Tumor Endothelial Inflammation Predicts Clinical Outcome in Diverse Human Cancers

Background

Vascular endothelial cells contribute to the pathogenesis of numerous human diseases by actively regulating the stromal inflammatory response; however, little is known regarding the role of endothelial inflammation in the growth of human tumors and its influence on the prognosis of human cancers.

Methods

Using an experimental model of tumor necrosis factor-alpha (TNF-α)-mediated inflammation, we characterized inflammatory gene expression in immunopurified tumor-associated endothelial cells. These genes formed the basis of a multivariate molecular predictor of overall survival that was trained and validated in four types of human cancer.

Results

We report that expression of experimentally derived tumor endothelial genes distinguished pathologic tissue specimens from normal controls in several human diseases associated with chronic inflammation. We trained these genes in human cancer datasets and defined a six-gene inflammatory signature that predicted significantly reduced overall survival in breast cancer, colon cancer, lung cancer, and glioma. This endothelial-derived signature predicted outcome independently of, but cooperatively with, standard clinical and pathological prognostic factors. Consistent with these findings, conditioned culture media from human endothelial cells stimulated by pro-inflammatory cytokines accelerated the growth of human colon and breast tumors in immunodeficient mice as compared with conditioned media from untreated endothelial cells.

Conclusions

This study provides the first prognostic cancer gene signature derived from an experimental model of tumor-associated endothelial inflammation. These findings support the notion that activation of inflammatory pathways in non-malignant tumor-infiltrating endothelial cells contributes to tumor growth and progression in multiple human cancers. Importantly, these results identify endothelial-derived factors that could serve as potential targets for therapy in diverse human cancers.     

Discovery and Validation of New Potential Biomarkers for Early Detection of Colon Cancer

Background

Accurate detection of characteristic proteins secreted by colon cancer tumor cells in biological fluids could serve as a biomarker for the disease. The aim of the present study was to identify and validate new serum biomarkers and demonstrate their potential usefulness for early diagnosis of colon cancer.

Methods

The study was organized in three sequential phases: 1) biomarker discovery, 2) technical and biological validation, and 3) proof of concept to test the potential clinical use of selected biomarkers. A prioritized subset of the differentially-expressed genes between tissue types (50 colon mucosa from cancer-free individuals and 100 normal-tumor pairs from colon cancer patients) was validated and further tested in a series of serum samples from 80 colon cancer cases, 23 patients with adenoma and 77 cancer-free controls.

Results

In the discovery phase, 505 unique candidate biomarkers were identified, with highly significant results and high capacity to discriminate between the different tissue types. After a subsequent prioritization, all tested genes (N = 23) were successfully validated in tissue, and one of them, COL10A1, showed relevant differences in serum protein levels between controls, patients with adenoma (p = 0.0083) and colon cancer cases (p = 3.2e-6).

Conclusion

We present a sequential process for the identification and further validation of biomarkers for early detection of colon cancer that identifies COL10A1 protein levels in serum as a potential diagnostic candidate to detect both adenoma lesions and tumor.

Impact

The use of a cheap serum test for colon cancer screening should improve its participation rates and contribute to decrease the burden of this disease.     

Cytosolic Malic Enzyme 1 (ME1) Mediates High Fat Diet-Induced Adiposity,Endocrine Profile,and Gastrointestinal Tract Proliferation-Associated Biomarkers in Male Mice

Background

Obesity and associated hormonal disturbances are risk factors for colon cancer. Cytosolic Malic Enzyme (ME1) generates NADPH used for lipogenesis in gastrointestinal (GI), liver and adipose tissues. We have reported that inclusion of soy protein isolate (SPI) in the diet lowered body fat content and colon tumor incidence of rats fed AIN-93G diet, while others have demonstrated SPI inhibition of rat hepatic ME1 expression. The present study examined the individual and combined effects of dietary SPI and absence of ME1 on: 1) serum concentrations of hormones implicated in colon cancer development, 2) expression of lipogenic and proliferation-associated genes in the mouse colon and small intestine, and 3) liver and adipose expression of lipogenic and adipocytokine genes that may contribute to colon cancer predisposition.

