Search

Exploring complex modularization of intracellular signal transduction pathways is critical to understanding aberrant cellular responses during disease development and drug treatment. IMPALA (Inferred Modularization of PAthway LAndscapes) integrates information from high throughput gene expression experiments and genome-scale knowledge databases to identify aberrant pathway modules, thereby providing a powerful sampling strategy to reconstruct and explore pathway landscapes. Here IMPALA identifies pathway modules associated with breast cancer recurrence and Tamoxifen resistance. Focusing on estrogen-receptor (ER) signaling, IMPALA identifies alternative pathways from gene expression data of Tamoxifen treated ER positive breast cancer patient samples. These pathways were often interconnected through cytoplasmic genes such as IRS1/2, JAK1, YWHAZ, CSNK2A1, MAPK1 and HSP90AA1 and significantly enriched with ErbB, MAPK, and JAK-STAT signaling components. Characterization of the pathway landscape revealed key modules associated with ER signaling and with cell cycle and apoptosis signaling. We validated IMPALA-identified pathway modules using data from four different breast cancer cell lines including sensitive and resistant models to Tamoxifen. Results showed that a majority of genes in cell cycle/apoptosis modules that were up-regulated in breast cancer patients with short survivals (

Motivation High-throughput RNA sequencing has revolutionized the scope and depth of transcriptome analysis. Accurate reconstruction of a phenotype-specific transcriptome is challenging due to the noise and variability of RNA-seq data. This requires computational identification of transcripts from multiple samples of the same phenotype, given the underlying consensus transcript structure. Results We present a Bayesian method, integrated assembly of phenotype-specific transcripts (IntAPT), that identifies phenotype-specific isoforms from multiple RNA-seq profiles. IntAPT features a novel two-layer Bayesian model to capture the presence of isoforms at the group layer and to quantify the abundance of isoforms at the sample layer. A spike-and-slab prior is used to model the isoform expression and to enforce the sparsity of expressed isoforms. Dependencies between the existence of isoforms and their expression are modeled explicitly to facilitate parameter estimation. Model parameters are estimated iteratively using Gibbs sampling to infer the joint posterior distribution, from which the presence and abundance of isoforms can reliably be determined. Studies using both simulations and real datasets show that IntAPT consistently outperforms existing methods for the IntAPT. Experimental results demonstrate that, despite sequencing errors, IntAPT exhibits a robust performance among multiple samples, resulting in notably improved identification of expressed isoforms of low abundance. Availability and implementation The IntAPT package is available at http://github.com/henryxushi/IntAPT. Supplementary information Supplementary data are available at Bioinformatics online.

De novo transcriptome assembly from billions of RNA-seq reads is very challenging due to alternative splicing and various levels of expression, which often leads to incorrect, mis-assembled transcripts. BayesDenovo addresses this problem by using both a read-guided strategy to accurately reconstruct splicing graphs from the RNA-seq data and a Bayesian strategy to estimate, from these graphs, the probability of transcript expression without penalizing poorly expressed transcripts. Simulation and cell line benchmark studies demonstrate that BayesDenovo is very effective in reducing false positives and achieves much higher accuracy than other assemblers, especially for alternatively spliced genes and for highly or poorly expressed transcripts. Moreover, BayesDenovo is more robust on multiple replicates by assembling a larger portion of common transcripts. When applied to breast cancer data, BayesDenovo identifies phenotype-specific transcripts associated with breast cancer recurrence. National Institutes of Health (NIH)United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [CA149653, CA164384, CA149147, GM125878] Published version This work is supported by National Institutes of Health (NIH) (CA149653, CA164384, CA149147 and GM125878).

Somatic inactivating mutations of ARID1A, a SWI/SNF chromatin remodeling gene, are prevalent in human endometrium-related malignancies. To elucidate the mechanisms underlying how ARID1A deleterious mutation contributes to tumorigenesis, we establish genetically engineered murine models with Arid1a and/or Pten conditional deletion in the endometrium. Transcriptomic analyses on endometrial cancers and precursors derived from these mouse models show a close resemblance to human uterine endometrioid carcinomas. We identify transcriptional networks that are controlled by Arid1a and have an impact on endometrial tumor development. To verify findings from the murine models, we analyze ARID1AWT and ARID1AKO human endometrial epithelial cells. Using a system biology approach and functional studies, we demonstrate that ARID1A-deficiency lead to loss of TGF-β tumor suppressive function and that inactivation of ARID1A/TGF-β axis promotes migration and invasion of PTEN-deleted endometrial tumor cells. These findings provide molecular insights into how ARID1A inactivation accelerates endometrial tumor progression and dissemination, the major causes of cancer mortality., ARID1A, which is often mutated in human endometrial cancer, is a component of the SWI/SNF chromatin remodelling complex. Here, the authors show that Arid1a mutations in the mouse endometrium and primary human endometrial epithelial cells cause widespread reprogramming of gene transcription and result in a loss of response to TGFβ.

Background Spleen tyrosine kinase (SYK) is frequently upregulated in recurrent ovarian carcinomas, for which effective therapy is urgently needed. SYK phosphorylates several substrates, but their translational implications remain unclear. Here, we show that SYK interacts with EGFR and ERBB2, and directly enhances their phosphorylation. Methods We used immunohistochemistry and immunoblotting to assess SYK and EGFR phosphorylation in ovarian serous carcinomas. Association with survival was determined by Kaplan-Meier analysis and the log-rank test. To study its role in EGFR signaling, SYK activity was modulated using a small molecule inhibitor, a syngeneic knockout, and an active kinase inducible system. We applied RNA-seq and phosphoproteomic mass spectrometry to investigate the SYK-regulated EGF-induced transcriptome and downstream substrates. Findings Induced expression of constitutively active SYK130E reduced cellular response to EGFR/ERBB2 inhibitor, lapatinib. Expression of EGFRWT, but not SYK non-phosphorylatable EGFR3F mutant, resulted in paclitaxel resistance, a phenotype characteristic to SYK active ovarian cancers. In tumor xenografts, SYK inhibitor reduces phosphorylation of EGFR substrates. Compared to SYKWT cells, SYKKO cells have an attenuated EGFR/ERBB2-transcriptional activity and responsiveness to EGF-induced transcription. In ovarian cancer tissues, pSYK (Y525/526) levels showed a positive correlation with pEGFR (Y1187). Intense immunoreactivity of pSYK (Y525/526) correlated with poor overall survival in ovarian cancer patients. Interpretation These findings indicate that SYK activity positively modulates the EGFR pathway, providing a biological foundation for co-targeting SYK and EGFR. Fund Department of Gynecology and Obstetrics, Johns Hopkins University School of Medicine, NIH/NCI, Ovarian Cancer Research Foundation Alliance, HERA Women's Cancer Foundation and Roseman Foundation. Funders had no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript and eventually in the decision to submit the manuscript.

