Exploring team dynamics during the development of a multi-institutional cross-disciplinary translational team: Implications for potential best practices.

Introduction: A recent literature review revealed no studies that explored teams that used an explicit theoretical framework for multiteam systems in academic settings, such as the increasingly important multi-institutional cross-disciplinary translational team (MCTT) form. We conducted an exploratory 30-interview grounded theory study over two rounds to analyze participants' experiences from three universities who assembled an MCTT in order to pursue a complex grant proposal related to research on post-acute sequelae of COVID-19, also called "long COVID." This article considers activities beginning with preliminary discussions among principal investigators through grant writing and submission, and completion of reviews by the National Center for Advancing Translational Sciences, which resulted in the proposal not being scored. Methods: There were two stages to this interview study with MCTT members: pre-submission, and post-decision. Round one focused on the process of developing structures to collaborate on proposal writing and assembly, whereas round two focused on evaluation of the complete process. A total of 15 participants agreed to be interviewed in each round. Findings: The first round of interviews was conducted prior to submission and explored issues during proposal writing, including (1) importance of the topic; (2) meaning and perception of "team" within the MCTT context; and (3) leadership at different levels of the team. The second round explored best practices-related issues including (1) leadership and design; (2) specific proposal assembly tasks; (3) communication; and (4) critical events. Conclusion: We conclude with suggestions for developing best practices for assembling MCTTs involving multi-institutional teams.

Subtyping Social Determinants of Health in All of Us: Network Analysis and Visualization Approach.

Background: Social determinants of health (SDoH), such as financial resources and housing stability, account for between 30-55% of people's health outcomes. While many studies have identified strong associations among specific SDoH and health outcomes, most people experience multiple SDoH that impact their daily lives. Analysis of this complexity requires the integration of personal, clinical, social, and environmental information from a large cohort of individuals that have been traditionally underrepresented in research, which is only recently being made available through the All of Us research program. However, little is known about the range and response of SDoH in All of Us, and how they co-occur to form subtypes, which are critical for designing targeted interventions. Objective: To address two research questions: (1) What is the range and response to survey questions related to SDoH in the All of Us dataset? (2) How do SDoH co-occur to form subtypes, and what are their risk for adverse health outcomes? Methods: For Question-1, an expert panel analyzed the range of SDoH questions across the surveys with respect to the 5 domains in Healthy People 2030 (HP-30), and analyzed their responses across the full All of Us data (n=372,397, V6). For Question-2, we used the following steps: (1) due to the missingness across the surveys, selected all participants with valid and complete SDoH data, and used inverse probability weighting to adjust their imbalance in demographics compared to the full data; (2) an expert panel grouped the SDoH questions into SDoH factors for enabling a more consistent granularity; (3) used bipartite modularity maximization to identify SDoH biclusters, their significance, and their replicability; (4) measured the association of each bicluster to three outcomes (depression, delayed medical care, emergency room visits in the last year) using multiple data types (surveys, electronic health records, and zip codes mapped to Medicaid expansion states); and (5) the expert panel inferred the subtype labels, potential mechanisms that precipitate adverse health outcomes, and interventions to prevent them. Results: For Question-1, we identified 110 SDoH questions across 4 surveys, which covered all 5 domains in HP-30. However, the results also revealed a large degree of missingness in survey responses (1.76%-84.56%), with later surveys having significantly fewer responses compared to earlier ones, and significant differences in race, ethnicity, and age of participants of those that completed the surveys with SDoH questions, compared to those in the full All of Us dataset. Furthermore, as the SDoH questions varied in granularity, they were categorized by an expert panel into 18 SDoH factors. For Question-2, the subtype analysis (n=12,913, d=18) identified 4 biclusters with significant biclusteredness (Q=0.13, random-Q=0.11, z=7.5, P<0.001), and significant replication (Real-RI=0.88, Random-RI=0.62, P<.001). Furthermore, there were statistically significant associations between specific subtypes and the outcomes, and with Medicaid expansion, each with meaningful interpretations and potential targeted interventions. For example, the subtype Socioeconomic Barriers included the SDoH factors not employed, food insecurity, housing insecurity, low income, low literacy, and low educational attainment, and had a significantly higher odds ratio (OR=4.2, CI=3.5-5.1, P-corr<.001) for depression, when compared to the subtype Sociocultural Barriers. Individuals that match this subtype profile could be screened early for depression and referred to social services for addressing combinations of SDoH such as housing insecurity and low income. Finally, the identified subtypes spanned one or more HP-30 domains revealing the difference between the current knowledge-based SDoH domains, and the data-driven subtypes. Conclusions: The results revealed that the SDoH subtypes not only had statistically significant clustering and replicability, but also had significant associations with critical adverse health outcomes, which had translational implications for designing targeted SDoH interventions, decision-support systems to alert clinicians of potential risks, and for public policies. Furthermore, these SDoH subtypes spanned multiple SDoH domains defined by HP-30 revealing the complexity of SDoH in the real-world, and aligning with influential SDoH conceptual models such as by Dahlgren-Whitehead. However, the high-degree of missingness warrants repeating the analysis as the data becomes more complete. Consequently we designed our machine learning code to be generalizable and scalable, and made it available on the All of Us workbench, which can be used to periodically rerun the analysis as the dataset grows for analyzing subtypes related to SDoH, and beyond.

