A network approach for managing and processing big cancer data in clouds

Translational cancer research requires integrative analysis of multiple levels of big cancer data to identify and treat cancer. In order to address the issues that data is decentralised, growing and continually being updated, and the content living or archiving on different information sources partially overlaps creating redundancies as well as contradictions and inconsistencies, we develop a data network model and technology for constructing and managing big cancer data. To support our data network approach for data process and analysis, we employ a semantic content network approach and adopt the CELAR cloud platform. The prototype implementation shows that the CELAR cloud can satisfy the on-demanding needs of various data resources for management and process of big cancer data

Homeostatic competition drives tumor growth and metastasis nucleation

We propose a mechanism for tumor growth emphasizing the role of homeostatic regulation and tissue stability. We show that competition between surface and bulk effects leads to the existence of a critical size that must be overcome by metastases to reach macroscopic sizes. This property can qualitatively explain the observed size distributions of metastases, while size-independent growth rates cannot account for clinical and experimental data. In addition, it potentially explains the observed preferential growth of metastases on tissue surfaces and membranes such as the pleural and peritoneal layers, suggests a mechanism underlying the seed and soil hypothesis introduced by Stephen Paget in 1889 and yields realistic values for metastatic inefficiency. We propose a number of key experiments to test these concepts. The homeostatic pressure as introduced in this work could constitute a quantitative, experimentally accessible measure for the metastatic potential of early malignant growths.Comment: 13 pages, 11 figures, to be published in the HFSP Journa

Immunodetection of nmt55/p54(nrb) isoforms in human breast cancer

BACKGROUND: We previously identified and characterized a novel 55 kDa nuclear protein, termed nmt55/p54(nrb), whose expression was decreased in a subset of human breast tumors. The objective of this study was to determine if this reduced expression in human breast tumors was attributed to the regulation of mRNA transcription or the presence of altered forms of this protein. RESULTS: Northern blot analysis and ribonuclease protection assay indicated that nmt55/p54(nrb) mRNA is expressed at varying levels in estrogen receptor positive (ER+) and estrogen receptor negative (ER-) human breast tumors suggesting that reduced expression of nmt55/p54(nrb) protein in ER- tumors was not due to transcriptional regulation. To determine if multiple protein isoforms are expressed in breast cancer, we utilized Western blot and immunohistochemical analyses, which revealed the expression of an nmt55/p54(nrb) protein isoform in a subset of ER+ tumors. This subset of ER+ human breast tumors expressed an altered form of nmt55/p54(nrb) that was undetectable with an amino-terminal specific antibody suggesting that this isoform contains alterations or modifications within the amino terminal domain. CONCLUSIONS: Our study indicates that nmt55/p54(nrb) protein is post-transcriptionally regulated in human breast tumors leading to reduced expression in ER- tumors and the expression of an amino terminal altered isoform in a subset of ER+ tumors. The potential involvement of nmt55/p54(nrb) in RNA binding and pre-mRNA splicing may be important for normal cell growth and function; thus, loss or alteration of protein structure may contribute to tumor growth and progression

Modeling of non-steroidal anti-inflammatory drug effect within signaling pathways and miRNA-regulation pathways

