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Cancers
Special Issues
Cancer Modeling and Network Biology
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Cancer Modeling and Network Biology

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Informatics and Big Data".

Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 52101

Special Issue Editors

Dr. Andrei Zinovyev

E-Mail Website
Guest Editor
1. Institut Curie, PSL Research University, 75248 Paris, France
2. INSERM, U900, 75248 Paris, France
3. CBIO-Centre for Computational Biology, Mines ParisTech, PSL Research University, 75272 Paris, France
4. Laboratory of Advanced Methods for High-Dimensional Data Analysis, Lobachevsky University, 603105 Nizhniy Novgorod, Russia
Interests: machine learning; mathematical modeling in biology; single cell data analysis; cancer systems biology
Dr. Laurence Calzone

E-Mail Website
Guest Editor
Institut Curie, PSL Research University, 75005 Paris, France
Interests: mathematical modeling in biology; boolean network models; biological networks; cancer systems biology
Dr. Inna Kuperstein

E-Mail Website
Guest Editor
Institut Curie, PSL Research University, 75005 Paris, France
Interests: knowledge formalization in biology; disease maps; biological pathway databases; cancer systems biology

Special Issue Information

Dear Colleagues,

Cancer progression is believed to be driven by abnormal dynamical behavior of signaling and metabolic pathways, transcriptional, or intercellular communication networks. Our knowledge about the structure and the complexity of molecular networks is rapidly growing, allowing the creation of larger-scale mathematical models of cancer-related processes. Modeling cancer networks and using them in multi-omics data analysis can drastically improve our understanding of inter-patient and intra-tumoral heterogeneity at the genetic and epigenetic levels and contribute to prognosis and individualized treatment suggestions. Current challenges in achieving this goal include constructing tissue-specific and high confidence network maps, automating mathematical model creation, choosing the appropriate level of abstraction andand integrating into modeling increasing amounts of omics data related to the cancer biology field, including multi-omics single-cell profiles.

This Special Issue focuses on the latest advances in modeling cancer-related networks and molecular mechanisms, including formalizing the knowledge of molecular networks involved in cancer progression, creating mechanistic and statistical models, and performing network-based analyses of cancer data.

Dr. Andrei Zinovyev
Dr. Laurence Calzone
Dr. Inna Kuperstein
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cancers is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • comprehensive maps of cancer networks
  • dynamical modeling of cancer mechanisms
  • multi-scale models in cancer
  • single-cell models in cancer
  • network-based cancer data analysis
  • networks and single-cell omics profiles
  • multilayer networks of cancer
  • molecular interaction and pathway databases in cancer
  • machine learning and network biology in studying cancer

Published Papers (12 papers)

