Human alveolar macrophages display marked hypo-responsiveness to IFN-γ in both proteomic and gene expression analysis

Alveolar macrophages (AM) perform a primary defense mechanism in the lung through phagocytosis of inhaled particles and microorganisms. AM are known to be relatively immunosuppressive consistent with the aim to limit alveolar inflammation and maintain effective gas exchange in the face of these constant challenges. How AM respond to T cell derived cytokine signals, which are critical to the defense against inhaled pathogens, is less well understood. For example, successful containment of Mycobacterium tuberculosis (Mtb) in lung macrophages is highly dependent on IFN-γ secreted by Th-1 lymphocytes, however, the proteomic IFN-γ response profile in AM remains mostly unknown. In this study, we measured IFN-γ induced protein abundance changes in human AM and autologous blood monocytes (MN). AM cells were activated by IFN-γ stimulation resulting in STAT1 phosphorylation and production of MIG/CXCL9 chemokine. However, the global proteomic response to IFN-γ in AM was dramatically limited in comparison to that of MN (9 AM vs 89 MN differentially abundant proteins). AM hypo-responsiveness was not explained by reduced JAK-STAT1 signaling nor increased SOCS1 expression. These findings suggest that AM have a tightly regulated response to IFN-γ which may prevent excessive pulmonary inflammation but may also provide a niche for the initial survival and growth of Mtb and other intracellular pathogens in the lung.

nSEA: n-Node Subnetwork Enumeration Algorithm Identifies Lower Grade Glioma Subtypes with Altered Subnetworks and Distinct Prognostics

Advances in molecular characterization have reshaped our understanding of low-grade glioma (LGG) subtypes, emphasizing the need for comprehensive classification beyond histology. Lever-aging this, we present a novel approach, network-based Subnetwork Enumeration, and Analysis (nSEA), to identify distinct LGG patient groups based on dysregulated molecular pathways. Using gene expression profiles from 516 patients and a protein-protein interaction network we generated 25 million sub-networks. Through our unsupervised bottom-up approach, we selected 92 subnetworks that categorized LGG patients into five groups. Notably, a new LGG patient group with a lack of mutations in EGFR, NF1, and PTEN emerged as a previously unidentified patient subgroup with unique clinical features and subnetwork states. Validation of the patient groups on an independent dataset demonstrated the robustness of our approach and revealed consistent survival traits across different patient populations. This study offers a comprehensive molecular classification of LGG, providing insights beyond traditional genetic markers. By integrating network analysis with patient clustering, we unveil a previously overlooked patient subgroup with potential implications for prognosis and treatment strategies. Our approach sheds light on the synergistic nature of driver genes and highlights the biological relevance of the identified subnetworks. With broad implications for glioma research, our findings pave the way for further investigations into the mechanistic underpinnings of LGG subtypes and their clinical relevance.Availability: Source code and supplementary data are available at https://github.com/bebeklab/nSEA.

Transcriptomic analysis of lung development in wildtype and CFTR-/- sheep suggests an early inflammatory signature in the CF distal lung

The precise molecular events initiating human lung disease are often poorly characterized. Investigating prenatal events that may underlie lung disease in later life is challenging in man, but insights from the well-characterized sheep model of lung development are valuable. Here, we determine the transcriptomic signature of lung development in wild-type sheep (WT) and use a sheep model of cystic fibrosis (CF) to characterize disease associated changes in gene expression through the pseudoglandular, canalicular, saccular, and alveolar stages of lung growth and differentiation. Using gene ontology process enrichment analysis of differentially expressed genes at each developmental time point, we define changes in biological processes (BP) in proximal and distal lung from WT or CF animals. We also compare divergent BP in WT and CF animals at each time point. Next, we establish the developmental profile of key genes encoding components of ion transport and innate immunity that are pivotal in CF lung disease and validate transcriptomic data by RT-qPCR. Consistent with the known pro-inflammatory phenotype of the CF lung after birth, we observe upregulation of inflammatory response processes in the CF sheep distal lung during the saccular stage of prenatal development. These data suggest early commencement of therapeutic regimens may be beneficial.

TNFRSF1B Gene Variants and Related Soluble TNFR2 Levels Impact Resilience in Alzheimer's Disease

Tumor necrosis factor receptor 2 (TNFR2) promotes neuronal survival downstream. This longitudinal study evaluated whether the TNFRSF1B gene encoding TNFR2 and levels of its soluble form (sTNFR2) affect Alzheimer disease (AD) biomarkers and clinical outcomes. Data analyzed included 188 patients in the Alzheimer's Disease Neuroimaging Initiative (ADNI) who had mild cognitive impairment (MCI) and AD dementia. Further, a replication study was performed in 48 patients with MCI with positive AD biomarkers who were treated at a memory clinic. Cerebrospinal fluid (CSF) sTNFR2 levels along with two related TNFRSF1B gene single nucleotide polymorphisms (SNPs) rs976881 and rs1061622 were assessed. General linear models were used to evaluate the effect of CSF sTNFR2 levels and each SNP in relationship to CSF t-tau and p-tau, cognitive domains, MRI brain measures, and longitudinal cognitive changes after adjustments were made for covariates such as APOE ε4 status. In the ADNI cohort, a significant interaction between rs976881 and CSF sTNFR2 modulates CSF t-tau and p-tau levels; hippocampal and whole brain volumes; and Digit Span Forwards subtest scores. In the replication cohort, a significant interaction between rs976881 and CSF sTNFR2 modulates CSF p-tau. A significant interaction between rs976881 and CSF sTNFR2 also impacts Clinical Dementia Rating Sum of Boxes scores over 12 months in the ADNI cohort. The interaction between TNFRSF1B variant rs976881 and CSF sTNFR2 levels was noted to modulate multiple AD-associated severity markers and cognitive domains. This interaction impacts resilience-related clinical outcomes in AD and lends support to sTNFR2 as a promising candidate for therapeutic targeting to improve clinical outcomes of interest.

Piwil1 Regulates Glioma Stem Cell Maintenance and Glioblastoma Progression

Piwi proteins are a subfamily of Argonaute proteins that maintain germ cells in eukaryotes. However, the role of their human homologs in cancer stem cells, and more broadly in cancer, is poorly understood. Here, we report that Piwi-like family members are overexpressed in glioblastoma (GBM), with Piwil1 (Hiwi) most frequently overexpressed (88%). Piwil1 is enriched in glioma stem-like cells (GSCs) to maintain self-renewal. Silencing Piwil1 in GSCs leads to global changes in gene expression resulting in cell-cycle arrest, senescence, or apoptosis. Piwil1 knockdown increases expression of the transcriptional co-regulator BTG2 and the E3-ubiquitin ligase FBXW7, leading to reduced c-Myc expression, as well as loss of expression of stem cell factors Olig2 and Nestin. Piwil1 regulates mRNA stability of BTG2, FBXW7, and CDKN1B. In animal models of GBM, Piwil1 knockdown suppresses tumor growth and promotes mouse survival. These findings support a role of Piwil1 in GSC maintenance and glioblastoma progression.

Huang et al. find that Piwil1 protein is overexpressed in glioblastoma and glioma stem cells (GSCs). Piwil1 maintains GSC self-renewal and survival by regulating gene expression. Targeting Piwil1 extends survival in mouse models of glioblastoma. Piwil1 represents a therapeutic vulnerability.

Frequent Subgraph Mining of Functional Interaction Patterns Across Multiple Cancers

Molecular mechanisms characterizing cancer development and progression are complex and process through thousands of interacting elements in the cell. Understanding the underlying structure of interactions requires the integration of cellular networks with extensive combinations of dysregulation patterns. Recent pan-cancer studies focused on identifying common dysregulation patterns in a confined set of pathways or targeting a manually curated set of genes. However, the complex nature of the disease presents a challenge for finding pathways that would constitute a basis for tumor progression and requires evaluation of subnetworks with functional interactions. Uncovering these relationships is critical for translational medicine and the identification of future therapeutics. We present a frequent subgraph mining algorithm to find functional dysregulation patterns across the cancer spectrum. We mined frequent subgraphs coupled with biased random walks utilizing genomic alterations, gene expression profiles, and protein-protein interaction networks. In this unsupervised approach, we have recovered expert-curated pathways previously reported for explaining the underlying biology of cancer progression in multiple cancer types. Furthermore, we have clustered the genes identified in the frequent subgraphs into highly connected networks using a greedy approach and evaluated biological significance through pathway enrichment analysis. Gene clusters further elaborated on the inherent heterogeneity of cancer samples by both suggesting specific mechanisms for cancer type and common dysregulation patterns across different cancer types. Survival analysis of sample level clusters also revealed significant differences among cancer types (p < 0.001). These results could extend the current understanding of disease etiology by identifying biologically relevant interactions.Supplementary Information: Supplementary methods, figures, tables and code are available at https://github.com/bebeklab/FSM_Pancancer.