The Pre-Adaptation of a Stroke-Specific Self-Management Program Among Older Adults.

Objectives: Managing multimorbidity as aging stroke patients is complex; standard self-management programs necessitate adaptations. We used visual analytics to examine complex relationships among aging stroke survivors' comorbidities. These findings informed pre-adaptation of a component of the Chronic Disease Self-Management Program. Methods: Secondary analysis of 2013-2014 Medicare claims with stroke as an index condition, hospital readmission within 90 days (n = 42,938), and 72 comorbidities. Visual analytics identified patient subgroups and co-occurring comorbidities. Guided by the framework for reporting adaptations and modifications to evidence-based interventions, an interdisciplinary team developed vignettes that highlighted multimorbidity to customize the self-management program. Results: There were five significant subgroups (z = 6.19, p < .001) of comorbidities such as obesity and cancer. We constructed 6 vignettes based on the 5 subgroups. Discussion: Aging stroke patients often face substantial disease-management hurdles. We used visual analytics to inform pre-adaptation of a self-management program to fit the needs of older adult stroke survivors.

A Framework for Modeling and Interpreting Patient Subgroups Applied to Hospital Readmission: Visual Analytical Approach.

BACKGROUND: A primary goal of precision medicine is to identify patient subgroups and infer their underlying disease processes with the aim of designing targeted interventions. Although several studies have identified patient subgroups, there is a considerable gap between the identification of patient subgroups and their modeling and interpretation for clinical applications. OBJECTIVE: This study aimed to develop and evaluate a novel analytical framework for modeling and interpreting patient subgroups (MIPS) using a 3-step modeling approach: visual analytical modeling to automatically identify patient subgroups and their co-occurring comorbidities and determine their statistical significance and clinical interpretability; classification modeling to classify patients into subgroups and measure its accuracy; and prediction modeling to predict a patient's risk of an adverse outcome and compare its accuracy with and without patient subgroup information. METHODS: The MIPS framework was developed using bipartite networks to identify patient subgroups based on frequently co-occurring high-risk comorbidities, multinomial logistic regression to classify patients into subgroups, and hierarchical logistic regression to predict the risk of an adverse outcome using subgroup membership compared with standard logistic regression without subgroup membership. The MIPS framework was evaluated for 3 hospital readmission conditions: chronic obstructive pulmonary disease (COPD), congestive heart failure (CHF), and total hip arthroplasty/total knee arthroplasty (THA/TKA) (COPD: n=29,016; CHF: n=51,550; THA/TKA: n=16,498). For each condition, we extracted cases defined as patients readmitted within 30 days of hospital discharge. Controls were defined as patients not readmitted within 90 days of discharge, matched by age, sex, race, and Medicaid eligibility. RESULTS: In each condition, the visual analytical model identified patient subgroups that were statistically significant (Q=0.17, 0.17, 0.31; P<.001, <.001, <.05), significantly replicated (Rand Index=0.92, 0.94, 0.89; P<.001, <.001, <.01), and clinically meaningful to clinicians. In each condition, the classification model had high accuracy in classifying patients into subgroups (mean accuracy=99.6%, 99.34%, 99.86%). In 2 conditions (COPD and THA/TKA), the hierarchical prediction model had a small but statistically significant improvement in discriminating between readmitted and not readmitted patients as measured by net reclassification improvement (0.059, 0.11) but not as measured by the C-statistic or integrated discrimination improvement. CONCLUSIONS: Although the visual analytical models identified statistically and clinically significant patient subgroups, the results pinpoint the need to analyze subgroups at different levels of granularity for improving the interpretability of intra- and intercluster associations. The high accuracy of the classification models reflects the strong separation of patient subgroups, despite the size and density of the data sets. Finally, the small improvement in predictive accuracy suggests that comorbidities alone were not strong predictors of hospital readmission, and the need for more sophisticated subgroup modeling methods. Such advances could improve the interpretability and predictive accuracy of patient subgroup models for reducing the risk of hospital readmission, and beyond.

