scientific problems involving high-throughput technology, inference must be made involving several hundreds or thousands of hypotheses. Recent attention has focused on how to address the multiple testing issue; much focus has been devoted toward the use of the false discovery rate. In this article, we consider an alternative estimation procedure titled shrunken p-values for assessing differential expression (SPADE). The estimators are motivated by risk considerations from decision theory and lead to a completely new method for adjustment in the multiple testing problem. In addition, the decision-theoretic framework can be used to derive a decision rule for controlling the number of false positive results. Some theoretical results are outlined. The proposed methodology is illustrated using simulation studies and with application to data from a prostate cancer gene expression profiling study.     

Clustering with exclusion zones: genomic applications

Methods for formally evaluating the clustering of events in space or time, notably the scan statistic, have been richly developed and widely applied. In order to utilize the scan statistic and related approaches, it is necessary to know the extent of the spatial or temporal domains wherein the events arise. Implicit in their usage is that these domains have no "holes"-hereafter "exclusion zones"-regions in which events a priori cannot occur. However, in many contexts, this requirement is not met. When the exclusion zones are known, it is straightforward to correct the scan statistic for their occurrence by simply adjusting the extent of the domain. Here, we tackle the more ambitious objective of formally evaluating clustering in the presence of "unknown" exclusion zones. We develop an algorithm for estimating total exclusion zone extent, the quantity needed to correct scan statistic-based inference, using distributional properties of "spacings," and show how bias correction for this estimator can be effected. Performance of the algorithm is assessed via simulation study. We showcase applications to genomic settings for differing marker (event) types-binding sites, housekeeping genes, and microRNAs-wherein exclusion zones can arise through a variety of mechanisms. In several instances, dramatic changes to unadjusted inference that does not accommodate exclusions are evidenced.     

Novel biotechnology approaches in colorectal cancer diagnosis and therapy

With ever-increasing molecular information about colorectal cancer (CRC), there is an expectation to detect more sensitive and specific molecular markers for new advanced diagnostic methods that can surpass the limitations of current screening tests. Moreover, enhanced molecular pathology knowledge about cancer has led to the development of targeted therapies, designed to interfere with specific aberrant biological pathways in cancer. Furthermore, biotechnology has opened a new window in CRC diagnosis and treatment by introducing different application of antibodies, antibody fragments, non-Ig scaffold proteins, and aptamers in targeted therapy and drug delivery. This review summarizes the molecular diagnostic and therapeutic approaches in CRC with a focus on genetic and epigenetic alterations, protein and metabolite markers as well as targeted therapy and drug delivery by Ig-scaffold proteins, non-Ig scaffold proteins, nanobodies, and aptamers.     

Optimized multilayer perceptrons for molecular classification and diagnosis using genomic data

MOTIVATION: Multilayer perceptrons (MLP) represent one of the widely used and effective machine learning methods currently applied to diagnostic classification based on high-dimensional genomic data. Since the dimensionalities of the existing genomic data often exceed the available sample sizes by orders of magnitude, the MLP performance may degrade owing to the curse of dimensionality and over-fitting, and may not provide acceptable prediction accuracy. RESULTS: Based on Fisher linear discriminant analysis, we designed and implemented an MLP optimization scheme for a two-layer MLP that effectively optimizes the initialization of MLP parameters and MLP architecture. The optimized MLP consistently demonstrated its ability in easing the curse of dimensionality in large microarray datasets. In comparison with a conventional MLP using random initialization, we obtained significant improvements in major performance measures including Bayes classification accuracy, convergence properties and area under the receiver operating characteristic curve (A(z)). SUPPLEMENTARY INFORMATION: The Supplementary information is available on http://www.cbil.ece.vt.edu/publications.htm     

TableView: portable genomic data visualization

TableView is a generalized scientific visualization program for exploration of various biological data, including EST, SAGE, microarray and annotation data. Written in Java, TableView is portable, is easily used together with other software including DBMSs and is versatile enough to be applied to any tabular data AVAILABILITY: TableView is freely available at: http://ccgb.umn.edu/software/java/apps/TableView/.