The transcriptional repressor BCL11A promotes breast cancer metastasis

The phenotypes of each breast cancer subtype are defined by their transcriptomes. However, the transcription factors that regulate differential patterns of gene expression that contribute to specific disease outcomes are not well understood. Here, using gene silencing and overexpression approaches, RNA-Seq, and splicing analysis, we report that the transcription factor B-cell leukemia/lymphoma 11A (BCL11A) is highly expressed in triple-negative breast cancer (TNBC) and drives metastatic disease. Moreover, BCL11A promotes cancer cell invasion by suppressing the expression of muscleblind-like splicing regulator 1 (MBNL1), a splicing regulator that suppresses metastasis. This ultimately increases the levels of an alternatively spliced isoform of integrin-α6 (ITGA6), which is associated with worse patient outcomes. These results suggest that BCL11A sustains TNBC cell invasion and metastatic growth by repressing MBNL1-directed splicing of ITGA6 Our findings also indicate that BCL11A lies at the interface of transcription and splicing and promotes aggressive TNBC phenotypes.

A functional genomics approach to investigate the differentiation of iPSCs into lung epithelium at air-liquid interface

The availability of robust protocols to differentiate induced pluripotent stem cells (iPSCs) into many human cell lineages has transformed research into the origins of human disease. The efficacy of differentiating iPSCs into specific cellular models is influenced by many factors including both intrinsic and extrinsic features. Among the most challenging models is the generation of human bronchial epithelium at air‐liquid interface (HBE‐ALI), which is the gold standard for many studies of respiratory diseases including cystic fibrosis. Here, we perform open chromatin mapping by ATAC‐seq and transcriptomics by RNA‐seq in parallel, to define the functional genomics of key stages of the iPSC to HBE‐ALI differentiation. Within open chromatin peaks, the overrepresented motifs include the architectural protein CTCF at all stages, while motifs for the FOXA pioneer and GATA factor families are seen more often at early stages, and those regulating key airway epithelial functions, such as EHF, are limited to later stages. The RNA‐seq data illustrate dynamic pathways during the iPSC to HBE‐ALI differentiation, and also the marked functional divergence of different iPSC lines at the ALI stages of differentiation. Moreover, a comparison of iPSC‐derived and lung donor‐derived HBE‐ALI cultures reveals substantial differences between these models.

Inflammatory pathway analytes predicting rapid cognitive decline in MCI stage of Alzheimer's disease

OBJECTIVE

To determine the inflammatory analytes that predict clinical progression and evaluate their performance against biomarkers of neurodegeneration.

METHODS

A longitudinal study of MCI-AD patients in a Discovery cohort over 15 months, with replication in the Alzheimer's Disease Neuroimaging Initiative (ADNI) MCI cohort over 36 months. Fifty-three inflammatory analytes were measured in the CSF and plasma with a RBM multiplex analyte platform. Inflammatory analytes that predict clinical progression on Clinical Dementia Rating Scale-Sum of Boxes (CDR-SB) and Mini Mental State Exam scores were assessed in multivariate regression models. To provide context, key analyte results in ADNI were compared against biomarkers of neurodegeneration, hippocampal volume, and CSF neurofilament light (NfL), in receiver operating characteristic (ROC) analyses evaluating highest quartile of CDR-SB change over two years (≥3 points).

RESULTS

Cerebrospinal fluid inflammatory analytes in relation to cognitive decline were best described by gene ontology terms, natural killer cell chemotaxis, and endothelial cell apoptotic process and in plasma, extracellular matrix organization, blood coagulation, and fibrin clot formation described the analytes. CSF CCL2 was most robust in predicting rate of cognitive change and analytes that correlated to CCL2 suggest IL-10 pathway dysregulation. The ROC curves for ≥3 points change in CDR-SB over 2 years when comparing baseline hippocampal volume, CSF NfL, and CCL2 were not significantly different.

INTERPRETATION

Baseline levels of immune cell chemotactic cytokine CCL2 in the CSF and IL-10 pathway dysregulation impact longitudinal cognitive and functional decline in MCI-AD. CCL2's utility appears comparable to biomarkers of neurodegeneration in predicting rapid decline.

PARP1 Is Up-Regulated in Non-small Cell Lung Cancer Tissues in the Presence of the Cyanobacterial Toxin Microcystin

Non-small cell lung cancer (NSCLC) is the major form of lung cancer, with adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) being its major subtypes. Smoking alone cannot completely explain the lung cancer etiology. We hypothesize that altered lung microbiome and chronic inflammatory insults in lung tissues contribute to carcinogenesis. Here we explore the microbiome composition of LUAD samples, compared to LUSC and normal samples. Extraction of microbiome DNA in formalin-fixed, paraffin-embedded (FFPE) lung tumor and normal adjacent tissues was meticulously performed. The 16S rRNA product from extracted microbiota was subjected to microbiome amplicon sequencing. To assess the contribution of the host genome, CD36 expression levels were analyzed then integrated with altered NSCLC subtype-specific microbe sequence data. Surprisingly phylum Cyanobacteria was consistently observed in LUAD samples. Across the NSCLC subtypes, differential abundance across four phyla (Proteobacteria, Bacteroidetes, Actinobacteria, and Firmicutes) was identified based on the univariate analysis (p-value < 6.4e-4 to 3.2e-2). In silico metagenomic and pathway analyses show that presence of microcystin correlates with reduced CD36 and increased PARP1 levels. This was confirmed in microcystin challenged NSCLC (A427) cell lines and Cyanobacteria positive LUAD tissues. Controlling the influx of Cyanobacteria-like particles or microcystin and the inhibition of PARP1 can provide a potential targeted therapy and prevention of inflammation-associated lung carcinogenesis.

Hypoxic Induction of Vasorin Regulates Notch1 Turnover to Maintain Glioma Stem-like Cells

Tumor hypoxia is associated with poor patient survival and is a characteristic of glioblastoma. Notch signaling is implicated in maintaining glioma stem-like cells (GSCs) within the hypoxic niche, although the molecular mechanisms linking hypoxia to Notch activation have not been clearly delineated. Here we show that Vasorin is a critical link between hypoxia and Notch signaling in GSCs. Vasorin is preferentially induced in GSCs by a HIF1α/STAT3 co-activator complex and stabilizes Notch1 protein at the cell membrane. This interaction prevents Numb from binding Notch1, rescuing it from Numb-mediated lysosomal degradation. Thus, Vasorin acts as a switch to augment Notch signaling under hypoxic conditions. Vasorin promotes tumor growth and reduces survival in mouse models of glioblastoma, and its expression correlates with increased aggression of human gliomas. These findings provide mechanistic insights into how hypoxia promotes Notch signaling in glioma and identify Vasorin as a potential therapeutic target.

Mitotic Vulnerability in Triple-Negative Breast Cancer Associated with LIN9 Is Targetable with BET Inhibitors

Triple-negative breast cancers (TNBC) are highly aggressive, lack FDA-approved targeted therapies, and frequently recur, making the discovery of novel therapeutic targets for this disease imperative. Our previous analysis of the molecular mechanisms of action of bromodomain and extraterminal protein inhibitors (BETi) in TNBC revealed these drugs cause multinucleation, indicating BET proteins are essential for efficient mitosis and cytokinesis. Here, using live cell imaging, we show that BET inhibition prolonged mitotic progression and induced mitotic cell death, both of which are indicative of mitotic catastrophe. Mechanistically, the mitosis regulator LIN9 was a direct target of BET proteins that mediated the effects of BET proteins on mitosis in TNBC. Although BETi have been proposed to function by dismantling super-enhancers (SE), the LIN9 gene lacks an SE but was amplified or overexpressed in the majority of TNBCs. In addition, its mRNA expression predicted poor outcome across breast cancer subtypes. Together, these results provide a mechanism for cancer selectivity of BETi that extends beyond modulation of SE-associated genes and suggest that cancers dependent upon LIN9 overexpression may be particularly vulnerable to BETi. Cancer Res; 77(19); 5395-408. ©2017 AACR.

Microbiomic differences in tumor and paired-normal tissue in head and neck squamous cell carcinomas

BACKGROUND

While the role of the gut microbiome in inflammation and colorectal cancers has received much recent attention, there are few data to support an association between the oral microbiome and head and neck squamous cell carcinomas. Prior investigations have been limited to comparisons of microbiota obtained from surface swabs of the oral cavity. This study aims to identify microbiomic differences in paired tumor and non-tumor tissue samples in a large group of 121 patients with head and neck squamous cell carcinomas and correlate these differences with clinical-pathologic features.

METHODS

Total DNA was extracted from paired normal and tumor resection specimens from 169 patients; 242 samples from 121 patients were included in the final analysis. Microbiomic content of each sample was determined using 16S rDNA amplicon sequencing. Bioinformatic analysis was performed using QIIME algorithms. F-testing on cluster strength, Wilcoxon signed-rank testing on differential relative abundances of paired tumor-normal samples, and Wilcoxon rank-sum testing on the association of T-stage with relative abundances were conducted in R.

RESULTS

We observed no significant difference in measures of alpha diversity between tumor and normal tissue (Shannon index: p = 0.13, phylogenetic diversity: p = 0.42). Similarly, although we observed statistically significantly differences in both weighted (p = 0.01) and unweighted (p = 0.04) Unifrac distances between tissue types, the tumor/normal grouping explained only a small proportion of the overall variation in the samples (weighted R² = 0.01, unweighted R² < 0.01). Notably, however, when comparing the relative abundances of individual taxa between matched pairs of tumor and normal tissue, we observed that Actinomyces and its parent taxa up to the phylum level were significantly depleted in tumor relative to normal tissue (q < 0.01), while Parvimonas was increased in tumor relative to normal tissue (q = 0.01). These differences were more pronounced among patients with more extensive disease as measured by higher T-stage.