Motivation Whole genome DNA-sequencing (WGS) of paired tumor and normal samples has enabled the identification of somatic DNA changes in an unprecedented detail. Large-scale identification of somatic structural variations (SVs) for a specific cancer type will deepen our understanding of driver mechanisms in cancer progression. However, the limited number of WGS samples, insufficient read coverage, and the impurity of tumor samples that contain normal and neoplastic cells, limit reliable and accurate detection of somatic SVs. Results We present a novel pattern-based probabilistic approach, PSSV, to identify somatic structural variations from WGS data. PSSV features a mixture model with hidden states representing different mutation patterns; PSSV can thus differentiate heterozygous and homozygous SVs in each sample, enabling the identification of those somatic SVs with heterozygous mutations in normal samples and homozygous mutations in tumor samples. Simulation studies demonstrate that PSSV outperforms existing tools. PSSV has been successfully applied to breast cancer data to identify somatic SVs of key factors associated with breast cancer development. Availability and Implementation An R package of PSSV is available at http://www.cbil.ece.vt.edu/software.htm. Supplementary information Supplementary data are available at Bioinformatics online.

Breast cancer is the most commonly diagnosed cancer in women and about 1 million new cases per year are diagnosed worldwide. About 70% of all breast cancers are estrogen receptor alpha positive (ER+). Antiestrogens (e.g., Tamoxifen or Faslodex) or aromatase inhibitors (e.g., Letrozole) are often used to treat ER+ breast cancers. However, resistance to these therapies (endocrine resistance) is prevalent in the clinic and the underlying mechanisms remain unclear. We have recently shown that the oncogene MYC is overexpressed in ER+ breast cancer and up-regulates glucose and glutamine uptake in endocrine resistant breast cancer cells, which suggests that the metabolomic profile of endocrine resistant breast cancer cells may contain features that are distinct from sensitive cells. In this study, to identify the biochemical pathways that are differentially regulated in endocrine resistance in breast cancer cells, we have analyzed gene expression data and untargeted metabolite profiles of ER+ MCF7-derived breast cancer cells that are antiestrogen sensitive (LCC1) or antiestrogen resistant (LCC9) under basal conditions. Glycolysis and glutamine-dependent pathways were increased in endocrine resistant cells. Integration of the transcriptomics and metabolomics data predicted an essential role for a gene-metabolite network associated with early growth response (EGR1) and glutamine metabolism in endocrine resistant cells. EGR1 is an immediate-early gene induced by E2, growth factors, or stress signals, and has been reported to exhibit both tumor suppressor and promoter activities, based on cellular context. While EGR1 mediated signaling is important for the normal development of female reproductive organs, its precise role in breast cancer remains unknown. EGR1 gene expression and protein levels were significantly higher in LCC1 cells compared with LCC9 cells. Kaplan-Meier survival curves with gene expression data obtained from ER+ human breast tumors treated with endocrine therapy show that higher EGR1 expression is associated with a more favorable prognosis: GSE17705 [HR=0.38 (0.21-0.69); p=0.00083], GSE6532 (ER+ samples on GPL96 platform) [HR=0.55 (0.34-0.9); p=0.017]. In GSE20181, pre-treatment vs 90 days post-treatment comparisons show significantly increased levels of EGR1 expression (p Citation Format: Ayesha N. Shajahan-Haq, Lu Jin, Amrita K. Cheema, Simina M. Boca, Yuriy Gusev, Krithika Bhuvaneshwar, Diane M. Demas, Habtom Ressom, Ryan Michalek, Xi Chen, Jianhua Xuan, Subha Madhavan, Robert Clarke. Early growth response (EGR1) is a critical regulator of cellular metabolism and predicts increased responsiveness to antiestrogens in breast cancer. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B1-23.

Purpose: Statins are among the most frequently prescribed drugs because of their efficacy and low toxicity in treating hypercholesterolemia. Recently, statins have been reported to inhibit the proliferative activity of cancer cells, especially those with TP53 mutations. Because TP53 mutations occur in almost all ovarian high-grade serous carcinoma (HGSC), we determined whether statins suppressed tumor growth in animal models of ovarian cancer. Experimental Design: Two ovarian cancer mouse models were used. The first one was a genetically engineered model, mogp-TAg, in which the promoter of oviduct glycoprotein-1 was used to drive the expression of SV40 T-antigen in gynecologic tissues. These mice spontaneously developed serous tubal intraepithelial carcinomas (STICs), which are known as ovarian cancer precursor lesions. The second model was a xenograft tumor model in which human ovarian cancer cells were inoculated into immunocompromised mice. Mice in both models were treated with lovastatin, and effects on tumor growth were monitored. The molecular mechanisms underlying the antitumor effects of lovastatin were also investigated. Results: Lovastatin significantly reduced the development of STICs in mogp-TAg mice and inhibited ovarian tumor growth in the mouse xenograft model. Knockdown of prenylation enzymes in the mevalonate pathway recapitulated the lovastatin-induced antiproliferative phenotype. Transcriptome analysis indicated that lovastatin affected the expression of genes associated with DNA replication, Rho/PLC signaling, glycolysis, and cholesterol biosynthesis pathways, suggesting that statins have pleiotropic effects on tumor cells. Conclusions: The above results suggest that repurposing statin drugs for ovarian cancer may provide a promising strategy to prevent and manage this devastating disease. Clin Cancer Res; 21(20); 4652–62. ©2015 AACR.

cope Soy flour diet (MS) prevented isoflavones from stimulating MCF-7 tumor growth in athymic nude mice, indicating that other bioactive compounds in soy can negate the estrogenic properties of isoflavones. The underlying signal transduction pathways to explain the protective effects of soy flour consumption were studied here. Methods and results Ovariectomized athymic nude mice inoculated with MCF-7 human breast cancer cells were fed either Soy flour diet (MS) or purified isoflavone mix diet (MI), both with equivalent amounts of genistein. Positive controls received estradiol pellets and negative controls received sham pellets. GeneChip Human Genome U133 Plus 2.0 Array platform was used to evaluate gene expressions, and results were analyzed using bioinformatics approaches. Tumors in MS-fed mice exhibited higher expression of tumor growth suppressing genes ATP2A3 and BLNK and lower expression of oncogene MYC. Tumors in MI-fed mice expressed a higher level of oncogene MYB and a lower level of MHC-I and MHC-II, allowing tumor cells to escape immunosurveillance. MS-induced gene expression alterations were predictive of prolonged survival among estrogen-receptor-positive breast cancer patients, whilst MI-induced gene changes were predictive of shortened survival. Conclusion Our findings suggest that dietary soy flour affects gene expression differently than purified isoflavones, which may explain why soy foods prevent isoflavones-induced stimulation of MCF-7 tumor growth in athymic nude mice.

Background Maternal and paternal high-fat (HF) diet intake before and/or during pregnancy increases mammary cancer risk in several preclinical models. We studied if maternal consumption of a HF diet that began at a time when the fetal primordial germ cells travel to the genital ridge and start differentiating into germ cells would result in a transgenerational inheritance of increased mammary cancer risk. Methods Pregnant C57BL/6NTac mouse dams were fed either a control AIN93G or isocaloric HF diet composed of corn oil high in n-6 polyunsaturated fatty acids between gestational days 10 and 20. Offspring in subsequent F1–F3 generations were fed only the control diet. Results Mammary tumor incidence induced by 7,12-dimethylbenz[a]anthracene was significantly higher in F1 (p

Reliable inference of transcription regulatory networks is a challenging task in computational biology. Network component analysis (NCA) has become a powerful scheme to uncover regulatory networks behind complex biological processes. However, the performance of NCA is impaired by the high rate of false connections in binding information. In this paper, we integrate stability analysis with NCA to form a novel scheme, namely stability-based NCA (sNCA), for regulatory network identification. The method mainly addresses the inconsistency between gene expression data and binding motif information. Small perturbations are introduced to prior regulatory network, and the distance among multiple estimated transcript factor (TF) activities is computed to reflect the stability for each TF's binding network. For target gene identification, multivariate regression and t-statistic are used to calculate the significance for each TF-gene connection. Simulation studies are conducted and the experimental results show that sNCA can achieve an improved and robust performance in TF identification as compared to NCA. The approach for target gene identification is also demonstrated to be suitable for identifying true connections between TFs and their target genes. Furthermore, we have successfully applied sNCA to breast cancer data to uncover the role of TFs in regulating endocrine resistance in breast cancer.