Evaluation of Information Theoretic Network Meta-analysis to Rank First-Line Anticancer Regimens for Hormone Receptor-Positive, ERBB2-Negative Metastatic Breast Cancer.

Importance: Hormone receptor-positive, ERBB2 (formerly HER2/neu)-negative metastatic breast cancer (HR-positive, ERBB2-negative MBC) is treated with targeted therapy, endocrine therapy, chemotherapy, or combinations of these modalities; however, evaluating the increasing number of treatment options is challenging because few regimens have been directly compared in randomized clinical trials (RCTs), and evidence has evolved over decades. Information theoretic network meta-analysis (IT-NMA) is a graph theory-based approach for regimen ranking that takes effect sizes and temporality of evidence into account. Objective: To examine the performance of an IT-NMA approach to rank HR-positive, ERBB2-negative MBC treatment regimens. Data Sources: HemOnc.org, a freely available medical online resource of interventions, regimens, and general information relevant to the fields of hematology and oncology, was used to identify relevant RCTs. Study Selection: All primary and subsequent reports of RCTs of first-line systemic treatments for HR-positive, ERBB2-negative MBC that were referenced on HemOnc.org and published between 1974 and 2019 were included. Additional RCTs that were evaluated by a prior traditional network meta-analysis on HR-positive, ERBB2-negative MBC were also included. Data Extraction and Synthesis: RCTs were independently extracted from HemOnc.org and a traditional NMA by separate observers. This study followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline for NMA with several exceptions: the risk of bias within individual studies and inconsistency in the treatment network were not assessed. Main Outcomes and Measures: Regimen rankings generated by IT-NMA based on clinical trial variables, including primary end point, enrollment number per trial arm, P value, effect size, years of enrollment, and year of publication. Results: A total of 203 RCTs with 63 629 patients encompassing 252 distinct regimens were compared by IT-NMA, which resulted in 151 rankings as of 2019. Combinations of targeted and endocrine therapy were highly ranked, especially the combination of endocrine therapy with cyclin-dependent kinase 4 and 6 (CDK4/6) inhibitors. For example, letrozole plus palbociclib was ranked first and letrozole plus ribociclib, third. Older monotherapies that continue to be used in RCTs in comparator groups, such as anastrozole (251 of 252) and letrozole (252), fell to the bottom of the rankings. Many regimens gravitated toward indeterminacy by 2019. Conclusions and Relevance: In this network meta-analysis study, combination therapies appeared to be associated with better outcomes than monotherapies in the treatment of HR-positive, ERBB2-negative MBC. These findings suggest that IT-NMA is a promising method for longitudinal ranking of anticancer regimens from RCTs with different end points, sparse interconnectivity, and decades-long timeframes.

SMART COVID Navigator, a Clinical Decision Support Tool for COVID-19 Treatment: Design and Development Study.

BACKGROUND: COVID-19 caused by SARS-CoV-2 has infected 219 million individuals at the time of writing of this paper. A large volume of research findings from observational studies about disease interactions with COVID-19 is being produced almost daily, making it difficult for physicians to keep track of the latest information on COVID-19's effect on patients with certain pre-existing conditions. OBJECTIVE: In this paper, we describe the creation of a clinical decision support tool, the SMART COVID Navigator, a web application to assist clinicians in treating patients with COVID-19. Our application allows clinicians to access a patient's electronic health records and identify disease interactions from a large set of observational research studies that affect the severity and fatality due to COVID-19. METHODS: The SMART COVID Navigator takes a 2-pronged approach to clinical decision support. The first part is a connection to electronic health record servers, allowing the application to access a patient's medical conditions. The second is accessing data sets with information from various observational studies to determine the latest research findings about COVID-19 outcomes for patients with certain medical conditions. By connecting these 2 data sources, users can see how a patient's medical history will affect their COVID-19 outcomes. RESULTS: The SMART COVID Navigator aggregates patient health information from multiple Fast Healthcare Interoperability Resources-enabled electronic health record systems. This allows physicians to see a comprehensive view of patient health records. The application accesses 2 data sets of over 1100 research studies to provide information on the fatality and severity of COVID-19 for several pre-existing conditions. We also analyzed the results of the collected studies to determine which medical conditions result in an increased chance of severity and fatality of COVID-19 progression. We found that certain conditions result in a higher likelihood of severity and fatality probabilities. We also analyze various cancer tissues and find that the probabilities for fatality vary greatly depending on the tissue being examined. CONCLUSIONS: The SMART COVID Navigator allows physicians to predict the fatality and severity of COVID-19 progression given a particular patient's medical conditions. This can allow physicians to determine how aggressively to treat patients infected with COVID-19 and to prioritize different patients for treatment considering their prior medical conditions.

