Understanding DNA Methylation patterns in Breast Cancer

poster abstractBreast Cancer is the most frequently diagnosed cancer among women worldwide. According to the estimates of the American Cancer Society, 231,840 new cases of invasive breast cancer will be diagnosed in US women in 2015. Early diagnosis of breast cancer relies on extensive understanding of the molecular mechanisms underlying its development and progression. In addition to the genetic and hormonal risk factors that are responsible for breast carcinogenesis, other factors such as life-styles, environmental and nutritional factors also plays a part in development of this complex disease. The study of these factors which modifies the genome without altering the DNA sequence is termed as cancer epigenetics. DNA methylation is an extensively studied epigenetic dysregulation which governs cell differentiation, and other aberrancies which can steer cells towards a malignant phenotype. This heritable, tissue- and species-specific DNA modification primarily occurs at cytosine-guanine (CpG) dinucleotides and often causes silencing of gene expression. Since altered DNA methylation patterns are commonly observed in various types of malignancies including Breast cancer, it is a potential target to investigate for diagnosis and prognosis of cancer. The main aim of our study was to identify DNA methylation pattern in Breast cancer patients and correlate it with the disease progression. We evaluated 5 Breast tumor samples and 2 Breast normal samples (Stages II and III) obtained from The Cancer Genome Atlas. The DNA from the samples were Bisulfite sequenced which provides the wholegenome coverage at a single-nucleotide resolution and is considered the gold-standard approach for quantitative measurement of DNA methylation level. The mapped bisulfite reads where processed to obtain the methylation ratio of all cytosine positions. The data was analyzed to identify the differentially methylated positions on all chromosomes. The results of our study shows that 97.3% of the differentially methylated positions overlap with intergenic regions, 0.1% in promoter regions and remaining in the exon and intronic regions. Out of 160803 differentially methylated positions, 139579 positions were hypo-methylated and 21224 were hyper-methylated positions. We observed that majority of the differentially methylated positions are were hypo-methylated which traditionally affects gene transcription. Highest number of hypo-methylated and hyper- methylated positions were observed on chromosome 1 and 5 respectively. As per our study NR5A2, BCAS3, PRR11, VMP1, PBX1 are the top 5 genes which are aberrantly methylated in Breast cancer patients

Identification of Immuno-Oncology Crosstalk Pathways in Lung Adenocarcinoma

poster abstractIdentifying dysregulated pathways from the high throughput data for biomarker detection is the rate limiting step in the complex diseases cure. Pathways don’t perform alone; they interact with each other through the overlapping genes. This phenomenon is known as crosstalk of pathways. The aim of the study is develop a methodology to find the highly interacting (cross-talk) immuneoncological pathways and their drug-gene-pathway modules which can be further validated invivo using Lung Adenocarcinoma (LUAD) as a case study. The reference pathway cross-talk matrix is built using the KEGG Knowledgebase, which consists of the 302 KEGG pathways associated with 6996 genes. The LUAD gene expression data available in The Cancer Genome Atlas (TCGA) is used for the study. The data of 32 patients was used in the study and of these, 9 patients were treated with immunotherapy drugs. A set of 3018 significant genes associated with 296 pathways [C.I. =95%, p-value <=0.05] are identified in this dataset, and a disease crosstalk matrix is constructed. Each cell in the matrix gives the cross-talk score of the pathways computed using the formula: ∩ ∪ . The interaction among the significant genes (3018 genes) in the crosstalk pathways were identified using the BioGrid physical gene-gene interaction map and a gene interaction network (10102 interaction) is generated. The significant genes in the network are annotated to their drugs as given in the clinical data of TCGA. The drug-genepathway modules of LUAD are identified using Seed-Based-Network Propagation Algorithm. These modules give the profile of the highest cross-talk pathways of LUAD that can be studied further for alternative drug targets. The study identified T-cell receptor signaling pathway and B cell receptor signaling pathway of LUAD have high crosstalk scores with Erbb Signaling pathway (18.67, 15.15) Vegf signaling pathway (17.77, 22.45); Osteoclast differentiation (16.35, 14.89)

ELUCIDATING GENE SIGNATURES THAT CONTROL THE CIRCADIAN RHYTHM IN CYANOBACTERIA USING BIOINFORMATICS METHODS

poster abstractBackground: The daily light-dark cycle govern rhythmic changes in the behavior and physiology of most species. This circadian rhythm, or bi-ological “clock,” allows the organism to anticipate and prepare for the changes in the physical environment that are associated with day and night, thereby ensuring that the organism carry our specific processes at the right time of the day. Studies have found that the internal clock con-sists of an array of genes and the protein products they encode, which regulate various physiological processes throughout the body. Cyanothece sp. ATCC 51142 is an organism that has both photosynthetic (producing oxygen) and nitrogen fixing ability. The N2-fixing enzyme, nitrogenase, is highly sensitive to oxygen for which it has developed a temporal regula-tion in which N2 fixation and photosynthesis occur at different times throughout a diurnal cycle with very high levels of CO2 fixation during the light and high levels of N2 fixation in the dark. The mechanisms underly-ing the circadian rhythm and the signature genes elucidating this mecha-nism are addressed in this research. Objective: The objective is to integrate gene expression data with da-ta and knowledge from prior studies using bibliomics techniques, in the de novo construction of quasi-complete transcriptional regulatory networks to identify gene signatures in functional motifs and elucidate their role in circadian rhythms in cyanothece sp. ATCC 51142. Methodology: The sequence data of Transcription profiling time se-ries of cyanothece sp. ATCC 51142 grown in 12-hour light/12 hour dark then 24 h light from Array Express was used to construct the initial global regulatory network. Different network topological features (degree, betweeness and eccentricity) are used to identify the signature pathways during the day and night. The genes of the global regulatory network were used to construct networks of homologous species. The functions of the already known genes in well-studied homologous species were mapped to the function of the unannotated genes of cynaothece sp. ATCC 51142. Results: We have identified significant (p<0.05) signature pathways like photosynthesis, pantothenate and CoA biosynthesis and Glyoxylate and dicarboxylate metabolism that operate during the day. And during the night, pathways such as ribosome, riboflavin metabolism, and fatty acid biosynthesis sulfur metabolism were found to be significant (p<0.05). We will further investigate the genes that were already known to be significant using cyanobase database in a particular biological path-way and the novel genes that are identified by bibliomics approach

CLIQUES FOR IDENTIFICATION OF GENE SIGNATURES FOR COLORECTAL CANCER ACROSS POPULATION

poster abstractIntroduction: Colorectal cancer (CRC) is one of the most common cancers diagnosed worldwide. Studies have correlated CRC with dietary habits and environmental conditions. We developed a novel network based approach where cliques and their connectivity profiles explained the variation and similarity in CRC across four populations- China, Germany, Saudi Arabia and USA. Methods: Networks generated after data preprocessing were analyzed individually based on topological and biological features. Using greedy algorithm, cliques of various sizes were identified in each network and size 7 cliques were further analyzed based on their clique connectivity profile (CCP). Our algorithm considered the interaction of cliques based on two parameters: (i) Identification of common (links) genes; (ii) CliqueStrength. The cliques were evaluated by two conditions (a) Maximum number of common genes across cliques and highest CliqueStrength and (b) Minimum number of common genes across cliques and highest CliqueStrength. Results: Large numbers of genes are found to be common between USA, China and Germany. Highly scored nodes based on topological parameters are TP53, SRC, ESR1, SMAD3, GRB2, CREBBP, EGFR, SMAD2, and CSN2KA1. Signal transduction, protein phosphorylation etc., were found to be important GO biological processes. Number of unique size 7 cliques identified in all the population is 650. 49 common cliques identified included genes- EGFR, GRB2, PIK3R1, PTPN6, BRCA1, SMAD2, TP53, CSN2 etc. We found 20 cliques that are uniquely identified for USA, 10 for Germany and one for China. Cliques include genes that are both well studied, less-studied in CRC; but are targets in other cancers. Conclusion: With CCP, we were able to identify commonality, uniqueness and divergence among the populations. Furthermore, comparing all cliques (their CCP) as gene-signatures across populations can help to identify efficient drug targets. Results were consistent with other studies and demonstrate the power of cliques to study CRC across populations

Social Exclusion and Social Change: Access To, and Influence of, Community-Based Collective Action Programs in Nepal.