CONCLUSIONS

Matched pairs analysis of individual tumor-normal pairs revealed significant differences in relative abundance of specific taxa, namely in the genus Actinomyces. These differences were more pronounced among patients with higher T-stage. Our observations suggest further experiments to interrogate potential novel mechanisms relevant to carcinogenesis associated with alterations of the oral microbiome that may have consequences for the human host.

Identifying Gene Interaction Networks

In this chapter, we introduce interaction networks by describing how they are generated, where they are stored, and how they are shared. We focus on publicly available interaction networks and describe a simple way of utilizing these resources. This chapter features two case studies, both of which utilize Cytoscape, an open source and user-friendly network visualization and analysis tool. In the first example, we demonstrate the basic functionalities of Cytoscape by building an interaction network from a publicly available database, analyzing its topological features, and performing gene ontology enrichment. For the second section, we constructed a network from scratch starting with an experimental gene expression dataset. From there, we implement more advanced visual annotations of the network and perform subnetwork enrichment. The methods described are applicable to larger networks that can be collected from various resources.

Network based model of social media big data predicts contagious disease diffusion

PURPOSE–

Predicting future outbreaks and understanding how they are spreading from location to location can improve patient care provided. Recently, mining social media big data provided the ability to track patterns and trends across the world. This study aims to analyze social media micro-blogs and geographical locations to understand how disease outbreaks spread over geographies and to enhance forecasting of future disease outbreaks.

DESIGN/METHODOLOGY/APPROACH –

In this paper, the authors use Twitter data as the social media data source, influenza-like illnesses (ILI) as disease epidemic and states in the USA as geographical locations. They present a novel network-based model to make predictions about the spread of diseases a week in advance utilizing social media big data.

FINDINGS–

The authors showed that flu-related tweets align well with ILI data from the Centers for Disease Control and Prevention (CDC) (p < 0.049). The authors compared this model to earlier approaches that utilized airline traffic, and showed that ILI activity estimates of their model were more accurate. They also found that their disease diffusion model yielded accurate predictions for upcoming ILI activity (p < 0.04), and they predicted the diffusion of flu across states based on geographical surroundings at 76 per cent accuracy. The equations and procedures can be translated to apply to any social media data, other contagious diseases and geographies to mine large data sets.

ORIGINALITY/VALUE–

First, while extensive work has been presented utilizing time-series analysis on single geographies, or post-analysis of highly contagious diseases, no previous work has provided a generalized solution to identify how contagious diseases diffuse across geographies, such as states in the USA. Secondly, due to nature of the social media data, various statistical models have been extensively used to address these problems.

FREQUENT SUBGRAPH MINING OF PERSONALIZED SIGNALING PATHWAY NETWORKS GROUPS PATIENTS WITH FREQUENTLY DYSREGULATED DISEASE PATHWAYS AND PREDICTS PROGNOSIS

MOTIVATION

Large scale genomics studies have generated comprehensive molecular characterization of numerous cancer types. Subtypes for many tumor types have been established; however, these classifications are based on molecular characteristics of a small gene sets with limited power to detect dysregulation at the patient level. We hypothesize that frequent graph mining of pathways to gather pathways functionally relevant to tumors can characterize tumor types and provide opportunities for personalized therapies.

RESULTS

In this study we present an integrative omics approach to group patients based on their altered pathway characteristics and show prognostic differences within breast cancer (p < 9:57E - 10) and glioblastoma multiforme (p < 0:05) patients. We were able validate this approach in secondary RNA-Seq datasets with p < 0:05 and p < 0:01 respectively. We also performed pathway enrichment analysis to further investigate the biological relevance of dysregulated pathways. We compared our approach with network-based classifier algorithms and showed that our unsupervised approach generates more robust and biologically relevant clustering whereas previous approaches failed to report specific functions for similar patient groups or classify patients into prognostic groups.

CONCLUSIONS

These results could serve as a means to improve prognosis for future cancer patients, and to provide opportunities for improved treatment options and personalized interventions. The proposed novel graph mining approach is able to integrate PPI networks with gene expression in a biologically sound approach and cluster patients in to clinically distinct groups. We have utilized breast cancer and glioblastoma multiforme datasets from microarray and RNA-Seq platforms and identified disease mechanisms differentiating samples.

SUPPLEMENTARY INFORMATION

Supplementary methods, figures, tables and code are available at https://github.com/bebeklab/dysprog.

Proteomic and bioinformatics profile of paired human alveolar macrophages and peripheral blood monocytes

Little is known about proteomic differences between pluripotent human peripheral blood monocytes (MN) and their terminally-differentiated pulmonary counterparts, alveolar macrophages (AM). To better characterize these cell populations, we performed a label-free shotgun proteomics assessment of matched AM and MN preparations from eight healthy volunteers. With an FDR of less than 0.45%, we identified 1754 proteins within AM and 1445 from MN. Comparison of the two proteomes revealed that 1239 of the proteins found in AM were shared with MN, whereas 206 proteins were uniquely identified in MN and 515 were unique to AM. Molecular and cellular functions, protein classes, development associations, and membership in physiological systems and canonical pathways were identified among the detected proteins. Analysis of biologic processes represented by these proteomes indicated that MN were most prominently enriched for proteins involved in cellular movement and immune cell trafficking. In contrast, AM were enriched for proteins involved in protein trafficking, molecular transport, and cellular assembly and organization. These findings provide a baseline proteomic resource for further studies aimed at better understanding of the functional differences between MN and AM in both health and disease.

Proteome Dynamics Reveals Pro-Inflammatory Remodeling of Plasma Proteome in a Mouse Model of NAFLD

Nonalcoholic fatty liver disease (NAFLD) is associated with an increased risk of cardiovascular disease. Because the liver is the major source of circulatory proteins, it is not surprising that hepatic disease could lead to alterations in the plasma proteome, which are therein implicated in atherosclerosis. The current study used low-density lipoprotein receptor-deficient (LDLR(-/-)) mice to examine the impact of Western diet (WD)-induced NAFLD on plasma proteome homeostasis. Using a (2)H2O-metabolic labeling method, we found that a WD led to a proinflammatory distribution of circulatory proteins analyzed in apoB-depleted plasma, which was attributed to an increased production. The fractional turnover rates of short-lived proteins that are implicated in stress-response, lipid metabolism, and transport functions were significantly increased with WD (P < 0.05). Pathway analyses revealed that alterations in plasma proteome dynamics were related to the suppression of hepatic PPARα, which was confirmed based on reduced gene and protein expression of PPARα in mice fed a WD. These changes were associated with ∼4-fold increase (P < 0.0001) in the proinflammatory property of apoB-depleted plasma. In conclusion, the proteome dynamics method reveals proinflammatory remodeling of the plasma proteome relevant to liver disease. The approach used herein may provide a useful metric of in vivo liver function and better enable studies of novel therapies surrounding NAFLD and other diseases.

Abstract 4647: BET protein inhibition blocks growth of triple-negative breast cancer by inducing mitotic and cytokinetic dysfunction

Bromodomain and Extra Terminal (BET) proteins are epigenetic “readers” that recognize acetylated histones and mark areas of the genome for transcription. BRD4, a BET family member protein, has been implicated in a number of types of cancer. It has been recently found to associate with super-enhancers, and elevated levels of BRD4 have been linked to increased expression of MYC as well as other oncogenes. BET protein inhibitors are currently being tested for their potential use in the treatment of HIV, heart failure, and cancer, all of which are diseases of aberrant transcription. However, little is known regarding their efficacy in triple-negative breast cancer (TNBC). We found JQ1, a prototypical BET inhibitor, impedes growth of seven TNBC cell lines in a dose-dependent manner within 72 hours of treatment. JQ1 also suppresses growth of three different TNBC xenografts, including a patient-derived model of basal-like breast cancer. Growth arrest, in vitro, is followed by either apoptosis or senescence. However, prior to the induction of these two terminal responses, prolonged treatment results in polyploidy in many of the cell lines, suggesting BETi disrupts mitosis/cytokinesis. Live-cell imaging revealed JQ1 significantly increased the duration of mitosis, and microarray analyses showed a significant JQ1-mediated downregulation of genes critical for cell cycle progression, mitosis, and cytokinesis. In all TNBC cell lines tested, Aurora kinases, proteins critical for proper progression through mitosis and cytokinesis, are suppressed in response to JQ1. Treatment with AZD1152, an Aurora kinase B inhibitor, elicits the same cellular responses as BET inhibition, indicating that the suppression of Aurora kinases plays a key role in the response of TNBC cells to BET inhibitors. These findings reveal that BET inhibitors block the growth of highly aggressive TNBC cells by inducing mitotic dysfunction and that these drugs are promising potential therapeutics for the treatment of TNBC.

Citation Format: Jennifer M. Brancato, Sylvia Gayle, Kristen Weber-Bonk, Matthew Summers, Gurkan Bebek, Ruth Keri. BET protein inhibition blocks growth of triple-negative breast cancer by inducing mitotic and cytokinetic dysfunction. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 4647.

