Search

Background Cardiometabolic diseases are highly comorbid, but their relationship with female‐specific or overwhelmingly female‐predominant health conditions (breast cancer, endometriosis, pregnancy complications) is understudied. This study aimed to estimate the cross‐trait genetic overlap and influence of genetic burden of cardiometabolic traits on health conditions unique to women. Methods and Results Using electronic health record data from 71 008 ancestrally diverse women, we examined relationships between 23 obstetrical/gynecological conditions and 4 cardiometabolic phenotypes (body mass index, coronary artery disease, type 2 diabetes, and hypertension) by performing 4 analyses: (1) cross‐trait genetic correlation analyses to compare genetic architecture, (2) polygenic risk score–based association tests to characterize shared genetic effects on disease risk, (3) Mendelian randomization for significant associations to assess cross‐trait causal relationships, and (4) chronology analyses to visualize the timeline of events unique to groups of women with high and low genetic burden for cardiometabolic traits and highlight the disease prevalence in risk groups by age. We observed 27 significant associations between cardiometabolic polygenic scores and obstetrical/gynecological conditions (body mass index and endometrial cancer, body mass index and polycystic ovarian syndrome, type 2 diabetes and gestational diabetes, type 2 diabetes and polycystic ovarian syndrome). Mendelian randomization analysis provided additional evidence of independent causal effects. We also identified an inverse association between coronary artery disease and breast cancer. High cardiometabolic polygenic scores were associated with early development of polycystic ovarian syndrome and gestational hypertension. Conclusions We conclude that polygenic susceptibility to cardiometabolic traits is associated with elevated risk of certain female‐specific health conditions.

The UK Biobank is a prospective study of 502,543 individuals, combining extensive phenotypic and genotypic data with streamlined access for researchers around the world1. Here we describe the release of exome-sequence data for the first 49,960 study participants, revealing approximately 4 million coding variants (of which around 98.6% have a frequency of less than 1%). The data include 198,269 autosomal predicted loss-of-function (LOF) variants, a more than 14-fold increase compared to the imputed sequence. Nearly all genes (more than 97%) had at least one carrier with a LOF variant, and most genes (more than 69%) had at least ten carriers with a LOF variant. We illustrate the power of characterizing LOF variants in this population through association analyses across 1,730 phenotypes. In addition to replicating established associations, we found novel LOF variants with large effects on disease traits, including PIEZO1 on varicose veins, COL6A1 on corneal resistance, MEPE on bone density, and IQGAP2 and GMPR on blood cell traits. We further demonstrate the value of exome sequencing by surveying the prevalence of pathogenic variants of clinical importance, and show that 2% of this population has a medically actionable variant. Furthermore, we characterize the penetrance of cancer in carriers of pathogenic BRCA1 and BRCA2 variants. Exome sequences from the first 49,960 participants highlight the promise of genome sequencing in large population-based studies and are now accessible to the scientific community., Exome sequences from the first 49,960 participants in the UK Biobank highlight the promise of genome sequencing in large population-based studies and are now accessible to the scientific community.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19), a respiratory illness that can result in hospitalization or death. We used exome sequence data to investigate associations between rare genetic variants and seven COVID-19 outcomes in 586,157 individuals, including 20,952 with COVID-19. After accounting for multiple testing, we did not identify any clear associations with rare variants either exome wide or when specifically focusing on (1) 13 interferon pathway genes in which rare deleterious variants have been reported in individuals with severe COVID-19, (2) 281 genes located in susceptibility loci identified by the COVID-19 Host Genetics Initiative, or (3) 32 additional genes of immunologic relevance and/or therapeutic potential. Our analyses indicate there are no significant associations with rare protein-coding variants with detectable effect sizes at our current sample sizes. Analyses will be updated as additional data become available, and results are publicly available through the Regeneron Genetics Center COVID-19 Results Browser.

SARS-CoV-2 enters host cells by binding angiotensin-converting enzyme 2 (ACE2). Through a genome-wide association study, we show that a rare variant (MAF = 0.3%, odds ratio 0.60, P=4.5x10-13) that down-regulates ACE2 expression reduces risk of COVID-19 disease, providing human genetics support for the hypothesis that ACE2 levels influence COVID-19 risk. Further, we show that common genetic variants define a risk score that predicts severe disease among COVID-19 cases.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) causes coronavirus disease-19 (COVID-19), a respiratory illness that can result in hospitalization or death. We investigated associations between rare genetic variants and seven COVID-19 outcomes in 543,213 individuals, including 8,248 with COVID-19. After accounting for multiple testing, we did not identify any clear associations with rare variants either exome-wide or when specifically focusing on (i) 14 interferon pathway genes in which rare deleterious variants have been reported in severe COVID-19 patients; (ii) 167 genes located in COVID-19 GWAS risk loci; or (iii) 32 additional genes of immunologic relevance and/or therapeutic potential. Our analyses indicate there are no significant associations with rare protein-coding variants with detectable effect sizes at our current sample sizes. Analyses will be updated as additional data become available, with results publicly browsable athttps://rgc-covid19.regeneron.com.

Chemical investigation of root bark of Glycosmis pentaphylla and stem bark of Tabernaemontana coronaria led to the isolation of three carbazole alkaloids glycozoline, glycozolidine and methyl carbazole 3-carboxylate, two furoquinoline alkaloids skimmianine and dictamine, an acridone alkaloid arborinine, three monomeric indole alkaloids coronaridine, 10-methoxy coronaridine and tabernaemontanine, and two dimeric indole alkaloids voacamine and tabernaelegantine B. Their structures were established by detailed spectral analysis. Mutagenic and antimutagenic potential of methanol extract of both plant materials were evaluated by Ames test against known positive mutagens 2-aminofluorine, 4-nitro-O-phenylenediamine and sodium azide using Salmonella typhimurium TA 98 and TA 100 bacterial strains both in the presence and absence of S9. Both the extracts were non-mutagenic in nature. Both the extracts of G. pentaphylla and T. coronaria exhibited significant antimutagenic activity against NPD and sodium azide for S. typhimurium TA98 and TA100 strains. The results indicated that the extracts could counteract the mutagenicity induced by different genotoxic compounds.

