Effects of triggers of senescence and senolysis in murine pancreatic cancer cells.

BACKGROUND: The combination of senescence triggers with senolytic drugs is considered a promising new approach to cancer therapy. Here, we studied the efficacy of the genotoxic agent etoposide (Eto) and irradiation in inducing senescence of Panc02 pancreatic cancer cells, and the capability of the Bcl-2 inhibitor navitoclax (ABT-263; Nav) to trigger senolysis. METHODS: Panc02 cells were treated with Eto or irradiated with 5-20 Gy before exposure to Nav. Cell survival, proliferation, and senescence were assessed by trypan blue staining, quantification of DNA synthesis, and staining of senescence-associated ß-galactosidase (SA-ß-Gal)-positive cells, respectively. Levels of mRNA were determined by real-time polymerase chain reaction, and protein expression was analyzed by immunoblotting. Panc02 cells were also grown as pancreatic tumors in mice, which were subsequently treated with Eto and Nav. RESULTS: Eto and irradiation had an antiproliferative effect on Panc02 cells that was significantly or tendentially enhanced by Nav. In vivo, Eto and Nav together, but not Eto alone, significantly reduced the proportion of proliferating cells. The expression of the senescence marker γH2AX and tumor infiltration with T-cells were not affected by the treatment. In vitro, almost all Eto-exposed cells and a significant proportion of cells irradiated with 20 Gy were SA-ß-Gal-positive. Application of Nav reduced the percentage of SA-ß-Gal-positive cells after irradiation but not after pretreatment with Eto. In response to triggers of senescence, cultured Panc02 cells showed increased protein levels of γH2AX and the autophagy marker LC3B-II, and higher mRNA levels of Cdkn1a, Mdm2, and PAI-1, while the effects of Nav were variable. CONCLUSIONS: In vitro and in vivo, the combination of senescence triggers with Nav inhibited tumor cell growth more effectively than the triggers alone. Our data also provide some evidence for senolytic effects of Nav in vitro.

Logic-based modeling and drug repurposing for the prediction of novel therapeutic targets and combination regimens against E2F1-driven melanoma progression.

Melanoma presents increasing prevalence and poor outcomes. Progression to aggressive stages is characterized by overexpression of the transcription factor E2F1 and activation of downstream prometastatic gene regulatory networks (GRNs). Appropriate therapeutic manipulation of the E2F1-governed GRNs holds the potential to prevent metastasis however, these networks entail complex feedback and feedforward regulatory motifs among various regulatory layers, which make it difficult to identify druggable components. To this end, computational approaches such as mathematical modeling and virtual screening are important tools to unveil the dynamics of these signaling networks and identify critical components that could be further explored as therapeutic targets. Herein, we integrated a well-established E2F1-mediated epithelial-mesenchymal transition (EMT) map with transcriptomics data from E2F1-expressing melanoma cells to reconstruct a core regulatory network underlying aggressive melanoma. Using logic-based in silico perturbation experiments of a core regulatory network, we identified that simultaneous perturbation of Protein kinase B (AKT1) and oncoprotein murine double minute 2 (MDM2) drastically reduces EMT in melanoma. Using the structures of the two protein signatures, virtual screening strategies were performed with the FDA-approved drug library. Furthermore, by combining drug repurposing and computer-aided drug design techniques, followed by molecular dynamics simulation analysis, we identified two potent drugs (Tadalafil and Finasteride) that can efficiently inhibit AKT1 and MDM2 proteins. We propose that these two drugs could be considered for the development of therapeutic strategies for the management of aggressive melanoma.

In Silico Analysis of MicroRNA Expression Data in Liver Cancer.

