Human Cancer Datasets Research Articles

The activated transforming growth factor‐beta signaling pathway in peritoneal metastases is a potential therapeutic target in ovarian cancer

Peritoneal dissemination including omental metastasis is the most frequent route of metastasis and an important prognostic factor in advanced ovarian cancer. We analyzed the publicly available microarray dataset (GSE2109) using binary regression and found that the transforming growth factor (TGF)-beta signaling pathway was activated in omental metastases as compared to primary sites of disease. Immunohistochemical analysis of TGF-beta receptor type 2 and phosphorylated SMAD2 indicated that both were upregulated in omental metastases as compared to primary disease sites. Treatment of the mouse ovarian cancer cell line HM-1 with recombinant TGF-β1 promoted invasiveness, cell motility and cell attachment while these were suppressed by treatment with A-83-01, an inhibitor of the TGF-β signaling pathway. Microarray analysis of HM-1 cells treated with TGF-β1 and/or A-83-01 revealed that A-83-01 efficiently inhibited transcriptional changes that are induced by TGF-β1. Using gene set enrichment analysis, we found that genes upregulated by TGF-β1 in HM-1 cells were also significantly upregulated in omental metastases compared to primary sites in the human ovarian cancer dataset, GSE2109 (false discovery rate (FDR) q = 0.086). Therapeutic effects of A-83-01 in a mouse model of peritoneal dissemination were examined. Intraperitoneal injection of A-83-01 (150 μg given three times weekly) significantly improved survival (p = 0.015). In summary, these results show that the activated TGF-β signaling pathway in peritoneal metastases is a potential therapeutic target in ovarian cancer.

BARTMAP: A viable structure for biclustering

Clustering has been used extensively in the analysis of high-throughput messenger RNA (mRNA) expression profiling with microarrays. Furthermore, clustering has proven elemental in microRNA expression profiling, which demonstrates enormous promise in the areas of cancer diagnosis and treatment, gene function identification, therapy development and drug testing, and genetic regulatory network inference. However, such a practice is inherently limited due to the existence of many uncorrelated genes with respect to sample or condition clustering, or many unrelated samples or conditions with respect to gene clustering. Biclustering offers a solution to such problems by performing simultaneous clustering on both dimensions, or automatically integrating feature selection to clustering without any prior information, so that the relations of clusters of genes (generally, features) and clusters of samples or conditions (data objects) are established. However, the NP-complete computational complexity raises a great challenge to computational methods for identifying such local relations. Here, we propose and demonstrate that a neural-based classifier, ARTMAP, can be modified to perform biclustering in an efficient way, leading to a biclustering algorithm called Biclustering ARTMAP (BARTMAP). Experimental results on multiple human cancer data sets show that BARTMAP can achieve clustering structures with higher qualities than those achieved with other commonly used biclustering or clustering algorithms, and with fast run times.

Ranking analysis of correlation coefficients in gene expressions

Development of statistical methods has become very necessary for large-scale correlation analysis in the current “omic” data. We propose ranking analysis of correlation coefficients (RAC) based on transforming correlation matrix into correlation vector and conducting a “locally ranking” strategy that significantly reduces computational complexity and load. RAC gives estimation of null correlation distribution and an estimator of false discovery rate (FDR) for finding gene pairs of being correlated in expressions obtained by comparison between the ranked observed correlation coefficients and the ranked estimated ones at a given threshold level. The simulated and real data show that the estimated null correlation distribution is exactly the same with the true one and the FDR estimator works well in various scenarios. By applying our RAC, in the null dataset, no gene pairs were found but, in the human cancer dataset, 837 gene pairs were found to have positively correlated expression variations at FDR ≤ 5%. RAC performs well in multiple conditions (classes), each with 3 or more replicate observations.