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Research

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25 pages, 2172 KiB  
Article
Network-Based Analysis to Identify Drivers of Metastatic Prostate Cancer Using GoNetic
by Louise de Schaetzen van Brienen, Giles Miclotte, Maarten Larmuseau, Jimmy Van den Eynden and Kathleen Marchal
Cancers 2021, 13(21), 5291; https://doi.org/10.3390/cancers13215291 - 21 Oct 2021
Cited by 2 | Viewed by 2054
Abstract
Most known driver genes of metastatic prostate cancer are frequently mutated. To dig into the long tail of rarely mutated drivers, we performed network-based driver identification on the Hartwig Medical Foundation metastatic prostate cancer data set (HMF cohort). Hereto, we developed GoNetic, a [...] Read more.
Most known driver genes of metastatic prostate cancer are frequently mutated. To dig into the long tail of rarely mutated drivers, we performed network-based driver identification on the Hartwig Medical Foundation metastatic prostate cancer data set (HMF cohort). Hereto, we developed GoNetic, a method based on probabilistic pathfinding, to identify recurrently mutated subnetworks. In contrast to most state-of-the-art network-based methods, GoNetic can leverage sample-specific mutational information and the weights of the underlying prior network. When applied to the HMF cohort, GoNetic successfully recovered known primary and metastatic drivers of prostate cancer that are frequently mutated in the HMF cohort (TP53, RB1, and CTNNB1). In addition, the identified subnetworks contain frequently mutated genes, reflect processes related to metastatic prostate cancer, and contain rarely mutated driver candidates. To further validate these rarely mutated genes, we assessed whether the identified genes were more mutated in metastatic than in primary samples using an independent cohort. Then we evaluated their association with tumor evolution and with the lymph node status of the patients. This resulted in forwarding several novel putative driver genes for metastatic prostate cancer, some of which might be prognostic for disease evolution. Full article
(This article belongs to the Special Issue Cancer Modeling and Network Biology)
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27 pages, 928 KiB  
Article
A Topic Modeling Analysis of TCGA Breast and Lung Cancer Transcriptomic Data
by Filippo Valle, Matteo Osella and Michele Caselle
Cancers 2020, 12(12), 3799; https://doi.org/10.3390/cancers12123799 - 16 Dec 2020
Cited by 9 | Viewed by 4348
Abstract
Topic modeling is a widely used technique to extract relevant information from large arrays of data. The problem of finding a topic structure in a dataset was recently recognized to be analogous to the community detection problem in network theory. Leveraging on this [...] Read more.
Topic modeling is a widely used technique to extract relevant information from large arrays of data. The problem of finding a topic structure in a dataset was recently recognized to be analogous to the community detection problem in network theory. Leveraging on this analogy, a new class of topic modeling strategies has been introduced to overcome some of the limitations of classical methods. This paper applies these recent ideas to TCGA transcriptomic data on breast and lung cancer. The established cancer subtype organization is well reconstructed in the inferred latent topic structure. Moreover, we identify specific topics that are enriched in genes known to play a role in the corresponding disease and are strongly related to the survival probability of patients. Finally, we show that a simple neural network classifier operating in the low dimensional topic space is able to predict with high accuracy the cancer subtype of a test expression sample. Full article
(This article belongs to the Special Issue Cancer Modeling and Network Biology)
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23 pages, 1163 KiB  
Article
Insights on TAM Formation from a Boolean Model of Macrophage Polarization Based on In Vitro Studies
by Malvina Marku, Nina Verstraete, Flavien Raynal, Miguel Madrid-Mencía, Marcin Domagala, Jean-Jacques Fournié, Loïc Ysebaert, Mary Poupot and Vera Pancaldi
Cancers 2020, 12(12), 3664; https://doi.org/10.3390/cancers12123664 - 7 Dec 2020
Cited by 10 | Viewed by 4153
Abstract
The tumour microenvironment is the surrounding of a tumour, including blood vessels, fibroblasts, signaling molecules, the extracellular matrix and immune cells, especially neutrophils and monocyte-derived macrophages. In a tumour setting, macrophages encompass a spectrum between a tumour-suppressive (M1) or tumour-promoting (M2) state. The [...] Read more.
The tumour microenvironment is the surrounding of a tumour, including blood vessels, fibroblasts, signaling molecules, the extracellular matrix and immune cells, especially neutrophils and monocyte-derived macrophages. In a tumour setting, macrophages encompass a spectrum between a tumour-suppressive (M1) or tumour-promoting (M2) state. The biology of macrophages found in tumours (Tumour Associated Macrophages) remains unclear, but understanding their impact on tumour progression is highly important. In this paper, we perform a comprehensive analysis of a macrophage polarization network, following two lines of enquiry: (i) we reconstruct the macrophage polarization network based on literature, extending it to include important stimuli in a tumour setting, and (ii) we build a dynamical model able to reproduce macrophage polarization in the presence of different stimuli, including the contact with cancer cells. Our simulations recapitulate the documented macrophage phenotypes and their dependencies on specific receptors and transcription factors, while also unravelling the formation of a special type of tumour associated macrophages in an in vitro model of chronic lymphocytic leukaemia. This model constitutes the first step towards elucidating the cross-talk between immune and cancer cells inside tumours, with the ultimate goal of identifying new therapeutic targets that could control the formation of tumour associated macrophages in patients. Full article