To date, it is widely recognized that Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) can exert considerable anti-tumor effects regarding many types of cancers. The prolonged use of NSAIDs is highly associated with diverse side effects. Therefore, tailoring down the NSAID application onto individual patients has become a necessary and relevant step towards personalized medicine. This study conducts the systemsbiological approach to construct a molecular model (NSAID model) containing a cyclooxygenase (COX)-pathway and its related signaling pathways. Four cancer hallmarks are integrated into the model to reflect different developmental aspects of tumorigenesis. In addition, a Flux-Comparative-Analysis (FCA) based on Petri net is developed to transfer the dynamic properties (including drug responsiveness) of individual cellular system into the model. The gene expression profiles of different tumor-types with available drug-response information are applied to validate the predictive ability of the NSAID model. Moreover, two therapeutic developmental strategies, synthetic lethality and microRNA (miRNA) biomarker discovery, are investigated based on the COX-pathway. In conclusion, the result of this study demonstrates that the NSAID model involving gene expression, gene regulation, signal transduction, protein interaction and other cellular processes, is able to predict the individual cellular responses for different therapeutic interventions (such as NS-398 and COX-2 specific siRNA inhibition). This strongly indicates that this type of model is able to reflect the physiological, developmental and pathological processes of an individual. The approach of miRNA biomarker discovery is demonstrated for identifying miRNAs with oncogenic and tumor suppressive functions for individual cell lines of breast-, colon- and lung-tumor. The achieved results are in line with different independent studies that investigated miRNA biomarker related to diagnostics of cancer treatments, therefore it might shed light on the development of biomarker discovery at individual level. Particular results of this study might contribute to step further towards personalized medicine with the systemsbiological approach

Modeling Core Metabolism in Cancer Cells: Surveying the Topology Underlying the Warburg Effect

BACKGROUND: Alterations on glucose consumption and biosynthetic activity of amino acids, lipids and nucleotides are metabolic changes for sustaining cell proliferation in cancer cells. Irrevocable evidence of this fact is the Warburg effect which establishes that cancer cells prefers glycolysis over oxidative phosphorylation to generate ATP. Regulatory action over metabolic enzymes has opened a new window for designing more effective anti-cancer treatments. This enterprise is not trivial and the development of computational models that contribute to identifying potential enzymes for breaking the robustness of cancer cells is a priority. METHODOLOGY/PRINCIPAL FINDINGS: This work presents a constraint-base modeling of the most experimentally studied metabolic pathways supporting cancer cells: glycolysis, TCA cycle, pentose phosphate, glutaminolysis and oxidative phosphorylation. To evaluate its predictive capacities, a growth kinetics study for Hela cell lines was accomplished and qualitatively compared with in silico predictions. Furthermore, based on pure computational criteria, we concluded that a set of enzymes (such as lactate dehydrogenase and pyruvate dehydrogenase) perform a pivotal role in cancer cell growth, findings supported by an experimental counterpart. CONCLUSIONS/SIGNIFICANCE: Alterations on metabolic activity are crucial to initiate and sustain cancer phenotype. In this work, we analyzed the phenotype capacities emerged from a constructed metabolic network conformed by the most experimentally studied pathways sustaining cancer cell growth. Remarkably, in silico model was able to resemble the physiological conditions in cancer cells and successfully identified some enzymes currently studied by its therapeutic effect. Overall, we supplied evidence that constraint-based modeling constitutes a promising computational platform to: 1) integrate high throughput technology and establish a crosstalk between experimental validation and in silico prediction in cancer cell phenotype; 2) explore the fundamental metabolic mechanism that confers robustness in cancer; and 3) suggest new metabolic targets for anticancer treatments. All these issues being central to explore cancer cell metabolism from a systems biology perspective

A Context-Specific Role for Retinoblastoma Protein-Dependent Negative Growth Control in Suppressing Mammary Tumorigenesis

The ability to respond to anti-growth signals is critical to maintain tissue homeostasis and loss of this negative growth control safeguard is considered a hallmark of cancer. Negative growth regulation generally occurs during the G0/G1 phase of the cell cycle, yet the redundancy and complexity among components of this regulatory network has made it difficult to discern how negative growth cues protect cells from aberrant proliferation.The retinoblastoma protein (pRB) acts as the final barrier to prevent cells from entering into the cell cycle. By introducing subtle changes in the endogenous mouse Rb1 gene (Rb1(ΔL)), we have previously shown that interactions at the LXCXE binding cleft are necessary for the proper response to anti-growth signals such as DNA damage and TGF-β, with minimal effects on overall development. This disrupts the balance of pro- and anti-growth signals in mammary epithelium of Rb1(ΔL/ΔL) mice. Here we show that Rb1(ΔL/ΔL) mice are more prone to mammary tumors in the Wap-p53(R172H) transgenic background indicating that negative growth regulation is important for tumor suppression in these mice. In contrast, the same defect in anti-growth control has no impact on Neu-induced mammary tumorigenesis.Our work demonstrates that negative growth control by pRB acts as a crucial barrier against oncogenic transformation. Strikingly, our data also reveals that this tumor suppressive effect is context-dependent