Characterizing the origin of high-grade serous ovarian cancer has significant practical importance for advancing biological knowledge and improving clinical treatments. Rapid advances in molecular profiling technologies and machine learning based data analytics provide new opportunities to investigate this important question using data-driven approaches at the molecular and network levels. We now report novel analytic results in assessing the origin of high-grade serous ovarian carcinoma. Using genome-wide gene expression data and effective machine learning approaches, we design proper statistical significance tests and perform both genomic and network analyses to discriminate among three possible origins. The experimental results are consistent with recent scientific hypothesis and independent findings.

NOTCH3 gene amplification plays an important role in the progression of many ovarian and breast cancers, but the targets of NOTCH3 signaling are unclear. Here, we report the use of an integrated systems biology approach to identify direct target genes for NOTCH3. Transcriptome analysis showed that suppression of NOTCH signaling in ovarian and breast cancer cells led to downregulation of genes in pathways involved in cell-cycle regulation and nucleotide metabolism. Chromatin immunoprecipitation (ChIP)-on-chip analysis defined promoter target sequences, including a new CSL binding motif (N1) in addition to the canonical CSL binding motif, that were occupied by the NOTCH3/CSL transcription complex. Integration of transcriptome and ChIP-on-chip data showed that the ChIP target genes overlapped significantly with the NOTCH-regulated transcriptome in ovarian cancer cells. From the set of genes identified, we showed that the mitotic apparatus organizing protein DLGAP5 (HURP/DLG7) was a critical target. Both the N1 motif and the canonical CSL binding motif were essential to activate DLGAP5 transcription. DLGAP5 silencing in cancer cells suppressed tumorigenicity and inhibited cellular proliferation by arresting the cell cycle at the G2–M phase. In contrast, enforced expression of DLGAP5 partially counteracted the growth inhibitory effects of a pharmacologic or RNA interference–mediated NOTCH inhibition in cancer cells. Our findings define direct target genes of NOTCH3 and highlight the role of DLGAP5 in mediating the function of NOTCH3. Cancer Res; 72(9); 2294–303. ©2012 AACR.

How breast cancer cells respond to the stress of endocrine therapies determines whether they will acquire a resistant phenotype or execute a cell-death pathway. After a survival signal is successfully executed, a cell must decide whether it should replicate. How these cell-fate decisions are regulated is unclear, but evidence suggests that the signals that determine these outcomes are highly integrated. Central to the final cell-fate decision is signaling from the unfolded protein response, which can be activated following the sensing of stress within the endoplasmic reticulum. The duration of the response to stress is partly mediated by the duration of inositol-requiring enzyme-1 activation following its release from heat shock protein A5. The resulting signals appear to use several B-cell lymphoma-2 family members to both suppress apoptosis and activate autophagy. Changes in metabolism induced by cellular stress are key components of this regulatory system, and further adaptation of the metabolome is affected in response to stress. Here we describe the unfolded protein response, autophagy, and apoptosis, and how the regulation of these processes is integrated. Central topologic features of the signaling network that integrate cell-fate regulation and decision execution are discussed. Cancer Res; 72(6); 1321–31. ©2012 AACR.

Somatic mutations in ARID1A, a SWI/SNF chromatin remodeling gene, are prevalent in human cancer and represent an emerging mechanism by which a molecular genetic alteration reprograms epigenomes and propels tumorigenesis. Through comprehensive sequencing of chromatin immunoprecipitation and chromatin accessibility, we found chromatin binding regions for ARID1A/BRG1-containing SWI/SNF remodeling complexes, which were enriched at enhancers and corresponded to euchromatin state. ARID1A deletion caused global re-distribution of BRG1-containing complexes in chromatin. Integrative analyses of ATAC-seq, ChIP-seq, and transcriptome data obtained from endometrial epithelium and human endometrioid carcinomas identified high-confidence ARID1A-regulated genes that participate in myriad key tumor and developmental signaling circuitry tissue. Deletion of Arid1a was found to inactivate the TGF-β pathway and to accelerate tumor progression from pre-cancerous lesions to endometrioid carcinomas. This study provides compelling evidence that Arid1a loss is required for developing endometrial cancer from its precursor. Collectively, targeting epigenomic modifications may hold promise for suppressing invasive behaviors of neoplasms carrying ARID1A mutations. Citation Format: Yohan Suryo Rahmanto, Xu Shi, Wenjing Shen, Tsutomu Miyamoto, Yu Yu, Xi Chen, Meng-Horng Lee, Vivek Singh, Michele I. Vitolo, Denis Wirtz, Ronny Drapkin, Jianhua Xuan, Ie-Ming Shih, Tian-Li Wang. Inactivation of ARID1A-SWI/SNF complex alters chromatin compactness at enhancer regions and affects transcription of key tumor signaling circuitry [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2493.

Background:Late recurrence (emergence from dormancy) is characteristic of ER+ breast cancers. Despite adjuvant endocrine therapy, many breast cancers recur decades after their initial diagnosis and treatment. Why this occurs is poorly understood. Methods: We studied 2 independent datasets of endocrine treated, ER+ breast cancers with up to 20 years follow-up. The 1st comprised matched samples from the primary tumor pretreatment at diagnosis and the first recurrence after or during adjuvant endocrine therapy (all FFPE). The 2nd dataset comprised pretreatment biopsies only (all snap frozen). For both datasets, high quality RNA was amplified, labelled, and subjected to transcriptome analysis using the Affymetrix technology (U133 Plus 2.0). Low quality data were identified using 'simpleaffy' and 'ffpe', and removed; all tools were from the R package unless otherwise noted. Remaining data were normalized using 'frma'. Genes differentially expressed between early (≤3 years) and late (≥ 5 years) were selected using limma. Unsupervised hierarchical clustering and PCA explored the structure of the data. A similar molecular analysis was done on the 2nd dataset. A classification scheme that robustly separated early from late recurrences was validated in an independent public dataset of comparable patients, array platform, and frozen tissues. We also explored features in pretreatment samples that predetermined response duration. Results: Genes that separated pretreatment specimens by recurrence time did not separate posttreatment specimens. Specimens did not cluster in patient pairs or by site of recurrence. 8245 genes were differentially expressed between early and late recurrences in the FFPE samples, while 2400 genes were significantly different in the same comparison in the frozen samples. Initial pathway analysis was done on each dataset independently using IPA (Ingenuity® Systems, www.ingenuity.com). 70 canonical pathways were identified in common between the two datasets (pretreatment). We then looked for genes regulated in both datasets (ignores FFPE and frozen tissue as source). There were 279 genes in common that differentially regulated in the same direction (upregulated; downregulated). IPA analysis of these genes identified 49 canonical pathways. We also explored the differentially expressed gene sets using 'GSEA' (www.software.broadinstitute.org/gsea/index.jsp). Pathways consistently associated with early vs. late recurrence include integrin signaling, the unfolded protein response, endoplasmic reticulum stress, actin-based motility, and estrogen biosynthesis. Conclusion: Analysis of pretreatment tumors can predict early recurrences from those that will remain dormant and recur much later. Recurrent tumors exhibit a remodeled molecular landscape that likely reflects the effects of treatments and/or a recreation of a niche with potentially common features at the site of recurrence. Changes in molecular signaling associated with duration of recurrence are consistent with our experimental model studies in vitro implicating UPR signaling as a major integrator of response to endocrine therapy and duration of survival. Additional data sets are being arrayed and more detailed molecular signaling studies are in progress. Citation Format: Clarke R, Dixon MJ, Jin L, Turnbull A, Hu R, Zwart A, Wang Y, Xuan J, Sengupta S, Renshaw L, Sims A, Liu MC. Molecular features of dormancy in ER+ breast cancers [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P4-04-10.