(This article belongs to the Special Issue Cancer Modeling and Network Biology)
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19 pages, 1138 KiB  
Article
Modelling of Immune Checkpoint Network Explains Synergistic Effects of Combined Immune Checkpoint Inhibitor Therapy and the Impact of Cytokines in Patient Response
by Maria Kondratova, Emmanuel Barillot, Andrei Zinovyev and Laurence Calzone
Cancers 2020, 12(12), 3600; https://doi.org/10.3390/cancers12123600 - 2 Dec 2020
Cited by 8 | Viewed by 3513
Abstract
After the success of the new generation of immune therapies, immune checkpoint receptors have become one important center of attention of molecular oncologists. The initial success and hopes of anti-programmed cell death protein 1 (anti-PD1) and anti-cytotoxic T-lymphocyte-associated protein 4 (anti-CTLA4) therapies have [...] Read more.
After the success of the new generation of immune therapies, immune checkpoint receptors have become one important center of attention of molecular oncologists. The initial success and hopes of anti-programmed cell death protein 1 (anti-PD1) and anti-cytotoxic T-lymphocyte-associated protein 4 (anti-CTLA4) therapies have shown some limitations since a majority of patients have continued to show resistance. Other immune checkpoints have raised some interest and are under investigation, such as T cell immunoglobulin and ITIM (immunoreceptor tyrosine-based inhibition motif) domain (TIGIT), inducible T-cell costimulator (ICOS), and T cell immunoglobulin and mucin domain-containing protein 3 (TIM3), which appear as promising targets for immunotherapy. To explore their role and study possible synergetic effects of these different checkpoints, we have built a model of T cell receptor (TCR) regulation including not only PD1 and CTLA4, but also other well studied checkpoints (TIGIT, TIM3, lymphocyte activation gene 3 (LAG3), cluster of differentiation 226 (CD226), ICOS, and tumour necrosis factor receptors (TNFRs)) and simulated different aspects of T cell biology. Our model shows good correspondence with observations from available experimental studies of anti-PD1 and anti-CTLA4 therapies and suggest efficient combinations of immune checkpoint inhibitors (ICI). Among the possible candidates, TIGIT appears to be the most promising drug target in our model. The model predicts that signal transducer and activator of transcription 1 (STAT1)/STAT4-dependent pathways, activated by cytokines such as interleukin 12 (IL12) and interferon gamma (IFNG), could improve the effect of ICI therapy via upregulation of Tbet, suggesting that the effect of the cytokines related to STAT3/STAT1 activity is dependent on the balance between STAT1 and STAT3 downstream signalling. Full article
(This article belongs to the Special Issue Cancer Modeling and Network Biology)
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13 pages, 2625 KiB  
Article
A Computational Framework for Prediction and Analysis of Cancer Signaling Dynamics from RNA Sequencing Data—Application to the ErbB Receptor Signaling Pathway
by Hiroaki Imoto, Suxiang Zhang and Mariko Okada
Cancers 2020, 12(10), 2878; https://doi.org/10.3390/cancers12102878 - 7 Oct 2020
Cited by 14 | Viewed by 5944
Abstract
A current challenge in systems biology is to predict dynamic properties of cell behaviors from public information such as gene expression data. The temporal dynamics of signaling molecules is critical for mammalian cell commitment. We hypothesized that gene expression levels are tightly linked [...] Read more.
A current challenge in systems biology is to predict dynamic properties of cell behaviors from public information such as gene expression data. The temporal dynamics of signaling molecules is critical for mammalian cell commitment. We hypothesized that gene expression levels are tightly linked with and quantitatively control the dynamics of signaling networks regardless of the cell type. Based on this idea, we developed a computational method to predict the signaling dynamics from RNA sequencing (RNA-seq) gene expression data. We first constructed an ordinary differential equation model of ErbB receptor → c-Fos induction using a newly developed modeling platform BioMASS. The model was trained with kinetic parameters against multiple breast cancer cell lines using autologous RNA-seq data obtained from the Cancer Cell Line Encyclopedia (CCLE) as the initial values of the model components. After parameter optimization, the model proceeded to prediction in another untrained breast cancer cell line. As a result, the model learned the parameters from other cells and was able to accurately predict the dynamics of the untrained cells using only the gene expression data. Our study suggests that gene expression levels of components within the ErbB network, rather than rate constants, can explain the cell-specific signaling dynamics, therefore playing an important role in regulating cell fate. Full article
(This article belongs to the Special Issue Cancer Modeling and Network Biology)
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34 pages, 2569 KiB  
Article
Functional Transcription Factor Target Networks Illuminate Control of Epithelial Remodelling
by Ian M. Overton, Andrew H. Sims, Jeremy A. Owen, Bret S. E. Heale, Matthew J. Ford, Alexander L. R. