Joint estimation of multiple related biological networks

Graphical models are widely used to make inferences concerning interplay in multivariate systems. In many applications, data are collected from multiple related but nonidentical units whose underlying networks may differ but are likely to share features. Here we present a hierarchical Bayesian formulation for joint estimation of multiple networks in this nonidentically distributed setting. The approach is general: given a suitable class of graphical models, it uses an exchangeability assumption on networks to provide a corresponding joint formulation. Motivated by emerging experimental designs in molecular biology, we focus on time-course data with interventions, using dynamic Bayesian networks as the graphical models. We introduce a computationally efficient, deterministic algorithm for exact joint inference in this setting. We provide an upper bound on the gains that joint estimation offers relative to separate estimation for each network and empirical results that support and extend the theory, including an extensive simulation study and an application to proteomic data from human cancer cell lines. Finally, we describe approximations that are still more computationally efficient than the exact algorithm and that also demonstrate good empirical performance.Comment: Published in at http://dx.doi.org/10.1214/14-AOAS761 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Cyclin D(1) expression during rat mammary tumor development and its potential role in the resistance of the Copenhagen rat

BACKGROUND: Resistance to mammary tumorigenesis in Copenhagen rats is associated with loss of early preneoplastic lesions known as intraductal proliferations. The cause of this disappearance, however, is unknown. RESULTS: There were no differences in the numbers of lesions in mammary whole-mounts prepared from Copenhagen or Wistar-Furth rats at 20 or 30 days after N-methyl-N-nitrosourea treatment, but at 37 days there were significantly fewer lesions in Copenhagen glands. Furthermore, lesions in Copenhagen glands were exclusively intraductal proliferations, whereas in Wistar-Furth glands more advanced lesions were also present. Immunohistochemical staining showed frequent cyclin D(1) overexpression in Wistar-Furth lesions at 37 days, but not in Copenhagen lesions. There were, however, no differences in p16(INK4a) protein expression, bromodeoxyuridine labeling and apoptotic indices, or mast cell infiltration between Copenhagen and Wistar-Furth lesions at any time. CONCLUSIONS: Overexpression of cyclin D(1) in preneoplastic lesions may be important in the development of mammary tumors in susceptible rats, although this overexpression does not appear to cause significant changes in cell kinetics. Furthermore, the low levels of cyclin D(1) expression in Copenhagen intraductal proliferations may play a role in the resistance of these rats to mammary tumorigenesis

Causal network inference using biochemical kinetics

Motivation: Networks are widely used as structural summaries of biochemical systems. Statistical estimation of networks is usually based on linear or discrete models. However, the dynamics of biochemical systems are generally non-linear, suggesting that suitable non-linear formulations may offer gains with respect to causal network inference and aid in associated prediction problems. Results: We present a general framework for network inference and dynamical prediction using time course data that is rooted in nonlinear biochemical kinetics. This is achieved by considering a dynamical system based on a chemical reaction graph with associated kinetic parameters. Both the graph and kinetic parameters are treated as unknown; inference is carried out within a Bayesian framework. This allows prediction of dynamical behavior even when the underlying reaction graph itself is unknown or uncertain. Results, based on (i) data simulated from a mechanistic model of mitogen-activated protein kinase signaling and (ii) phosphoproteomic data from cancer cell lines, demonstrate that non-linear formulations can yield gains in causal network inference and permit dynamical prediction and uncertainty quantification in the challenging setting where the reaction graph is unknown. © The Author 2014. Published by Oxford University Press

Procedural results and acute complications in stenting native and recurrent coarctation of the aorta in patients over 4 years of age: A multi-institutional study

Background: We report a multi-institutional experience with intravascular stenting (IS) for treatment of coarctation of the aorta. Methods and Results: Data was collected retrospectively by review of medical records from 17 institutions. The data was broken down to prior to 2002 and after 2002 for further analysis. A total of 565 procedures were performed with a median age of 15 years (mean = 18.1 years). Successful reduction in the post stent gradient (0.8 was achieved in 97.9% of procedures. There was significant improvement ( P < 0.01) in pre versus post stent coarctation dimensions (7.4 mm ± 3.0 mm vs. 14.3 ± 3.2mm), systolic gradient (31.6 mm Hg ± 16.0 mm Hg vs. 2.7 mm Hg ± 4.2 mm Hg) and ratio of the coarctation segment to the DAo (0.43 ± 0.17 vs. 0.85 ± 0.15). Acute complications were encountered in 81/565 (14.3%) procedures. There were two procedure related deaths. Aortic wall complications included: aneurysm formation ( n = 6), intimal tears ( n = 8), and dissections ( n = 9). The risk of aortic dissection increased significantly in patients over the age of 40 years. Technical complications included stent migration ( n = 28), and balloon rupture ( n = 13). Peripheral vascular complications included cerebral vascular accidents (CVA) ( n = 4), peripheral emboli ( n = 1), and significant access arterial injury ( n = 13). Older age was significantly associated with occurrence of CVAs. A significant decrease in the technical complication rate from 16.3% to 6.1% ( P < 0.001) was observed in procedures performed after January 2002. Conclusions: Stent placement for coarctation of aorta is an effective treatment option, though it remains a technically challenging procedure. Technical and aortic complications have decreased over the past 3 years due to, in part, improvement in balloon and stent design. Improvement in our ability to assess aortic wall compliance is essential prior to placement of ISs in older patients with coarctation of the aorta. © 2007 Wiley-Liss, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/56159/1/21164_ftp.pd