One-third of all estrogen receptor (ER)–positive breast tumors treated with endocrine therapy fail to respond, and the remainder is likely to relapse in the future. Almost all data on endocrine resistance has been obtained in models of invasive ductal carcinoma (IDC). However, invasive lobular carcinomas (ILC) comprise up to 15% of newly diagnosed invasive breast cancers each year and, whereas the incidence of IDC has remained relatively constant during the last 20 years, the prevalence of ILC continues to increase among postmenopausal women. We report a new model of Tamoxifen (TAM)-resistant invasive lobular breast carcinoma cells that provides novel insights into the molecular mechanisms of endocrine resistance. SUM44 cells express ER and are sensitive to the growth inhibitory effects of antiestrogens. Selection for resistance to 4-hydroxytamoxifen led to the development of the SUM44/LCCTam cell line, which exhibits decreased expression of ERα and increased expression of the estrogen-related receptor γ (ERRγ). Knockdown of ERRγ in SUM44/LCCTam cells by siRNA restores TAM sensitivity, and overexpression of ERRγ blocks the growth-inhibitory effects of TAM in SUM44 and MDA-MB-134 VI lobular breast cancer cells. ERRγ-driven transcription is also increased in SUM44/LCCTam, and inhibition of activator protein 1 (AP1) can restore or enhance TAM sensitivity. These data support a role for ERRγ/AP1 signaling in the development of TAM resistance and suggest that expression of ERRγ may be a marker of poor TAM response. [Cancer Res 2008;68(21):8908–17]

Background Recent advances in RNA sequencing (RNA-Seq) technology have offered unprecedented scope and resolution for transcriptome analysis. However, precise quantification of mRNA abundance and identification of differentially expressed genes are complicated due to biological and technical variations in RNA-Seq data. Results We systematically study the variation in count data and dissect the sources of variation into between-sample variation and within-sample variation. A novel Bayesian framework is developed for joint estimate of gene level mRNA abundance and differential state, which models the intrinsic variability in RNA-Seq to improve the estimation. Specifically, a Poisson-Lognormal model is incorporated into the Bayesian framework to model within-sample variation; a Gamma-Gamma model is then used to model between-sample variation, which accounts for over-dispersion of read counts among multiple samples. Simulation studies, where sequencing counts are synthesized based on parameters learned from real datasets, have demonstrated the advantage of the proposed method in both quantification of mRNA abundance and identification of differentially expressed genes. Moreover, performance comparison on data from the Sequencing Quality Control (SEQC) Project with ERCC spike-in controls has shown that the proposed method outperforms existing RNA-Seq methods in differential analysis. Application on breast cancer dataset has further illustrated that the proposed Bayesian model can 'blindly' estimate sources of variation caused by sequencing biases. Conclusions We have developed a novel Bayesian hierarchical approach to investigate within-sample and between-sample variations in RNA-Seq data. Simulation and real data applications have validated desirable performance of the proposed method. The software package is available at http://www.cbil.ece.vt.edu/software.htm. Published version

High coverage whole genome DNA-sequencing enables identification of somatic structural variation (SSV) more evident in paired tumor and normal samples. Recent studies show that simultaneous analysis of paired samples provides a better resolution of SSV detection than subtracting shared SVs. However, available tools can neither identify all types of SSVs nor provide any rank information regarding their somatic features. In this paper, we have developed a Bayesian framework, by integrating read alignment information from both tumor and normal samples, called BSSV, to calculate the significance of each SSV. Tested by simulated data, the precision of BSSV is comparable to that of available tools and the false negative rate is significantly lowered. We have also applied this approach to The Cancer Genome Atlas breast cancer data for SSV detection. Many known breast cancer specific mutated genes like RAD51, BRIP1, ER, PGR and PTPRD have been successfully identified.

Summary: Phenotypic Up-regulated Gene Support Vector Machine (PUGSVM) is a cancer Biomedical Informatics Grid (caBIG™) analytical tool for multiclass gene selection and classification. PUGSVM addresses the problem of imbalanced class separability, small sample size and high gene space dimensionality, where multiclass gene markers are defined by the union of one-versus-everyone phenotypic upregulated genes, and used by a well-matched one-versus-rest support vector machine. PUGSVM provides a simple yet more accurate strategy to identify statistically reproducible mechanistic marker genes for characterization of heterogeneous diseases. Availability: http://www.cbil.ece.vt.edu/caBIG-PUGSVM.htm. Contact: yuewang@vt.edu Supplementary information: Supplementary data are available at Bioinformatics online.

ChlP-chip experiments are performed to determine binding sites for transcription factors (TFs). Conventional TF-gene regulation is generated based on p-value cutoff of the binding sites as well as their distance to nearest genes. Taking into account that binding sites of one ChlP-chip experiment should follow the same specific location distribution, we proposed a statistical model using both location and significance information to weigh target genes. With multiple ChlP-chip experiments and gene expression data, we identified co-regulatory and differentially expressed gene modules with a joint clustering and Metropolis sampling approach. We demonstrated the efficiency of our method on a ChlP-chip data set with 38 breast cancer related TFs.

The reliability and reproducibility of gene biomarkers for classification of cancer patients has been challenged due to measurement noise and biological heterogeneity among patients. In this paper, we propose a novel module-based feature selection framework, which integrates biological network information and gene expression data to identify biomarkers not as individual genes but as functional modules. Results from four breast cancer studies demonstrate that the identified module biomarkers i) achieve higher classification accuracy in independent validation datasets; ii) are more reproducible than individual gene markers; iii) improve the biological interpretability of results; and iv) are enriched in cancer “disease drivers”.