Prognostic Performance of Peripheral Blood Biomarkers in Identifying Seropositive Individuals at Risk of Developing Clinically Symptomatic Chagas Cardiomyopathy.

Biomarkers for prognosis-based detection of Trypanosoma cruzi-infected patients presenting no clinical symptoms to cardiac Chagas disease (CD) are not available. In this study, we examined the performance of seven biomarkers in prognosis and risk of symptomatic CD development. T. cruzi-infected patients clinically asymptomatic (C/A; n = 30) or clinically symptomatic (C/S; n = 30) for cardiac disease and humans who were noninfected and healthy (N/H; n = 24) were enrolled (1 - ß = 80%, α = 0.05). Serum, plasma, and peripheral blood mononuclear cells (PBMCs) were analyzed for heterogeneous nuclear ribonucleoprotein A1 (hnRNPA1), vimentin, poly(ADP-ribose) polymerase (PARP1), 8-hydroxy-2-deoxyguanosine (8-OHdG), copeptin, endostatin, and myostatin biomarkers by enzyme-linked immunosorbent assay (ELISA) and Western blotting. Secreted hnRNPA1, vimentin, PARP1, 8-OHdG, copeptin, and endostatin were increased by 1.4- to 7.0-fold in CD subjects versus N/H subjects (P < 0.001) and showed excellent predictive value in identifying the occurrence of infection (area under the receiver operating characteristic [ROC] curve [AUC], 0.935 to 0.999). Of these, vimentin, 8-OHdG, and copeptin exhibited the best performance in prognosis of C/S (versus C/A) CD, determined by binary logistic regression analysis with the Cox and Snell test (R2C&S = 0.492 to 0.688). A decline in myostatin and increase in hnRNPA1 also exhibited good predictive value in identifying C/S and C/A CD status, respectively. Furthermore, circulatory 8-OHdG (Wald χ2 = 15.065), vimentin (Wald χ2 = 14.587), and endostatin (Wald χ2 = 17.902) levels exhibited a strong association with changes in left ventricular ejection fraction and diastolic diameter (P = 0.001) and predicted the risk of cardiomyopathy development in CD patients. We have identified four biomarkers (vimentin, 8-OHdG, copeptin, and endostatin) that offer excellent value in prognosis and risk of symptomatic CD development. Decline in these four biomarkers and increase in hnRNPA1 would be useful in monitoring the efficacy of therapies and vaccines in halting CD. IMPORTANCE There is a lack of validated biomarkers for diagnosis of T. cruzi-infected individuals at risk of developing heart disease. Of the seven potential biomarkers that were screened, vimentin, 8-OHdG, copeptin, and endostatin exhibited excellent performance in distinguishing the clinical severity of Chagas disease. A decline in these four biomarkers can also be used for monitoring the therapeutic responses of infected patients to established or newly developed drugs and vaccines and precisely inform the patients about their progress. These biomarkers can easily be screened using the readily available plasma/serum samples in the clinical setting by an ELISA that is inexpensive, fast, and requires low-tech resources at the facility, equipment, and personnel levels.

Heterogeneity in COVID-19 Patients at Multiple Levels of Granularity: From Biclusters to Clinical Interventions.

Several studies have shown that COVID-19 patients with prior comorbidities have a higher risk for adverse outcomes, resulting in a disproportionate impact on older adults and minorities that fit that profile. However, although there is considerable heterogeneity in the comorbidity profiles of these populations, not much is known about how prior comorbidities co-occur to form COVID-19 patient subgroups, and their implications for targeted care. Here we used bipartite networks to quantitatively and visually analyze heterogeneity in the comorbidity profiles of COVID-19 inpatients, based on electronic health records from 12 hospitals and 60 clinics in the greater Minneapolis region. This approach enabled the analysis and interpretation of heterogeneity at three levels of granularity (cohort, subgroup, and patient), each of which enabled clinicians to rapidly translate the results into the design of clinical interventions. We discuss future extensions of the multigranular heterogeneity framework, and conclude by exploring how the framework could be used to analyze other biomedical phenomena including symptom clusters and molecular phenotypes, with the goal of accelerating translation to targeted clinical care.

