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

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

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

Modeling precision treatment of breast cancer

Background: First-generation molecular profiles for human breast cancers have enabled the identification of features that can predict therapeutic response; however, little is known about how the various data types can best be combined to yield optimal predictors. Collections of breast cancer cell lines mirror many aspects of breast cancer molecular pathobiology, and measurements of their omic and biological therapeutic responses are well-suited for development of strategies to identify the most predictive molecular feature sets. Results: We used least squares-support vector machines and random forest algorithms to identify molecular features associated with responses of a collection of 70 breast cancer cell lines to 90 experimental or approved therapeutic agents. The datasets analyzed included measurements of copy number aberrations, mutations, gene and isoform expression, promoter methylation and protein expression. Transcriptional subtype contributed strongly to response predictors for 25% of compounds, and adding other molecular data types improved prediction for 65%. No single molecular dataset consistently out-performed the others, suggesting that therapeutic response is mediated at multiple levels in the genome. Response predictors were developed and applied to TCGA data, and were found to be present in subsets of those patient samples. Conclusions: These results suggest that matching patients to treatments based on transcriptional subtype will improve response rates, and inclusion of additional features from other profiling data types may provide additional benefit. Further, we suggest a systems biology strategy for guiding clinical trials so that patient cohorts most likely to respond to new therapies may be more efficiently identified

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

Thrombospondin-4 is a putative tumour-suppressor gene in colorectal cancer that exhibits age-related methylation

<p>Abstract</p> <p>Background</p> <p><it>Thrombospondin-4 </it>(<it>THBS4</it>) is a member of the extracellular calcium-binding protein family and is involved in cell adhesion and migration. The aim of this study was to evaluate the potential role of deregulation of <it>THBS4 </it>expression in colorectal carcinogenesis. Of particular interest was the possible silencing of expression by methylation of the CpG island in the gene promoter.</p> <p>Methods</p> <p>Fifty-five sporadic colorectal tumours stratified for the CpG Island Methylator Phenotype (CIMP) were studied. Immunohistochemical staining of THBS4 protein was assessed in normal and tumour specimens. Relative levels of <it>THBS4 </it>transcript expression in matched tumours and normal mucosa were also determined by quantitative RT-PCR. Colony forming ability was examined in 8 cell lines made to overexpress THBS4. Aberrant promoter hypermethylation was investigated as a possible mechanism of gene disruption using MethyLight. Methylation was also assessed in the normal colonic tissue of 99 patients, with samples biopsied from four regions along the length of the colon.</p> <p>Results</p> <p><it>THBS4 </it>expression was significantly lower in tumour tissue than in matched normal tissue. Immunohistochemical examination demonstrated that THBS4 protein was generally absent from normal epithelial cells and tumours, but was occasionally expressed at low levels in the cytoplasm towards the luminal surface in vesicular structures. Forced THBS4 over-expression caused a 50-60% repression of tumour colony growth in all eight cell lines examined compared to control cell lines. Tumours exhibited significantly higher levels of methylation than matched normal mucosa, and <it>THBS4 </it>methylation correlated with the CpG island methylator phenotype. There was a trend towards decreased gene expression in tumours exhibiting high <it>THBS4 </it>methylation, but the correlation was not significant. <it>THBS4 </it>methylation was detectable in normal mucosal biopsies where it correlated with increasing patient age and negatively with the occurrence of adenomas elsewhere in the colon.</p> <p>Conclusions</p> <p><it>THBS4 </it>shows increased methylation in colorectal cancer, but this is not strongly associated with altered gene expression, either because methylation has not always reached a critical level or because other factors influence <it>THBS4 </it>expression. <it>THBS4 </it>may act as a tumour suppressor gene, demonstrated by its suppression of tumour colony formation <it>in vitro</it>. <it>THBS4 </it>methylation is detectable in normal colonic mucosa and its level may be a biomarker for the occurrence of adenomas and carcinoma.</p

