PhenoNet: identification of key networks associated with disease phenotype

Identification of phenotype-specific networks from paired gene expression-cell shape imaging data

The morphology of breast cancer cells is often used as an indicator of tumor severity and prognosis. Additionally, morphology can be used to identify more fine-grained, molecular developments within a cancer cell, such as transcriptomic changes and signaling pathway activity. Delineating the interface between morphology and signaling is important to understand the mechanical cues that a cell processes in order to undergo epithelial-to-mesenchymal transition and consequently metastasize. However, the exact regulatory systems that define these changes remain poorly characterized. In this study, we used a network-systems approach to integrate imaging data and RNA-seq expression data. Our workflow allowed the discovery of unbiased and context-specific gene expression signatures and cell signaling subnetworks relevant to the regulation of cell shape, rather than focusing on the identification of previously known, but not always representative, pathways. By constructing a cell-shape signaling network from shape-correlated gene expression modules and their upstream regulators, we found central roles for developmental pathways such as WNT and Notch, as well as evidence for the fine control of NF-kB signaling by numerous kinase and transcriptional regulators. Further analysis of our network implicates a gene expression module enriched in the RAP1 signaling pathway as a mediator between the sensing of mechanical stimuli and regulation of NF-kB activity, with specific relevance to cell shape in breast cancer.

Robust edge-based biomarker discovery improves prediction of breast cancer metastasis

Background

The abundance of molecular profiling of breast cancer tissues entailed active research on molecular marker-based early diagnosis of metastasis. Recently there is a surging interest in combining gene expression with gene networks such as protein-protein interaction (PPI) network, gene co-expression (CE) network and pathway information to identify robust and accurate biomarkers for metastasis prediction, reflecting the common belief that cancer is a systems biology disease. However, controversy exists in the literature regarding whether network markers are indeed better features than genes alone for predicting as well as understanding metastasis. We believe much of the existing results may have been biased by the overly complicated prediction algorithms, unfair evaluation, and lack of rigorous statistics. In this study, we propose a simple approach to use network edges as features, based on two types of networks respectively, and compared their prediction power using three classification algorithms and rigorous statistical procedure on one of the largest datasets available. To detect biomarkers that are significant for the prediction and to compare the robustness of different feature types, we propose an unbiased and novel procedure to measure feature importance that eliminates the potential bias from factors such as different sample size, number of features, as well as class distribution.

Results

Experimental results reveal that edge-based feature types consistently outperformed gene-based feature type in random forest and logistic regression models under all performance evaluation metrics, while the prediction accuracy of edge-based support vector machine (SVM) model was poorer, due to the larger number of edge features compared to gene features and the lack of feature selection in SVM model. Experimental results also show that edge features are much more robust than gene features and the top biomarkers from edge feature types are statistically more significantly enriched in the biological processes that are well known to be related to breast cancer metastasis.

Conclusions

Overall, this study validates the utility of edge features as biomarkers but also highlights the importance of carefully designed experimental procedures in order to achieve statistically reliable comparison results.

Network approaches to systems biology analysis of complex disease: integrative methods for multi-omics data

In the past decade, significant progress has been made in complex disease research across multiple omics layers from genome, transcriptome and proteome to metabolome. There is an increasing awareness of the importance of biological interconnections, and much success has been achieved using systems biology approaches. However, because of the typical focus on one single omics layer at a time, existing systems biology findings explain only a modest portion of complex disease. Recent advances in multi-omics data collection and sharing present us new opportunities for studying complex diseases in a more comprehensive fashion, and yet simultaneously create new challenges considering the unprecedented data dimensionality and diversity. Here, our goal is to review extant and emerging network approaches that can be applied across multiple biological layers to facilitate a more comprehensive and integrative multilayered omics analysis of complex diseases.

hsa-miR-9 controls the mobility behavior of glioblastoma cells via regulation of MAPK14 signaling elements

Background

Glioblastoma Multiforme (GBM) is the most common and lethal primary tumor of the brain. GBM is associated with one of the worst 5-year survival rates among all human cancers, despite much effort in different modes of treatment.