How High-Risk Comorbidities Co-Occur in Readmitted Patients With Hip Fracture: Big Data Visual Analytical Approach.

BACKGROUND: When older adult patients with hip fracture (HFx) have unplanned hospital readmissions within 30 days of discharge, it doubles their 1-year mortality, resulting in substantial personal and financial burdens. Although such unplanned readmissions are predominantly caused by reasons not related to HFx surgery, few studies have focused on how pre-existing high-risk comorbidities co-occur within and across subgroups of patients with HFx. OBJECTIVE: This study aims to use a combination of supervised and unsupervised visual analytical methods to (1) obtain an integrated understanding of comorbidity risk, comorbidity co-occurrence, and patient subgroups, and (2) enable a team of clinical and methodological stakeholders to infer the processes that precipitate unplanned hospital readmission, with the goal of designing targeted interventions. METHODS: We extracted a training data set consisting of 16,886 patients (8443 readmitted patients with HFx and 8443 matched controls) and a replication data set consisting of 16,222 patients (8111 readmitted patients with HFx and 8111 matched controls) from the 2010 and 2009 Medicare database, respectively. The analyses consisted of a supervised combinatorial analysis to identify and replicate combinations of comorbidities that conferred significant risk for readmission, an unsupervised bipartite network analysis to identify and replicate how high-risk comorbidity combinations co-occur across readmitted patients with HFx, and an integrated visualization and analysis of comorbidity risk, comorbidity co-occurrence, and patient subgroups to enable clinician stakeholders to infer the processes that precipitate readmission in patient subgroups and to propose targeted interventions. RESULTS: The analyses helped to identify (1) 11 comorbidity combinations that conferred significantly higher risk (ranging from P<.001 to P=.01) for a 30-day readmission, (2) 7 biclusters of patients and comorbidities with a significant bicluster modularity (P<.001; Medicare=0.440; random mean 0.383 [0.002]), indicating strong heterogeneity in the comorbidity profiles of readmitted patients, and (3) inter- and intracluster risk associations, which enabled clinician stakeholders to infer the processes involved in the exacerbation of specific combinations of comorbidities leading to readmission in patient subgroups. CONCLUSIONS: The integrated analysis of risk, co-occurrence, and patient subgroups enabled the inference of processes that precipitate readmission, leading to a comorbidity exacerbation risk model for readmission after HFx. These results have direct implications for (1) the management of comorbidities targeted at high-risk subgroups of patients with the goal of pre-emptively reducing their risk of readmission and (2) the development of more accurate risk prediction models that incorporate information about patient subgroups.

Opportunities and Challenges for Analyzing Cancer Data at the Inter- and Intra-Institutional Levels.

PURPOSE: Our goal was to identify the opportunities and challenges in analyzing data from the American Association of Cancer Research Project Genomics Evidence Neoplasia Information Exchange (GENIE), a multi-institutional database derived from clinically driven genomic testing, at both the inter- and the intra-institutional level. Inter-institutionally, we identified genotypic differences between primary and metastatic tumors across the 3 most represented cancers in GENIE. Intra-institutionally, we analyzed the clinical characteristics of the Vanderbilt-Ingram Cancer Center (VICC) subset of GENIE to inform the interpretation of GENIE as a whole. METHODS: We performed overall cohort matching on the basis of age, ethnicity, and sex of 13,208 patients stratified by cancer type (breast, colon, or lung) and sample site (primary or metastatic). We then determined whether detected variants, at the gene level, were associated with primary or metastatic tumors. We extracted clinical data for the VICC subset from VICC's clinical data warehouse. Treatment exposures were mapped to a 13-class schema derived from the HemOnc ontology. RESULTS: Across 756 genes, there were significant differences in all cancer types. In breast cancer, ESR1 variants were over-represented in metastatic samples (odds ratio, 5.91; q < 10-6). TP53 mutations were over-represented in metastatic samples across all cancers. VICC had a significantly different cancer type distribution than that of GENIE but patients were well matched with respect to age, sex, and sample type. Treatment data from VICC was used for a bipartite network analysis, demonstrating clusters with a mix of histologies and others being more histology specific. CONCLUSION: This article demonstrates the feasibility of deriving meaningful insights from GENIE at the inter- and intra-institutional level and illuminates the opportunities and challenges of the data GENIE contains. The results should help guide future development of GENIE, with the goal of fully realizing its potential for accelerating precision medicine.

Efficient identification of multiple pathways: RNA-Seq analysis of livers from 56Fe ion irradiated mice.