Abnormal miRNA expression has been evidenced to be directly linked to HCC initiation and progression. This study was designed to detect possible prognostic, diagnostic, and/or therapeutic miRNAs for HCC using computational analysis of miRNAs expression. Methods: miRNA expression datasets meta-analysis was performed using the YM500v2 server to compare miRNA expression in normal and cancerous liver tissues. The most significant differentially regulated miRNAs in our study undergone target gene analysis using the mirWalk tool to obtain their validated and predicted targets. The combinatorial target prediction tool; miRror Suite was used to obtain the commonly regulated target genes. Functional enrichment analysis was performed on the resulting targets using the DAVID tool. A network was constructed based on interactions among microRNAs, their targets, and transcription factors. Hub nodes and gatekeepers were identified using network topological analysis. Further, we performed patient data survival analysis based on low and high expression of identified hubs and gatekeeper nodes, patients were stratified into low and high survival probability groups. Results: Using the meta-analysis option in the YM500v2 server, 34 miRNAs were found to be significantly differentially regulated (P-value ⩽ .05); 5 miRNAs were down-regulated while 29 were up-regulated. The validated and predicted target genes for each miRNA, as well as the combinatorially predicted targets, were obtained. DAVID enrichment analysis resulted in several important cellular functions that are directly related to the main cancer hallmarks. Among these functions are focal adhesion, cell cycle, PI3K-Akt signaling, insulin signaling, Ras and MAPK signaling pathways. Several hub genes and gatekeepers were found that could serve as potential drug targets for hepatocellular carcinoma. POU2F1 and PPARA showed a significant difference between low and high survival probabilities (P-value ⩽ .05) in HCC patients. Our study sheds light on important biomarker miRNAs for hepatocellular carcinoma along with their target genes and their regulated functions.

A Metabolic Model of Intestinal Secretions: The Link between Human Microbiota and Colorectal Cancer Progression.

The human gut microbiota plays a dual key role in maintaining human health or inducing disorders, for example, obesity, type 2 diabetes, and cancers such as colorectal cancer (CRC). High-throughput data analysis, such as metagenomics and metabolomics, have shown the diverse effects of alterations in dynamic bacterial populations on the initiation and progression of colorectal cancer. However, it is well established that microbiome and human cells constantly influence each other, so it is not appropriate to study them independently. Genome-scale metabolic modeling is a well-established mathematical framework that describes the dynamic behavior of these two axes at the system level. In this study, we created community microbiome models of three conditions during colorectal cancer progression, including carcinoma, adenoma and health status, and showed how changes in the microbial population influence intestinal secretions. Conclusively, our findings showed that alterations in the gut microbiome might provoke mutations and transform adenomas into carcinomas. These alterations include the secretion of mutagenic metabolites such as H2S, NO compounds, spermidine and TMA (trimethylamine), as well as the reduction of butyrate. Furthermore, we found that the colorectal cancer microbiome can promote inflammation, cancer progression (e.g., angiogenesis) and cancer prevention (e.g., apoptosis) by increasing and decreasing certain metabolites such as histamine, glutamine and pyruvate. Thus, modulating the gut microbiome could be a promising strategy for the prevention and treatment of CRC.

WITHDRAWN: The Atlas of Inflammation Resolution (AIR).

The Publisher regrets that this article is an accidental duplication of an article that has already been published, https://doi.org/10.1016/j.mam.2020.100894. The duplicate article has therefore been withdrawn. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/our-business/policies/article-withdrawal.

The Atlas of Inflammation Resolution (AIR).

Acute inflammation is a protective reaction by the immune system in response to invading pathogens or tissue damage. Ideally, the response should be localized, self-limited, and returning to homeostasis. If not resolved, acute inflammation can result in organ pathologies leading to chronic inflammatory phenotypes. Acute inflammation and inflammation resolution are complex coordinated processes, involving a number of cell types, interacting in space and time. The biomolecular complexity and the fact that several biomedical fields are involved, make a multi- and interdisciplinary approach necessary. The Atlas of Inflammation Resolution (AIR) is a web-based resource capturing an essential part of the state-of-the-art in acute inflammation and inflammation resolution research. The AIR provides an interface for users to search thousands of interactions, arranged in inter-connected multi-layers of process diagrams, covering a wide range of clinically relevant phenotypes. By mapping experimental data onto the AIR, it can be used to elucidate drug action as well as molecular mechanisms underlying different disease phenotypes. For the visualization and exploration of information, the AIR uses the Minerva platform, which is a well-established tool for the presentation of disease maps. The molecular details of the AIR are encoded using international standards. The AIR was created as a freely accessible resource, supporting research and education in the fields of acute inflammation and inflammation resolution. The AIR connects research communities, facilitates clinical decision making, and supports research scientists in the formulation and validation of hypotheses. The AIR is accessible through https://air.bio.informatik.uni-rostock.de.