Identification of a robust gene signature that predicts breast cancer outcome in independent data sets

BACKGROUND: Breast cancer is a heterogeneous disease, presenting with a wide range of histologic, clinical, and genetic features. Microarray technology has shown promise in predicting outcome in these patients. METHODS: We profiled 162 breast tumors using expression microarrays to stratify tumors based on gene expression. A subset of 55 tumors with extensive follow-up was used to identify gene sets that predicted outcome. The predictive gene set was further tested in previously published data sets. RESULTS: We used different statistical methods to identify three gene sets associated with disease free survival. A fourth gene set, consisting of 21 genes in common to all three sets, also had the ability to predict patient outcome. To validate the predictive utility of this derived gene set, it was tested in two published data sets from other groups. This gene set resulted in significant separation of patients on the basis of survival in these data sets, correctly predicting outcome in 62–65% of patients. By comparing outcome prediction within subgroups based on ER status, grade, and nodal status, we found that our gene set was most effective in predicting outcome in ER positive and node negative tumors. CONCLUSION: This robust gene selection with extensive validation has identified a predictive gene set that may have clinical utility for outcome prediction in breast cancer patients

Context Specificity in Causal Signaling Networks Revealed by Phosphoprotein Profiling.

Signaling networks downstream of receptor tyrosine kinases are among the most extensively studied biological networks, but new approaches are needed to elucidate causal relationships between network components and understand how such relationships are influenced by biological context and disease. Here, we investigate the context specificity of signaling networks within a causal conceptual framework using reverse-phase protein array time-course assays and network analysis approaches. We focus on a well-defined set of signaling proteins profiled under inhibition with five kinase inhibitors in 32 contexts: four breast cancer cell lines (MCF7, UACC812, BT20, and BT549) under eight stimulus conditions. The data, spanning multiple pathways and comprising ∼70,000 phosphoprotein and ∼260,000 protein measurements, provide a wealth of testable, context-specific hypotheses, several of which we experimentally validate. Furthermore, the data provide a unique resource for computational methods development, permitting empirical assessment of causal network learning in a complex, mammalian setting.This work was supported by the National Institutes of Health National Cancer Institute (grant U54 CA112970 to J.W.G., G.B.M., S.M., and P.T.S.). S.M.H. and S.M. were supported by the UK Medical Research Council (unit program numbers MC_UP_1302/1 and MC_UP_1302/3). S.M. was a recipient of a Royal Society Wolfson Research Merit Award. The MD Anderson Cancer Center RPPA Core Facility is funded by the National Institutes of Health National Cancer Institute (Cancer Center Core Grant CA16672)

Cellular senescence in cancer: from mechanisms to detection

Senescence refers to a cellular state featuring a stable cell‐cycle arrest triggered in response to stress. This response also involves other distinct morphological and intracellular changes including alterations in gene expression and epigenetic modifications, elevated macromolecular damage, metabolism deregulation and a complex pro‐inflammatory secretory phenotype. The initial demonstration of oncogene‐induced senescence in vitro established senescence as an important tumour‐suppressive mechanism, in addition to apoptosis. Senescence not only halts the proliferation of premalignant cells but also facilitates the clearance of affected cells through immunosurveillance. Failure to clear senescent cells owing to deficient immunosurveillance may, however, lead to a state of chronic inflammation that nurtures a pro‐tumorigenic microenvironment favouring cancer initiation, migration and metastasis. In addition, senescence is a response to post‐therapy genotoxic stress. Therefore, tracking the emergence of senescent cells becomes pivotal to detect potential pro‐tumorigenic events. Current protocols for the in vivo detection of senescence require the analysis of fixed or deep‐frozen tissues, despite a significant clinical need for real‐time bioimaging methods. Accuracy and efficiency of senescence detection are further hampered by a lack of universal and more specific senescence biomarkers. Recently, in an attempt to overcome these hurdles, an assortment of detection tools has been developed. These strategies all have significant potential for clinical utilisation and include flow cytometry combined with histo‐ or cytochemical approaches, nanoparticle‐based targeted delivery of imaging contrast agents, OFF‐ON fluorescent senoprobes, positron emission tomography senoprobes and analysis of circulating SASP factors, extracellular vesicles and cell‐free nucleic acids isolated from plasma. Here, we highlight the occurrence of senescence in neoplasia and advanced tumours, assess the impact of senescence on tumorigenesis and discuss how the ongoing development of senescence detection tools might improve early detection of multiple cancers and response to therapy in the near future

Kinome rewiring reveals AURKA limits PI3K-pathway inhibitor efficacy in breast cancer.

