Challenges and opportunities for competency-based health professional education in Bangladesh: an interview, observation and mapping study.

BACKGROUND: Bangladesh has a shortfall of health professionals. The World Health Organization states that improving education will increase recruitment and retention of health workers. Traditional learning approaches, in medical education particularly, focus on didactic teaching, teaching of subjects and knowledge testing. These approaches have been superseded in some programmes, with a greater focus on active learning, integrated teaching and learning of knowledge, application, skills and attitudes or values and associated testing of competencies as educational outcomes. In addition, some regions do not have continuous professional development or clinical placements for health worker students, contributing to difficulties in retention of health workers. This study aims to explore the experiences of health professional education in Bangladesh, focusing on what is through observation of health professional education sessions and experiences of educators. METHODS: This mixed method study included 22 observations of teaching sessions in clinical and educational settings, detailed analysis of 8 national curricula documents mapped to Global Competency and Outcomes Framework for Universal Health Coverage and 15 interviews of professionals responsible for health education. An observational checklist was created based on previous literature which assessed training of within dimensions of basic clinical skills; diagnosis and management; professionalism; professional development; and effective communication. Interviews explored current practices within health education in Bangladesh, as well as barriers and facilitators to incorporating different approaches to learning. RESULTS: Observations revealed a variety of approaches and frameworks followed across institutions. Only one observation included all sub-competencies of the checklist. National curricula documents varied in their coverage of the Global Competency and Outcomes Framework domains. Three key themes were generated from a thematic analysis of interview transcripts: (1) education across the career span; (2) challenges for health professional education; (3) contextual factors and health professional education. Opportunities for progression and development post qualification are limited and certain professions are favoured over others. CONCLUSION: Traditional approaches seem to predominate but there is some enthusiasm for a more clinical focus to education and for more competency based approaches to teaching, learning and assessment.

Impact of community-based first responder development for the management of drowning casualties in rural areas of Bangladesh.

BACKGROUND: Drowning is the leading cause of childhood death in Bangladesh. In order to minimize the number of casualties Centre for Injury Prevention and Research, Bangladesh (CIPRB) incorporated a 'first responder' program which includes Cardio Pulmonary Resuscitation (CPR), in community based drowning prevention program, SwimSafe. Along with swimming lessons, swimming instructors provide first responder services in the community. The objective of this study was to describe the results of the volunteer based first responder services for the management of drowned casualties between 2012 and 2015 in the rural communities of Bangladesh. METHODS: Adolescents and youths who volunteered as community swimming instructors were trained as first responders to provide first aid and resuscitation in the community. Trainers from the International Drowning Research Centre Bangladesh (IDRC-B) of CIPRB delivered the training. The first responders were also trained on the documentation of the first responder services they provided in the community. The documented records were collected from the volunteers on a regular basis; when drowning cases were reported CIPRB management followed up with an in depth data collection, using a structured form. RESULTS: 2,305 community volunteers were trained between 2012 and 2015. Of them 1,461 reported providing first responder services among 6,773 casualties, including 184 drowning casualties. Of the drowning casualties, volunteers treated 31 casualties with Cardiopulmonary Resuscitation (CPR), 51 casualties by putting into the recovery position and 102 casualties were treated for the shock on site. Of those given CPR, 22 (71%) survived and 9 (29%) died. After receiving treatment from the first responder 104 (56.5%) of the drowning casualties were referred to health facilities for further treatment. CONCLUSIONS: The training of community first responders seems to be an effective way of managing and reducing drowning causalities in countries like Bangladesh, where drowning is a significant public health hazard.

Effect of an Enhanced Self-Care Protocol on Lymphedema Status among People Affected by Moderate to Severe Lower-Limb Lymphedema in Bangladesh, a Cluster Randomized Controlled Trial.