Defining the Molecular Basis of Tumor Metabolism: a Continuing Challenge Since Warburg's Discovery

Cancer cells are the product of genetic disorders that alter crucial intracellular signaling pathways associated with the regulation of cell survival, proliferation, differentiation and death mechanisms. the role of oncogene activation and tumor suppressor inhibition in the onset of cancer is well established. Traditional antitumor therapies target specific molecules, the action/expression of which is altered in cancer cells. However, since the physiology of normal cells involves the same signaling pathways that are disturbed in cancer cells, targeted therapies have to deal with side effects and multidrug resistance, the main causes of therapy failure. Since the pioneering work of Otto Warburg, over 80 years ago, the subversion of normal metabolism displayed by cancer cells has been highlighted by many studies. Recently, the study of tumor metabolism has received much attention because metabolic transformation is a crucial cancer hallmark and a direct consequence of disturbances in the activities of oncogenes and tumor suppressors. in this review we discuss tumor metabolism from the molecular perspective of oncogenes, tumor suppressors and protein signaling pathways relevant to metabolic transformation and tumorigenesis. We also identify the principal unanswered questions surrounding this issue and the attempts to relate these to their potential for future cancer treatment. As will be made clear, tumor metabolism is still only partly understood and the metabolic aspects of transformation constitute a major challenge for science. Nevertheless, cancer metabolism can be exploited to devise novel avenues for the rational treatment of this disease. Copyright (C) 2011 S. Karger AG, BaselFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Univ Fed ABC UFABC, CCNH, Santo Andre, BrazilUniversidade Federal de São Paulo UNIFESP, Dept Ciencias Biol, São Paulo, BrazilUniversidade Federal de São Paulo UNIFESP, Dept Bioquim, São Paulo, BrazilUniv Fed Sao Carlos UFSCar, DFQM, Sorocaba, BrazilUniversidade Federal de São Paulo UNIFESP, Dept Ciencias Biol, São Paulo, BrazilUniversidade Federal de São Paulo UNIFESP, Dept Bioquim, São Paulo, BrazilFAPESP: 10/16050-9FAPESP: 10/11475-1FAPESP: 08/51116-0Web of Scienc

In silico Experimentation of Glioma Microenvironment Development and Anti-tumor Therapy

Tumor cells do not develop in isolation, but co-evolve with stromal cells and tumor-associated immune cells in a tumor microenvironment mediated by an array of soluble factors, forming a complex intercellular signaling network. Herein, we report an unbiased, generic model to integrate prior biochemical data and the constructed brain tumor microenvironment in silico as characterized by an intercellular signaling network comprising 5 types of cells, 15 cytokines, and 69 signaling pathways. The results show that glioma develops through three distinct phases: pre-tumor, rapid expansion, and saturation. We designed a microglia depletion therapy and observed significant benefit for virtual patients treated at the early stages but strikingly no therapeutic efficacy at all when therapy was given at a slightly later stage. Cytokine combination therapy exhibits more focused and enhanced therapeutic response even when microglia depletion therapy already fails. It was further revealed that the optimal combination depends on the molecular profile of individual patients, suggesting the need for patient stratification and personalized treatment. These results, obtained solely by observing the in silico dynamics of the glioma microenvironment with no fitting to experimental/clinical data, reflect many characteristics of human glioma development and imply new venues for treating tumors via selective targeting of microenvironmental components