Quantitative H2S-mediated protein sulfhydration reveals metabolic reprogramming during the integrated stress response

The sulfhydration of cysteine residues in proteins is an important mechanism involved in diverse biological processes. We have developed a proteomics approach to quantitatively profile the changes of sulfhydrated cysteines in biological systems. Bioinformatics analysis revealed that sulfhydrated cysteines are part of a wide range of biological functions. In pancreatic β cells exposed to endoplasmic reticulum (ER) stress, elevated H2S promotes the sulfhydration of enzymes in energy metabolism and stimulates glycolytic flux. We propose that transcriptional and translational reprogramming by the integrated stress response (ISR) in pancreatic β cells is coupled to metabolic alternations triggered by sulfhydration of key enzymes in intermediary metabolism.

Abstract PR04: Cancer drug response networks built for comparative cancer pharmacogenomics identifies combination therapies for repositioning

We present an integrative framework for classifying drug response: CAncer Drug Response nEtworks (CADREs). We built CADREs for drugs with differential sensitivity profiles in cancer cell lines by integrating baseline copy number, gene expression, and drug sensitivity data for the cell lines. The nodes in the network are drugs and the edges are weighted according to the number of shared copy number variations, differentially expressed genes, and dysregulated pathways present in sensitive cell lines at baseline. These baseline features were mapped into pathways to generate a Pathway CADRE, and were also integrated into a differential gene expression and copy number Feature CADRE. CADREs were built for breast, ovarian, and endometrial cancers individually and combined to assess shared and unique features of drug sensitivity. Analyzing CADRE clusters associated multiple gene features and pathways with sensitivity to frontline therapies used in combination in these cancers. We identified four clusters of breast cancer drugs whose effectiveness is driven by a shared core of pathways. Compounds that were used in combination in clinical trials tended to not share an edge or are completely disconnected in networks. Specifically, ten such drug pairs in our breast CADREs have been found to work in combination in breast cancer and an additional five have been used in combination in other cancers, representing opportunities for repositioning these combinations in breast cancer. Examining highly interconnected CADRE nodes gave us an understanding for compounds that might work best together.

Citation Format: Doug Brubaker, Svenson Elena, Kai Smith, Maya Monroe, Christopher Ryu, Mark R. Chance, Goutham Narla, Gurkan Bebek. Cancer drug response networks built for comparative cancer pharmacogenomics identifies combination therapies for repositioning. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr PR04.

Abstract B1-43: Cancer drug response networks built for comparative cancer pharmacogenomics identifies combination therapies for repositioning

We present an integrative framework for classifying drug response: CAncer Drug Response nEtworks (CADREs). We built CADREs for drugs with differential sensitivity profiles in cancer cell lines by integrating baseline copy number, gene expression, and drug sensitivity data for the cell lines. The nodes in the network are drugs and the edges are weighted according to the number of shared copy number variations, differentially expressed genes, and dysregulated pathways present in sensitive cell lines at baseline. These baseline features were mapped into pathways to generate a Pathway CADRE, and were also integrated into a differential gene expression and copy number Feature CADRE. CADREs were built for breast, ovarian, and endometrial cancers individually and combined to assess shared and unique features of drug sensitivity. Analyzing CADRE clusters associated multiple gene features and pathways with sensitivity to frontline therapies used in combination in these cancers. We identified four clusters of breast cancer drugs whose effectiveness is driven by a shared core of pathways. Compounds that were used in combination in clinical trials tended to not share an edge or are completely disconnected in networks. Specifically, ten such drug pairs in our breast CADREs have been found to work in combination in breast cancer and an additional five have been used in combination in other cancers, representing opportunities for repositioning these combinations in breast cancer. Examining highly interconnected CADRE nodes gave us an understanding for compounds that might work best together.

Citation Format: Douglas Brubaker, Elena Svenson, Smith Kai, Maya Monroe, Christopher Ryu, Goutham Narla, Mark R. Chance, Gurkan Bebek. Cancer drug response networks built for comparative cancer pharmacogenomics identifies combination therapies for repositioning. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B1-43.

Abstract 358: Integrative pathway analysis of molecular profiles of glioblastoma multiforme for predicting patient survival

Glioblastoma multiforme (GBM) is the most common form of malignant brain tumor in adults, characterized by median survival of one year and overall poor prognosis. Subtypes based on gene expression signatures have been reported; however, this classification varies based on molecular characteristics of patient samples, and mostly lacks prognostic utility. An important and challenging goal of stratifying patients by molecular markers is to identify functionally relevant sets of genes that are prognostically relevant. In this study we present a new computational approach that integrates multiple datasets to group patients based on their altered pathway characteristics and show prognostic differences within GBM patients (p&lt;0.05). We first enumerate signaling paths from publicly available pathway databases, and calculate a score for signaling pathway segments utilizing gene expression profiles. Patient samples are clustered into groups based on these scores, and Kaplan-Meier curves based on their survival are plotted. Statistical assessment of the survival curves through a Log-Rank Test is calculated to observe prognostic utility of the groups based on pathway segments. We validated our approach on publicly available datasets (TCGA and GSE4271), and showed prognostic differences in patient groups (p&lt;0.05). The two groups we have identified are different than commonly defined two GBM subtypes, namely proneural (PN) and mesenchymal (MES). While some PN tumors might carry features that might favor better prognosis, such as IDH1 mutations, these alterations are not generalizable. We performed gene set enrichment analysis (GSEA) to further investigate the pathway consequences of dysregulated genes in these patient groups, and identified differential enrichment (p&lt;0.01) of various signaling pathways associated with GBM pathogenesis. Recently, Krishna et al. (Cancer Cell, 2013) identified NFKB pathway in aggressive GBM tumors that might have poor prognosis. Furthermore, our analysis identified genes enriched for the longer survivor patient group in PTEN dependent cell cycle arrest and apoptosis pathway, EGF Signaling Pathway, PDGF Signaling Pathway, IL2 Signaling Pathway, and mTOR signaling pathway. While some of key signaling genes (PTEN, PDGFRA, EGFR etc.) included in these pathways were previously associated with GBM subtypes and pathogenesis (Verhaak et al. Cancer Cell. 2010), we provide further utility for their prognostic function within these pathways. These results could serve as a means to improve prognosis and treatment options for future GBM patients as well as to provide opportunities for alternative interventions in multiple other cancers.

Citation Format: Arda Durmaz, Douglas Brubaker, Gurkan Bebek. Integrative pathway analysis of molecular profiles of glioblastoma multiforme for predicting patient survival. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 358. doi:10.1158/1538-7445.AM2014-358

Germline alterations in RASAL1 in Cowden syndrome patients presenting with follicular thyroid cancer and in individuals with apparently sporadic epithelial thyroid cancer

CONTEXT

RASAL1 has recently been identified as an important tumor suppressor for sporadic thyroid tumorigenesis, particularly for follicular thyroid cancer (FTC) and anaplastic thyroid cancer. Thyroid cancer is an important component of Cowden syndrome (CS). Patients with germline PTEN mutations have an overrepresentation of FTC over other histological subtypes.

OBJECTIVE

To determine the prevalence of germline RASAL1 mutations in PTEN mutation-positive and wild type CS patients.

SETTING AND DESIGN

We reviewed our prospective database of more than 3000 CS/CS-like patients and retrospectively identified a subset of patients who presented with thyroid cancer for RASAL1 mutation analysis. We reviewed data from The Cancer Genome Atlas (TCGA) sporadic papillary thyroid cancer (PTC) database with germline data for RASAL1 mutations to determine the prevalence of germline RASAL1 mutations in CS-related thyroid cancer patients.

RESULTS

We scanned 155 CS/CS-like patients with thyroid cancer for germline RASAL1 mutations. Of the 155 patients, 39 had known germline pathogenic PTEN mutations (PTEN(mut+)) and 116 were PTEN mutation negative (PTEN(WT)). Among these 155 patients, we identified RASAL1 germline alterations suspected as being deleterious in two patients. Both were patients with PTEN(WT) who had FTC (2/48, 4.1%). This was in contrast to patients with PTEN(mut+) who had thyroid cancer (0/39). Of 339 sporadic patients with PTC from the TCGA study, 62 (18%) had germline RASAL1 variants predicted to be deleterious. TCGA patients with follicular-variant PTC were statistically overrepresented (21/62, 34%) among patients with deleterious RASAL1 variants compared with those without (57/277, 21%).

CONCLUSIONS

Germline RASAL1 alterations are uncommon in patients with CS but may not be infrequent in patients with apparently sporadic follicular-variant PTC.