This dissertation examines how the ascribed status of caste influences participation in community-based organizations within a context marked by hierarchy and discrimination. It also analyzes how this association changes over time in Western Chitwan, Nepal. Finally, it examines the association between individuals’ participation in community-based organizations and changes in their gender- and family-related attitudes within this rapidly changing, non-industrial context. I utilize frameworks of social stratification and social exclusion to examine how formal and informal institutions and practices work to disconnect groups and individuals from social relations, and create barriers that prevent them from being able to participate in activities which would be normal and accessible for others. I expand on the framework of family modes of social organization to better understand how the introduction of new institutions and opportunities influences changes in gender- and family-related attitudes in a more egalitarian direction. I use long-term panel data to empirically test these theoretical models. Data from the first wave of the Chitwan Valley Family Study shows that compared to the upper caste privileged groups, all other caste/ethnic groups have significantly lower rates of participation in community groups. However, examining changes in the rates of participation over time show that participation of lower caste groups is no longer statistically different from the upper caste group. Yet, the most marginalized Tarai-Janajati group continues to show less likelihood of participation. Women have increased their participation at greater rates than men have. An examination of intersectional caste/ethnic and gender identities reveals insights about how rates of participation differ based on distinctive socio-cultural experiences. I examine the effect of participation in community-based organizations on changes in a set of four individual attitudes representing gendered relations in the context of Nepal - timing of marriage for girls, re-marriage of young widows, role expectations for daughters-in-law, and the role of men in household decision-making. Participation in community groups has a strong, highly significant and independent effect on changes in these attitudes towards more egalitarianism. This demonstrates the importance of expanding our understanding of the diversity of experiences that influence changes in such attitudes in settings undergoing social changes.Ph.D.SociologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/86357/1/meetasp_1.pd

Novel Approach to Cluster Patient-Generated Data Into Actionable Topics: Case Study of a Web-Based Breast Cancer Forum

Background: The increasing use of social media and mHealth apps has generated new opportunities for health care consumers to share information about their health and well-being. Information shared through social media contains not only medical information but also valuable information about how the survivors manage disease and recovery in the context of daily life. Objective: The objective of this study was to determine the feasibility of acquiring and modeling the topics of a major online breast cancer support forum. Breast cancer patient support forums were selected to discover the hidden, less obvious aspects of disease management and recovery. Methods: First, manual topic categorization was performed using qualitative content analysis (QCA) of each individual forum board. Second, we requested permission from the Breastcancer.org Community for a more in-depth analysis of the postings. Topic modeling was then performed using open source software Machine Learning Language Toolkit, followed by multiple linear regression (MLR) analysis to detect highly correlated topics among the different website forums. Results: QCA of the forums resulted in 20 categories of user discussion. The final topic model organized >4 million postings into 30 manageable topics. Using qualitative analysis of the topic models and statistical analysis, we grouped these 30 topics into 4 distinct clusters with similarity scores of ≥0.80; these clusters were labeled Symptoms & Diagnosis, Treatment, Financial, and Family & Friends. A clinician review confirmed the clinical significance of the topic clusters, allowing for future detection of actionable items within social media postings. To identify the most significant topics across individual forums, MLR demonstrated that 6 topics—based on the Akaike information criterion values ranging from −642.75 to −412.32—were statistically significant. Conclusions: The developed method provides an insight into the areas of interest and concern, including those not ascertainable in the clinic. Such topics included support from lay and professional caregivers and late side effects of therapy that consumers discuss in social media and may be of interest to clinicians. The developed methods and results indicate the potential of social media to inform the clinical workflow with regards to the impact of recovery on daily life. [JMIR Med Inform 2018;6(4):e45

A Dynamic, User-centric Big Data Analytics Framework for Genome Data

poster abstractThe cost to sequence DNA today has reduced from $100million to mere over$1000 and this has significantly increased the generation of genomic data multifold. However, analysis of such large data requires meeting user needs and computational challenges. There are different tools that exist to process the sequenced DNA information for alignment and research. These tools are made adaptive to work in a big data processing environment like Hadoop. However, the analysis of such sequence data is dependent on user specific needs, and hence, a unique data analysis pipeline is needed for each user. We propose a barcode driven technology to instruct a Hadoop-based big data analytics system that would allow the user to select the necessary tools to process the input genome data file. The proposed framework can dynamically generate customized barcodes for each user based on the user’s data analysis need and a pipeline is created and driven by the barcode. This approach will revolutionize the way NGS data analytics pipelines are being setup by the user. This new method will provide the user with a seamless way to analyze the data. The time taken to process a genomic file was significantly reduced from 2 hours on a traditional Linux server to just 3.81 minutes on Hadoop. Our results indicate that a barcode-based approach will enable the user to customize NGS data analysis in a very efficient manner