Results

Here, we demonstrate specific GBM cancer phenotypes that are governed by modifications to the MAPAKAP network. We then demonstrate a novel regulation mode by which a set of five key factors of the MAPKAP pathway are regulated by the same microRNA, hsa-miR-9.

We demonstrate that hsa-miR-9 overexpression leads to MAPKAP signaling inhibition, partially by interfering with the MAPK14/MAPKAP3 complex. Further, hsa-miR-9 overexpression initiates re-arrangement of actin filaments, which leads us to hypothesize a mechanism for the observed phenotypic shift.

Conclusion

The work presented here exposes novel microRNA features and situates hsa-miR-9 as a therapeutic target, which governs metastasis and thus determines prognosis in GBM through MAPKAP signaling.

ChiPPI: a novel method for mapping chimeric protein-protein interactions uncovers selection principles of protein fusion events in cancer

Fusion proteins, comprising peptides deriving from the translation of two parental genes, are produced in cancer by chromosomal aberrations. The expressed fusion protein incorporates domains of both parental proteins. Using a methodology that treats discrete protein domains as binding sites for specific domains of interacting proteins, we have cataloged the protein interaction networks for 11 528 cancer fusions (ChiTaRS-3.1). Here, we present our novel method, chimeric protein–protein interactions (ChiPPI) that uses the domain–domain co-occurrence scores in order to identify preserved interactors of chimeric proteins. Mapping the influence of fusion proteins on cell metabolism and pathways reveals that ChiPPI networks often lose tumor suppressor proteins and gain oncoproteins. Furthermore, fusions often induce novel connections between non-interactors skewing interaction networks and signaling pathways. We compared fusion protein PPI networks in leukemia/lymphoma, sarcoma and solid tumors finding distinct enrichment patterns for each disease type. While certain pathways are enriched in all three diseases (Wnt, Notch and TGF β), there are distinct patterns for leukemia (EGFR signaling, DNA replication and CCKR signaling), for sarcoma (p53 pathway and CCKR signaling) and solid tumors (FGFR and EGFR signaling). Thus, the ChiPPI method represents a comprehensive tool for studying the anomaly of skewed cellular networks produced by fusion proteins in cancer.

Brachyury, a vaccine target, is overexpressed in triple-negative breast cancer

Patients diagnosed with triple-negative breast cancer (TNBC) have a high rate of tumor metastasis and a poor prognosis. The treatment option for these patients is currently chemotherapy, which results in very low response rates. Strategies that exploit the immune system for the treatment of cancer have now shown the ability to improve survival in several tumor types. Identifying potential targets for immune therapeutic interventions is an important step in developing novel treatments for TNBC. In this study, in silico analysis of publicly available datasets and immunohistochemical analysis of primary and metastatic tumor biopsies from TNBC patients were conducted to evaluate the expression of the transcription factor brachyury, which is a driver of tumor metastasis and resistance and a target for cancer vaccine approaches. Analysis of breast cancer datasets demonstrated a predominant expression of brachyury mRNA in TNBC and in basal vs luminal or HER2 molecular breast cancer subtypes. At the protein level, variable levels of brachyury expression were detected both in primary and metastatic TNBC lesions. A strong association was observed between nuclear brachyury protein expression and the stage of disease, with nuclear brachyury being more predominant in metastatic vs primary tumors. Survival analysis also demonstrated an association between high levels of brachyury in the primary tumor and poor prognosis. Two brachyury-targeting cancer vaccines are currently undergoing clinical evaluation; the data presented here provide rationale for using brachyury-targeting immunotherapy approaches for the treatment of TNBC.