BACKGROUND: Lymphatic filariasis (LF) is a major cause of lymphedema, affecting over 16 million people globally. A daily, hygiene-centered self-care protocol is recommended and effective in reducing acute attacks caused by secondary infections. It may also reverse lymphedema status in early stages, but less so as lymphedema advances. Lymphatic stimulating activities such as self-massage and deep-breathing have proven beneficial for cancer-related lymphedema, but have not been tested in LF-settings. Therefore, an enhanced self-care protocol was trialed among people affected by moderate to severe LF-related lymphedema in northern Bangladesh. METHODS: Cluster randomization was used to allocate participants to either standard- or enhanced-self-care groups. Lymphedema status was determined by lymphedema stage, mid-calf circumference, and mid-calf tissue compressibility. RESULTS: There were 71 patients in each group and at 24 weeks, both groups had experienced significant improvement in lymphedema status and reduction in acute attacks. There was a significant and clinically relevant between-group difference in mid-calf tissue compressibility with the biggest change observed on legs affected by severe lymphedema in the enhanced self-care group (∆ 21.5%, -0.68 (-0.91, -0.45), p < 0.001). CONCLUSION: This study offers the first evidence for including lymphatic stimulating activities in recommended self-care for people affected by moderate and severe LF-related lymphedema.

Developing the first national database and map of lymphatic filariasis clinical cases in Bangladesh: Another step closer to the elimination goals.

BACKGROUND: The Bangladesh Lymphatic Filariasis (LF) Elimination Programme has made significant progress in interrupting transmission through mass drug administration (MDA) and has now focussed its efforts on scaling up managing morbidity and preventing disability (MMDP) activities to deliver the minimum package of care to people affected by LF clinical conditions. This paper highlights the Bangladesh LF Programme's success in conducting a large-scale cross-sectional survey to determine the number of people affected by lymphoedema and hydrocoele, which enabled clinical risk maps to be developed for targeted interventions across the 34 endemic districts (19 high endemic; 15 low endemic). METHODOLOGY/PRINCIPAL FINDINGS: In the 19 high endemic districts, 8,145 community clinic staff were trained to identify and report patients in their catchment area. In the 15 low endemic districts, a team of 10 trained field assistants conducted active case finding with cases reported via a SMS mHealth tool. Disease burden and prevalence maps were developed, with morbidity hotspots identified at sub-district level based on a combination of the highest prevalence rates per 100,000 and case-density rates per square kilometre (km2). The relationship between morbidity and baseline microfilaria (mf) prevalence was also examined. In total 43,678 cases were identified in the 19 high endemic districts; 30,616 limb lymphoedema (70.1%; female 55.3%), 12,824 hydrocoele (29.4%), and 238 breast/female genital swelling (0.5%). Rangpur Division reported the highest cases numbers and prevalence of lymphoedema (26,781 cases, 195 per 100,000) and hydrocoele (11661 cases, 169.6 per 100,000), with lymphoedema predominately affecting females (n = 21,652). Rangpur and Lalmonirhat Districts reported the highest case numbers (n = 11,199), and prevalence (569 per 100,000) respectively, with five overlapping lymphoedema and hydrocoele sub-district hotspots. In the 15 low endemic districts, 732 cases were identified; 661 lymphoedema (90.2%; female 39.6%), 56 hydrocoele (7.8%), and 15 both conditions (2.0%). Spearman's correlation analysis found morbidity and mf prevalence significantly positively correlated (r = 0.904; p<0.01). CONCLUSIONS/SIGNIFICANCE: The Bangladesh LF Programme has developed one of the largest, most comprehensive country databases on LF clinical conditions in the world. It provides an essential database for health workers to identify local morbidity hotspots, deliver the minimum package of care, and address the dossier elimination requirements.

Epidemiology of Fall Injury in Rural Bangladesh.

Globally, falls are the second leading cause of unintentional injury deaths, with 80% occurring in low-and middle-income countries. The overall objective of this study is to describe the burden and risk factors of falls in rural Bangladesh. In 2013, a large household survey covering a population of 1,169,593 was conducted in seven rural sub-districts of Bangladesh to assess the burden of all injuries, including falls. The recall periods for non-fatal and fatal injuries were six and 12 months, respectively. Descriptive, bivariate and multiple logistic regression analyses were conducted. The rates of non-fatal and fatal falls were 36.3 per 1000 and 5 per 100,000 population, respectively. The rates of both fatal and non-fatal falls were highest among the elderly. The risk of non-fatal falls was higher at extremes of age. Lower limb and waist injuries were frequent following a fall. Head injuries were frequent among infants (35%), while lower limb and waist injuries were frequent among the elderly (>65 years old). Injuries to all body parts (except the waist) were most frequent among men. More than half of all non-fatal falls occurred in a home environment. The injury patterns and risk factors of non-fatal falls differ by sociodemographic factors.