Expression Signature Developed from a Complex Series of Mouse Models Accurately Predicts Human Breast Cancer Survival

The capability of microarray platform to interrogate thousands of genes has led to the development of molecular diagnostic tools for cancer patients. Although large-scale comparative studies on clinical samples are often limited by the access of human tissues, expression profiling databases of various human cancer types are publicly available for researchers. Given that mouse models have been instrumental to our current understanding of cancer progression, we aimed to test the hypothesis that novel gene signatures possessing predictability in clinical outcome can be derived by coupling genomic analyses in mouse models of cancer with publicly available human cancer data sets. We established a complex series of syngeneic metastatic animal models using a murine breast cancer cell line. Tumor RNA was hybridized on Affymetrix MouseGenome-430A2.0 GeneChips. With the use of Venn logic, gene signatures that represent metastatic competency were derived and tested against publicly available human breast and lung cancer data sets. Survival analyses showed that the spontaneous metastasis gene signature was significantly associated with metastasis-free and overall survival (P < 0.0005). Consequently, the six-gene model was determined and showed statistical predictability in predicting survival in breast cancer patients. In addition, the model was able to stratify poor from good prognosis for lung cancer patients in most data sets analyzed. Together, our data support that novel gene signature derived from mouse models of cancer can be used for predicting human cancer outcome. Our approaches set precedence that similar strategies may be used to decipher novel gene signatures for clinical utility.

Biomarker detection in the integration of multiple multi-class genomic studies

Systematic information integration of multiple-related microarray studies has become an important issue as the technology becomes mature and prevalent in the past decade. The aggregated information provides more robust and accurate biomarker detection. So far, published meta-analysis methods for this purpose mostly consider two-class comparison. Methods for combining multi-class studies and considering expression pattern concordance are rarely explored. In this article, we develop three integration methods for biomarker detection in multiple multi-class microarray studies: ANOVA-maxP, min-MCC and OW-min-MCC. We first consider a natural extension of combining P-values from the traditional ANOVA model. Since P-values from ANOVA do not guarantee to reflect the concordant expression pattern information across studies, we propose a multi-class correlation (MCC) measure to specifically seek for biomarkers of concordant inter-class patterns across a pair of studies. For both ANOVA and MCC approaches, we use extreme order statistics to identify biomarkers differentially expressed (DE) in all studies (i.e. ANOVA-maxP and min-MCC). The min-MCC method is further extended to identify biomarkers DE in partial studies by incorporating a recently developed optimally weighted (OW) technique (OW-min-MCC). All methods are evaluated by simulation studies and by three meta-analysis applications to multi-tissue mouse metabolism datasets, multi-condition mouse trauma datasets and multi-malignant-condition human prostate cancer datasets. The results show complementary strength of the three methods for different biological purposes. http://www.biostat.pitt.edu/bioinfo/. Supplementary data is available at Bioinformatics online.

Gene Expression Data Analysis Using a Novel Approach to Biclustering Combining Discrete and Continuous Data

Many different methods exist for pattern detection in gene expression data. In contrast to classical methods, biclustering has the ability to cluster a group of genes together with a group of conditions (replicates, set of patients or drug compounds). However, since the problem is NP-complex, most algorithms use heuristic search functions and therefore might converge towards local maxima. By using the results of biclustering on discrete data as a starting point for a local search function on continuous data, our algorithm avoids the problem of heuristic initialization. Similar to OPSM, our algorithm aims to detect biclusters whose rows and columns can be ordered such that row values are growing across the bicluster's columns and vice-versa. Results have been generated on the yeast genome (Saccharomyces cerevisiae), a human cancer dataset and random data. Results on the yeast genome showed that 89% of the one hundred biggest non-overlapping biclusters were enriched with Gene Ontology annotations. A comparison with OPSM and ISA demonstrated a better efficiency when using gene and condition orders. We present results on random and real datasets that show the ability of our algorithm to capture statistically significant and biologically relevant biclusters.

A genomic strategy for targeting therapy in castration-Independent Prostate Cancer