Hypomethylation of the MMP7 promoter and increased expression of MMP7 distinguishes the basal-like breast cancer subtype from other triple-negative tumors

Identification of novel targets for the treatment of basal-like breast cancer is essential for improved outcomes in patients with this disease. This study investigates the association of MMP7 expression and MMP7 promoter methylation with subtype and outcome in breast cancer patient cohorts. Immunohistochemical analysis was performed on a breast cancer tissue microarray and validated in independent histological samples. MMP7 expression significantly correlated with patient age, tumor size, triple-negative (TN) status, and recurrence. Analysis of publically available datasets confirmed MMP7 gene expression as a prognostic marker of breast cancer metastasis, particularly metastasis to the brain and lungs. Methylation of the MMP7 promoter was assessed by methylation-specific PCR in a panel of breast cancer cell lines and patient tumor samples. Hypomethylation of the MMP7 promoter significantly correlated with TN status in DNA from patient tumor samples, and this association was confirmed using The Cancer Genome Atlas (TCGA) dataset. Evaluation of a panel of breast cancer cell lines and data from the Curtis and TCGA breast carcinoma datasets revealed that elevated MMP7 expression and MMP7 promoter hypomethylation are specific biomarkers of the basal-like molecular subtype which shares considerable, but not complete, overlap with the clinical TN subtype. Importantly, MMP7 expression was identified as an independent predictor of pathological complete response in a large breast cancer patient cohort. Combined, these data suggest that MMP7 expression and MMP7 promoter methylation may be useful as prognostic biomarkers. Furthermore, MMP7 expression and promoter methylation analysis may be effective mechanisms to distinguish basal-like breast cancers from other triple-negative subtypes. Finally, these data implicate MMP7 as a potential therapeutic target for the treatment of basal-like breast cancers.

Drug Intervention Response Predictions with PARADIGM (DIRPP) identifies drug resistant cancer cell lines and pathway mechanisms of resistance

The revolution in sequencing techniques in the past decade has provided an extensive picture of the molecular mechanisms behind complex diseases such as cancer. The Cancer Cell Line Encyclopedia (CCLE) and The Cancer Genome Project (CGP) have provided an unprecedented opportunity to examine copy number, gene expression, and mutational information for over 1000 cell lines of multiple tumor types alongside IC50 values for over 150 different drugs and drug related compounds. We present a novel pipeline called DIRPP, Drug Intervention Response Predictions with PARADIGM7, which predicts a cell line's response to a drug intervention from molecular data. PARADIGM (Pathway Recognition Algorithm using Data Integration on Genomic Models) is a probabilistic graphical model used to infer patient specific genetic activity by integrating copy number and gene expression data into a factor graph model of a cellular network. We evaluated the performance of DIRPP on endometrial, ovarian, and breast cancer related cell lines from the CCLE and CGP for nine drugs. The pipeline is sensitive enough to predict the response of a cell line with accuracy and precision across datasets as high as 80 and 88% respectively. We then classify drugs by the specific pathway mechanisms governing drug response. This classification allows us to compare drugs by cellular response mechanisms rather than simply by their specific gene targets. This pipeline represents a novel approach for predicting clinical drug response and generating novel candidates for drug repurposing and repositioning.

Proteomic analysis of synovial fluid from the osteoarthritic knee: comparison with transcriptome analyses of joint tissues

OBJECTIVE

The pathophysiology of the most common joint disease, osteoarthritis (OA), remains poorly understood. Since synovial fluid (SF) bathes joint cartilage and synovium, we reasoned that a comparative analysis of its protein constituents in health and OA could identify pathways involved in joint damage. We undertook this study to perform a proteomic analysis of knee SF from OA patients and control subjects and to compare the results to microarray expression data from cartilage and synovium.

METHODS

Age-matched knee SF samples from 10 control subjects, 10 patients with early-stage OA, and 10 patients with late-stage OA were compared using 2-dimensional difference-in-gel electrophoresis and mass spectrometry (MS). MS with a multiplexed peptide selected reaction monitoring assay was used to confirm differential expression of a subset of proteins in an independent OA patient cohort. Proteomic results were analyzed by Ingenuity Pathways Analysis and compared to published synovial tissue and cartilage messenger RNA profiles.

RESULTS

Sixty-six proteins were differentially present in healthy and OA SF. Three major pathways were identified among these proteins: the acute-phase response signaling pathway, the complement pathway, and the coagulation pathway. Differential expression of 5 proteins was confirmed by selected reaction monitoring assay. A focused analysis of transcripts corresponding to the differentially present proteins indicated that both synovial and cartilage tissues may contribute to the OA SF proteome.

CONCLUSION

Proteins involved in the acute-phase response signaling pathway, the complement pathway, and the coagulation pathway are differentially regulated in SF from OA patients, suggesting that they contribute to joint damage. Validation of these pathways and their utility as biomarkers or therapeutic targets in OA is warranted.

Abstract 3217: Identification of mutated peptides/proteins driving cancer phenotype utilizing improved shotgun proteomics and data analysis approaches

Background: Mutations in cancer can either abolish or alter protein function. In the latter case, mutated proteins may rewire regulatory networks to promote tumor growth or metastasis. Identifying and understanding the role of potential mutated oncogenes is important for developing effective therapies for individual patients. Unfortunately, detecting mutated proteins in tumors is challenging because the level of mutated proteins is typically low and identification of potential mutations is challenging due to limited database search engines. Utilizing analytical methods incorporating modified protein search engines can facilitate detection of protein variants.

Methods: Bottom up proteomics was performed on colorectal cancer cell line HCT-116, whereby five million cells were harvested and cell lysates/protein fraction was fractionated ten-fold using a gel-based approach. Unbiased discovery-mode LC-MS analysis was performed in LTQ Velos-Orbitrap Mass Spectrometer for all the fractions using nano LC-MS/MS. A novel computational approach integrated into the MassMatrix search engine has been developed to analyze the pooled tandem MS dataset and identify significant variant peptides/proteins. Peptide and protein level FDR was carefully controlled to yield variant candidates. Selected peptides with mutations of interest were then synthesized and analyzed in the same instrument for validation purposes.

Results: Analysis of the HCT-116 cell line data returned 14000 peptides overall, indicating satisfactory proteome mining. By utilizing the new variant detection pipeline, we have identified variant proteins in HCT-116 compared to the reference sequence database, including 43 SNPs and 12 mutations. Based on close manual evaluation of the quality of the tandem MS spectra, six candidates were selected for validation. These variant peptide sequences were confirmed by comparing them to the synthesized peptide MS/MS patterns. The specific mutations in all of these proteins, including PTPN11, FYN, DUSP23, PRPF8, FUBP1 and RRM2B, have been reported in earlier gene sequencing studies. We have also examined public HCT-116 RNAseq data to further validate expression of these mutated sequences.

Conclusions: We have created a novel proto-genomic variant detection pipeline developed on top of existing protein search engines. This method can be married with popular search engines such as MassMatrix, and shows great potential for variant peptide detection. The identified mutated proteins in this study represent targets of immense biological interest for rewiring of the HCT-116 regulatory landscape. Functional studies to understand the molecular basis of the phenotype are ongoing, specifically we are testing the potential gain or loss of function of these proteins due to mutations, and we are probing the likely roles they play in maintaining the tumor phenotype.

Citation Format: Gurkan Bebek, Giridharan Gokulrangan, Hua Xu, Mark R. Chance. Identification of mutated peptides/proteins driving cancer phenotype utilizing improved shotgun proteomics and data analysis approaches. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 3217. doi:10.1158/1538-7445.AM2013-3217

Note: This abstract was not presented at the AACR Annual Meeting 2013 because the presenter was unable to attend.

FOXA1 represses the molecular phenotype of basal breast cancer cells

Breast cancer is a heterogeneous disease that comprises multiple subtypes. Luminal subtype tumors confer a more favorable patient prognosis, which is, in part, attributed to estrogen receptor (ER)-α positivity and antihormone responsiveness. Expression of the forkhead box transcription factor, FOXA1, similarly correlates with the luminal subtype and patient survival, but is also present in a subset of ER-negative tumors. FOXA1 is also consistently expressed in luminal breast cancer cell lines even in the absence of ER. In contrast, breast cancer cell lines representing the basal subtype do not express FOXA1. To delineate an ER-independent role for FOXA1 in maintaining the luminal phenotype, and hence a more favorable prognosis, we performed expression microarray analyses on FOXA1-positive and ER-positive (MCF7, T47D), or FOXA1-positive and ER-negative (MDA-MB-453, SKBR3) luminal cell lines in the presence or absence of transient FOXA1 silencing. This resulted in three FOXA1 transcriptomes: (1) a luminal signature (consistent across cell lines), (2) an ER-positive signature (restricted to MCF7 and T47D) and (3) an ER-negative signature (restricted to MDA-MB-453 and SKBR3). Gene set enrichment analyses revealed FOXA1 silencing causes a partial transcriptome shift from luminal to basal gene expression signatures. FOXA1 binds to a subset of both luminal and basal genes within luminal breast cancer cells, and loss of FOXA1 increases enhancer RNA transcription for a representative basal gene (CD58). These data suggest FOXA1 directly represses a subset of basal signature genes. Functionally, FOXA1 silencing increases migration and invasion of luminal cancer cells, both of which are characteristics of basal subtype cells. We conclude FOXA1 controls plasticity between basal and luminal breast cancer cells, not only by inducing luminal genes but also by repressing the basal phenotype, and thus aggressiveness. Although it has been proposed that FOXA1-targeting agents may be useful for treating luminal tumors, these data suggest that this approach may promote transitions toward more aggressive cancers.