Methods

Weanling wild type (WT) and ME1 null (MOD-1) male mice were fed high-fat (HF), iso-caloric diets containing either casein (CAS) or SPI as sole protein source for 5 wks. Somatic growth, serum hormone and glucose levels, liver and adipose tissue weights, GI tissue parameters, and gene expression were evaluated.

Results

The MOD-1 genotype and SPI-HF diet resulted in decreases in: body and retroperitoneal fat weights, serum insulin, serum leptin, leptin/adiponectin ratio, adipocyte size, colon mTOR and cyclin D1 mRNA abundance, and jejunum FASN mRNA abundance, when compared to WT mice fed CAS-HF. Regardless of diet, MOD-1 mice had reductions in liver weight, liver steatosis, and colon crypt depth, and increases in adipose tissue expression of IRS1 and IRS2, compared to WT mice. SPI-HF diet reduced ME1 gene expression only in retroperitoneal fat.

Conclusions

Data suggest that the pharmacological targeting of ME1 or the inclusion of soy protein in the diet may provide avenues to reduce obesity and its associated pro-tumorigenic endocrine environment and improve insulin sensitivity, potentially disrupting the obesity-colon cancer connection.     

Expression Profiling of Mitochondrial Voltage-Dependent Anion Channel-1 Associated Genes Predicts Recurrence-Free Survival in Human Carcinomas

Background

Mitochondrial voltage-dependent anion channels (VDACs) play a key role in mitochondria-mediated apoptosis. Both in vivo and in vitro evidences indicate that VDACs are actively involved in tumor progression. Specifically, VDAC-1, one member of the VDAC family, was thought to be a potential anti-cancer therapeutic target. Our previous study demonstrated that the human gene VDAC1 (encoding the VDAC-1 isoform) was significantly up-regulated in lung tumor tissue compared with normal tissue. Also, we found a significant positive correlation between the gene expression of VDAC1 and histological grade in breast cancer. However, the prognostic power of VDAC1 and its associated genes in human cancers is largely unknown.

Methods

We systematically analyzed the expression pattern of VDAC1 and its interacting genes in breast, colon, liver, lung, pancreatic, and thyroid cancers. The genes differentially expressed between normal and tumor tissues in human carcinomas were identified.

Results

The expression level of VDAC1 was uniformly up-regulated in tumor tissue compared with normal tissue in breast, colon, liver, lung, pancreatic, and thyroid cancers. Forty-four VDAC1 interacting genes were identified as being commonly differentially expressed between normal and tumor tissues in human carcinomas. We designated VDAC1 and the 44 dysregulated interacting genes as the VDAC1 associated gene signature (VAG). We demonstrate that the VAG signature is a robust prognostic biomarker to predict recurrence-free survival in breast, colon, and lung cancers, and is independent of standard clinical and pathological prognostic factors.

Conclusions

VAG represents a promising prognostic biomarker in human cancers, which may enhance prediction accuracy in identifying patients at higher risk for recurrence. Future therapies aimed specifically at VDAC1 associated genes may lead to novel agents in the treatment of cancer.     

Operator Dependent Choice of Prostate Cancer Biopsy Has Limited Impact on a Gene Signature Analysis for the Highly Expressed Genes IGFBP3 and F3 in Prostate Cancer Epithelial Cells

Background

Predicting the prognosis of prostate cancer disease through gene expression analysis is receiving increasing interest. In many cases, such analyses are based on formalin-fixed, paraffin embedded (FFPE) core needle biopsy material on which Gleason grading for diagnosis has been conducted. Since each patient typically has multiple biopsy samples, and since Gleason grading is an operator dependent procedure known to be difficult, the impact of the operator''s choice of biopsy was evaluated.

Methods

Multiple biopsy samples from 43 patients were evaluated using a previously reported gene signature of IGFBP3, F3 and VGLL3 with potential prognostic value in estimating overall survival at diagnosis of prostate cancer. A four multiplex one-step qRT-PCR test kit, designed and optimized for measuring the signature in FFPE core needle biopsy samples was used. Concordance of gene expression levels between primary and secondary Gleason tumor patterns, as well as benign tissue specimens, was analyzed.