It is biologically important to integrate high-throughput data to identify aberrant signal transduction pathways in cancer research. The high-throughput data acquired from The Cancer Genome Atlas (TCGA) Project offer a comprehensive picture of the genomic and transcriptional changes across hundreds of tumor samples. In this paper we propose a novel method, namely Gibbs sampler to Infer Signal Transduction pathways (GIST), to detect aberrant pathways that are highly associated with biological phenotypes or clinical information. GIST endeavors to estimate the edge probability by using a Markov Chain Monte Carlo (MCMC) method (i.e., a Gibbs sampling strategy). Through the sampling process, GIST is able to infer the correct signal transduction direction because the sampled edge probabilities are jointly determined by gene expression data and network topology. We first tested the efficacy of the GIST algorithm on yeast data and successfully uncovered several biologically meaningful signaling pathways. A case study on TCGA ovarian cancer data was further designed, aiming to unravel diverse signaling pathways associated with the development of ovarian cancer. The experimental results demonstrated the feasibility of applying GIST to identify and prioritize important signaling pathways in ovarian cancer for further biological validation.

Ovarian cancer is often called the ‘silent killer’ since it is difficult to have early detection and prognosis. Understanding the biological mechanism related to ovarian cancer becomes extremely important for the purpose of treatment. We propose an integrative framework to identify pathway related networks based on large-scale TCGA copy number data and gene expression profiles. The integrative approach first detects highly conserved copy number altered genes and regards them as seed genes, and then applies a network-based method to identify subnetworks that can differentiate gene expression patterns between different phenotypes of ovarian cancer patients. The identified subnetworks are further validated on an independent gene expression data set using a network-based classification method. The experimental results show that our approach can not only achieve good prediction performance across different data sets, but also identify biological meaningful subnetworks involved in many signaling pathways related to ovarian cancer.

Background One of the major goals in gene and protein expression profiling of cancer is to identify biomarkers and build classification models for prediction of disease prognosis or treatment response. Many traditional statistical methods, based on microarray gene expression data alone and individual genes' discriminatory power, often fail to identify biologically meaningful biomarkers thus resulting in poor prediction performance across data sets. Nonetheless, the variables in multivariable classifiers should synergistically interact to produce more effective classifiers than individual biomarkers. Results We developed an integrated approach, namely network-constrained support vector machine (netSVM), for cancer biomarker identification with an improved prediction performance. The netSVM approach is specifically designed for network biomarker identification by integrating gene expression data and protein-protein interaction data. We first evaluated the effectiveness of netSVM using simulation studies, demonstrating its improved performance over state-of-the-art network-based methods and gene-based methods for network biomarker identification. We then applied the netSVM approach to two breast cancer data sets to identify prognostic signatures for prediction of breast cancer metastasis. The experimental results show that: (1) network biomarkers identified by netSVM are highly enriched in biological pathways associated with cancer progression; (2) prediction performance is much improved when tested across different data sets. Specifically, many genes related to apoptosis, cell cycle, and cell proliferation, which are hallmark signatures of breast cancer metastasis, were identified by the netSVM approach. More importantly, several novel hub genes, biologically important with many interactions in PPI network but often showing little change in expression as compared with their downstream genes, were also identified as network biomarkers; the genes were enriched in signaling pathways such as TGF-beta signaling pathway, MAPK signaling pathway, and JAK-STAT signaling pathway. These signaling pathways may provide new insight to the underlying mechanism of breast cancer metastasis. Conclusions We have developed a network-based approach for cancer biomarker identification, netSVM, resulting in an improved prediction performance with network biomarkers. We have applied the netSVM approach to breast cancer gene expression data to predict metastasis in patients. Network biomarkers identified by netSVM reveal potential signaling pathways associated with breast cancer metastasis, and help improve the prediction performance across independent data sets.

Identification of intracellular signal transduction pathways plays an important role in understanding the mechanisms of how cells respond to external stimuli. The availability of high throughput microarray expression data and accumulating knowledge of protein-protein interactions have provided us with useful information to infer condition-specific signal transduction pathways. We propose a novel method called Gibbs sampler to Infer Signal Transduction pathways (GIST) to search dys-regulated pathways from large-scale protein-protein interaction networks. GIST incorporates different knowledge sources to extract paths that are highly associated with biological phenotypes or clinical information. One of the most attractive features of GIST is that the algorithm will not only provide the single optimal path according to the defined cost function but also reveal multiple suboptimal paths as alternative solutions, which can be utilized to study the pathway crosstalk. As a proof-of-concept, we test our GIST algorithm on yeast PPI networks and the identified MAPK signaling pathways are well supported by existing biological knowledge. We also apply the GIST algorithm onto a breast cancer patient dataset to show its feasibility of identifying potential pathways for further biological validation.

Background Genes work coordinately as gene modules or gene networks. Various computational approaches have been proposed to find gene modules based on gene expression data; for example, gene clustering is a popular method for grouping genes with similar gene expression patterns. However, traditional gene clustering often yields unsatisfactory results for regulatory module identification because the resulting gene clusters are co-expressed but not necessarily co-regulated. Results We propose a novel approach, motif-guided sparse decomposition (mSD), to identify gene regulatory modules by integrating gene expression data and DNA sequence motif information. The mSD approach is implemented as a two-step algorithm comprising estimates of (1) transcription factor activity and (2) the strength of the predicted gene regulation event(s). Specifically, a motif-guided clustering method is first developed to estimate the transcription factor activity of a gene module; sparse component analysis is then applied to estimate the regulation strength, and so predict the target genes of the transcription factors. The mSD approach was first tested for its improved performance in finding regulatory modules using simulated and real yeast data, revealing functionally distinct gene modules enriched with biologically validated transcription factors. We then demonstrated the efficacy of the mSD approach on breast cancer cell line data and uncovered several important gene regulatory modules related to endocrine therapy of breast cancer. Conclusion We have developed a new integrated strategy, namely motif-guided sparse decomposition (mSD) of gene expression data, for regulatory module identification. The mSD method features a novel motif-guided clustering method for transcription factor activity estimation by finding a balance between co-regulation and co-expression. The mSD method further utilizes a sparse decomposition method for regulation strength estimation. The experimental results show that such a motif-guided strategy can provide context-specific regulatory modules in both yeast and breast cancer studies.

Lack of understanding of endocrine resistance remains one of the major challenges for breast cancer researchers, clinicians, and patients. Current reductionist approaches to understanding the molecular signaling driving resistance have offered mostly incremental progress over the past 10 years. As the field of systems biology has begun to mature, the approaches and network modeling tools being developed and applied therein offer a different way to think about how molecular signaling and the regulation of crucial cellular functions are integrated. To gain novel insights, we first describe some of the key challenges facing network modeling of endocrine resistance, many of which arise from the properties of the data spaces being studied. We then use activation of the unfolded protein response (UPR) following induction of endoplasmic reticulum stress in breast cancer cells by antiestrogens, to illustrate our approaches to computational modeling. Activation of UPR is a key determinant of cell fate decision-making and regulation of autophagy and apoptosis. These initial studies provide insight into a small subnetwork topology obtained using differential dependency network analysis and focused on the UPR gene XBP1. The XBP1 subnetwork topology incorporates BCAR3, BCL2, BIK, NF-κB, and other genes as nodes; the connecting edges represent the dependency structures among these nodes. As data from ongoing cellular and molecular studies become available, we will build detailed mathematical models of this XBP1-UPR network.