Proteomic and bioinformatic profile of primary human oral epithelial cells

Wounding of the oral mucosa occurs frequently in a highly septic environment. Remarkably, these wounds heal quickly and the oral cavity, for the most part, remains healthy. Deciphering the normal human oral epithelial cell (NHOEC) proteome is critical for understanding the mechanism(s) of protection elicited when the mucosal barrier is intact, as well as when it is breached. Combining 2D gel electrophoresis with shotgun proteomics resulted in identification of 1662 NHOEC proteins. Proteome annotations were performed based on protein classes, molecular functions, disease association and membership in canonical and metabolic signaling pathways. Comparing the NHOEC proteome with a database of innate immunity-relevant interactions (InnateDB) identified 64 common proteins associated with innate immunity. Comparison with published salivary proteomes revealed that 738/1662 NHOEC proteins were common, suggesting that significant numbers of salivary proteins are of epithelial origin. Gene ontology analysis showed similarities in the distributions of NHOEC and saliva proteomes with regard to biological processes, and molecular functions. We also assessed the interindividual variability of the NHOEC proteome and observed it to be comparable with other primary cells. The baseline proteome described in this study should serve as a resource for proteome studies of the oral mucosa, especially in relation to disease processes.

Network biology methods integrating biological data for translational science

The explosion of biomedical data, both on the genomic and proteomic side as well as clinical data, will require complex integration and analysis to provide new molecular variables to better understand the molecular basis of phenotype. Currently, much data exist in silos and is not analyzed in frameworks where all data are brought to bear in the development of biomarkers and novel functional targets. This is beginning to change. Network biology approaches, which emphasize the interactions between genes, proteins and metabolites provide a framework for data integration such that genome, proteome, metabolome and other -omics data can be jointly analyzed to understand and predict disease phenotypes. In this review, recent advances in network biology approaches and results are identified. A common theme is the potential for network analysis to provide multiplexed and functionally connected biomarkers for analyzing the molecular basis of disease, thus changing our approaches to analyzing and modeling genome- and proteome-wide data.

Chromosome 3 status in uveal melanoma: a comparison of fluorescence in situ hybridization and single-nucleotide polymorphism array

PURPOSE

To compare fluorescence in situ hybridization (FISH) using a centromeric probe for chromosome 3 (CEP3) and 3p26 locus-specific probe with single-nucleotide polymorphism array (SNP-A) analysis in the detection of high-risk uveal melanoma.

METHODS

Fifty cases of uveal melanoma (28 males, 22 females) treated by enucleation between 2004 and 2010 were analyzed. Fresh tissue was used for FISH and SNP-A analysis. FISH was performed using a CEP3 and a 3p26 locus-specific probe. Tumor size, location, and clinical outcome were recorded during the 7-year study period (median follow-up: 35.5 months; mean: 38.5 months). The sensitivity, specificity, positive predictive value, and negative predictive value were calculated.

RESULTS

Monosomy 3 was detected by FISH-CEP3 in 27 tumors (54%), FISH-3p26 deletion was found in 30 (60%), and SNP-A analysis identified 31 (62%) of the tumors with monosomy 3. Due to technical failures, FISH and SNP-A were noninterpretable in one case (2%) and two cases (4%), respectively. In both cases of SNP-A failure, tumors were positive for FISH 3p26 deletion and in a single case of FISH failure, monosomy 3 was found using SNP-A. No statistically significant differences were observed in any of the sensitivity or specificity measures.

CONCLUSIONS

For prediction of survival at 36 months, FISH CEP3, FISH 3p26, and SNP-A were comparable. A combination of prognostication techniques should be used in an unlikely event of technical failure (2%-4%).

MAGNET: MicroArray Gene expression and Network Evaluation Toolkit

MicroArray Gene expression and Network Evaluation Toolkit (MAGNET) is a web-based application that provides tools to generate and score both protein-protein interaction networks and coexpression networks. MAGNET integrates user-provided experimental measurements with high-throughput proteomic datasets, generating weighted gene-gene and protein-protein interaction networks. MAGNET allows users to weight edges of protein-protein interaction networks using a logistic regression model integrating tissue-specific gene expression data, sub-cellular localization data, co-clustering of interacting proteins and the number of observations of the interaction. This provides a way to quantitatively measure the plausibility of interactions in protein-protein interaction networks given protein/gene expression measurements. Secondly, MAGNET generates filtered coexpression networks, where genes are represented as nodes, and their correlations are represented with edges. Overall, MAGNET provides researchers with a new framework with which to analyze and generate gene-gene and protein-protein interaction networks, based on both the user's own data and publicly available -omics datasets. The freely available service and documentation can be accessed at http://gurkan.case.edu/software or http://magnet.case.edu.

Correlation of microbiomic profiles with disease status and MDR1 methylation in head and neck squamous cell carcinoma (HNSCC)

5573

Background: Recent studies of the aerodigestive tract have uncovered the functional importance of the microbiome, the total collection of microorganisms that reside within the human host. Furthermore, specific bacterial species have been shown to be etiologic agents or show potential as a screening biomarker in cancers including gastric, colon and pancreas. Here, we hypothesize that specific microbial populations may contribute to HNSCC pathogenesis via epigenetic modifications in inflammatory- and HNSCC-associated genes. Methods: Matched tumor and adjacent normal fresh frozen tissue specimens were collected from 55 prospectively enrolled HNSCC patients. Microbiomic profiles were obtained using Sanger sequencing of 16S rDNA PCR products. Methylation status of four genes previously linked to HNSCC or inflammation (MDR1, IL8, RARB, TGFBR2) was assessed in 49 samples by combined bisulfite restriction analysis and by Illumina HumanMethylation27 chip. Principle component analysis and multivariate analysis of covariance (MANCOVA) were used to identify bacterial subpopulations significantly associated with HNSCC and relevant clinical variables. Results: In this cohort, median age is 62, male:female ratio is 3:2, 16% are HPV+. Specific bacterial subpopulations associate with HNSCC over normal tissue and correlate with larger tumor size and higher nodal stage (p<0.05). Furthermore, microbial populations can separate tumors by tobacco (p<0.005) but not alcohol (p>0.7) status. Bacterial profiles are independent of HPV status. MDR1 promoter methylation is seen in tumor samples but not in normal oral mucosa (22/49 vs 0/49), and associates with specific microbiomic subpopulations in the order Enterobacteriales and phylum Tenericutes (p<0.001). Additionally, MDR1 methylation correlates with regional nodal metastases. Conclusions: Specific bacterial subpopulations differ between normal and tumor tissue and show association to specific clinicopathologic features, suggesting a role for microbial profiling in HNSCC as a novel area of investigation for diagnosis, prognostication, and therapeutic targeting.

Abstract 4087: Microbiomic subprofiles and MDR1 promoter methylation in head and neck squamous cell carcinoma

Background: Clinical observations and epidemiologic studies suggest that the incidence of head and neck squamous cell carcinoma (HNSCC) correlates with dental hygiene, implying a role for bacteria-induced inflammation in its pathogenesis. Differences in microbiota in HNSCC compared to matched normal oral mucosa, and whether these differences are accompanied by epigenetic modifications in selected HNSCC-associated genes and clinico-pathologic features has not yet been investigated. Standard microbiologic culturing techniques miss many fastidious organisms. Methods: Human genomic DNA and associated microbial DNA were isolated from 42 prospectively accrued consecutive HNSCC tumors and paired normal oral mucosae. Microbiomic profiling was carried out by serially characterizing bacteria-specific 16S rRNA and taxa classified by the presence of specific polymorphisms in the 16S rDNA genes. Promoter methylation and microbial populations of each sample was determined. Microbiomic profiles were compared between HNSCC and matched normal mucosa, to specific promoter methylation in four genes (MDR1, IL8, RARB, TGFBR2) previously linked to HNSCC or inflammation, and to clinico-pathologic features. Results: Specific microbiomic profiles significantly associate with HNSCC over normal mucosae. While methylation of both MDR1 (p&lt;10-9) and IL8 (p&lt;0.02) genes significantly associate with HNSCC, only MDR1 methylation associates with specific microbiomic profiles in HNSCC over normal mucosae. Furthermore, MDR1 methylation correlates with regional nodal metastases in the context of two specific bacterial taxa, Enterobacteriaceae and Tenericutes. Additionally, microbial populations can separate tumors by tobacco status (p&lt;0.005), but not by alcohol status (p=0.37). Conclusions: Specific microbial populations may contribute to HNSCC pathogenesis, possibly by triggering inflammation and consequent aberrant DNA methylation. These associations could lead to better understanding of the pathogenesis and treatment of HNSCC, suggesting novel roles for demethylating agents and probiotic adjuncts particularly for patients with advanced or refractory disease.

Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 4087. doi:1538-7445.AM2012-4087

Integrative genomic analysis reveals extended germline homozygosity with lung cancer risk in the PLCO cohort

Susceptibility to common cancers is multigenic resulting from low-to-high penetrance predisposition-factors and environmental exposure. Genomic studies suggest germline homozygosity as a novel low-penetrance factor contributing to common cancers. We hypothesized that long homozygous regions (tracts-of-homozygosity [TOH]) harbor tobacco-dependent and independent lung-cancer predisposition (or protection) genes. We performed in silico genome-wide SNP-array-based analysis of lung-cancer patients of European-ancestry from the PLCO screening-trial cohort to identify TOH regions amongst 788 cancer-cases and 830 ancestry-matched controls. Association analyses was then performed between presence of lung cancer and common(c)TOHs (operationally defined as 10 or more subjects sharing ≥100 identical homozygous calls), aTOHs (allelically-matched groups within a cTOH), demographics and tobacco-exposure. Finally, integration of significant c/aTOH with transcriptome was performed to functionally-map lung-cancer risk-genes. After controlling for demographics and smoking, we identified 7 cTOHs and 5 aTOHs associated with lung cancer (adjusted p<0.01). Three cTOHs were over-represented in cases over controls (OR = 1.75–2.06, p = 0.007–0.001), whereas 4 were under-represented (OR = 0.28–0.69, p = 0.006–0.001). Interaction between smoking status and cTOH3/aTOH2 (2p16.3–2p16.1) was observed (adjusted p<0.03). The remaining significant aTOHs have ORs 0.23–0.50 (p = 0.004–0.006) and 2.95–3.97 (p = 0.008–0.001). After integrating significant cTOH/aTOHs with publicly-available lung-cancer transcriptome datasets followed by filtering based on lung cancer and its relevant pathways revealed 9 putative predisposing genes (p<0.0001). In conclusion, differentially-distributed cTOH/aTOH genomic variants between cases and controls harbor sets of plausible differentially-expressed genes accounting for the complexity of lung-cancer predisposition.

Identifying gene interaction networks

In this chapter, we introduce interaction networks by describing how they are generated, where they are stored, and how they are shared. We focus on publicly available interaction networks and describe a simple way of utilizing these resources. As a case study, we used Cytoscape, an open source and easy-to-use network visualization and analysis tool to first gather and visualize a small network. We have analyzed this network's topological features and have looked at functional enrichment of the network nodes by integrating the gene ontology database. The methods described are applicable to larger networks that can be collected from various resources.

Microbiomic subprofiles and MDR1 promoter methylation in head and neck squamous cell carcinoma

Clinical observations and epidemiologic studies suggest that the incidence of head and neck squamous cell carcinoma (HNSCC) correlates with dental hygiene, implying a role for bacteria-induced inflammation in its pathogenesis. Here we begin to explore the pilot hypothesis that specific microbial populations may contribute to HNSCC pathogenesis via epigenetic modifications in inflammatory- and HNSCC-associated genes. Microbiomic profiling by 16S rRNA sequencing of matched tumor and adjacent normal tissue specimens in 42 individuals with HNSCC demonstrate a significant association of specific bacterial subpopulations with HNSCC over normal tissue (P < 0.01). Furthermore, microbial populations can separate tumors by tobacco status (P < 0.008), but not by alcohol status (P = 0.41). If our subhypothesis regarding a mechanistic link from microorganism to carcinogenesis via inflammation and consequent aberrant DNA methylation is correct, then we should see hypermethylation of relevant genes associate with specific microbiomic profiles. Methylation analysis in four genes (MDR1, IL8, RARB, TGFBR2) previously linked to HNSCC or inflammation shows significantly increased methylation in tumor samples compared with normal oral mucosa. Of these, MDR1 promoter methylation associates with specific microbiomic profiles in tumor over normal mucosa. Additionally, we report that MDR1 methylation correlates with regional nodal metastases in the context of two specific bacterial subpopulations, Enterobacteriaceae and Tenericutes (P < 0.001 for each). These associations may lead to a different, and potentially more comprehensive, perspective on the pathogenesis of HNSCC, and support further exploration of mechanistic linkage and, if so, novel therapeutic strategies such as demethylating agents and probiotic adjuncts, particularly for patients with advanced or refractory disease.

Identification of a central role for complement in osteoarthritis

Osteoarthritis, characterized by the breakdown of articular cartilage in synovial joints, has long been viewed as the result of “wear and tear”¹. Although low-grade inflammation is detected in osteoarthritis, its role is unclear^2–4. Here we identify a central role for the inflammatory complement system in the pathogenesis of osteoarthritis. Through proteomic and transcriptomic analyses of synovial fluids and membranes from individuals with osteoarthritis, we find that expression and activation of complement is abnormally high in human osteoarthritic joints. Using mice genetically deficient in C5, C6, or CD59a, we show that complement, and specifically the membrane attack complex (MAC)-mediated arm of complement, is critical to the development of arthritis in three different mouse models of osteoarthritis. Pharmacological modulation of complement in wild-type mice confirmed the results obtained with genetically deficient mice. Expression of inflammatory and degradative molecules was lower in chondrocytes from destabilized joints of C5-deficient mice than C5-sufficient mice, and MAC induced production of these molecules in cultured chondrocytes. Furthermore, MAC co-localized with matrix metalloprotease (MMP)-13 and with activated extracellular signal-regulated kinase (ERK) around chondrocytes in human osteoarthritic cartilage. Our findings indicate that dysregulation of complement in synovial joints plays a critical role in the pathogenesis of osteoarthritis.

Vavien: an algorithm for prioritizing candidate disease genes based on topological similarity of proteins in interaction networks

Genome-wide linkage and association studies have demonstrated promise in identifying genetic factors that influence health and disease. An important challenge is to narrow down the set of candidate genes that are implicated by these analyses. Protein-protein interaction (PPI) networks are useful in extracting the functional relationships between known disease and candidate genes, based on the principle that products of genes implicated in similar diseases are likely to exhibit significant connectivity/proximity. Information flow?based methods are shown to be very effective in prioritizing candidate disease genes. In this article, we utilize the topology of PPI networks to infer functional information in the context of disease association. Our approach is based on the assumption that PPI networks are organized into recurrent schemes that underlie the mechanisms of cooperation among different proteins. We hypothesize that proteins associated with similar diseases would exhibit similar topological characteristics in PPI networks. Utilizing the location of a protein in the network with respect to other proteins (i.e., the "topological profile" of the proteins), we develop a novel measure to assess the topological similarity of proteins in a PPI network. We then use this measure to prioritize candidate disease genes based on the topological similarity of their products and the products of known disease genes. We test the resulting algorithm, Vavien, via systematic experimental studies using an integrated human PPI network and the Online Mendelian Inheritance in Man (OMIM) database. Vavien outperforms other network-based prioritization algorithms as shown in the results and is available at www.diseasegenes.org.

Microenvironmental genomic alterations reveal signaling networks for head and neck squamous cell carcinoma

Background

Advanced stage head and neck squamous cell carcinoma (HNSCC) is an aggressive cancer with low survival rates. Loss-of-heterozygosity/allelic imbalance (LOH/AI) analysis has been widely used to identify genomic alterations in solid tumors and the tumor microenvironment (stroma). We hypothesize that these identified alterations can point to signaling networks functioning in HNSCC epithelial-tumor and surrounding stroma (tumor microenvironment).

Results

Under the assumption that genes in proximity to identified LOH/AI regions are correlated with the tumorigenic phenotype, we mined publicly available biological information to identify pathway segments (signaling proteins connected to each other in a network) and identify the role of tumor microenvironment in HNSCC. Across both neoplastic epithelial cells and the surrounding stromal cells, genetic alterations in HNSCC were successfully identified, and 75 markers were observed to have significantly different LOH/AI frequencies in these compartments (p < 0.026). We applied a network identification approach to the genes in proximity to these 75 markers in cancer epithelium and stroma in order to identify biological networks that can describe functional associations amongst these marker-associated genes.

Conclusions

We verified the involvement of T-cell receptor signaling pathways in HNSCC as well as associated oncogenes such as LCK and PLCB1, and tumor suppressors such as STAT5A, PTPN6, PARK2. We identified expression levels of genes within significant LOH/AI regions specific to stroma networks that correlate with better outcome in radiation therapy. By integrating various levels of high-throughput data, we were able to precisely focus on specific proteins and genes that are germane to HNSCC.

Germline mutations in MSR1, ASCC1, and CTHRC1 in patients with Barrett esophagus and esophageal adenocarcinoma

CONTEXT

Barrett esophagus (BE) occurs in 1% to 10% of the general population and is believed to be the precursor of esophageal adenocarcinoma (EAC). The incidence of EAC has increased 350% in the last 3 decades without clear etiology. Finding predisposition genes may improve premorbid risk assessment, genetic counseling, and management. Genome-wide multiplatform approaches may lead to the identification of genes important in BE/EAC development.

OBJECTIVE

To identify risk alleles or mutated genes associated with BE/EAC.

DESIGN, SETTING, AND PATIENTS

Model-free linkage analyses of 21 concordant-affected sibling pairs with BE/EAC and 11 discordant sibling pairs (2005-2006). Significant germline genomic regions in independent prospectively accrued series of 176 white patients with BE/EAC and 200 ancestry-matched controls (2007-2010) were validated and fine mapped. Integrating data from these significant genomic regions with somatic gene expression data from 19 BE/EAC tissues yielded 12 "priority" candidate genes for mutation analysis (2010). Genes that showed mutations in cases but not in controls were further screened in an independent prospectively accrued validation series of 58 cases (2010).

MAIN OUTCOME MEASURES

Identification of germline mutations in genes associated with BE/EAC cases. Functional interrogation of the most commonly mutated gene.

RESULTS

Three major genes, MSR1, ASCC1, and CTHRC1 were associated with BE/EAC (all P < .001). In addition, 13 patients (11.2%) with BE/EAC carried germline mutations in MSR1, ASCC1, or CTHRC1. MSR1 was the most frequently mutated, with 8 of 116 (proportion, 0.069; 95% confidence interval [CI], 0.030-0.130; P < .001) cases with c.877C>T (p.R293X). An independent validation series confirmed germline MSR1 mutations in 2 of 58 cases (proportion, 0.035; 95% CI, 0.004-0.120; P = .09). MSR1 mutation resulted in CCND1 up-regulation in peripheral-protein lysate. Immunohistochemistry of BE tissues in MSR1-mutation carriers showed increased nuclear expression of CCND1.