Results

The gene expression levels of IGFBP3 and F3 in prostate cancer epithelial cell-containing tissue representing the primary and secondary Gleason patterns were high and consistent, while the low expressed VGLL3 showed more variation in its expression levels.

Conclusion

The assessment of IGFBP3 and F3 gene expression levels in prostate cancer tissue is independent of Gleason patterns, meaning that the impact of operator''s choice of biopsy is low.     

Integration of DNA copy number alterations and transcriptional expression analysis in human gastric cancer

Background

Genomic instability with frequent DNA copy number alterations is one of the key hallmarks of carcinogenesis. The chromosomal regions with frequent DNA copy number gain and loss in human gastric cancer are still poorly defined. It remains unknown how the DNA copy number variations contributes to the changes of gene expression profiles, especially on the global level.

Principal Findings

We analyzed DNA copy number alterations in 64 human gastric cancer samples and 8 gastric cancer cell lines using bacterial artificial chromosome (BAC) arrays based comparative genomic hybridization (aCGH). Statistical analysis was applied to correlate previously published gene expression data obtained from cDNA microarrays with corresponding DNA copy number variation data to identify candidate oncogenes and tumor suppressor genes. We found that gastric cancer samples showed recurrent DNA copy number variations, including gains at 5p, 8q, 20p, 20q, and losses at 4q, 9p, 18q, 21q. The most frequent regions of amplification were 20q12 (7/72), 20q12–20q13.1 (12/72), 20q13.1–20q13.2 (11/72) and 20q13.2–20q13.3 (6/72). The most frequent deleted region was 9p21 (8/72). Correlating gene expression array data with aCGH identified 321 candidate oncogenes, which were overexpressed and showed frequent DNA copy number gains; and 12 candidate tumor suppressor genes which were down-regulated and showed frequent DNA copy number losses in human gastric cancers. Three networks of significantly expressed genes in gastric cancer samples were identified by ingenuity pathway analysis.

Conclusions

This study provides insight into DNA copy number variations and their contribution to altered gene expression profiles during human gastric cancer development. It provides novel candidate driver oncogenes or tumor suppressor genes for human gastric cancer, useful pathway maps for the future understanding of the molecular pathogenesis of this malignancy, and the construction of new therapeutic targets.     

Comparison of Microarray Platforms for Measuring Differential MicroRNA Expression in Paired Normal/Cancer Colon Tissues

Background

Microarray technology applied to microRNA (miRNA) profiling is a promising tool in many research fields; nevertheless, independent studies characterizing the same pathology have often reported poorly overlapping results. miRNA analysis methods have only recently been systematically compared but only in few cases using clinical samples.

Methodology/Principal Findings

We investigated the inter-platform reproducibility of four miRNA microarray platforms (Agilent, Exiqon, Illumina, and Miltenyi), comparing nine paired tumor/normal colon tissues. The most concordant and selected discordant miRNAs were further studied by quantitative RT-PCR. Globally, a poor overlap among differentially expressed miRNAs identified by each platform was found. Nevertheless, for eight miRNAs high agreement in differential expression among the four platforms and comparability to qRT-PCR was observed. Furthermore, most of the miRNA sets identified by each platform are coherently enriched in data from the other platforms and the great majority of colon cancer associated miRNA sets derived from the literature were validated in our data, independently from the platform. Computational integration of miRNA and gene expression profiles suggested that anti-correlated predicted target genes of differentially expressed miRNAs are commonly enriched in cancer-related pathways and in genes involved in glycolysis and nutrient transport.

Conclusions

Technical and analytical challenges in measuring miRNAs still remain and further research is required in order to increase consistency between different microarray-based methodologies. However, a better inter-platform agreement was found by looking at miRNA sets instead of single miRNAs and through a miRNAs – gene expression integration approach.     