Background Precise regulation of the cell cycle is crucial to the growth and development of all organisms. Understanding the regulatory mechanism of the cell cycle is crucial to unraveling many complicated diseases, most notably cancer. Multiple sources of biological data are available to study the dynamic interactions among many genes that are related to the cancer cell cycle. Integrating these informative and complementary data sources can help to infer a mutually consistent gene transcriptional regulatory network with strong similarity to the underlying gene regulatory relationships in cancer cells. Results and Principal Findings We propose an integrative framework that infers gene regulatory modules from the cell cycle of cancer cells by incorporating multiple sources of biological data, including gene expression profiles, gene ontology, and molecular interaction. Among 846 human genes with putative roles in cell cycle regulation, we identified 46 transcription factors and 39 gene ontology groups. We reconstructed regulatory modules to infer the underlying regulatory relationships. Four regulatory network motifs were identified from the interaction network. The relationship between each transcription factor and predicted target gene groups was examined by training a recurrent neural network whose topology mimics the network motif(s) to which the transcription factor was assigned. Inferred network motifs related to eight well-known cell cycle genes were confirmed by gene set enrichment analysis, binding site enrichment analysis, and comparison with previously published experimental results. Conclusions We established a robust method that can accurately infer underlying relationships between a given transcription factor and its downstream target genes by integrating different layers of biological data. Our method could also be beneficial to biologists for predicting the components of regulatory modules in which any candidate gene is involved. Such predictions can then be used to design a more streamlined experimental approach for biological validation. Understanding the dynamics of these modules will shed light on the processes that occur in cancer cells resulting from errors in cell cycle regulation. Published version

Motivation: The identification of gene regulatory modules is an important yet challenging problem in computational biology. While many computational methods have been proposed to identify regulatory modules, their initial success is largely compromised by a high rate of false positives, especially when applied to human cancer studies. New strategies are needed for reliable regulatory module identification.Results: We present a new approach, namely multilevel support vector regression (ml-SVR), to systematically identify condition-specific regulatory modules. The approach is built upon a multilevel analysis strategy designed for suppressing false positive predictions. With this strategy, a regulatory module becomes ever more significant as more relevant gene sets are formed at finer levels. At each level, a two-stage support vector regression (SVR) method is utilized to help reduce false positive predictions by integrating binding motif information and gene expression data; a significant analysis procedure is followed to assess the significance of each regulatory module. To evaluate the effectiveness of the proposed strategy, we first compared the ml-SVR approach with other existing methods on simulation data and yeast cell cycle data. The resulting performance shows that the ml-SVR approach outperforms other methods in the identification of both regulators and their target genes. We then applied our method to breast cancer cell line data to identify condition-specific regulatory modules associated with estrogen treatment. Experimental results show that our method can identify biologically meaningful regulatory modules related to estrogen signaling and action in breast cancer.Availability and implementation: The ml-SVR MATLAB package can be downloaded at http://www.cbil.ece.vt.edu/software.htmContact: xuan@vt.eduSupplementary information: Supplementary data are available at Bioinformatics online.

In this paper, we propose to apply non-uniform weighs to the genes in the pathway based analysis and present two weighting schemes for the genes in pathway. Specifically, we incorporate our weighting schemes into the global test pathway based analysis approach and investigate the effects of our weighting schemes. We observe that when non-uniform weights are applied, some originally lower ranked pathways are elevated to the high ranks, prediction performances of the selected genes are improved, and some genes associated with the related phenotype are identified, which are missed by uniform weighting approach.

Resistance to endocrine therapies, whether de novo or acquired, remains a major limitation in the ability to cure many tumors that express detectable levels of the estrogen receptor alpha protein (ER). While several resistance phenotypes have been described, endocrine unresponsiveness in the context of therapy-induced tumor growth appears to be the most prevalent. The signaling that regulates endocrine resistant phenotypes is poorly understood but it involves a complex signaling network with a topology that includes redundant and degenerative features. To be relevant to clinical outcomes, the most pertinent features of this network are those that ultimately affect the endocrine-regulated components of the cell fate and cell proliferation machineries. We show that autophagy, as supported by the endocrine regulation of monodansylcadaverine staining, increased LC3 cleavage, and reduced expression of p62/SQSTM1, plays an important role in breast cancer cells responding to endocrine therapy. We further show that the cell fate machinery includes both apoptotic and autophagic functions that are potentially regulated through integrated signaling that flows through key members of the BCL2 gene family and beclin-1 (BECN1). This signaling links cellular functions in mitochondria and endoplasmic reticulum, the latter as a consequence of induction of the unfolded protein response. We have taken a seed-gene approach to begin extracting critical nodes and edges that represent central signaling events in the endocrine regulation of apoptosis and autophagy. Three seed nodes were identified from global gene or protein expression analyses and supported by subsequent functional studies that established their abilities to affect cell fate. The seed nodes of nuclear factor kappa B (NFkappaB), interferon regulatory factor-1 (IRF1), and X-box binding protein-1 (XBP1)are linked by directional edges that support signal flow through a preliminary network that is grown to include key regulators of their individual function: NEMO/IKKgamma, nucleophosmin and ER respectively. Signaling proceeds through BCL2 gene family members and BECN1 ultimately to regulate cell fate.

Identifying breast cancer susceptibility genes is one of the key challenges in breast cancer research. Conventional gene-based approaches can identify patterns of gene activity that sub-classify tumors, by which genes with known breast cancer mutations are typically not detected. In this study, we present a novel network motif-based approach that integrates biological network topology and high-throughput gene expression data to identify markers not as individual genes but as network motifs. We observed that the network motifs are more reproducible than individual marker genes selected without biological network information, and that they achieve higher accuracy in the classification of metastatic versus non-metastatic tumors.

Many computational methods for identification of transcription regulatory modules often result in many false positives in practice due to noise sources of binding information and gene expression profiling data. In this paper, we propose a multi-level strategy for condition-specific gene regulatory module identification by integrating motif binding information and gene expression data through support vector regression and significant analysis. We have demonstrated the feasibility of the proposed method on a yeast cell cycle data set. The study on a breast cancer microarray data set shows that it can successfully identify the significant and reliable regulatory modules associated with breast cancer.

Microarray technology and high-throughput proteomics have revolutionized cancer biology by generating vast amount of data for various cancers. To gain insights into the biological processes that drive breast cancer recurrence, we integrate gene expression profiles of breast cancer and protein interaction networks in search for affected regulatory and signaling networks statistically associated with endocrine resistance. To perform this integration systematically, we introduce a systems biology approach to screen related protein-protein interaction networks for active cliques, i.e., connected regions of the network that show significant changes in expression between recurrent and non-recurrent tumors. The experimental results from two breast cancer data sets show that the identified sub-networks can be effectively used to stratify patients treated with endocrine into groups with different outcomes. More importantly, the sub-networks can further help us gain mechanistic insights into estrogen action and endocrine resistance.

In studying the relationship between risk factors and breast cancer, the growth patterns of fat pads and glandular tissues are considered as important biomarkers. The aim of this study is to measure the growth pattern statistics of rat mammary pads and glandular tissues with magnetic resonance (MR) time sequence images. In this paper, we proposed methods containing sequential steps to extract and analyze imaging biomarkers of rat mammary pad and glandular tissues. Firstly, to accurately segment out pads in MR images with noisy bias filed, we proposed a level set method combining local binary fitting (LBF) and geodesic active contour (GAC). The salient glandular tissue regions within the fat pads are further extracted by a scale-space analysis procedure. Then, the volume data of a single rat at different time points are aligned through profile correlation analysis. Finally, the growth rates are calculated and compared to show the changing patterns of fat pads and glandular tissues within separate groups. The experimental results showed the great utility of this approach in providing accurate measurements for novel risk factors of breast cancer.