SUMMARYThe UK Biobank is a prospective study of 502,543 individuals, combining extensive phenotypic and genotypic data with streamlined access for researchers around the world. Here we describe the first tranche of large-scale exome sequence data for 49,960 study participants, revealing approximately 4 million coding variants (of which ~98.4% have frequency < 1%). The data includes 231,631 predicted loss of function variants, a >10-fold increase compared to imputed sequence for the same participants. Nearly all genes (>97%) had ≥1 predicted loss of function carrier, and most genes (>69%) had ≥10 loss of function carriers. We illustrate the power of characterizing loss of function variation in this large population through association analyses across 1,741 phenotypes. In addition to replicating a range of established associations, we discover novel loss of function variants with large effects on disease traits, including PIEZO1 on varicose veins, COL6A1 on corneal resistance, MEPE on bone density, and IQGAP2 and GMPR on blood cell traits. We further demonstrate the value of exome sequencing by surveying the prevalence of pathogenic variants of clinical significance in this population, finding that 2% of the population has a medically actionable variant. Additionally, we leverage the phenotypic data to characterize the relationship between rare BRCA1 and BRCA2 pathogenic variants and cancer risk. Exomes from the first 49,960 participants are now made accessible to the scientific community and highlight the promise offered by genomic sequencing in large-scale population-based studies.

To best define biomarkers of response, and to shed insight on mechanism of action of certain clinically important agents for early breast cancer, we used a brief-exposure paradigm in the preoperative setting to study transcriptional changes in patient tumors that occur with one dose of therapy prior to combination chemotherapy. Tumor biopsies from breast cancer patients enrolled in two preoperative clinical trials were obtained at baseline and after one dose of bevacizumab (HER2-negative), trastuzumab (HER2-positive) or nab-paclitaxel, followed by treatment with combination chemo-biologic therapy. RNA-Sequencing based PAM50 subtyping at baseline of 46 HER2-negative patients revealed a strong association between the basal-like subtype and pathologic complete response (pCR) to chemotherapy plus bevacizumab (p ≤ 0.0027), but did not provide sufficient specificity to predict response. However, a single dose of bevacizumab resulted in down-regulation of a well-characterized TGF-β activity signature in every single breast tumor that achieved pCR (p ≤ 0.004). The TGF-β signature was confirmed to be a tumor-specific read-out of the canonical TGF-β pathway using pSMAD2 (p ≤ 0.04), with predictive power unique to brief-exposure to bevacizumab (p ≤ 0.016), but not trastuzumab or nab-paclitaxel. Down-regulation of TGF-β activity was associated with reduction in tumor hypoxia by transcription and protein levels, suggesting therapy-induced disruption of an autocrine-loop between tumor stroma and malignant cells. Modulation of the TGF-β pathway upon brief-exposure to bevacizumab may provide an early functional readout of pCR to preoperative anti-angiogenic therapy in HER2-negative breast cancer, thus providing additional avenues for exploration in both preclinical and clinical settings with these agents.

Background: Genomics is expected to transform oncology clinical practice. However, converting genomic data into clinically actionable information is a daunting task. Genomic high-throughput technologies produce massive amounts of raw data, and its complexity presents a formidable challenge for clinical adoption. While algorithms exist to convert genomic data into meaningful biological information, they are geared towards the bioinformatics expert user, lack clinical annotation and interpretation, and are not addressing the needs of clinical experts. Furthermore, the main problem is that sequencing results should not be treated like a test as they are geared towards precision diagnostics which require patient clinical data. Methods: We present PAPAyA, a genome informatics platform that provides an overall solution to the genomic data overload which includes analysis of whole transcriptome and exome data, secure storage and management of this data and the interactive presentation of patient genome information in a contextualized manner. It is a continuum of analytics and user experiences with deep understanding of the clinical questions and the workflow. PAPAyA is a framework for hosting multiple genome informatics applications that bring information that is Connected, Digital and in Real Time. Connected in many dimensions - across hospital systems, across time, across many hospitals and their affiliates. Digital means retrievable (without complicated SQL queries) not just stored bits across modalities (genomics is a single vertical that pulls other information towards precise treatment). Real time means up to date ACTGs get converted into actionable information fast, and get to the right clinical expert without manual actions and printing reports. The framework sits on top of a digital health platform that offers core capabilities such as: persisting data and provisioning service (elastic computing), security, auditing, business workflow engine, reports service, user management service, patient identity service, provider registry service. At its core, PAPAyA provides full pipelines that analyze genomic high-throughput data, and further extract and prioritize clinically-meaningful information from the patient's genome. Furthermore, PAPAyA enables storage and secure management of this complex data which allows clinicians to query the data in a clinical action-oriented framework, as opposed to data aggregation and reporting framework. Results: We demonstrate the usability of PAPAyA using public data from TCGA breast cancer cohort. We show the utility of executing in-silico assays applied on RNAseq data, such as ER/PR/Her2, differential expression of long-noncoding RNAs, gene fusions, subtyping, hypoxia index, and other known signatures from the biomedical literature. In parallel, we annotate full exomes in order to find clinically relevant information for variants of unknown significance as well as variants with known disease and response phenotype using biological, functional, and clinical annotation resources. Conclusion: We have implemented an automated open learning system for processing genomic data to aid in clinical decision making. We already started the pilot phase and clinical evaluation with key academic collaborators. Citation Format: Nilanjana Banerjee, Konstantin Volyanskyy, Vartika Agrawal, Yong Mao, Yee-Him Cheung, Sitharthan Kamalakaran, Laurens Boekhorst, Arjan Claassen, Nevenka Dimitrova. PAPAyA – The genome informatics framework for oncology applications and other clinical domains [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P4-08-02.