GWAS Meets Microarray: Are the Results of Genome-Wide Association Studies and Gene-Expression Profiling Consistent? Prostate Cancer as an Example

Background

Genome-wide association studies (GWASs) and global profiling of gene expression (microarrays) are two major technological breakthroughs that allow hypothesis-free identification of candidate genes associated with tumorigenesis. It is not obvious whether there is a consistency between the candidate genes identified by GWAS (GWAS genes) and those identified by profiling gene expression (microarray genes).

Methodology/Principal Findings

We used the Cancer Genetic Markers Susceptibility database to retrieve single nucleotide polymorphisms from candidate genes for prostate cancer. In addition, we conducted a large meta-analysis of gene expression data in normal prostate and prostate tumor tissue. We identified 13,905 genes that were interrogated by both GWASs and microarrays. On the basis of P values from GWASs, we selected 1,649 most significantly associated genes for functional annotation by the Database for Annotation, Visualization and Integrated Discovery. We also conducted functional annotation analysis using same number of the top genes identified in the meta-analysis of the gene expression data. We found that genes involved in cell adhesion were overrepresented among both the GWAS and microarray genes.

Conclusions/Significance

We conclude that the results of these analyses suggest that combining GWAS and microarray data would be a more effective approach than analyzing individual datasets and can help to refine the identification of candidate genes and functions associated with tumor development.     

Silencing of DLGAP5 by siRNA Significantly Inhibits the Proliferation and Invasion of Hepatocellular Carcinoma Cells

Background

The dysregulation of oncogenes and tumor suppressor genes plays an important role in many cancers, including hepatocellular carcinoma (HCC), which is one of the most common cancers in the world. In a previous microarray experiment, we found that DLGAP5 is overexpressed in HCCs. However, whether the up-regulation of DLGAP5 contributes to hepatocarcinogenesis remains unclear.

Methodology/Principal Findings

In this study, we showed that DLGAP5 was significantly up-regulated in 76.4% (168 of 220) of the analyzed HCC specimens when compared with adjacent liver tissue. DLGAP5 overexpression was evident in 25% (22 of 88) of the HCC specimens without AFP expression, suggesting that DLGAP5 may be a novel biomarker for HCC pathogenesis. The silencing of DLGAP5 gene expression by RNA interference significantly suppressed cell growth, migration and colony formation in vitro. The expression level of DLGAP5 was also found to be related to the methylation level of its promoter in the HCC specimens.

Conclusions/Significance

Taken together, these data suggest that the expression of DLGAP5 is regulated by methylation and that the up-regulation of DLGAP5 contributes to HCC tumorigenesis by promoting cell proliferation.     

GTI: a novel algorithm for identifying outlier gene expression profiles from integrated microarray datasets

Background

Meta-analysis of gene expression microarray datasets presents significant challenges for statistical analysis. We developed and validated a new bioinformatic method for the identification of genes upregulated in subsets of samples of a given tumour type (‘outlier genes’), a hallmark of potential oncogenes.

Methodology

A new statistical method (the gene tissue index, GTI) was developed by modifying and adapting algorithms originally developed for statistical problems in economics. We compared the potential of the GTI to detect outlier genes in meta-datasets with four previously defined statistical methods, COPA, the OS statistic, the t-test and ORT, using simulated data. We demonstrated that the GTI performed equally well to existing methods in a single study simulation. Next, we evaluated the performance of the GTI in the analysis of combined Affymetrix gene expression data from several published studies covering 392 normal samples of tissue from the central nervous system, 74 astrocytomas, and 353 glioblastomas. According to the results, the GTI was better able than most of the previous methods to identify known oncogenic outlier genes. In addition, the GTI identified 29 novel outlier genes in glioblastomas, including TYMS and CDKN2A. The over-expression of these genes was validated in vivo by immunohistochemical staining data from clinical glioblastoma samples. Immunohistochemical data were available for 65% (19 of 29) of these genes, and 17 of these 19 genes (90%) showed a typical outlier staining pattern. Furthermore, raltitrexed, a specific inhibitor of TYMS used in the therapy of tumour types other than glioblastoma, also effectively blocked cell proliferation in glioblastoma cell lines, thus highlighting this outlier gene candidate as a potential therapeutic target.