Dysregulation of the PI3K-AKT-mTOR signaling network is a prominent feature of breast cancers. However, clinical responses to drugs targeting this pathway have been modest, possibly because of dynamic changes in cellular signaling that drive resistance and limit drug efficacy. Using a quantitative chemoproteomics approach, we mapped kinome dynamics in response to inhibitors of this pathway and identified signaling changes that correlate with drug sensitivity. Maintenance of AURKA after drug treatment was associated with resistance in breast cancer models. Incomplete inhibition of AURKA was a common source of therapy failure, and combinations of PI3K, AKT or mTOR inhibitors with the AURKA inhibitor MLN8237 were highly synergistic and durably suppressed mTOR signaling, resulting in apoptosis and tumor regression in vivo. This signaling map identifies survival factors whose presence limits the efficacy of targeted therapies and reveals new drug combinations that may unlock the full potential of PI3K-AKT-mTOR pathway inhibitors in breast cancer

Brachyury oncogene is a prognostic factor in high-risk testicular germ cell tumors

The T-box transcription factor Brachyury has been considered a cancer-specific marker and a novel oncotarget in solid tumors. Brachyury overexpression has been described in various cancers, being associated with epithelial-mesenchymal transition, metastasis, and poor prognosis. However, its clinical association with testicular germ cell tumor is unknown. We analyzed the expression of Brachyury by immunohistochemistry in a series of well-characterized testicular germ cell tumor samples and at transcript level by in silico analysis. Additionally, we aimed to investigate the clinical significance of Brachyury in testicular germ cell tumor. Brachyury cytoplasm immunostaining was present in 89.6% (86/96) of cases with nuclear staining observed in 24% (23/96) of testicular germ cell tumor. Bioinformatics microarray expression analysis of two independent cohorts of testicular germ cell tumors showed similar results with increased levels of Brachyury in testicular germ cell tumors and metastasis compared with normal testis. Clinically, Brachyury nuclear staining was statistically associated with lower event-free survival (p = 0.04) and overall survival (p = 0.01) in intermediate/high-risk testicular germ cell tumors. Univariate analysis showed that Brachyury nuclear subcellular localization was a predictor of poor prognosis (p = 0.02), while a tendency was observed by multivariate analysis (HR: 3.56, p = 0.06). In conclusion, these results indicate that Brachyury plays an oncogenic role in testicular germ cell tumors and its subcellular localization in the nucleus may constitute a novel biomarker of poor prognosis and a putative oncotarget for intermediate/high-risk testicular germ cell tumor patients.ICVS internal research funds, by the Portuguese FCT project (PTDC/SAU‐TOX/114549/2009‐FCOMP‐01‐0124‐FEDER‐016057) to Reis RM and Barretos Cancer Hospital Internal Research Fund. F. Pinto received a fellowship from FCT ref SFRH/BD/81369/2011 and SFRH/BPD/115730/2016). Project ON.2 SR&TD Integrated Program (NORTE‐07‐0124‐FEDER‐000017) cofinanced by Programa Operacional Regional do Norte (ON.2—O Novo Norte), Quadro de Referência Estratégico Nacional (QREN), Fundo Europeu de Desenvolvimento Regional (FEDER)info:eu-repo/semantics/publishedVersio

Genome co-amplification upregulates a mitotic gene network activity that predicts outcome and response to mitotic protein inhibitors in breast cancer.

BACKGROUND: High mitotic activity is associated with the genesis and progression of many cancers. Small molecule inhibitors of mitotic apparatus proteins are now being developed and evaluated clinically as anticancer agents. With clinical trials of several of these experimental compounds underway, it is important to understand the molecular mechanisms that determine high mitotic activity, identify tumor subtypes that carry molecular aberrations that confer high mitotic activity, and to develop molecular markers that distinguish which tumors will be most responsive to mitotic apparatus inhibitors. METHODS: We identified a coordinately regulated mitotic apparatus network by analyzing gene expression profiles for 53 malignant and non-malignant human breast cancer cell lines and two separate primary breast tumor datasets. We defined the mitotic network activity index (MNAI) as the sum of the transcriptional levels of the 54 coordinately regulated mitotic apparatus genes. The effect of those genes on cell growth was evaluated by small interfering RNA (siRNA). RESULTS: High MNAI was enriched in basal-like breast tumors and was associated with reduced survival duration and preferential sensitivity to inhibitors of the mitotic apparatus proteins, polo-like kinase, centromere associated protein E and aurora kinase designated GSK462364, GSK923295 and GSK1070916, respectively. Co-amplification of regions of chromosomes 8q24, 10p15-p12, 12p13, and 17q24-q25 was associated with the transcriptional upregulation of this network of 54 mitotic apparatus genes, and we identify transcription factors that localize to these regions and putatively regulate mitotic activity. Knockdown of the mitotic network by siRNA identified 22 genes that might be considered as additional therapeutic targets for this clinically relevant patient subgroup. CONCLUSIONS: We define a molecular signature which may guide therapeutic approaches for tumors with high mitotic network activity