BACKGROUND: mRNA interaction with other mRNAs and other signaling molecules determine different biological pathways and functions. Gene co-expression network analysis methods have been widely used to identify correlation patterns between genes in various biological contexts (e.g., cancer, mouse genetics, yeast genetics). A challenge remains to identify an optimal partition of the networks where the individual modules (clusters) are neither too small to make any general inferences, nor too large to be biologically interpretable. Clustering thresholds for identification of modules are not systematically determined and depend on user-settable parameters requiring optimization. The absence of systematic threshold determination may result in suboptimal module identification and a large number of unassigned features. RESULTS: In this study, we propose a new pipeline to perform gene co-expression network analysis. The proposed pipeline employs WGCNA, a software widely used to perform different aspects of gene co-expression network analysis, and Modularity Maximization algorithm, to analyze novel RNA-Seq data to understand the effects of low-dose 56Fe ion irradiation on the formation of hepatocellular carcinoma in mice. The network results, along with experimental validation, show that using WGCNA combined with Modularity Maximization, provides a more biologically interpretable network in our dataset, than that obtainable using WGCNA alone. The proposed pipeline showed better performance than the existing clustering algorithm in WGCNA, and identified a module that was biologically validated by a mitochondrial complex I assay. CONCLUSIONS: We present a pipeline that can reduce the problem of parameter selection that occurs with the existing algorithm in WGCNA, for applicable RNA-Seq datasets. This may assist in the future discovery of novel mRNA interactions, and elucidation of their potential downstream molecular effects.

Methylation differences reveal heterogeneity in preterm pathophysiology: results from bipartite network analyses.

BACKGROUND: Recent studies have shown that epigenetic differences can increase the risk of spontaneous preterm birth (PTB). However, little is known about heterogeneity underlying such epigenetic differences, which could lead to hypotheses for biological pathways in specific patient subgroups, and corresponding targeted interventions critical for precision medicine. Using bipartite network analysis of fetal DNA methylation data we demonstrate a novel method for classification of PTB. METHODS: The data consisted of DNA methylation across the genome (HumanMethylation450 BeadChip) in cord blood from 50 African-American subjects consisting of 22 cases of early spontaneous PTB (24-34 weeks of gestation) and 28 controls (>39 weeks of gestation). These data were analyzed using a combination of (1) a supervised method to select the top 10 significant methylation sites, (2) unsupervised "subject-variable" bipartite networks to visualize and quantitatively analyze how those 10 methylation sites co-occurred across all the subjects, and across only the cases with the goal of analyzing subgroups and their underlying pathways, and (3) a simple linear regression to test whether there was an association between the total methylation in the cases, and gestational age. RESULTS: The bipartite network analysis of all subjects and significant methylation sites revealed statistically significant clustering consisting of an inverse symmetrical relationship in the methylation profiles between a case-enriched subgroup and a control-enriched subgroup: the former was predominantly hypermethylated across seven methylation sites, and hypomethylated across three methylation sites, whereas the latter was predominantly hypomethylated across the above seven methylation sites and hypermethylated across the three methylation sites. Furthermore, the analysis of only cases revealed one subgroup that was predominantly hypomethylated across seven methylation sites, and another subgroup that was hypomethylated across all methylation sites suggesting the presence of heterogeneity in PTB pathophysiology. Finally, the analysis found a strong inverse linear relationship between total methylation and gestational age suggesting that methylation differences could be used as predictive markers for gestational length. CONCLUSIONS: The results demonstrate that unsupervised bipartite networks helped to identify a complex but comprehensible data-driven hypotheses related to patient subgroups and inferences about their underlying pathways, and therefore were an effective complement to supervised approaches currently used.

Enabling Comprehension of Patient Subgroups and Characteristics in Large Bipartite Networks: Implications for Precision Medicine.

A primary goal of precision medicine is to identify patient subgroups based on their characteristics (e.g., comorbidities or genes) with the goal of designing more targeted interventions. While network visualization methods such as Fruchterman-Reingold have been used to successfully identify such patient subgroups in small to medium sized data sets, they often fail to reveal comprehensible visual patterns in large and dense networks despite having significant clustering. We therefore developed an algorithm called ExplodeLayout, which exploits the existence of significant clusters in bipartite networks to automatically "explode" a traditional network layout with the goal of separating overlapping clusters, while at the same time preserving key network topological properties that are critical for the comprehension of patient subgroups. We demonstrate the utility of ExplodeLayout by visualizing a large dataset extracted from Medicare consisting of readmitted hip-fracture patients and their comorbidities, demonstrate its statistically significant improvement over a traditional layout algorithm, and discuss how the resulting network visualization enabled clinicians to infer mechanisms precipitating hospital readmission in specific patient subgroups.