5067 Background: Response to secondary hormonal manipulations and cytotoxic therapy is variable in men with castration resistant prostate cancer (CRPC). Androgen receptor (AR) signaling persists in CRPC but there are no established biomarkers which facilitate the choice between continued hormonal manipulations or initiation of cytotoxic therapy in CRPC. Methods: A transcription-based AR activity signature was developed using established analytic methods from an androgen-sensitive prostate cancer cell line (LNCaP). The AR signature assigns a probability of AR activity to individual samples (range 0 to 1) and was applied to independent datasets of LNCaP cells and human prostate samples with known androgen status. The AR signature was subsequently applied to multiple human prostate cancer datasets to determine if AR activity in individual tumor specimens correlates with clinical and molecular phenotypes. Results: A robust expression-based signature of AR activity can accurately predict the AR activity status of independent LNCaP samples, anticipates the hormonal status of individuals (castrate v non-castrate), and assigns an AR activity score that correlates with intraprostatic androgen levels in human biopsy specimens (two-tail Spearman p=0.022 for dihydrotestosterone). The mean difference between AR activity between replicates was minimal (5%) and intra-individual consistency across different metastatic sites was also consistent. In two independent datasets, AR activity was predicted to be higher in local, untreated prostate tumors (mean 0.80 ± 0.23, n=16 and 0.72 ± 0.21, n=10) and decreased but more heterogeneous in CRPC (0.42 ± 0.33, n=32 and 0.40 ± 0.33, n=10)(Mann Whitney P=0.0001 and 0.036, respectively). Low probability of AR activity correlated strongly with predicted SRC activity in local (p = 0.006, n=79) and metastatic (p=0.059, n=32) prostate tumors and predicted sensitivity to dasatinib, a src-targeting kinase inhibitor. Conclusions: Determining the level of AR activity in an individual’s CRPC has the potential to identify patients most likely to benefit from continued hormonal manipulation versus those who are more likely to benefit from other biologically targeted or cytotoxic therapies. No significant financial relationships to disclose.

Edge-based scoring and searching method for identifying condition-responsive protein–protein interaction sub-network

Current high-throughput protein-protein interaction (PPI) data do not provide information about the condition(s) under which the interactions occur. Thus, the identification of condition-responsive PPI sub-networks is of great importance for investigating how a living cell adapts to changing environments. In this article, we propose a novel edge-based scoring and searching approach to extract a PPI sub-network responsive to conditions related to some investigated gene expression profiles. Using this approach, what we constructed is a sub-network connected by the selected edges (interactions), instead of only a set of vertices (proteins) as in previous works. Furthermore, we suggest a systematic approach to evaluate the biological relevance of the identified responsive sub-network by its ability of capturing condition-relevant functional modules. We apply the proposed method to analyze a human prostate cancer dataset and a yeast cell cycle dataset. The results demonstrate that the edge-based method is able to efficiently capture relevant protein interaction behaviors under the investigated conditions. Supplementary data are available at Bioinformatics online.

Possibilistic approach for biclustering microarray data

Biclustering has emerged as an important method for analyzing gene expression data from microarray technology. It allows to identify groups of genes which behave similarly under a subset of conditions. As a gene may play more than one biological role in conjunction with distinct groups of genes, non-exclusive biclustering algorithms are required. In this paper we propose a new method to obtain potentially-overlapping biclusters, the Possibilistic Spectral Biclustering algorithm (PSB), based on Fuzzy Technology and Spectral Clustering. We tested our method on S. cerevisiae cell cycle expression data and on a human cancer dataset, validating the obtained biclusters using known classifications of conditions and GO Term Finder for functional annotations of genes. Results are available at http://decsai.ugr.es/ ∼ ccano/psb .

GO-2D: identifying 2-dimensional cellular-localized functional modules in Gene Ontology

BackgroundRapid progress in high-throughput biotechnologies (e.g. microarrays) and exponential accumulation of gene functional knowledge make it promising for systematic understanding of complex human diseases at functional modules level. Based on Gene Ontology, a large number of automatic tools have been developed for the functional analysis and biological interpretation of the high-throughput microarray data.ResultsDifferent from the existing tools such as Onto-Express and FatiGO, we develop a tool named GO-2D for identifying 2-dimensional functional modules based on combined GO categories. For example, it refines biological process categories by sorting their genes into different cellular component categories, and then extracts those combined categories enriched with the interesting genes (e.g., the differentially expressed genes) for identifying the cellular-localized functional modules. Applications of GO-2D to the analyses of two human cancer datasets show that very specific disease-relevant processes can be identified by using cellular location information.ConclusionFor studying complex human diseases, GO-2D can extract functionally compact and detailed modules such as the cellular-localized ones, characterizing disease-relevant modules in terms of both biological processes and cellular locations. The application results clearly demonstrate that 2-dimensional approach complementary to current 1-dimensional approach is powerful for finding modules highly relevant to diseases.