FISHtrees 3.0: Tumor Phylogenetics Using a Ploidy Probe.

Advances in fluorescence in situ hybridization (FISH) make it feasible to detect multiple copy-number changes in hundreds of cells of solid tumors. Studies using FISH, sequencing, and other technologies have revealed substantial intra-tumor heterogeneity. The evolution of subclones in tumors may be modeled by phylogenies. Tumors often harbor aneuploid or polyploid cell populations. Using a FISH probe to estimate changes in ploidy can guide the creation of trees that model changes in ploidy and individual gene copy-number variations. We present FISHtrees 3.0, which implements a ploidy-based tree building method based on mixed integer linear programming (MILP). The ploidy-based modeling in FISHtrees includes a new formulation of the problem of merging trees for changes of a single gene into trees modeling changes in multiple genes and the ploidy. When multiple samples are collected from each patient, varying over time or tumor regions, it is useful to evaluate similarities in tumor progression among the samples. Therefore, we further implemented in FISHtrees 3.0 a new method to build consensus graphs for multiple samples. We validate FISHtrees 3.0 on a simulated data and on FISH data from paired cases of cervical primary and metastatic tumors and on paired breast ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC). Tests on simulated data show improved accuracy of the ploidy-based approach relative to prior ploidyless methods. Tests on real data further demonstrate novel insights these methods offer into tumor progression processes. Trees for DCIS samples are significantly less complex than trees for paired IDC samples. Consensus graphs show substantial divergence among most paired samples from both sets. Low consensus between DCIS and IDC trees may help explain the difficulty in finding biomarkers that predict which DCIS cases are at most risk to progress to IDC. The FISHtrees software is available at ftp://ftp.ncbi.nih.gov/pub/FISHtrees.

Phylogenetic analysis of multiple FISH markers in oral tongue squamous cell carcinoma suggests that a diverse distribution of copy number changes is associated with poor prognosis.

Oral tongue squamous cell carcinoma (OTSCC) is associated with poor prognosis. To improve prognostication, we analyzed four gene probes (TERC, CCND1, EGFR and TP53) and the centromere probe CEP4 as a marker of chromosomal instability, using fluorescence in situ hybridization (FISH) in single cells from the tumors of sixty-five OTSCC patients (Stage I, n = 15; Stage II, n = 30; Stage III, n = 7; Stage IV, n = 13). Unsupervised hierarchical clustering of the FISH data distinguished three clusters related to smoking status. Copy number increases of all five markers were found to be correlated to non-smoking habits, while smokers in this cohort had low-level copy number gains. Using the phylogenetic modeling software FISHtrees, we constructed models of tumor progression for each patient based on the four gene probes. Then, we derived test statistics on the models that are significant predictors of disease-free and overall survival, independent of tumor stage and smoking status in multivariate analysis. The patients whose tumors were modeled as progressing by a more diverse distribution of copy number changes across the four genes have poorer prognosis. This is consistent with the view that multiple genetic pathways need to become deregulated in order for cancer to progress.

Inferring models of multiscale copy number evolution for single-tumor phylogenetics.

MOTIVATION: Phylogenetic algorithms have begun to see widespread use in cancer research to reconstruct processes of evolution in tumor progression. Developing reliable phylogenies for tumor data requires quantitative models of cancer evolution that include the unusual genetic mechanisms by which tumors evolve, such as chromosome abnormalities, and allow for heterogeneity between tumor types and individual patients. Previous work on inferring phylogenies of single tumors by copy number evolution assumed models of uniform rates of genomic gain and loss across different genomic sites and scales, a substantial oversimplification necessitated by a lack of algorithms and quantitative parameters for fitting to more realistic tumor evolution models. RESULTS: We propose a framework for inferring models of tumor progression from single-cell gene copy number data, including variable rates for different gain and loss events. We propose a new algorithm for identification of most parsimonious combinations of single gene and single chromosome events. We extend it via dynamic programming to include genome duplications. We implement an expectation maximization (EM)-like method to estimate mutation-specific and tumor-specific event rates concurrently with tree reconstruction. Application of our algorithms to real cervical cancer data identifies key genomic events in disease progression consistent with prior literature. Classification experiments on cervical and tongue cancer datasets lead to improved prediction accuracy for the metastasis of primary cervical cancers and for tongue cancer survival. AVAILABILITY AND IMPLEMENTATION: Our software (FISHtrees) and two datasets are available at ftp://ftp.ncbi.nlm.nih.gov/pub/FISHtrees.