Following recent work [e.g., J. Park et al., J. Rheol. 56, 1057–1082 (2012); T. Yaoita et al., Macromolecules 45, 2773–2782 (2012); and G. Ianniruberto et al., Macromolecules 45, 8058–8066 (2012)], we introduce the idea of a configuration dependent friction coefficient (CDFC) based on the relative orientation of Kuhn bonds of the test and surrounding matrix chains. We incorporate CDFC into the “toy” model of Mead et al. [Macromolecules 31, 7895–7914 (1998)] in a manner akin to Yaoita et al. [Nihon Reoroji Gakkaishi 42, 207–213 (2014)]. Additionally, we incorporate entanglement dynamics (ED) of discrete entanglement pairs into the new Mead–Banerjee–Park (MBP) model in a way similar to Ianniruberto and Marrucci [J. Rheol. 58, 89–102 (2014)]. The MBP model predicts a deformation dependent entanglement microstructure which is physically reflected in a reduced modulus that heals slowly following cessation of deformation. Incorporating ED into the model allows “shear modification” to be qualitatively captured. The MBP model is tested against experimental data in steady and transient extensional and shear flows. The MBP model captures the monotonic thinning of the extensional flow curve of entangled monodisperse polystyrene (PS) melts [A. Bach et al., Macromolecules 36, 5174–5179 (2003)] while simultaneously predicting the extension hardening found in PS semidilute solutions where CDFC is diluted out [P. K. Bhattacharjee et al., Macromolecules 35, 10131–10148 (2002)]. The simulation results also show that the rheological properties in nonlinear extensional flows of PS melts are sensitive to CDFC but not to convective constraint release (CCR) while those for shear flows are influenced more by CCR. The monodisperse MBP toy model is generalized to arbitrary polydispersity.

MicroRNAs (miRNAs) are known to play an important role in cancer development by post-transcriptionally affecting the expression of critical genes. The aims of this study were two-fold: (i) to develop a robust method to isolate miRNAs from small volumes of saliva and (ii) to develop a panel of saliva-based diagnostic biomarkers for the detection of head and neck squamous cell carcinoma (HNSCC). Five differentially expressed miRNAs were selected from miScript™ miRNA microarray data generated using saliva from five HNSCC patients and five healthy controls. Their differential expression was subsequently confirmed by RT-qPCR using saliva samples from healthy controls (n = 56) and HNSCC patients (n = 56). These samples were divided into two different cohorts, i.e., a first confirmatory cohort (n = 21) and a second independent validation cohort (n = 35), to narrow down the miRNA diagnostic panel to three miRNAs: miR-9, miR-134 and miR-191. This diagnostic panel was independently validated using HNSCC miRNA expression data from The Cancer Genome Atlas (TCGA), encompassing 334 tumours and 39 adjacent normal tissues. Receiver operating characteristic (ROC) curve analysis was performed to assess the diagnostic capacity of the panel. On average 60 ng/μL miRNA was isolated from 200 μL of saliva. Overall a good correlation was observed between the microarray data and the RT-qPCR data. We found that miR-9 (P

There is a growing discussion about the possibility of arsenic mitigation measures in Bengal and similar areas leading to undesirable substitution of water-borne-pathogen attributable risks pathogens for risks attributable to arsenic, in part because of uncertainties in relative pathogen concentrations in supplied and end-use water. We try to resolve this discussion, by assessing the relative contributions of water supply and end-user practices to water-borne-pathogen-attributable risks for arsenic mitigation options in a groundwater arsenic impacted area of West Bengal. Paired supplied arsenic-mitigated water and end-use drinking water samples from 102 households were collected and analyzed for arsenic and thermally tolerant coliforms [TTC], used as a proxy for microbiological water quality, We then estimated the DALYs related to key sequelae, diarrheal diseases and cancers, arising from water-borne pathogens and arsenic respectively. We found [TTC] in end-use drinking water to depend only weakly on [TTC] in source-water. End-user practices far outweighed the microbiological quality of supplied water in determining diarrheal disease burden. [TTC] in source water was calculated to contribute &lt, 1% of total diarrheal disease burden. No substantial demonstrable pathogen-for-arsenic risk substitution attributable to specific arsenic mitigation of supplied waters was observed, illustrating the benefits of arsenic mitigation measures in the area studied.

Background: Long non-coding RNAs (lncRNAs) are pervasively transcribed in the genome yet their role in human disease is not well understood. LncRNAs can have regulatory effects on coding mRNAs through a number of mechanisms, including repressing their sense-strand protein-coding partners. There is also emerging evidence that hormone signaling affects the expression of a wide variety of lncRNAs. However, the role of lncRNAs in the response mechanisms to endocrine therapy remains mostly unknown. We investigated MCF7 and T47D cell-line RNA-seq data to characterize changes in lncRNA expression and evaluate their response to estradiol (E2), progesterone (P4) and to their antagonists, tamoxifen (TAM) and mifepristone (RU486). Methods: RNA-seq data from MCF7 cell-lines with response to E2 and TAM and T47D cell-lines with response to P4 and RU486 were obtained from publicly available short read archive (SRA: ERP000992). Expression values after hormonal or antagonist treatments were compared to non-treated cells. We independently sequenced total RNA samples of T47D cell-lines with or without E2 treatment and created a robust list E2-responsive lncRNAs for further analysis. The RNA-sequencing data was aligned to a lncRNA database containing 14,572 unique lncRNAs. Significant log-fold changes in relative abundance of lncRNA transcripts were selected using percentile-p-value. ER-responsive binding sites on or near the ER-associated lncRNAs were investigated in a ChIP-seq study in MCF7 cells following estrogen treatment. Finally, we identified the subset of hormonally-responsive lncRNAs that are also evolutionarily conserved. Results: On average, in each cell-line experiment, 5000 lncRNAs were detectable. Expression of 55 lncRNAs were associated with differing response to E2 and TAM (p3-fold change between response to agonist and antagonist). Less than 10% of the lncRNAs were common among the two sets suggesting that while there might be some cross-talk, E2 and P4 are generally regulating unique sets of lncRNAs. For example, XLOC_013954, a bona-fide E2-responsive lncRNA shows 5-fold difference between response to E2 and TAM, but has a fold change of mere 1.1 between response to P2 and RU486. From the MCF7 ChIP-seq data we found that >50% of the ER-associated lncRNAs had ER binding site either overlapping or neighboring the lncRNA. Less than 30% of the response-associated lncRNAs were evolutionarily conserved suggesting they might be under different selection pressures. Discussion: We have shown that lncRNA expression levels are associated with hormonal influences and endocrine therapy. We have further established that the lncRNAs impacted by the different hormonal influences are quite exclusive and suggest separate regulatory networks. We are also exploring lncRNA-mRNA expression for coding partners of the response-associated lncRNAs. Understanding the regulatory effects of lncRNA expression opens up new opportunities for stratification and management of breast cancers. Citation Format: Nilanjana Banerjee, Wim Verhaegh, Patrick Cheung, Nevenka Dimitrova. RNA-seq reveals lncRNAs associated with hormonal influences and endocrine therapy [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P3-05-18.