Conclusions/Significance

Taken together, these results support the GTI as a novel approach to identify potential oncogene outliers and drug targets. The algorithm is implemented in an R package (Text S1).     

Ubiquitin ligase A20 regulates p53 protein in human colon epithelial cells

Background

Intestinal polyps may further develop into colon cancer; the pathogenesis is not clear. The p53 gene is an important anti-cancer gene in the body, which is suppressed in cancer. The ubiquitin E3 ligase A20 (A20) plays a role in regulating the activities of epithelial cells. This study was designed to investigate the role of the colon polyp epithelium-derived A20 in the pathogenesis of colon cancer.

Results

Eighty-eight colon cancer patients and 136 colon polyp patients were recruited into this study. Human colon cancer tissue, the epithelium of adenomas polyp and hyperplastic polyp showed high levels of A20, which had a positive correlation with the cancerous tendency of colon polyps. The levels of A20 were much higher in the adenomas and hyperplastic polyps than that in the inflammatory polyps; the latter showed less cancerous tendency. A20 bound p53 to form complexes in colon cancer tissue and colon polyps. Over expression of A20 suppresses P53 protein levels in the HEK293 cells.

Conclusions

A20 may play an important role in the cancerous tendency of colon polyposis.     

Cancer gene prioritization for targeted resequencing using FitSNP scores

Background

Although the throughput of next generation sequencing is increasing and at the same time the cost is substantially reduced, for the majority of laboratories whole genome sequencing of large cohorts of cancer samples is still not feasible. In addition, the low number of genomes that are being sequenced is often problematic for the downstream interpretation of the significance of the variants. Targeted resequencing can partially circumvent this problem; by focusing on a limited number of candidate cancer genes to sequence, more samples can be included in the screening, hence resulting in substantial improvement of the statistical power. In this study, a successful strategy for prioritizing candidate genes for targeted resequencing of cancer genomes is presented.

Results

Four prioritization strategies were evaluated on six different cancer types: genes were ranked using these strategies, and the positive predictive value (PPV) or mutation rate within the top-ranked genes was compared to the baseline mutation rate in each tumor type. Successful strategies generate gene lists in which the top is enriched for known mutated genes, as evidenced by an increase in PPV. A clear example of such an improvement is seen in colon cancer, where the PPV is increased by 2.3 fold compared to the baseline level when 100 top fitSNP genes are sequenced.

Conclusions

A gene prioritization strategy based on the fitSNP scores appears to be most successful in identifying mutated cancer genes across different tumor entities, with variance of gene expression levels as a good second best.     

Profiling Critical Cancer Gene Mutations in Clinical Tumor Samples

Background

Detection of critical cancer gene mutations in clinical tumor specimens may predict patient outcomes and inform treatment options; however, high-throughput mutation profiling remains underdeveloped as a diagnostic approach. We report the implementation of a genotyping and validation algorithm that enables robust tumor mutation profiling in the clinical setting.

Methodology

We developed and implemented an optimized mutation profiling platform (“OncoMap”) to interrogate ∼400 mutations in 33 known oncogenes and tumor suppressors, many of which are known to predict response or resistance to targeted therapies. The performance of OncoMap was analyzed using DNA derived from both frozen and FFPE clinical material in a diverse set of cancer types. A subsequent in-depth analysis was conducted on histologically and clinically annotated pediatric gliomas. The sensitivity and specificity of OncoMap were 93.8% and 100% in fresh frozen tissue; and 89.3% and 99.4% in FFPE-derived DNA. We detected known mutations at the expected frequencies in common cancers, as well as novel mutations in adult and pediatric cancers that are likely to predict heightened response or resistance to existing or developmental cancer therapies. OncoMap profiles also support a new molecular stratification of pediatric low-grade gliomas based on BRAF mutations that may have immediate clinical impact.

Conclusions

Our results demonstrate the clinical feasibility of high-throughput mutation profiling to query a large panel of “actionable” cancer gene mutations. In the future, this type of approach may be incorporated into both cancer epidemiologic studies and clinical decision making to specify the use of many targeted anticancer agents.