A Novel Subset of Human Tumors That Simultaneously Overexpress Multiple E2F-responsive Genes Found in Breast, Ovarian, and Prostate Cancers.

Reasoning that overexpression of multiple E2F-responsive genes might be a useful marker for RB1 dysfunction, we compiled a list of E2F-responsive genes from the literature and evaluated their expression in publicly available gene expression microarray data of patients with breast cancer, serous ovarian cancer, and prostate cancer. In breast cancer, a group of tumors was identified, each of which simultaneously overexpressed multiple E2F-responsive genes. Seventy percent of these genes were concerned with cell cycle progression, DNA repair, or mitosis. These E2F-responsive gene overexpressing (ERGO) tumors frequently exhibited additional evidence of Rb/E2F axis dysfunction, were mostly triple negative, and preferentially overexpressed multiple basal cytokeratins, suggesting that they overlapped substantially with the basal-like tumor subset. ERGO tumors were also identified in serous ovarian cancer and prostate cancer. In these cancer types, there was no evidence for a tumor subset comparable to the breast cancer basal-like subset. A core group of about 30 E2F-responsive genes were overexpressed in all three cancer types. Thus, it appears that disorders of the Rb/E2F axis can arise at multiple organ sites and produce tumors that simultaneously overexpress multiple E2F-responsive genes.

Algorithms to model single gene, single chromosome, and whole genome copy number changes jointly in tumor phylogenetics.

We present methods to construct phylogenetic models of tumor progression at the cellular level that include copy number changes at the scale of single genes, entire chromosomes, and the whole genome. The methods are designed for data collected by fluorescence in situ hybridization (FISH), an experimental technique especially well suited to characterizing intratumor heterogeneity using counts of probes to genetic regions frequently gained or lost in tumor development. Here, we develop new provably optimal methods for computing an edit distance between the copy number states of two cells given evolution by copy number changes of single probes, all probes on a chromosome, or all probes in the genome. We then apply this theory to develop a practical heuristic algorithm, implemented in publicly available software, for inferring tumor phylogenies on data from potentially hundreds of single cells by this evolutionary model. We demonstrate and validate the methods on simulated data and published FISH data from cervical cancers and breast cancers. Our computational experiments show that the new model and algorithm lead to more parsimonious trees than prior methods for single-tumor phylogenetics and to improved performance on various classification tasks, such as distinguishing primary tumors from metastases obtained from the same patient population.

Single-cell genetic analysis reveals insights into clonal development of prostate cancers and indicates loss of PTEN as a marker of poor prognosis.

Gauging the risk of developing progressive disease is a major challenge in prostate cancer patient management. We used genetic markers to understand genomic alteration dynamics during disease progression. By using a novel, advanced, multicolor fluorescence in situ hybridization approach, we enumerated copy numbers of six genes previously identified by array comparative genomic hybridization to be involved in aggressive prostate cancer [TBL1XR1, CTTNBP2, MYC (alias c-myc), PTEN, MEN1, and PDGFB] in six nonrecurrent and seven recurrent radical prostatectomy cases. An ERG break-apart probe to detect TMPRSS2-ERG fusions was included. Subsequent hybridization of probe panels and cell relocation resulted in signal counts for all probes in each individual cell analyzed. Differences in the degree of chromosomal and genomic instability (ie, tumor heterogeneity) or the percentage of cells with TMPRSS2-ERG fusion between samples with or without progression were not observed. Tumors from patients that progressed had more chromosomal gains and losses, and showed a higher degree of selection for a predominant clonal pattern. PTEN loss was the most frequent aberration in progressers (57%), followed by TBL1XR1 gain (29%). MYC gain was observed in one progresser, which was the only lesion with an ERG gain, but no TMPRSS2-ERG fusion. According to our results, a probe set consisting of PTEN, MYC, and TBL1XR1 would detect progressers with 86% sensitivity and 100% specificity. This will be evaluated further in larger studies.