Systems biology approach to obtain significant modules of immune therapy and colorectal cancer

poster abstractColorectal cancer (CRC) is the second leading cause of cancer death in the United States. There has been a lot of research around genes influencing CRC, despite its extensive understanding on the genetic perspective and the emergence of drugs targeting these genes, the tumor progression could be hardly mitigated. However, immune therapy has recently been observed to be effective in CRC treatment and diagnosis. This study focuses on developing a statistically validated multi-feature analytical approach to identify immuno-oncology targets. The features considered in this study were gene expression, DNA methylation, concepts from literature and immuno-cancer pathways. The network algorithm will identify the potentially relevant immuno-oncology modules of CRC. For the study level-3 data (7.2 gigabytes) of gene expression and DNA methylation was obtained from The Cancer Genome Atlas. Around 13000 genes were identified to be significant from the gene expression data analysis and 19000 genes significant in DNA methylation data. The CRC and Immuno-oncology concepts were manually annotated from 50 peer reviewed articles. The output of the preliminary analysis could predict 95 concepts annotated to the 1587 significant genes and were integrated into the network. The top rank concepts in terms of genes associated were ‘apoptosis’, ‘transforming growth factor’, ‘protein arginine methyltransferase’, ‘carcinoembryonic antigen’ and ‘methyl binding protein’. The gene annotated with highest number of concepts was ‘PRMT5’, ‘CSF2’, ‘CFLAR’ and ‘MLH1’. These genes were observed in the literature as targets of CRC

A Micro-Level Event-Centered Approach to Investigating Armed Conflict and Population Responses

In this article, we construct and test a micro-level event-centered approach to the study of armed conflict and behavioral responses in the general population. Event-centered approaches have been successfully used in the macro-political study of armed conflict but have not yet been adopted in micro-behavioral studies. The micro-level event-centered approach that we advocate here includes decomposition of a conflict into discrete political and violent events, examination of the mechanisms through which they affect behavior, and consideration of differential risks within the population. We focus on two mechanisms: instability and threat of harm. We test this approach empirically in the context of the recent decade-long armed conflict in Nepal, using detailed measurements of conflict-related events and a longitudinal study of first migration, first marriage, and first contraceptive use. Results demonstrate that different conflict-related events independently shaped migration, marriage, and childbearing and that they can simultaneously influence behaviors in opposing directions. We find that violent events increased migration, but political events slowed migration. Both violent and political events increased marriage and contraceptive use net of migration. Overall, this micro-level event-centered approach yields a significant advance for the study of how armed conflict affects civilian behavioral responses

Utilization of electronic health records for the assessment of adiponectin receptor autoantibodies during the progression of cardio-metabolic comorbidities

Background: Diabetes is a complex, multi-symptomatic disease whose complications drives increases in healthcare costs as the diabetes prevalence grows rapidly world-wide. Real-world electronic health records (EHRs) coupled with patient biospecimens, biological understanding, and technologies can characterize emerging diagnostic autoimmune markers resulting from proteomic discoveries. Methods: Circulating autoantibodies for C‑terminal fragments of adiponectin receptor 1 (IgG-CTF) were measured by immunoassay to establish the reference range using midpoint samples from 1862 participants in a 20-year observational study of type 2 diabetes and cardiovascular arterial disease (CVAD) conducted by the Fairbanks Institute. The White Blood Cell elastase activity in these patients was assessed using immunoassays for Bikunin and Uristatin. Participants were assigned to four cohorts (healthy, T2D, CV, CV+T2D) based on analysis of their EHRs and the diagnostic biomarkers values and patient status were assessed ten-years post-sample. Results: The IgG-CTF reference range was determined to be 75–821 ng/mL and IgG-CTF out-ofrange values did not predict cohort or comorbidity as determined from the EHRs at 10 years after sample collection nor did IgG-CTF demonstrate a significant risk for comorbidity or death. Many patients at sample collection time had other conditions (hypertension, hyperlipidemia, or other risk factors) of which only hypertension, Uristatin and Bikunin values correlated with increased risk of developing additional comorbidities (odds ratio 2.58–13.11, P<0.05). Conclusions: This study confirms that retrospective analysis of biorepositories coupled with EHRs can establish reference ranges for novel autoimmune diagnostic markers and provide insights into prediction of specific health outcomes and correlations to other markers