BackgroundMany statistical methods have been proposed to identify disease biomarkers from gene expression profiles. However, from gene expression profile data alone, statistical methods often fail to identify biologically meaningful biomarkers related to a specific disease under study. In this paper, we develop a novel strategy, namely knowledge-guided multi-scale independent component analysis (ICA), to first infer regulatory signals and then identify biologically relevant biomarkers from microarray data.ResultsSince gene expression levels reflect the joint effect of several underlying biological functions, disease-specific biomarkers may be involved in several distinct biological functions. To identify disease-specific biomarkers that provide unique mechanistic insights, a meta-data "knowledge gene pool" (KGP) is first constructed from multiple data sources to provide important information on the likely functions (such as gene ontology information) and regulatory events (such as promoter responsive elements) associated with potential genes of interest. The gene expression and biological meta data associated with the members of the KGP can then be used to guide subsequent analysis. ICA is then applied to multi-scale gene clusters to reveal regulatory modes reflecting the underlying biological mechanisms. Finally disease-specific biomarkers are extracted by their weighted connectivity scores associated with the extracted regulatory modes. A statistical significance test is used to evaluate the significance of transcription factor enrichment for the extracted gene set based on motif information. We applied the proposed method to yeast cell cycle microarray data and Rsf-1-induced ovarian cancer microarray data. The results show that our knowledge-guided ICA approach can extract biologically meaningful regulatory modes and outperform several baseline methods for biomarker identification.ConclusionWe have proposed a novel method, namely knowledge-guided multi-scale ICA, to identify disease-specific biomarkers. The goal is to infer knowledge-relevant regulatory signals and then identify corresponding biomarkers through a multi-scale strategy. The approach has been successfully applied to two expression profiling experiments to demonstrate its improved performance in extracting biologically meaningful and disease-related biomarkers. More importantly, the proposed approach shows promising results to infer novel biomarkers for ovarian cancer and extend current knowledge.

An important topic in computational biology is to identify transcriptionalmodules through sequence analysis and gene expression profiling. Atranscriptional module is formed by a group of genes under control of one orseveral transcription factors (TFs) that bind to cis-regulatory elements in thepromoter regions of those genes. In this paper, we develop an integrative approach,namely motif-guided sparse decomposition (mSD), to uncover transcriptionalmodules by combining motif information and gene expression data.The method exploits the interplay of co-expression and co-regulation to findregulated gene patterns guided by TF binding information. Specifically, a motif-guided clustering method is first developed to estimate transcription factorbinding activities (TFBAs); sparse component analysis is then followed to furtheridentify TFs' target genes. The experimental results show that the mSD approachcan successfully help uncover condition-specific transcriptional modulesthat may have important implications in endocrine therapy of breast cancer.

Network Component Analysis (NCA) has shown its effectiveness inregulator identification by inferring the transcription factor activity (TFA) whenboth microarray data and ChIP-on-chip data are available. However, the NCAscheme is not applicable to many biological studies due to the lack of completeChIP-on-chip data. In this paper, we propose an integrative NCA (iNCA) approachto combine motif information, limited ChIP-on-chip data, and geneexpression data for regulatory network inference. Specifically, a Bayesian frameworkis adopted to develop a novel strategy, namely stability analysis with topologicalsampling, to infer key TFAs and their downstream gene targets. TheiNCA approach with stability analysis reduces the computational cost by avoidinga direct estimation of the high-dimensional distribution in a traditionalBayesian approach. Stability indices are designed to measure the goodness of theestimated TFAs and their connectivity strengths. The approach can also be usedto evaluate the confidence level of different data sources, considering the inevitableinconsistency among the data sources. The iNCA approach has beenapplied to a time course microarray data set of muscle regeneration. The experimentalresults show that iNCA can effectively integrate motif information, ChIP-on-chip data and microarray data to identify key regulators and their gene targetsin muscle regeneration. In particular, several identified TFAs like those ofMyoD, myogenin and YY1 are well supported by biological experiments.

Background Integrating data from multiple global assays and curated databases is essential to understand the spatio-temporal interactions within cells. Different experiments measure cellular processes at various widths and depths, while databases contain biological information based on established facts or published data. Integrating these complementary datasets helps infer a mutually consistent transcriptional regulatory network (TRN) with strong similarity to the structure of the underlying genetic regulatory modules. Decomposing the TRN into a small set of recurring regulatory patterns, called network motifs (NM), facilitates the inference. Identifying NMs defined by specific transcription factors (TF) establishes the framework structure of a TRN and allows the inference of TF-target gene relationship. This paper introduces a computational framework for utilizing data from multiple sources to infer TF-target gene relationships on the basis of NMs. The data include time course gene expression profiles, genome-wide location analysis data, binding sequence data, and gene ontology (GO) information. Results The proposed computational framework was tested using gene expression data associated with cell cycle progression in yeast. Among 800 cell cycle related genes, 85 were identified as candidate TFs and classified into four previously defined NMs. The NMs for a subset of TFs are obtained from literature. Support vector machine (SVM) classifiers were used to estimate NMs for the remaining TFs. The potential downstream target genes for the TFs were clustered into 34 biologically significant groups. The relationships between TFs and potential target gene clusters were examined by training recurrent neural networks whose topologies mimic the NMs to which the TFs are classified. The identified relationships between TFs and gene clusters were evaluated using the following biological validation and statistical analyses: (1) Gene set enrichment analysis (GSEA) to evaluate the clustering results; (2) Leave-one-out cross-validation (LOOCV) to ensure that the SVM classifiers assign TFs to NM categories with high confidence; (3) Binding site enrichment analysis (BSEA) to determine enrichment of the gene clusters for the cognate binding sites of their predicted TFs; (4) Comparison with previously reported results in the literatures to confirm the inferred regulations. Conclusion The major contribution of this study is the development of a computational framework to assist the inference of TRN by integrating heterogeneous data from multiple sources and by decomposing a TRN into NM-based modules. The inference capability of the proposed framework is verified statistically (e.g., LOOCV) and biologically (e.g., GSEA, BSEA, and literature validation). The proposed framework is useful for inferring small NM-based modules of TF-target gene relationships that can serve as a basis for generating new testable hypotheses.