Integrative workflows for network analysis.

Due to genetic heterogeneity across patients, the identification of effective disease signatures and therapeutic targets is challenging. Addressing this challenge, we have previously developed a network-based approach, which integrates heterogeneous sources of biological information to identify disease specific core-regulatory networks. In particular, our workflow uses a multi-objective optimization function to calculate a ranking score for network components (e.g. feedback/feedforward loops) based on network properties, biomedical and high-throughput expression data. High ranked network components are merged to identify the core-regulatory network(s) that is then subjected to dynamical analysis using stimulus-response and in silico perturbation experiments for the identification of disease gene signatures and therapeutic targets. In a case study, we implemented our workflow to identify bladder and breast cancer specific core-regulatory networks underlying epithelial-mesenchymal transition from the E2F1 molecular interaction map.In this study, we review our workflow and described how it has developed over time to understand the mechanisms underlying disease progression and prediction of signatures for clinical decision making.

A web platform for the network analysis of high-throughput data in melanoma and its use to investigate mechanisms of resistance to anti-PD1 immunotherapy.

Cellular phenotypes are established and controlled by complex and precisely orchestrated molecular networks. In cancer, mutations and dysregulations of multiple molecular factors perturb the regulation of these networks and lead to malignant transformation. High-throughput technologies are a valuable source of information to establish the complex molecular relationships behind the emergence of malignancy, but full exploitation of this massive amount of data requires bioinformatics tools that rely on network-based analyses. In this report we present the Virtual Melanoma Cell, an online tool developed to facilitate the mining and interpretation of high-throughput data on melanoma by biomedical researches. The platform is based on a comprehensive, manually generated and expert-validated regulatory map composed of signaling pathways important in malignant melanoma. The Virtual Melanoma Cell is a tool designed to accept, visualize and analyze user-generated datasets. It is available at: https://www.vcells.net/melanoma. To illustrate the utilization of the web platform and the regulatory map, we have analyzed a large publicly available dataset accounting for anti-PD1 immunotherapy treatment of malignant melanoma patients.

A Network-Based Integrative Workflow to Unravel Mechanisms Underlying Disease Progression.

Unraveling mechanisms underlying diseases has motivated the development of systems biology approaches. The key challenges for the development of mathematical models and computational tool are (1) the size of molecular networks, (2) the nonlinear nature of spatio-temporal interactions, and (3) feedback loops in the structure of interaction networks. We here propose an integrative workflow that combines structural analyses of networks, high-throughput data, and mechanistic modeling. As an illustration of the workflow, we use prostate cancer as a case study with the aim of identifying key functional components associated with primary to metastasis transitions. Analysis carried out by the workflow revealed that HOXD10, BCL2, and PGR are the most important factors affected in primary prostate samples, whereas, in the metastatic state, STAT3, JUN, and JUNB are playing a central role. The identified key elements of each network are validated using patient survival analysis. The workflow presented here allows experimentalists to use heterogeneous data sources for the identification of diagnostic and prognostic signatures.

Unraveling a tumor type-specific regulatory core underlying E2F1-mediated epithelial-mesenchymal transition to predict receptor protein signatures.

Cancer is a disease of subverted regulatory pathways. In this paper, we reconstruct the regulatory network around E2F, a family of transcription factors whose deregulation has been associated to cancer progression, chemoresistance, invasiveness, and metastasis. We integrate gene expression profiles of cancer cell lines from two E2F1-driven highly aggressive bladder and breast tumors, and use network analysis methods to identify the tumor type-specific core of the network. By combining logic-based network modeling, in vitro experimentation, and gene expression profiles from patient cohorts displaying tumor aggressiveness, we identify and experimentally validate distinctive, tumor type-specific signatures of receptor proteins associated to epithelial-mesenchymal transition in bladder and breast cancer. Our integrative network-based methodology, exemplified in the case of E2F1-induced aggressive tumors, has the potential to support the design of cohort- as well as tumor type-specific treatments and ultimately, to fight metastasis and therapy resistance.Deregulation of E2F family transcription factors is associated with cancer progression and metastasis. Here, the authors construct a map of the regulatory network around the E2F family, and using gene expression profiles, identify tumour type-specific regulatory cores and receptor expression signatures associated with epithelial-mesenchymal transition in bladder and breast cancer.