CONCLUSION

MSR1 was significantly associated with the presence of BE/EAC in derivation and validation samples, although it was only present in a small percentage of the cases.

DADA: Degree-Aware Algorithms for Network-Based Disease Gene Prioritization

Background

High-throughput molecular interaction data have been used effectively to prioritize candidate genes that are linked to a disease, based on the observation that the products of genes associated with similar diseases are likely to interact with each other heavily in a network of protein-protein interactions (PPIs). An important challenge for these applications, however, is the incomplete and noisy nature of PPI data. Information flow based methods alleviate these problems to a certain extent, by considering indirect interactions and multiplicity of paths.

Results

We demonstrate that existing methods are likely to favor highly connected genes, making prioritization sensitive to the skewed degree distribution of PPI networks, as well as ascertainment bias in available interaction and disease association data. Motivated by this observation, we propose several statistical adjustment methods to account for the degree distribution of known disease and candidate genes, using a PPI network with associated confidence scores for interactions. We show that the proposed methods can detect loosely connected disease genes that are missed by existing approaches, however, this improvement might come at the price of more false negatives for highly connected genes. Consequently, we develop a suite called DADA, which includes different uniform prioritization methods that effectively integrate existing approaches with the proposed statistical adjustment strategies. Comprehensive experimental results on the Online Mendelian Inheritance in Man (OMIM) database show that DADA outperforms existing methods in prioritizing candidate disease genes.

Conclusions

These results demonstrate the importance of employing accurate statistical models and associated adjustment methods in network-based disease gene prioritization, as well as other network-based functional inference applications. DADA is implemented in Matlab and is freely available at http://compbio.case.edu/dada/.

BiC: a web server for calculating bimodality of coexpression between gene and protein networks

Bimodal patterns of expression have recently been shown to be useful not only in prioritizing genes that distinguish phenotypes, but also in prioritizing network models that correlate with proteomic evidence. In particular, subgroups of strongly coexpressed gene pairs result in an increased variance of the correlation distribution. This variance, a measure of association between sets of genes (or proteins), can be summarized as the bimodality of coexpression (BiC). We developed an online tool to calculate the BiC for user-defined gene lists and associated mRNA expression data. BiC is a comprehensive application that provides researchers with the ability to analyze both publicly available and user-collected array data.

AVAILABILITY

The freely available web service and the documentation can be accessed at http://gurkan.case.edu/software.

CONTACT

gurkan@case.edu.

Specific kinesin expression profiles associated with taxane resistance in basal-like breast cancer

Breast cancer is a genetically heterogenous disease with subtypes differing in prognosis and chemosensitivity. The basal-like breast cancer (BLBC) molecular subtype is associated with poorer outcomes, but is more responsive to taxane-based chemotherapy. Kinesins are intracellular transport proteins that interact with microtubules, which are also the mechanistic target for taxanes. We investigated the relationship between taxane resistance in BLBC and kinesins using both expression and functional studies. Kinesin (KIF) expression was evaluated in three settings in relation to taxane resistance: (i) the NCI-60 cancer cell lines, (ii) pre-treatment samples from four BLBC patient cohorts receiving neoadjuvant chemotherapy regimens with and without taxanes, and (iii) post-treatment samples from residual breast cancer following neoadjuvant taxane-containing chemotherapy. We used a novel functional approach to gene modification, validation-based insertional mutagenesis, to select kinesin-overexpressing clones of BLBC cells for evaluation of related mechanisms of taxane resistance. In the NCI-60 cell line dataset, overexpression of the kinesin KIFC3 is significantly correlated with resistance to both docetaxel (P < 0.001) and paclitaxel (P < 0.001), but not to platinum-based chemotherapy, including carboplatin (P = 0.49) and cisplatin (P = 0.10). Overexpression of KIFC3 and KIF5A in pre-chemotherapy samples similarly predicted resistance to paclitaxel in the MDACC cohorts (P = 0.01); no KIF predicted resistance to fluorouracil-epirubicin-cyclophosphamide or cisplatin in BLBC patient cohorts treated without taxanes. KIF12 is the most overexpressed KIF gene in post-chemotherapy taxane-resistant residual breast cancers (2.8-fold-change). Functional studies established that overexpression of KIFC3, KIF5A, and KIF12 were specific in mediating resistance to docetaxel and not vincristine or doxorubicin. Mutation of the ATP-binding domain of a kinesin abolished its ability to mediate docetaxel resistance. Overall, kinesin overexpression correlates with specific taxane resistance in BLBC cell lines and tissues. Our results suggest a novel approach for drug development to overcome taxane resistance in breast cancer through concurrent or sequential use of kinesin inhibitors.

PETALS: Proteomic Evaluation and Topological Analysis of a mutated Locus' Signaling

Background

Colon cancer is driven by mutations in a number of genes, the most notorious of which is Apc. Though much of Apc's signaling has been mechanistically identified over the years, it is not always clear which functions or interactions are operative in a particular tumor. This is confounded by the presence of mutations in a number of other putative cancer driver (CAN) genes, which often synergize with mutations in Apc.

Computational methods are, thus, required to predict which pathways are likely to be operative when a particular mutation in Apc is observed.

Results

We developed a pipeline, PETALS, to predict and test likely signaling pathways connecting Apc to other CAN-genes, where the interaction network originating at Apc is defined as a "blossom," with each Apc-CAN-gene subnetwork referred to as a "petal." Known and predicted protein interactions are used to identify an Apc blossom with 24 petals. Then, using a novel measure of bimodality, the coexpression of each petal is evaluated against proteomic (2 D differential In Gel Electrophoresis, 2D-DIGE) measurements from the Apc^1638N+/-mouse to test the network-based hypotheses.

Conclusions

The predicted pathways linking Apc and Hapln1 exhibited the highest amount of bimodal coexpression with the proteomic targets, prioritizing the Apc-Hapln1 petal over other CAN-gene pairs and suggesting that this petal may be involved in regulating the observed proteome-level effects. These results not only demonstrate how functional 'omics data can be employed to test in silico predictions of CAN-gene pathways, but also reveal an approach to integrate models of upstream genetic interference with measured, downstream effects.

Phylogenetic analysis of modularity in protein interaction networks

BACKGROUND

In systems biology, comparative analyses of molecular interactions across diverse species indicate that conservation and divergence of networks can be used to understand functional evolution from a systems perspective. A key characteristic of these networks is their modularity, which contributes significantly to their robustness, as well as adaptability. Consequently, analysis of modular network structures from a phylogenetic perspective may be useful in understanding the emergence, conservation, and diversification of functional modularity.

RESULTS

In this paper, we propose a phylogenetic framework for analyzing network modules, with applications that extend well beyond network-based phylogeny reconstruction. Our approach is based on identification of modular network components from each network separately, followed by projection of these modules onto the networks of other species to compare different networks. Subsequently, we use the conservation of various modules in each network to assess the similarity between different networks. Compared to traditional methods that rely on topological comparisons, our approach has key advantages in (i) avoiding intractable graph comparison problems in comparative network analysis, (ii) accounting for noise and missing data through flexible treatment of network conservation, and (iii) providing insights on the evolution of biological systems through investigation of the evolutionary trajectories of network modules. We test our method, MOPHY, on synthetic data generated by simulation of network evolution, as well as existing protein-protein interaction data for seven diverse species. Comprehensive experimental results show that MOPHY is promising in reconstructing evolutionary histories of extant networks based on conservation of modularity, it is highly robust to noise, and outperforms existing methods that quantify network similarity in terms of conservation of network topology.

CONCLUSION

These results establish modularity and network proximity as useful features in comparative network analysis and motivate detailed studies of the evolutionary histories of network modules.

PathFinder: mining signal transduction pathway segments from protein-protein interaction networks

BACKGROUND

A Signal transduction pathway is the chain of processes by which a cell converts an extracellular signal into a response. In most unicellular organisms, the number of signal transduction pathways influences the number of ways the cell can react and respond to the environment. Discovering signal transduction pathways is an arduous problem, even with the use of systematic genomic, proteomic and metabolomic technologies. These techniques lead to an enormous amount of data and how to interpret and process this data becomes a challenging computational problem.

RESULTS

In this study we present a new framework for identifying signaling pathways in protein-protein interaction networks. Our goal is to find biologically significant pathway segments in a given interaction network. Currently, protein-protein interaction data has excessive amount of noise, e.g., false positive and false negative interactions. First, we eliminate false positives in the protein-protein interaction network by integrating the network with microarray expression profiles, protein subcellular localization and sequence information. In addition, protein families are used to repair false negative interactions. Then the characteristics of known signal transduction pathways and their functional annotations are extracted in the form of association rules.

CONCLUSION

Given a pair of starting and ending proteins, our methodology returns candidate pathway segments between these two proteins with possible missing links (recovered false negatives). In our study, S. cerevisiae (yeast) data is used to demonstrate the effectiveness of our method.