Next‐generation sequencing (NGS) approaches for measuring RNA and DNA benefit from greatly increased sensitivity, dynamic range and detection of novel transcripts. These technologies are rapidly becoming the standard for molecular assays and represent huge potential value to the practice of oncology. However, many challenges exist in the transition of these technologies from research application to clinical practice. This review discusses the value of NGS in detecting mutations, copy number changes and RNA quantification and their applications in oncology, the challenges for adoption and the relevant steps that are needed for translating this potential to routine practice.

While the tumorigenic effects of specific recurrent mutations in known cancer driver-genes is well-characterized, not much is known about the functional relevance of the vast majority of recurrent mutations observed across cancers. Prior studies have attempted to identify functional genomic aberrations by integrating multi-omics measurements in cancer samples with community-curated biological pathway networks. However, the majority of these approaches overlook the following biological considerations: i) signaling pathway networks are highly tissue-specific and their regulatory interactions differ across tissue types; ii) regulatory factors exhibit heterogeneous influence on downstream gene transcription; iii) epigenetic and genomic alterations exhibit nonlinear impact on gene transcription. In order to accommodate these biological effects, we propose a hybrid Bayesian method to learn tissue-specific pairwise influence models amongst genes and to predict a gene's expression level as a nonlinear-function of its epigenetic and regulatory influences. We employ a novel tree-based depth-penalization mechanism in order to capture the higher regulatory impact of closer neighbors in the regulatory network. Using a breast cancer multi-omics dataset (N=1190), we show that our proposed method has superior prediction power over optimization-based regression models, with the additional advantage of revealing gene deregulations potentially driven by somatic mutations.

Cooperativity between transcription factors is critical to gene regulation. Current computational methods do not take adequate account of this salient aspect. To address this issue, we present a computational method based on multivariate adaptive regression splines to correlate the occurrences of transcription factor binding motifs in the promoter DNA and their interactions to the logarithm of the ratio of gene expression levels. This allows us to discover both the individual motifs and synergistic pairs of motifs that are most likely to be functional, and enumerate their relative contributions at any arbitrary time point for which mRNA expression data are available. We present results of simulations and focus specifically on the yeast cell-cycle data. Inclusion of synergistic interactions can increase the prediction accuracy over linear regression to as much as 1.5- to 3.5-fold. Significant motifs and combinations of motifs are appropriately predicted at each stage of the cell cycle. We believe our multivariate adaptive regression splines-based approach will become more significant when applied to higher eukaryotes, especially mammals, where cooperative control of gene regulation is absolutely essential.

Arsenic Induced Health Effects, Genetic Damage and Genetic Variants in the Population Exposed to Arsenic through Drinking Water in West Bengal

Background: Breast cancer subtyping using gene expression is well established in breast cancer research. While it is known that there are large chromosomal regions affected by copy number polymorphisms in breast cancer, it is not clear whether expression patterns reflect large genomic events affecting longer portion of the chromosome. We present a method to quantify long range gene expression patterns of larger genomic regions at 23kb, 100kb and 1mb. Methods: We used TCGA-level 2 breast cancer gene expression data (RNA-Seq) generated at the Carolina Center for Genome Sciences, UNC at Chapel Hill. We evaluated the long range expression patterns of 649 patients for which we had ER, PR and Her2 status data by IHC. For 221 samples we had ER, PR, Her2, age, menopausal status, p53 and PIK3CA mutation status. Our method defines long range expression within a window of a particular length (e.g. 23kb, 100kb). We take the mean expression scores for all genes that fall within each window and concatenate these windows to obtain larger chromosome-wide patterns. The final chromosome-wide vectors are concatenated to represent long range expression patterns across the entire genome. We then evaluate the variation of these window scores across all samples and keep top 2% varying windows. Then, we apply hierarchical clustering, and evaluate enrichment of clinically meaningful subtypes using hypergeometric test. Results: Simple hierarchical clustering revealed clear separation of triple negative breast cancer samples at any level of resolution: 23kb, 100kb in the available data set. While the 100kb resolution showed two distinct clusters, the 23kb resolution showed three distinct clusters. Interestingly, the hierarchical clustering of samples (n=649) using the top 214 (2%) highly varying long 100kb regions revealed a cluster which contained 99 samples and was enriched with 75 TNBC samples (out of 102 TNBC in the entire set) (p=1.7E-53). The long range expression of 23Kb regions on 221 samples revealed 319 (2%) such regions which further segregate the samples into three different clusters: Cluster1: Luminal enriched cluster with 147 samples out of which 104 are ER+PR+Her2- samples (p-value < Conclusions: Hierarchical clustering relying on long range expression regions produces clusters that are enriched with well known clinically relevant subtypes. The associations with known tumor biology are strong for different region sizes (23kb and 100kb). This is the first study to report long range gene expression patterns that reveal data-driven close association with tumor biology. Citation Format: Alex Mankovich, Vartika Agrawal, Nilanjana Banerjee, Nevenka Dimitrova. Long range expression patterns detected by RNASeq in breast cancer reveal cluster enriched with triple negative breast cancer [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P2-03-14.

Over 26 million people in West Bengal, India, are exposed to very high levels of arsenic through drinking water, leading to several deleterious endpoints including cancers. To elucidate the role of promoter methylation in arsenic-induced dermatological and nondermatological health effects, methylation status of p16 and DAPK genes was determined. A case-control study was conducted involving 72 individuals with arsenic-induced skin lesions (cases) and 50 individuals without skin lesions (controls), having similar arsenic exposure through drinking water.Methylation status was determined by bisulfite conversion of genomic DNA and methylation-specific PCR. Expression of the genes was determined by real-time PCR and Western blot analysis. Associations between the promoter methylation status and nondermatological health effects were determined from epidemiological survey data. Significant hypermethylation was found in the promoters of both DAPK and p16 genes in the cases compared with the controls resulting in downregulation of both the genes in the cases. There was a 3.4-fold decrease in the expression of death-associated protein kinase and 2.2-fold decrease in gene expression of p16 in the cases compared to the controls, the lowest expression being in the cancer tissues. Promoter hypermethylation of the genes was also associated with higher risk of developing arsenic-induced skin lesions, peripheral neuropathy, ocular and respiratory diseases. This study for the first time makes an attempt to correlate epigenetic modifications of the tumor suppressor genes with dermatological and nondermatological health outcomes in a population chronically exposed to arsenic