Monitor unit calculations for external photon and electron beams: Report of the AAPM Therapy Physics Committee Task Group No. 71.

A protocol is presented for the calculation of monitor units (MU) for photon and electron beams, delivered with and without beam modifiers, for constant source-surface distance (SSD) and source-axis distance (SAD) setups. This protocol was written by Task Group 71 of the Therapy Physics Committee of the American Association of Physicists in Medicine (AAPM) and has been formally approved by the AAPM for clinical use. The protocol defines the nomenclature for the dosimetric quantities used in these calculations, along with instructions for their determination and measurement. Calculations are made using the dose per MU under normalization conditions, D'0, that is determined for each user's photon and electron beams. For electron beams, the depth of normalization is taken to be the depth of maximum dose along the central axis for the same field incident on a water phantom at the same SSD, where D'0 = 1 cGy/MU. For photon beams, this task group recommends that a normalization depth of 10 cm be selected, where an energy-dependent D'0 ≤ 1 cGy/MU is required. This recommendation differs from the more common approach of a normalization depth of dm, with D'0 = 1 cGy/MU, although both systems are acceptable within the current protocol. For photon beams, the formalism includes the use of blocked fields, physical or dynamic wedges, and (static) multileaf collimation. No formalism is provided for intensity modulated radiation therapy calculations, although some general considerations and a review of current calculation techniques are included. For electron beams, the formalism provides for calculations at the standard and extended SSDs using either an effective SSD or an air-gap correction factor. Example tables and problems are included to illustrate the basic concepts within the presented formalism.

Hybrid modeling of the crosstalk between signaling and transcriptional networks using ordinary differential equations and multi-valued logic.

A decade of successful results indicates that systems biology is the appropriate approach to investigate the regulation of complex biochemical networks involving transcriptional and post-transcriptional regulations. It becomes mandatory when dealing with highly interconnected biochemical networks, composed of hundreds of compounds, or when networks are enriched in non-linear motifs like feedback and feedforward loops. An emerging dilemma is to conciliate models of massive networks and the adequate description of non-linear dynamics in a suitable modeling framework. Boolean networks are an ideal representation of massive networks that are humble in terms of computational complexity and data demand. However, they are inappropriate when dealing with nested feedback/feedforward loops, structural motifs common in biochemical networks. On the other hand, models of ordinary differential equations (ODEs) cope well with these loops, but they require enormous amounts of quantitative data for a full characterization of the model. Here we propose hybrid models, composed of ODE and logical sub-modules, as a strategy to handle large scale, non-linear biochemical networks that include transcriptional and post-transcriptional regulations. We illustrate the construction of this kind of models using as example a regulatory network centered on E2F1, a transcription factor involved in cancer. The hybrid modeling approach proposed is a good compromise between quantitative/qualitative accuracy and scalability when considering large biochemical networks with a small highly interconnected core, and module of transcriptionally regulated genes that are not part of critical regulatory loops. This article is part of a Special Issue entitled: Computational Proteomics, Systems Biology & Clinical Implications. Guest Editor: Yudong Cai.

Kinetic modeling-based detection of genetic signatures that provide chemoresistance via the E2F1-p73/DNp73-miR-205 network.