How Comorbidities Co-Occur in Readmitted Hip Fracture Patients: From Bipartite Networks to Insights for Post-Discharge Planning.

Although a majority of 30-day readmissions of hip-fracture (HFx) patients in the elderly are caused by non-surgical complications, little is known about which specific combinations of comorbidities are associated with increased risk of readmission. We therefore used bipartite network analysis to explore the complex associations between 70 comorbidities (defined by hierarchal condition categories as critical in this population) and (a) cases consisting of all 2,316 HFx patients without hospital complications in the 2010 Medicare claims database who were re-admitted within 30 days of discharge, and (b) controls consisting of an equal number of matched HFx patients who were not readmitted for at least 90 days since discharge. A network-wide analysis revealed nine patient/comorbidity co-clusters, of which two had a significantly different proportion of cases compared to the rest of the data. A cluster-specific analysis of the most significant co-cluster revealed that a pair of comorbidities (Renal Failure and Diabetes with no Complications) within the co-cluster had significantly higher risk of 30-day readmission, whereas another pair of comorbidities (Renal Failure and Diabetes with Renal or Peripheral Circulatory Manifestations), despite having a relatively more serious comorbidity, did not confer a higher risk. This counter-intuitive result suggests that HFx patients with more serious comorbidities may have better follow-up that reduces the risk of 30-day readmission, whereas those with specific relatively less-serious comorbidities may have less stringent follow-up resulting in unanticipated incidents that precipitate readmission. These analyses reveal the strengths and limitations of bipartite networks for identifying hypotheses for complex phenomena related to readmissions, with the goal of improving follow-up care for patients with specific combinations of comorbidities.

Analysis of the TGFß-induced program in primary airway epithelial cells shows essential role of NF-κB/RelA signaling network in type II epithelial mesenchymal transition.

BACKGROUND: The airway epithelial cell plays a central role in coordinating the pulmonary response to injury and inflammation. Here, transforming growth factor-ß (TGFß) activates gene expression programs to induce stem cell-like properties, inhibit expression of differentiated epithelial adhesion proteins and express mesenchymal contractile proteins. This process is known as epithelial mesenchymal transition (EMT); although much is known about the role of EMT in cellular metastasis in an oncogene-transformed cell, less is known about Type II EMT, that occurring in normal epithelial cells. In this study, we applied next generation sequencing (RNA-Seq) in primary human airway epithelial cells to understand the gene program controlling Type II EMT and how cytokine-induced inflammation modifies it. RESULTS: Generalized linear modeling was performed on a two-factor RNA-Seq experiment of 6 treatments of telomerase immortalized human small airway epithelial cells (3 replicates). Using a stringent cut-off, we identified 3,478 differentially expressed genes (DEGs) in response to EMT. Unbiased transcription factor enrichment analysis identified three clusters of EMT regulators, one including SMADs/TP63 and another NF-κB/RelA. Surprisingly, we also observed 527 of the EMT DEGs were also regulated by the TNF-NF-κB/RelA pathway. This Type II EMT program was compared to Type III EMT in TGFß stimulated A549 alveolar lung cancer cells, revealing significant functional differences. Moreover, we observe that Type II EMT modifies the outcome of the TNF program, reducing IFN signaling and enhancing integrin signaling. We confirmed experimentally that TGFß-induced the NF-κB/RelA pathway by observing a 2-fold change in NF-κB/RelA nuclear translocation. A small molecule IKK inhibitor blocked TGFß-induced core transcription factor (SNAIL1, ZEB1 and Twist1) and mesenchymal gene (FN1 and VIM) expression. CONCLUSIONS: These data indicate that NF-κB/RelA controls a SMAD-independent gene network whose regulation is required for initiation of Type II EMT. Type II EMT dramatically affects the induction and kinetics of TNF-dependent gene networks.

Unlocking proteomic heterogeneity in complex diseases through visual analytics.