Arsenic in drinking water may cause major deleterious health impacts including death. Although arsenic in rice has recently been demonstrated to be a potential exposure route for humans, there has been to date no direct evidence for the impact of such exposure on human health. Here we show for the first time, through a cohort study in West Bengal, India, involving over 400 human subjects not otherwise significantly exposed to arsenic through drinking water, elevated genotoxic effects, as measured by micronuclei (MN) in urothelial cells, associated with the staple consumption of cooked rice with >200 μg/kg arsenic. Further work is required to determine the applicability to populations with different dietary and genetic characteristics, but with over 3 billion people in the world consuming rice as a staple food and several percent of this rice containing such elevated arsenic concentrations, this study raises considerable concerns over the threat to human health.

article i nfo Objective. Ovarian cancers are highly heterogeneous and while chemotherapy is the preferred treatment many patients are intrinsically resistant or quickly develop resistance. Furthermore, all tumors that recur ultimately become resistant. Recent evidence suggests that epigenetic deregulation may be a key factor in the onset and maintenance of chemoresistance. We set out to identify epigenetically silenced genes that affect chemoresistance. Methods. The epigenomes of at otal of 45 ovarian samples were analyzed to identify epigenetically altered genes that segregate with platinum response, and further filtered with expression data to identify genes that were suppressed. A tissue culture carboplatin resistance screen was utilized to functionally validate this set of candidate platinum resistance genes. Results. Our screen correctly identified 19 genes that when suppressed altered the chemoresistance of the cells in culture. Of the genes identified in the screen we further characterized one gene, docking protein 2 (DOK2), an adapter protein downstream of tyrosine kinase, to determine if we could elucidate the mechanism by which it increased resistance. The loss of DOK2 decreased the level of apoptosis in response to carboplatin. Furthermore, in cells with reduced DOK2, the level of anoikis was decreased. Conclusions. We have developed a screening methodology that analyzes the epigenome and informatically identifiescandidate genesfollowed byinvitroculture screeningofthe candidate genes.Tovalidate ourscreening methodology we further characterized one candidate gene, DOK2, and showed that loss of DOK2 induces chemotherapy resistance by decreasing the level of apoptosis in response to treatment.

Background Arsenic is a natural drinking water contaminant affecting 26 million people in West Bengal, India. Chronic arsenic exposure causes cancer, cardiovascular disease, liver disease, neuropathies and ocular diseases. The aims of the present study were to assess bioindicators of hepatocellular injury as indicated by the levels of liver enzymes, to determine the auto immune status, as indicated by the amounts of anti-nuclear antibodies (ANA) and anti-dsDNA antibodies in their serum, and to predict cardiovascular risk in the arsenic exposed population. Methods Effect of chronic arsenic exposure on liver was determined by liver function tests. Autoimmune status was measured by measuring ANA and anti-dsDNA in serum. Inflammatory cytokines associated with increased cardiovascular disease risk, IL6, IL8 and MCP-1 were determined. Results Our results indicated that serum levels of bilirubin, alanine transaminase, aspartate transaminase, alkaline phosphatase and ANA were increased in the arsenic exposed population. Serum levels of IL6 and IL8 also increased in the arsenic exposed group. Conclusions Chronic arsenic exposure causes liver injury, increases the serum levels of autoimmune markers and imparts increased cardiovascular risk.

BackgroundAllium sativum leaf agglutinin (ASAL) is a 25-kDa homodimeric, insecticidal, mannose binding lectin whose subunits are assembled by the C-terminal exchange process. An attempt was made to convert dimeric ASAL into a monomeric form to correlate the relevance of quaternary association of subunits and their functional specificity. Using SWISS-MODEL program a stable monomer was designed by altering five amino acid residues near the C-terminus of ASAL.Methodology/principal findingsBy introduction of 5 site-specific mutations (-DNSNN-), a β turn was incorporated between the 11(th) and 12(th) β strands of subunits of ASAL, resulting in a stable monomeric mutant ASAL (mASAL). mASAL was cloned and subsequently purified from a pMAL-c2X system. CD spectroscopic analysis confirmed the conservation of secondary structure in mASAL. Mannose binding assay confirmed that molecular mannose binds efficiently to both mASAL and ASAL. In contrast to ASAL, the hemagglutination activity of purified mASAL against rabbit erythrocytes was lost. An artificial diet bioassay of Lipaphis erysimi with mASAL displayed an insignificant level of insecticidal activity compared to ASAL. Fascinatingly, mASAL exhibited strong antifungal activity against the pathogenic fungi Fusarium oxysporum, Rhizoctonia solani and Alternaria brassicicola in a disc diffusion assay. A propidium iodide uptake assay suggested that the inhibitory activity of mASAL might be associated with the alteration of the membrane permeability of the fungus. Furthermore, a ligand blot assay of the membrane subproteome of R. solani with mASAL detected a glycoprotein receptor having interaction with mASAL.Conclusions/significanceConversion of ASAL into a stable monomer resulted in antifungal activity. From an evolutionary aspect, these data implied that variable quaternary organization of lectins might be the outcome of defense-related adaptations to diverse situations in plants. Incorporation of mASAL into agronomically-important crops could be an alternative method to protect them from dramatic yield losses from pathogenic fungi in an effective manner.

In West Bengal, India, at present, more than 26 million people are exposed to arsenic through drinking water. Among them, only 15-20% manifest arsenic-induced noncancerous, precancerous, and cancerous skin lesions, indicating that genetic variants play important role in arsenic susceptibility. Chronic arsenic exposure has been associated with impairment of immune systems in the exposed individuals. Because cytokines are important immune mediators, alteration in expression of these gene products may lead to arsenic-specific disease manifestations. The aim of the present work was to investigate the association between the TNF-α-308G>A (rs1800629) and IL10 -3575T>A (rs1800890) polymorphisms and arsenic-induced dermatological and nondermatological health outcomes. A case-control study was conducted in West Bengal, India, involving 207 cases with arsenic-induced skin lesions and 190 controls without skin lesions having similar arsenic exposure. The polymorphisms were determined using conventional PCR-sequencing method. ELISA was done to determine the serum levels of the two cytokines tumor necrosis factor α (TNF-α) and interleukin 10 (IL10). Associations between the polymorphisms studied and nondermatological health effects in the study subjects were determined from our epidemiological survey data. Individuals with GA/AA (-308 TNF-α) and TA/AA (-3575 IL10) genotypes were at higher risk of developing arsenic-induced skin lesions, ocular, and respiratory diseases. Also the -308 TNF A allele corresponded to a higher production of TNF-α, and -3575 IL10 A allele corresponded to a lower production of IL10. Thus, the polymorphisms studied impart significant risk toward development of arsenic-induced dermatological and nondermatological health effects in the chronically exposed population of West Bengal, India.