Drug resistance is a major cause of deaths from cancer. E2F1 is a transcription factor involved in cell proliferation, apoptosis. and metastasis through an intricate regulatory network, which includes other transcription factors like p73 and cancer-related microRNAs like miR-205. To investigate the emergence of drug resistance, we developed a methodology that integrates experimental data with a network biology and kinetic modeling. Using a regulatory map developed to summarize knowledge on E2F1 and its interplay with p73/DNp73 and miR-205 in cancer drug responses, we derived a kinetic model that represents the network response to certain genotoxic and cytostatic anticancer drugs. By perturbing the model parameters, we simulated heterogeneous cell configurations referred to as in silico cell lines. These were used to detect genetic signatures characteristic for single or double drug resistance. We identified a signature composed of high E2F1 and low miR-205 expression that promotes resistance to genotoxic drugs. In this signature, downregulation of miR-205, can be mediated by an imbalance in the p73/DNp73 ratio or by dysregulation of other cancer-related regulators of miR-205 expression such as TGFß-1 or TWIST1. In addition, we found that a genetic signature composed of high E2F1, low miR-205, and high ERBB3 can render tumor cells insensitive to both cytostatic and genotoxic drugs. Our model simulations also suggested that conventional genotoxic drug treatment favors selection of chemoresistant cells in genetically heterogeneous tumors, in a manner requiring dysregulation of incoherent feedforward loops that involve E2F1, p73/DNp73, and miR-205.

Recommendations for clinical electron beam dosimetry: supplement to the recommendations of Task Group 25.

The goal of Task Group 25 (TG-25) of the Radiation Therapy Committee of the American Association of.Physicists in Medicine (AAPM) was to provide a methodology and set of procedures for a medical physicist performing clinical electron beam dosimetry in the nominal energy range of 5-25 MeV. Specifically, the task group recommended procedures for acquiring basic information required for acceptance testing and treatment planning of new accelerators with therapeutic electron beams. Since the publication of the TG-25 report, significant advances have taken place in the field of electron beam dosimetry, the most significant being that primary standards laboratories around the world have shifted from calibration standards based on exposure or air kerma to standards based on absorbed dose to water. The AAPM has published a new calibration protocol, TG-51, for the calibration of high-energy photon and electron beams. The formalism and dosimetry procedures recommended in this protocol are based on the absorbed dose to water calibration coefficient of an ionization chamber at 60Co energy, N60Co(D,w), together with the theoretical beam quality conversion coefficient k(Q) for the determination of absorbed dose to water in high-energy photon and electron beams. Task Group 70 was charged to reassess and update the recommendations in TG-25 to bring them into alignment with report TG-51 and to recommend new methodologies and procedures that would allow the practicing medical physicist to initiate and continue a high quality program in clinical electron beam dosimetry. This TG-70 report is a supplement to the TG-25 report and enhances the TG-25 report by including new topics and topics that were not covered in depth in the TG-25 report. These topics include procedures for obtaining data to commission a treatment planning computer, determining dose in irregularly shaped electron fields, and commissioning of sophisticated special procedures using high-energy electron beams. The use of radiochromic film for electrons is addressed, and radiographic film that is no longer available has been replaced by film that is available. Realistic stopping-power data are incorporated when appropriate along with enhanced tables of electron fluence data. A larger list of clinical applications of electron beams is included in the full TG-70 report available at http://www.aapm.org/pubs/reports. Descriptions of the techniques in the clinical sections are not exhaustive but do describe key elements of the procedures and how to initiate these programs in the clinic. There have been no major changes since the TG-25 report relating to flatness and symmetry, surface dose, use of thermoluminescent dosimeters or diodes, virtual source position designation, air gap corrections, oblique incidence, or corrections for inhomogeneities. Thus these topics are not addressed in the TG-70 report.

Application of measured pencil beam parameters for electron beam model evaluation.

Most current electron beam models, as are used in commercial treatment planning systems, combine measured broad beam central axis depth dose data with measured or modeled functions to approximate radial scatter and heterogeneity effects. In this paper, we extend a recently developed pencil beam model to calculate doses outside the field edge and doses in heterogeneous media. We have also explored use of this model as a tool for evaluating commercial electron planning programs. The algorithm we have developed, based on the concept of the lateral buildup ratio (LBR), enables calculation of dose at any point in an irregular electron field, and is capable of generating both on- and off-axis depth dose curves and isodose profiles. This model includes the effects of density and mass-angular scattering power in measured broad beam central axis depth dose data, which when combined with small field reference data, can be used to generate LBR ratios. From these ratios one can infer the depth dependent, effective pencil beam radial spread parameter a in water or other materials, which can be used to model any arbitrary field. We have used this approach to calculate fractional depth doses for small fields incident on aluminum and cork, which we have then compared against measurements and the calculations of several commercial planning systems.