Background Network Component Analysis (NCA) has shown its effectiveness in discovering regulators and inferring transcription factor activities (TFAs) when both microarray data and ChIP-on-chip data are available. However, a NCA scheme is not applicable to many biological studies due to limited topology information available, such as lack of ChIP-on-chip data. We propose a new approach, motif-directed NCA (mNCA), to integrate motif information and gene expression data to infer regulatory networks. Results We develop motif-directed NCA (mNCA) to incorporate motif information into NCA for regulatory network inference. While motif information is readily available from knowledge databases, it is a "noisy" source of network topology information consisting of many false positives. To overcome this problem, we develop a stability analysis procedure embedded in mNCA to resolve the inconsistency between motif information and gene expression data, and to enable the identification of stable TFAs. The mNCA approach has been applied to a time course microarray data set of muscle regeneration. The experimental results show that the inferred TFAs are not only numerically stable but also biologically relevant to muscle differentiation process. In particular, several inferred TFAs like those of MyoD, myogenin and YY1 are well supported by biological experiments. Conclusion A novel computational approach, mNCA, has been developed to integrate motif information and gene expression data for regulatory network reconstruction. Specifically, motif analysis is used to obtain initial network topology, and stability analysis is developed and applied with mNCA to extract stable TFAs. Experimental results on muscle regeneration microarray data have demonstrated that mNCA is a practical and reliable computational method for regulatory network inference and pathway discovery.

High-throughput genomic and proteomic technologies are widely used in cancer research to build better predictive models of diagnosis, prognosis and therapy, to identify and characterize key signalling networks and to find new targets for drug development. These technologies present investigators with the task of extracting meaningful statistical and biological information from high-dimensional data spaces, wherein each sample is defined by hundreds or thousands of measurements, usually concurrently obtained. The properties of high dimensionality are often poorly understood or overlooked in data modelling and analysis. From the perspective of translational science, this Review discusses the properties of high-dimensional data spaces that arise in genomic and proteomic studies and the challenges they can pose for data analysis and interpretation.

This paper gives an overview of statistical and machine learning-based feature selection and pattern classification algorithms and their application in molecular cancer classification or phenotype prediction. In particular, the paper focuses on the use of these computational methods for gene and peak selection from microarray and mass spectrometry data, respectively. The selected features are presented to a classifier for phenotype prediction.

Many statistical methods have been proposed to identify biomarkers from gene expression profiles. However, from expression data alone, statistical methods often fail to identify biologically meaningful biomarkers related to a specific biological process or disease under study. In this paper, we develop a novel strategy, namely knowledge-driven multi-scale independent component analysis (ICA), to infer regulatory signals and identify biologically relevant biomarkers from microarray data. Specifically, based on partial prior knowledge and clustering results, we apply ICA to find the most knowledge relevant linear regulatory mode in each subset of genes and then extract associated biomarkers according to their weighted loading factors. We have applied our method to a yeast cell cycle microarray dataset to find cell cycle regulated biomarkers. The experimental results indicate that our knowledge-driven multi-scale ICA method outperforms both baseline ICA method and correlation method significantly.

The design principles of gene transcriptional regulation networks in cells have been puzzles due to their unknown dynamic and nonlinear mechanisms. Although high-throughput biotechnologies have generated unprecedented amounts of data, the integration of multi-source data to better understand the process of gene regulation has been a challenge in post genomics era. Gene expression data are limited in providing information about the underlying causal relationships among genes. Prior biological knowledge such as protein binding data and gene ontology annotation, albeit limited in quantity, reflects physical processes of gene regulation. In this paper, we introduce a computational framework for utilizing time course gene expression patterns, protein binding data, and gene ontology information to infer transcriptional regulatory modules. The proposed method mainly consists of three parts: (1) a fuzzy c-means clustering approach that exploits gene functional category information to define gene clusters; (2) a network motif detection tool that classifies the transcription factors into different kinds of regulatory modules based on protein binding data; and (3) a recurrent neural network model for each transcription factor that mimics the architecture of the predicted regulatory module. A hybrid of genetic algorithm and particle swarm optimization method is applied to search for gene cluster that may be regulated by the transcription factor and to determine the parameters of the recurrent neural network. The proposed method is tested on yeast cell cycle process. The inferred gene transcriptional regulatory networks are compared with previously reported results in the literature.

For the critical task of gene module discovery in genomic research, we present a model-based hierarchical data clustering and visualization algorithm, visual statistical data analyzer (VISDA), which effectively exploits human-data interaction to improve the clustering outcome. Guided by a diagnostic tree, we apply VISDA to a muscular dystrophy dataset that contains a number of different phenotypic conditions. We then superimpose existing knowledge of gene regulation and gene function (ingenuity pathway analysis) to analyze the clustering results and generate novel hypotheses for further research on muscular dystrophies.

Though supervised and unsupervised analyses of genomic data have been intensively studied in recent years, little effort has been made to discover the structural information contained in the data. In this work, we propose a stability analysis guided supervised clustering and visualization method aiming to discover the hierarchical structure in gene expression data, which we call the "tree of phenotypes". We applied the method on two multiclass gene expression microarray data sets and presented the biological plausibility of the learned trees. We also tested the multiclass classifiers built on the learned trees and demonstrated their good classification performance.

We present a novel algorithm that combines a recurrent neural network (RNN) and two swarm intelligence (SI) methods to infer a gene regulatory network (GRN) from time course gene expression data. The algorithm uses ant colony optimization (ACO) to identify the optimal architecture of an RNN, while the weights of the RNN are optimized using particle swarm optimization (PSO). Our goal is to construct an RNN whose response mimics gene expression data generated by time course DNA microarray experiments. We observed promising results in applying the proposed hybrid SI-RNN algorithm to infer networks of interaction from simulated and real-world gene expression data.

We present a novel algorithm combining artificial neural networks and swarm intelligence (SI) methods to infer network interactions. The algorithm uses ant colony optimization (ACO) to identify the optimal architecture of a recurrent neural network (RNN), while the weights of the RNN are optimized using particle swarm optimization (PSO). Our goal is to construct an RNN that mimics the true structure of an unknown network and the time-series data that the network generated. We applied the proposed hybrid SI-RNN algorithm to infer a simulated genetic network. The results indicate that the algorithm has a promising potential to infer complex interactions such as gene regulatory networks from time-series gene expression data. I. INTRODUCTION inference algorithm based on GAs for the optimization of the influence matrix of gene regulatory network. In (13), GAs and ANNs are combined to determine gene interactions in temporal gene expression data. In this paper, we propose to apply a hybrid of ANNs and swarm intelligence (SI) methods (12) to infer network interactions from time-series data. The architecture and the synaptic weights of a recurrent neural network (RNN) are optimized using ant colony optimization (ACO) and particle swarm optimization (PSO) methods, respectively. Unlike previous computational methods, which targeted at one-step- ahead prediction of time-series data (13), our method enables a multi-step-ahead prediction. This is achieved through our RNN, which is self-evolutionary. The RNN starts with a given initial condition, evolves, and eventually reaches final states. The proposed hybrid SI-RNN algorithm selects the architecture of the RNN and weights not only to mimic the response of the unknown network at each time point but also to identify the structure of the network that generated the time-series data. This is a challenging task given that there may be many possible structures with responses that closely match the generated data. The algorithm evaluates various structures through the cross- validation method to avoid the selection of a wrong structure and to make sure that the correct structure is identified despite the presence of noise and complexity of the unknown network. We successfully applied the algorithm to infer simulated network interactions.

Gene module discovery can provide comprehensive molecular portrait of biological regulation and functional genomics. We present a new analytic strategy ? nonnegative Independent Component Analysis to reveal some gene module composite. The results show that by grouping genes in the latent space, we can find statistically more significant enrichment of gene annotations within clusters. Further, this approach has been applied to a muscular dystrophy data set for gene module discovery.