The diversity of breast cancers reflects variations in underlying biology and affects the clinical implications for patients. Gene expression studies have identified five major subtypes– Luminal A, Luminal B, basal‐like, ErbB2+ and Normal‐Like. We set out to determine the role of DNA methylation in subtypes by performing genome‐wide scans of CpG methylation in breast cancer samples with known expression‐based subtypes. Unsupervised hierarchical clustering using a set of most varying loci clustered the tumors into a Luminal A majority (82%) cluster, Basal‐like/ErbB2+ majority (86%) cluster and a non‐specific cluster with samples that were also inconclusive in their expression‐based subtype correlations. Contributing methylation loci were both gene associated loci (30%) and non‐gene associated (70%), suggesting subtype dependant genome‐wide alterations in the methylation landscape. The methylation patterns of significant differentially methylated genes in luminal A tumors are similar to those identified in CD24 + luminal epithelial cells and the patterns in basal‐like tumors similar to CD44 + breast progenitor cells. CpG islands in the HOXA cluster and other homeobox (IRX2, DLX2, NKX2‐2) genes were significantly more methylated in Luminal A tumors. A significant number of genes (2853, p

Chronic arsenic exposure through contaminated drinking water is a major environmental health issue. Chronic arsenic exposure is known to exert its toxic effects by a variety of mechanisms, of which generation of reactive oxygen species (ROS) is one of the most important. A high level of ROS, in turn, leads to DNA damage that might ultimately culminate in cancer. In order to keep the level of ROS in balance, an array of enzymes is present, of which catalase (CAT) and myeloperoxidase (MPO) are important members. Hence, in this study, we determined the activities of these two enzymes in the sera and chromosomal aberrations (CA) in peripheral blood lymphocytes in individuals exposed and unexposed to arsenic in drinking water. Arsenic in drinking water and in urine was used as a measure of exposure. Our results show that individuals chronically exposed to arsenic have significantly higher CAT and MPO activities and higher incidence of CA. We found moderate positive correlations between CAT and MPO activities, induction of CA and arsenic in urine and water. These results indicate that chronic arsenic exposure causes higher CAT and MPO activities in serum that correlates with induction of genetic damage. We conclude that the serum levels of these enzymes might be used as biomarkers of early arsenic exposure induced disease much before the classical dermatological symptoms of arsenicosis begin to appear.

Transcriptional interactions in a cell are modulated by a variety of mechanisms that prevent their representation as pure pairwise interactions between a transcription factor and its target(s). These include, among others, transcription factor activation by phosphorylation and acetylation, formation of active complexes with one or more co-factors, and mRNA/protein degradation and stabilization processes. This paper presents a first step towards the systematic, genome-wide computational inference of genes that modulate the interactions of specific transcription factors at the post-transcriptional level. The method uses a statistical test based on changes in the mutual information between a transcription factor and each of its candidate targets, conditional on the expression of a third gene. The approach was first validated on a synthetic network model, and then tested in the context of a mammalian cellular system. By analyzing 254 microarray expression profiles of normal and tumor related human B lymphocytes, we investigated the post transcriptional modulators of the MYC proto-oncogene, an important transcription factor involved in tumorigenesis. Our method discovered a set of 100 putative modulator genes, responsible for modulating 205 regulatory relationships between MYC and its targets. The set is significantly enriched in molecules with function consistent with their activities as modulators of cellular interactions, recapitulates established MYC regulation pathways, and provides a notable repertoire of novel regulators of MYC function. The approach has broad applicability and can be used to discover modulators of any other transcription factor, provided that adequate expression profile data are available.

Transcription regulation in eukaryotes is known to occur through the coordinated action of multiple transcription factors (TFs). Recently, a few genome-wide transcription studies have begun to explore the combinatorial nature of TF interactions. We propose a novel approach that reveals how multiple TFs cooperate to regulate transcription in the yeast cell cycle. Our method integrates genome-wide gene expression data and chromatin immunoprecipitation (ChIP-chip) data to discover more biologically relevant synergistic interactions between different TFs and their target genes than previous studies. Given any pair of TFs A and B, we define a novel measure of cooperativity between the two TFs based on the expression patterns of sets of target genes of only A, only B, and both A and B. If the cooperativity measure is significant then there is reason to postulate that the presence of both TFs is needed to influence gene expression. Our results indicate that many cooperative TFs that were previously characterized experimentally indeed have high values of cooperativity measures in our analysis. In addition, we propose several novel, experimentally testable predictions of cooperative TFs that play a role in the cell cycle and other biological processes. Many of them hold interesting clues for cross talk between the cell cycle and other processes including metabolism, stress response and pseudohyphal differentiation. Finally, we have created a web tool where researchers can explore the exhaustive list of cooperative TFs and survey the graphical representation of the target genes' expression profiles. The interface includes a tool to dynamically draw a TF cooperativity network of 113 TFs with user-defined significance levels. This study is an example of how systematic combination of diverse data types along with new functional genomic approaches can provide a rigorous platform to map TF interactions more efficiently.