Aberrant expression of the embryonic transcription factor brachyury in human tumors detected with a novel rabbit monoclonal antibody

The embryonic transcription factor brachyury is overexpressed in a variety of human tumors, including lung, breast, colon and prostate carcinomas, chordomas and hemangioblastomas. In human carcinoma cells, overexpression of brachyury associates with the occurrence of the phenomenon of epithelial-mesenchymal transition (EMT), acquisition of metastatic propensity and resistance to a variety of anti-cancer therapeutics. Brachyury is preferentially expressed in human tumors vs. normal adult tissues, and high levels of this molecule associate with poor prognosis in patients with lung, colon and prostate carcinomas, and in breast cancer patients treated with adjuvant tamoxifen. Brachyury is immunogenic in humans and vaccines against this novel oncotarget are currently undergoing clinical investigation. While our group and others have employed various anti-brachyury antibodies to interrogate the above findings, we report here on the development and thorough characterization of a novel rabbit monoclonal antibody (MAb 54-1) that reacts with distinct high affinity and specificity with human brachyury. MAb 54-1 was successfully used in ELISA, western blot, immunofluorescence and immunohistochemistry assays to evaluate expression of brachyury in various human tumor cell lines and tissues. We propose the use of this antibody to assist in research studies of EMT and in prognostic studies for a range of human tumors.

ChainRank, a chain prioritisation method for contextualisation of biological networks

BACKGROUND

Advances in high throughput technologies and growth of biomedical knowledge have contributed to an exponential increase in associative data. These data can be represented in the form of complex networks of biological associations, which are suitable for systems analyses. However, these networks usually lack both, context specificity in time and space as well as the distinctive borders, which are usually assigned in the classical pathway view of molecular events (e.g. signal transduction). This complexity and high interconnectedness call for automated techniques that can identify smaller targeted subnetworks specific to a given research context (e.g. a disease scenario).

RESULTS

Our method, named ChainRank, finds relevant subnetworks by identifying and scoring chains of interactions that link specific network components. Scores can be generated from integrating multiple general and context specific measures (e.g. experimental molecular data from expression to proteomics and metabolomics, literature evidence, network topology). The performance of the novel ChainRank method was evaluated on recreating selected signalling pathways from a human protein interaction network. Specifically, we recreated skeletal muscle specific signaling networks in healthy and chronic obstructive pulmonary disease (COPD) contexts. The analysis showed that ChainRank can identify main mediators of context specific molecular signalling. An improvement of up to factor 2.5 was shown in the precision of finding proteins of the recreated pathways compared to random simulation.

CONCLUSIONS

ChainRank provides a framework, which can integrate several user-defined scores and evaluate their combined effect on ranking interaction chains linking input data sets. It can be used to contextualise networks, identify signaling and regulatory path amongst targeted genes or to analyse synthetic lethality in the context of anticancer therapy. ChainRank is implemented in R programming language and freely available at https://github.com/atenyi/ChainRank.

Subtype prediction in pediatric acute myeloid leukemia: classification using differential network rank conservation revisited

Background

One of the most important application spectrums of transcriptomic data is cancer phenotype classification. Many characteristics of transcriptomic data, such as redundant features and technical artifacts, make over-fitting commonplace. Promising classification results often fail to generalize across datasets with different sources, platforms, or preprocessing. Recently a novel differential network rank conservation (DIRAC) algorithm to characterize cancer phenotypes using transcriptomic data. DIRAC is a member of a family of algorithms that have shown useful for disease classification based on the relative expression of genes. Combining the robustness of this family’s simple decision rules with known biological relationships, this systems approach identifies interpretable, yet highly discriminate networks. While DIRAC has been briefly employed for several classification problems in the original paper, the potentials of DIRAC in cancer phenotype classification, and especially robustness against artifacts in transcriptomic data have not been fully characterized yet.

Results

In this study we thoroughly investigate the potentials of DIRAC by applying it to multiple datasets, and examine the variations in classification performances when datasets are (i) treated and untreated for batch effect; (ii) preprocessed with different techniques. We also propose the first DIRAC-based classifier to integrate multiple networks. We show that the DIRAC-based classifier is very robust in the examined scenarios. To our surprise, the trained DIRAC-based classifier even translated well to a dataset with different biological characteristics in the presence of substantial batch effects that, as shown here, plagued the standard expression value based classifier. In addition, the DIRAC-based classifier, because of the integrated biological information, also suggests pathways to target in specific subtypes, which may enhance the establishment of personalized therapy in diseases such as pediatric AML. In order to better comprehend the prediction power of the DIRAC-based classifier in general, we also performed classifications using publicly available datasets from breast and lung cancer. Furthermore, multiple well-known classification algorithms were utilized to create an ideal test bed for comparing the DIRAC-based classifier with the standard gene expression value based classifier. We observed that the DIRAC-based classifier greatly outperforms its rival.

Conclusions

Based on our experiments with multiple datasets, we propose that DIRAC is a promising solution to the lack of generalizability in classification efforts that uses transcriptomic data. We believe that superior performances presented in this study may motivate other to initiate a new aline of research to explore the untapped power of DIRAC in a broad range of cancer types.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0737-3) contains supplementary material, which is available to authorized users.

Phase I Trial of a Yeast-Based Therapeutic Cancer Vaccine (GI-6301) Targeting the Transcription Factor Brachyury

The nuclear transcription factor brachyury has previously been shown to be a strong mediator of the epithelial-to-mesenchymal transition (EMT) in human carcinoma cells and a strong negative prognostic factor in several tumor types. Brachyury is overexpressed in a range of human carcinomas as well as in chordoma, a rare tumor for which there is no standard systemic therapy. Preclinical studies have shown that a recombinant Saccharomyces cerevisiae (yeast) vaccine encoding brachyury (GI-6301) can activate human T cells in vitro. A phase I dose-escalation (3+3 design) trial enrolled 34 patients at 4 dose levels [3, 3, 16, and 11 patients, respectively, at 4, 16, 40, and 80 yeast units (YU)]. Expansion cohorts were enrolled at 40- and 80-YU dose levels for analysis of immune response and clinical activity. We observed brachyury-specific T-cell immune responses in the majority of evaluable patients despite most having been heavily pretreated. No evidence of autoimmunity or other serious adverse events was observed. Two chordoma patients showed evidence of disease control (one mixed response and one partial response). A patient with colorectal carcinoma, who enrolled on study with a large progressing pelvic mass and rising carcinoembryonic antigen (CEA), remains on study for greater than 1 year with stable disease, evidence of decreased tumor density, and decreased serum CEA. This is the first-in-human study to demonstrate the safety and immunogenicity of this therapeutic cancer vaccine and provides the rationale for exploration in phase II studies. A randomized phase II chordoma study is now enrolling patients.

Immune Targeting of Tumor Epithelial-Mesenchymal Transition via Brachyury-Based Vaccines

As a manifestation of their inherent plasticity, carcinoma cells undergo profound phenotypic changes during progression toward metastasis. One such phenotypic modulation is the epithelial-mesenchymal transition (EMT), an embryonically relevant process that can be reinstated by tumor cells, resulting in the acquisition of metastatic propensity, stem-like cell properties, and resistance to a variety of anticancer therapies, including chemotherapy, radiation, and some small-molecule targeted therapies. Targeting of the EMT is emerging as a novel intervention against tumor progression. This review focuses on the potential use of cancer vaccine strategies targeting tumor cells that exhibit mesenchymal-like features, with an emphasis on the current status of development of vaccine platforms directed against the T-box transcription factor brachyury, a novel cancer target involved in tumor EMT, stemness, and resistance to therapies. Also presented is a summary of potential mechanisms of resistance to immune-mediated attack driven by EMT and the development of novel combinatorial strategies based on the use of agents that alleviate tumor EMT for an optimized targeting of plastic tumor cells that are responsible for tumor recurrence and the establishment of therapeutic refractoriness.