APRIL is overexpressed in cancer: link with tumor progression

<p>Abstract</p> <p>Background</p> <p>BAFF and APRIL share two receptors – TACI and BCMA – and BAFF binds to a third receptor, BAFF-R. Increased expression of BAFF and APRIL is noted in hematological malignancies. BAFF and APRIL are essential for the survival of normal and malignant B lymphocytes, and altered expression of BAFF or APRIL or of their receptors (BCMA, TACI, or BAFF-R) have been reported in various B-cell malignancies including B-cell non-Hodgkin's lymphoma, chronic lymphocytic leukemia, Hodgkin's lymphoma, multiple myeloma, and Waldenstrom's macroglobulinemia.</p> <p>Methods</p> <p>We compared the expression of <it>BAFF, APRIL, TACI and BAFF-R </it>gene expression in 40 human tumor types – brain, epithelial, lymphoid, germ cells – to that of their normal tissue counterparts using publicly available gene expression data, including the Oncomine Cancer Microarray database.</p> <p>Results</p> <p>We found significant overexpression of <it>TACI </it>in multiple myeloma and thyroid carcinoma and an association between TACI expression and prognosis in lymphoma. Furthermore, <it>BAFF and APRIL </it>are overexpressed in many cancers and we show that <it>APRIL </it>expression is associated with tumor progression. We also found overexpression of at least one proteoglycan with heparan sulfate chains (HS), which are coreceptors for APRIL and TACI, in tumors where APRIL is either overexpressed or is a prognostic factor. APRIL could induce survival or proliferation directly through HS proteoglycans.</p> <p>Conclusion</p> <p>Taken together, these data suggest that APRIL is a potential prognostic factor for a large array of malignancies.</p

Genome-wide methylation analysis identifies genes silenced in non-seminoma cell lines

Silencing of genes by DNA methylation is a common phenomenon in many types of cancer. However, the genome wide effect of DNA methylation on gene expression has been analysed in relatively few cancers. Germ cell tumours (GCTs) are a complex group of malignancies. They are unique in developing from a pluripotent progenitor cell. Previous analyses have suggested that non-seminomas exhibit much higher levels of DNA methylation than seminomas. The genomic targets that are methylated, the extent to which this results in gene silencing and the identity of the silenced genes most likely to play a role in the tumours’ biology have not yet been established. In this study, genome-wide methylation and expression analysis of GCT cell lines was combined with gene expression data from primary tumours to address this question. Genome methylation was analysed using the Illumina infinium HumanMethylome450 bead chip system and gene expression was analysed using Affymetrix GeneChip Human Genome U133 Plus 2.0 arrays. Regulation by methylation was confirmed by demethylation using 5-aza-2-deoxycytidine and reverse transcription–quantitative PCR. Large differences in the level of methylation of the CpG islands of individual genes between tumour cell lines correlated well with differential gene expression. Treatment of non-seminoma cells with 5-aza-2-deoxycytidine verified that methylation of all genes tested played a role in their silencing in yolk sac tumour cells and many of these genes were also differentially expressed in primary tumours. Genes silenced by methylation in the various GCT cell lines were identified. Several pluripotency-associated genes were identified as a major functional group of silenced genes

Gene expression profiling of rat spermatogonia and Sertoli cells reveals signaling pathways from stem cells to niche and testicular cancer cells to surrounding stroma

Background: Stem cells and their niches are studied in many systems, but mammalian germ stem cells (GSC) and their niches are still poorly understood. In rat testis, spermatogonia and undifferentiated Sertoli cells proliferate before puberty, but at puberty most spermatogonia enter spermatogenesis, and Sertoli cells differentiate to support this program. Thus, pre-pubertal spermatogonia might possess GSC potential and pre-pubertal Sertoli cells niche functions. We hypothesized that the different stem cell pools at pre-puberty and maturity provide a model for the identification of stem cell and niche-specific genes. We compared the transcript profiles of spermatogonia and Sertoli cells from pre-pubertal and pubertal rats and examined how these related to genes expressed in testicular cancers, which might originate from inappropriate communication between GSCs and Sertoli cells. Results: The pre-pubertal spermatogonia-specific gene set comprised known stem cell and spermatogonial stem cell (SSC) markers. Similarly, the pre-pubertal Sertoli cell-specific gene set comprised known niche gene transcripts. A large fraction of these specifically enriched transcripts encoded trans-membrane, extra-cellular, and secreted proteins highlighting stem cell to niche communication. Comparing selective gene sets established in this study with published gene expression data of testicular cancers and their stroma, we identified sets expressed genes shared between testicular tumors and pre-pubertal spermatogonia, and tumor stroma and pre-pubertal Sertoli cells with statistic significance. Conclusions: Our data suggest that SSC and their niche specifically express complementary factors for cell communication and that the same factors might be implicated in the communication between tumor cells and their micro-enviroment in testicular cancer