Ovarian cancers are highly heterogeneous and while chemotherapy is the preferred treatment many patients are intrinsically resistant or quickly develop resistance. Furthermore, all tumors that recur ultimately become resistant. Recent evidence suggests that epigenetic deregulation may be a key factor in the onset and maintenance of chemoresistance. Epigenetic events are reversible and the identification of genes altered by this mechanism may lead to studies on how to reprogram the process leading up to resistance. We set out to identify epigenetically silenced genes that affect chemoresistance. The epigenomes of a total of 45 ovarian samples were analyzed to identify epigenetically altered genes that segregate with platinum response, and further filtered with expression data to identify genes that were suppressed. A tissue culture carboplatin resistance screen was utilized to functionally validate this set of candidate platinum resistance genes. Our screen identified a number of genes that when suppressed altered the chemoresistance of the cells in culture. One of the validated candidate genes identified through the pooled shRNA screen was DOK2, an adapter protein downstream of tyrosine kinase, which has been shown by others to be a lung cancer tumor suppressor. We show that suppression of DOK2 by short hairpin RNAs in ovarian cell lines conferred resistance to platinum treatment. Functional analysis of loss of DOK2 effect on chemoresistance determined that there was no change in growth or influx or efflux of carboplatin. However, we determined that loss of DOK2 increased growth in soft agar and also increased migratory capabilities. Furthermore the loss of DOK2 decreased the level of apoptosis in response to carboplatin. Furthermore, in cells with reduced DOK2, the level of anoikis was decreased. We have developed a screening methodology that analyzes the epigenome and informatically identifies candidate genes followed by in vitro culture screening of the candidate genes. To validate our screening methodology we further characterized one candidate gene, DOK2, and showed that loss of DOK2 induces chemotherapy resistance by decreasing the level of apoptosis in response to treatment. Citation Format: Michele Vigliotti, Elena Lum, nilanjana Banerjee, Noelle Cutter, Kazimierz O. Wrzeszczynski, Sohail Khan, Sitharthan Kamalakaran, Douglas A. Levine, Nevenka Dimitrova, Robert Lucito. Loss of DOK2 induces carboplatin resistance in ovarian cancer via suppression of apoptosis. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research: From Concept to Clinic; Sep 18-21, 2013; Miami, FL. Philadelphia (PA): AACR; Clin Cancer Res 2013;19(19 Suppl):Abstract nr A46.

Background: Long non-coding RNAs (lncRNAs) are pervasively transcribed in the genome yet their role in human disease is not well understood. Recent studies show that expression of the lncRNA HOTAIR is increased in primary breast tumors and is a predictor of metastasis. However, to date, it is unclear how changes in lncRNA expression with treatment correlate with response. We performed an RNA-seq study to characterize changes in lncRNA expression following exposure to chemotherapy or biologic therapy and evaluate association to achievement of pathologic complete response (pCR) to neoadjuvant therapy. Methods: We sequenced transcriptomes of core biopsy RNA from 50 pairs of breast tumors obtained from neoadjuvant clinical trials BrUOG 211A/211B. Patients were given a run-in dose of bevacizumab (B), nab-paclitaxel (N) or trastuzumab (T), followed by combination biologic/chemotherapy (HER2- with B/carboplatin/N; HER2+ with T/carboplatin/N). RNA was derived from biopsy pairs obtained pre/post 10 day exposure to run-in monotherapy. Paired-end sequencing was performed with 74bp read length, yielding genome-wide transcriptomic data. Transcriptomic abundance and differential expression were estimated assuming Poisson-distributed read-counts. Paired-end sequence data was aligned to a lncRNA database containing 14,572 unique lncRNAs. Changes in relative abundance of lncRNA transcripts were tested for association with pCR using the Wilcoxon rank-sum test. Results: On average, in each patient 1500 lncRNAs were differentially expressed between pre and post samples (p Discussion: This is the first study to report that changes in lncRNA expression upon brief exposure to B are associated with achievement of pCR to subsequent neoadjuvant therapy. The overlapping changes in lncRNAs across different treatment arms suggest systematic lncRNA-mediated mechanisms at play. These preliminary observations need to be explored in a larger group of patients. Next generation sequencing technologies will likely allow further characterization of lncRNAs leading to improved methods of stratifying, subclassifying and managing breast cancers. Citation Format: Nilanjana Banerjee, Vinay Varadan, Sitharthan Kamalakaran, Angel Janevski, Kristy Miskimen, Nicole Williams, Maysa Abu-Khalaf, William Sikov, Lyndsay N. Harris, Nevenka Dimitrova. RNA-seq reveals association of pCR and changes in lncRNA expression in pre-operative bevacizumab treatment of breast cancer. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 1828. doi:10.1158/1538-7445.AM2013-1828

Background: Gene expression profiles from tumor biopsies taken during preoperative therapy may identify markers associated with therapy benefit. We have previously shown that changes in TGF-β pathway activity following one dose of bevacizumab (B) are associated with pathologic complete response (pCR) (SABCS, 2012). Here we evaluated the ability of this signature to predict pCR following brief exposure to trastuzumab (T) or nab-paclitaxel (N). Methods: We sequenced transcriptomes of core biopsy RNA from 50 breast tumor pairs obtained from neoadjuvant clinical trials BrUOG 211A/211B. Patients were given one dose of B (n=15) or N (n=17) or T (n=18), followed by combination biologic/chemotherapy (HER2- with B/carboplatin/N; HER2+ with T/carboplatin/N). Paired-end sequencing (74bp) was performed using amplified total RNA from biopsies obtained pre/post 10 day exposure to run-in monotherapy. Transcript abundance and differential expression were estimated assuming Poisson-distributed read-counts. Significance-adjusted log2 fold change (sa-log2FC) of gene expression was calculated by diminishing statistically insignificant (p > 0.05) log2FC values by 100. sa-log2FC values of the TGF-β activity signature genes were used to cluster patients and the clusters were tested for enrichment of pCR cases. Prioritization of the genes in the TGF-β activity signature was performed with a shrunken centroid classifier using 3-fold cross-validation to identify an appropriate shrinkage threshold. Results: PCA and RNA-seq based PAM50 subtyping showed baseline and post-brief exposure samples clustering together across all patients. We identified significantly differentially expressed genes upon brief exposure to therapy associated with pCR in the B arm. Pathway analysis revealed TGF-β signaling, Cell Cycle, DNA Replication and Steroid Biosynthesis pathways to be enriched (p≤0.05) after multiple testing correction. Following the lead on TGF-β signaling, we derived a 107-gene TGF-β activity signature by combining a published TGF-β activity signature (Padua et al, 2008) with the pCR-associated KEGG TGF-β signaling pathway genes in our study. This signature showed a strong separation of pCR cases (p=0.004) by clustering. We further refined this using a shrunken centroid classifier to identify a 61-gene signature of TGF-β activity whose down-regulation is predictive of pCR in the B arm. Evaluation of the TGF-β signature in the T and N arms showed no association with pCR. Conclusions: TGF-β pathway genes whose activity is altered by brief exposure to bevacizumab are associated with pCR; a similar pattern was not observed following exposure to trastuzumab or nab-paclitaxel. This signature may provide an early indication of potential benefit from preoperative treatment with bevacizumab and combination chemotherapy. We are currently in the process of validating these results using a custom RT-PCR panel. Citation Format: Vinay Varadan, Kristy Miskimen, Sitharthan Kamalakaran, Angel Janevski, Nilanjana Banerjee, Nicole Williams, Maysa Abu-Khalaf, William Sikov, Nevenka Dimitrova, Lyndsay Harris. RNA-seq identifies a TGF-β signature that predicts response to preoperative bevacizumab in breast cancer. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 4566. doi:10.1158/1538-7445.AM2013-4566