An integrated approach to blood-based cancer diagnosis and biomarker discovery.

Disrupted or abnormal biological processes responsible for cancers often quantitatively manifest as disrupted additive and multiplicative interactions of gene/protein expressions correlating with cancer progression. However, the examination of all possible combinatorial interactions between gene features in most case-control studies with limited training data is computationally infeasible. In this paper, we propose a practically feasible data integration approach, QUIRE (QUadratic Interactions among infoRmative fEatures), to identify discriminative complex interactions among informative gene features for cancer diagnosis and biomarker discovery directly based on patient blood samples. QUIRE works in two stages, where it first identifies functionally relevant gene groups for the disease with the help of gene functional annotations and available physical protein interactions, then it explores the combinatorial relationships among the genes from the selected informative groups. Based on our private experimentally generated data from patient blood samples using a novel SOMAmer (Slow Off-rate Modified Aptamer) technology, we apply QUIRE to cancer diagnosis and biomarker discovery for Renal Cell Carcinoma (RCC) and Ovarian Cancer (OVC). To further demonstrate the general applicability of our approach, we also apply QUIRE to a publicly available Colorectal Cancer (CRC) dataset that can be used to prioritize our SOMAmer design. Our experimental results show that QUIRE identifies gene-gene interactions that can better identify the different cancer stages of samples, as compared to other state-of-the-art feature selection methods. A literature survey shows that many of the interactions identified by QUIRE play important roles in the development of cancer.

Children's perceptions about falls and their prevention: a qualitative study from a rural setting in Bangladesh.

BACKGROUND: Childhood falls is a major public health problem in Bangladesh. In-depth understanding of the situation by the target groups and their families is necessary for successful development, implementation and evaluation of any intervention. The study aimed at knowing the views of Bangladeshi rural children about childhood falls and their suggestions for prevention. METHODS: Children of 10-17 were selected purposely from 4 villages of Sherpur Sadar upazila (sub-district), Sherpur district of Bangladesh. Six focus group discussions and ten in-depth interviews were conducted during July-August 2010 for this study. Gender and education of the participants were considered. Major themes were identified, coded and categorized from content analysis. RESULTS: Participants stated that young children (<5 years of age) and boys appeared to be the main victims of falls and majority of these injuries occurred in and around the households. Boys commonly fall from the tree around their premises and high places. Girls usually fall when they remain busy in household chores and playing with friends around their premises. Participants also mentioned that children mostly sustained injury when they are unsupervised. Supervision, public awareness and putting barriers (e.g. door barrier, putting pillow and use net around the bed etc.) were suggested as the preventive measures. CONCLUSION: Findings of this study could be considered as part of knowledge-base in designing interventions to address childhood falls.

Network signatures of survival in glioblastoma multiforme.

To determine a molecular basis for prognostic differences in glioblastoma multiforme (GBM), we employed a combinatorial network analysis framework to exhaustively search for molecular patterns in protein-protein interaction (PPI) networks. We identified a dysregulated molecular signature distinguishing short-term (survival<225 days) from long-term (survival>635 days) survivors of GBM using whole genome expression data from The Cancer Genome Atlas (TCGA). A 50-gene subnetwork signature achieved 80% prediction accuracy when tested against an independent gene expression dataset. Functional annotations for the subnetwork signature included "protein kinase cascade," "IκB kinase/NFκB cascade," and "regulation of programmed cell death" - all of which were not significant in signatures of existing subtypes. Finally, we used label-free proteomics to examine how our subnetwork signature predicted protein level expression differences in an independent GBM cohort of 16 patients. We found that the genes discovered using network biology had a higher probability of dysregulated protein expression than either genes exhibiting individual differential expression or genes derived from known GBM subtypes. In particular, the long-term survivor subtype was characterized by increased protein expression of DNM1 and MAPK1 and decreased expression of HSPA9, PSMD3, and CANX. Overall, we demonstrate that the combinatorial analysis of gene expression data constrained by PPIs outlines an approach for the discovery of robust and translatable molecular signatures in GBM.