Despite years of preclinical development, biological interventions designed to treat complex diseases such as asthma often fail in phase III clinical trials. These failures suggest that current methods to analyze biomedical data might be missing critical aspects of biological complexity such as the assumption that cases and controls come from homogeneous distributions. Here we discuss why and how methods from the rapidly evolving field of visual analytics can help translational teams (consisting of biologists, clinicians, and bioinformaticians) to address the challenge of modeling and inferring heterogeneity in the proteomic and phenotypic profiles of patients with complex diseases. Because a primary goal of visual analytics is to amplify the cognitive capacities of humans for detecting patterns in complex data, we begin with an overview of the cognitive foundations for the field of visual analytics. Next, we organize the primary ways in which a specific form of visual analytics called networks has been used to model and infer biological mechanisms, which help to identify the properties of networks that are particularly useful for the discovery and analysis of proteomic heterogeneity in complex diseases. We describe one such approach called subject-protein networks, and demonstrate its application on two proteomic datasets. This demonstration provides insights to help translational teams overcome theoretical, practical, and pedagogical hurdles for the widespread use of subject-protein networks for analyzing molecular heterogeneities, with the translational goal of designing biomarker-based clinical trials, and accelerating the development of personalized approaches to medicine.

The role of visual analytics in asthma phenotyping and biomarker discovery.

The exponential growth of biomedical data related to diseases such as asthma far exceeds our cognitive abilities to comprehend it for tasks such as biomarker discovery, pathway identification, and molecular-based phenotyping. This chapter discusses the cognitive and task-based reasons for why methods from visual analytics can help in analyzing such large and complex asthma data, and demonstrates how one such approach called network visualization and analysis can be used to reveal important translational insights related to asthma. The demonstration of the method helps to identify the strengths and limitations of network analysis, in addition to areas for future research that can enhance the use of networks to analyze vast and complex biomedical datasets related to diseases such as asthma.

Heterogeneity within and across Pediatric Pulmonary Infections: From Bipartite Networks to At-Risk Subphenotypes.

Although influenza (flu) and respiratory syncytial virus (RSV) infections are extremely common in children under two years and resolve naturally, a subset develop severe disease resulting in hospitalization despite having no identifiable clinical risk factors. However, little is known about inherent host-specific genetic and immune mechanisms in this at-risk subpopulation. We therefore conducted a secondary analysis of statistically significant, differentially-expressed genes from a whole genome-wide case-control study of children less than two years of age hospitalized with flu or RSV, through the use of bipartite networks. The analysis revealed three clusters of cases common to both types of infection: core cases with high expression of genes in the network core implicated in hyperimmune responsiveness; periphery cases with lower expression of the same set of genes indicating medium-responsiveness; and control-like cases with a gene signature resembling that of the controls, indicating normal-responsiveness. These results provide testable hypotheses for at-risk subphenotypes and their respective pathophysiologies in both types of infections. We conclude by discussing alternate hypotheses for the results, and insights about how bipartite networks of gene expression across multiple phenotypes can help to identify complex patterns related to subphenotypes, with the translational goal of identifying targeted therapeutics.

How Cytokines Co-occur across Rickettsioses Patients: From Bipartite Visual Analytics to Mechanistic Inferences of a Cytokine Storm.

Several intersecting host, vector, and environmental factors have led to a re-emergence of rickettsial diseases such as Mediterranean Spotted Fever (MSF), and Dermacentor spp.-borne necrosis-erythema lymphadenopathy (DEBONEL). Some rickettsiae produce diffuse endothelial infection and systemic microvascular leakage leading in some cases to high morbidity and mortality. Unfortunately, little is known about the molecular pathways triggered by these diseases in humans. We therefore analyzed how candidate cytokines co-occur across acutely-ill patients with either a localized (DEBONEL), or a systemic (MSF) form of rickettsiosis, using bipartite visual analytics. The results revealed a network core consisting of a small set of MSF patients exhibiting high expressions of cytokines implicated in microvascular leakage, endothelial repair, and pro-inflammatory immune responses, and a network periphery consisting of a mixture of MSF and DEBONEL patients with relatively lower overall cytokine expressions. These results provide evidence of pathways triggered by rickettsiae in humans, and a testable hypothesis for the mechanisms in a rickettsia-induced cytokine storm with the translational goal of identifying therapeutic targets.

Outlier Detection through Bipartite Visual Analytics.

A critical goal of outlier detection is to determine whether an outlying value was caused by experimental/human error, or by natural biological diversity. However, because univariate or multivariate methods (e.g., box plots and principle component analysis) typically used for outlier detection use unipartite representations, they cannot distinguish whether outliers across a set of variables represent, for example, a single patient or different patients. Here we propose a bipartite visual analytical approach to outlier detection, and demonstrate its usefulness for identifying complex bipartite outliers in a dataset of rickettsioses patients, which enabled domain experts to determine whether the outliers were caused by errors, or by biological diversity.