Ovarian cancers are highly heterogeneous and while chemotherapy is the preferred treatment, many patients are intrinsically resistant or quickly develop resistance. Furthermore, all tumors that recur will become resistant. Recent evidence suggests that epigenetic deregulation may be a key factor in the onset and maintenance of chemoresistance. To examine the ovarian epigenome, we first analyzed a set of 43 primary ovarian tumors and 9 normal ovarian samples. Since therapy response is a significant issue for ovarian cancer patients we analyzed the epigenetic differences that segregate with platinum response. We then associated expression data to identify genes with expression changes potentially altered by promoter methylation to generate a list of candidate platinum resistance genes. Next, we developed a tissue culture carboplatin resistance screen to determine which candidates functionally affect resistance. The screen utilized pools of shRNAs of the candidate genes to identify genes that when repressed allowed survival from carboplatin treatment, in order to validate that our epigenetics screen identified genes involved in resistance. Of the genes identified in the screen we further characterized one gene, docking protein 2 (DOK2), an adapter protein downstream of tyrosine kinase, to determine if we could elucidate what mechanism was used to increase resistance. Our analysis determined that loss of DOK2 decreased the level of apoptosis in response to carboplatin. In addition, we determined that in cells with reduced DOK2, the level of anoikis was decreased, a mechanism of possible importance in ovarian cancer where there is a large number of cells floating in ascites. Functional analysis of the DOK2 genes ability to affect resistance validates this approach to finding genes involved in carboplatin resistance. Citation Format: Elena Lum, Michele Vigliotti, Nilanjana Banerjee, Noelle Cutter, Sitharthan Kamalakaran, Kazimierz Wrzeszczynski, Sohail Khan, Nevenka Dimitrova, Douglas Levine, Robert Lucito. DOK2 suppression by methylation induces platinum resistance via suppression of apoptosis in ovarian cancer cells. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 2988. doi:10.1158/1538-7445.AM2013-2988

Background Whole genome sequencing enables a high resolution view of the human genome and provides unique insights into genome structure at an unprecedented scale. There have been a number of tools to infer copy number variation in the genome. These tools, while validated, also include a number of parameters that are configurable to genome data being analyzed. These algorithms allow for normalization to account for individual and population-specific effects on individual genome CNV estimates but the impact of these changes on the estimated CNVs is not well characterized. We evaluate in detail the effect of normalization methodologies in two CNV algorithms FREEC and CNV-seq using whole genome sequencing data from 8 individuals spanning four populations. Methods We apply FREEC and CNV-seq to a sequencing data set consisting of 8 genomes. We use multiple configurations corresponding to different read-count normalization methodologies in FREEC, and statistically characterize the concordance of the CNV calls between FREEC configurations and the analogous output from CNV-seq. The normalization methodologies evaluated in FREEC are: GC content, mappability and control genome. We further stratify the concordance analysis within genic, non-genic, and a collection of validated variant regions. Results The GC content normalization methodology generates the highest number of altered copy number regions. Both mappability and control genome normalization reduce the total number and length of copy number regions. Mappability normalization yields Jaccard indices in the 0.07 - 0.3 range, whereas using a control genome normalization yields Jaccard index values around 0.4 with normalization based on GC content. The most critical impact of using mappability as a normalization factor is substantial reduction of deletion CNV calls. The output of another method based on control genome normalization, CNV-seq, resulted in comparable CNV call profiles, and substantial agreement in variable gene and CNV region calls. Conclusions Choice of read-count normalization methodology has a substantial effect on CNV calls and the use of genomic mappability or an appropriately chosen control genome can optimize the output of CNV analysis.

Background The decision environment for cancer care is becoming increasingly complex due to the discovery and development of novel genomic tests that offer information regarding therapy response, prognosis and monitoring, in addition to traditional histopathology. There is, therefore, a need for translational clinical tools based on molecular bioinformatics, particularly in current cancer care, that can acquire, analyze the data, and interpret and present information from multiple diagnostic modalities to help the clinician make effective decisions. Results We present a platform for molecular signature discovery and clinical decision support that relies on genomic and epigenomic measurement modalities as well as clinical parameters such as histopathological results and survival information. Our Physician Accessible Preclinical Analytics Application (PAPAyA) integrates a powerful set of statistical and machine learning tools that leverage the connections among the different modalities. It is easily extendable and reconfigurable to support integration of existing research methods and tools into powerful data analysis and interpretation pipelines. A current configuration of PAPAyA with examples of its performance on breast cancer molecular profiles is used to present the platform in action. Conclusion PAPAyA enables analysis of data from (pre)clinical studies, formulation of new clinical hypotheses, and facilitates clinical decision support by abstracting molecular profiles for clinicians.

Background: Combinatorial interaction of transcription factors (TFs) is important for gene regulation. Although various genomic datasets are relevant to this issue, each dataset provides relatively weak evidence on its own. Developing methods that can integrate different sequence, expression and localization data have become important. Results: Here we use a novel method that integrates chromatin immunoprecipitation (ChIP) data with microarray expression data and with combinatorial TF-motif analysis. We systematically identify combinations of transcription factors and of motifs. The various combinations of TFs involved multiple binding mechanisms. We reconstruct a new combinatorial regulatory map of the yeast cell cycle in which cell-cycle regulation can be drawn as a chain of extended TF modules. We find that the pairwise combination of a TF for an early cell-cycle phase and a TF for a later phase is often used to control gene expression at intermediate times. Thus the number of distinct times of gene expression is greater than the number of transcription factors. We also see that some TF modules control branch points (cell-cycle entry and exit), and in the presence of appropriate signals they can allow progress along alternative pathways. Conclusions: Combining different data sources can increase statistical power as demonstrated by detecting TF interactions and composite TF-binding motifs. The original picture of a chain of simple cell-cycle regulators can be extended to a chain of composite regulatory modules: different modules may share a common TF component in the same pathway or a TF component crosstalking to other pathways.