Phylogenetic analysis of multiprobe fluorescence in situ hybridization data from tumor cell populations.

MOTIVATION: Development and progression of solid tumors can be attributed to a process of mutations, which typically includes changes in the number of copies of genes or genomic regions. Although comparisons of cells within single tumors show extensive heterogeneity, recurring features of their evolutionary process may be discerned by comparing multiple regions or cells of a tumor. A useful source of data for studying likely progression of individual tumors is fluorescence in situ hybridization (FISH), which allows one to count copy numbers of several genes in hundreds of single cells. Novel algorithms for interpreting such data phylogenetically are needed, however, to reconstruct likely evolutionary trajectories from states of single cells and facilitate analysis of tumor evolution. RESULTS: In this article, we develop phylogenetic methods to infer likely models of tumor progression using FISH copy number data and apply them to a study of FISH data from two cancer types. Statistical analyses of topological characteristics of the tree-based model provide insights into likely tumor progression pathways consistent with the prior literature. Furthermore, tree statistics from the resulting phylogenies can be used as features for prediction methods. This results in improved accuracy, relative to unstructured gene copy number data, at predicting tumor state and future metastasis. AVAILABILITY: Source code for software that does FISH tree building (FISHtrees) and the data on cervical and breast cancer examined here are available at ftp://ftp.ncbi.nlm.nih.gov/pub/FISHtrees. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Identifying stage-specific protein subnetworks for colorectal cancer.

BACKGROUND: In recent years, many algorithms have been developed for network-based analysis of differential gene expression in complex diseases. These algorithms use protein-protein interaction (PPI) networks as an integrative framework and identify subnetworks that are coordinately dysregulated in the phenotype of interest. MOTIVATION: While such dysregulated subnetworks have demonstrated significant improvement over individual gene markers for classifying phenotype, the current state-of-the-art in dysregulated subnetwork discovery is almost exclusively limited to binary phenotype classes. However, many clinical applications require identification of molecular markers for multiple classes. APPROACH: We consider the problem of discovering groups of genes whose expression signatures can discriminate multiple phenotype classes. We consider two alternate formulations of this problem (i) an all-vs-all approach that aims to discover subnetworks distinguishing all classes, (ii) a one-vs-all approach that aims to discover subnetworks distinguishing each class from the rest of the classes. For the one-vs-all formulation, we develop a set-cover based algorithm, which aims to identify groups of genes such that at least one gene in the group exhibits differential expression in the target class. RESULTS: We test the proposed algorithms in the context of predicting stages of colorectal cancer. Our results show that the set-cover based algorithm identifying "stage-specific" subnetworks outperforms the all-vs-all approaches in classification. We also investigate the merits of utilizing PPI networks in the search for multiple markers, and show that, with correct parameter settings, network-guided search improves performance. Furthermore, we show that assessing statistical significance when selecting features greatly improves classification performance.

Single-cell genetic analysis of ductal carcinoma in situ and invasive breast cancer reveals enormous tumor heterogeneity yet conserved genomic imbalances and gain of MYC during progression.

Ductal carcinoma in situ (DCIS) is a precursor lesion of invasive ductal carcinoma (IDC) of the breast. To understand the dynamics of genomic alterations in this progression, we used four multicolor fluorescence in situ hybridization probe panels consisting of the oncogenes COX2, MYC, HER2, CCND1, and ZNF217 and the tumor suppressor genes DBC2, CDH1, and TP53 to visualize copy number changes in 13 cases of synchronous DCIS and IDC based on single-cell analyses. The DCIS had a lower degree of chromosomal instability than the IDC. Despite enormous intercellular heterogeneity in DCIS and IDC, we observed signal patterns consistent with a nonrandom distribution of genomic imbalances. CDH1 was most commonly lost, and gain of MYC emerged during progression from DCIS to IDC. Four of 13 DCISs showed identical clonal imbalances in the IDCs. Six cases revealed a switch, and in four of those, the IDC had acquired a gain of MYC. In one case, the major clone in the IDC was one of several clones in the DCIS, and in another case, the major clone in the DCIS became one of the two major clones in the IDC. Despite considerable chromosomal instability, in most cases the evolution from DCIS to IDC is determined by recurrent patterns of genomic imbalances, consistent with a biological continuum.

Subnetwork state functions define dysregulated subnetworks in cancer.

Emerging research demonstrates the potential of protein-protein interaction (PPI) networks in uncovering the mechanistic bases of cancers, through identification of interacting proteins that are coordinately dysregulated in tumorigenic and metastatic samples. When used as features for classification, such coordinately dysregulated subnetworks improve diagnosis and prognosis of cancer considerably over single-gene markers. However, existing methods formulate coordination between multiple genes through additive representation of their expression profiles and utilize fast heuristics to identify dysregulated subnetworks, which may not be well suited to the potentially combinatorial nature of coordinate dysregulation. Here, we propose a combinatorial formulation of coordinate dysregulation and decompose the resulting objective function to cast the problem as one of identifying subnetwork state functions that are indicative of phenotype. Based on this formulation, we show that coordinate dysregulation of larger subnetworks can be bounded using simple statistics on smaller subnetworks. We then use these bounds to devise an efficient algorithm, Crane, that can search the subnetwork space more effectively than existing algorithms. Comprehensive cross-classification experiments show that subnetworks identified by Crane outperform those identified by additive algorithms in predicting metastasis of colorectal cancer (CRC).

Protein-protein interaction networks and subnetworks in the biology of disease.

The main goal of systems medicine is to provide predictive models of the patho-physiology of complex diseases as well as define healthy states. The reason is clear--we hope accurate models will ultimately lead to more specific and sensitive markers of disease that will help clinicians better stratify their patient populations and optimize treatment plans. In addition, we expect that these models will define novel targets for combating disease. However, for many complex diseases, particularly at the clinical level, it is becoming increasingly clear that one or a few genomic variations alone (e.g., simple models) cannot adequately explain the multiple phenotypes related to disease states, or the variable risks that attend disease progression. We suggest that models that account for the activities of many interacting proteins will explain a wider range of variability inherent in these phenotypes. These models, which encompass protein interaction networks dysregulated for specific diseases and specific patient sub-populations, will be constructed by integrating protein interaction data with multiple types of other relevant cellular information. Protein interaction databases are thus playing an increasingly important role in systems biology approaches to the study of disease. They present us with a static, but highly functional view of the cellular state, and thus give us a better understanding of not only the normal phenotype, but also the overall disease phenotype at the level of the whole organism when certain interactions become dysregulated.

Systems biology analyses of gene expression and genome wide association study data in obstructive sleep apnea.

The precise molecular etiology of obstructive sleep apnea (OSA) is unknown; however recent research indicates that several interconnected aberrant pathways and molecular abnormalities are contributors to OSA. Identifying the genes and pathways associated with OSA can help to expand our understanding of the risk factors for the disease as well as provide new avenues for potential treatment. Towards these goals, we have integrated relevant high dimensional data from various sources, such as genome-wide expression data (microarray), protein-protein interaction (PPI) data and results from genome-wide association studies (GWAS) in order to define sub-network elements that connect some of the known pathways related to the disease as well as define novel regulatory modules related to OSA. Two distinct approaches are applied to identify sub-networks significantly associated with OSA. In the first case we used a biased approach based on sixty genes/proteins with known associations with sleep disorders and/or metabolic disease to seed a search using commercial software to discover networks associated with disease followed by information theoretic (mutual information) scoring of the sub-networks. In the second case we used an unbiased approach and generated an interactome constructed from publicly available gene expression profiles and PPI databases, followed by scoring of the network with p-values from GWAS data derived from OSA patients to uncover sub-networks significant for the disease phenotype. A comparison of the approaches reveals a number of proteins that have been previously known to be associated with OSA or sleep. In addition, our results indicate a novel association of Phosphoinositide 3-kinase, the STAT family of proteins and its related pathways with OSA.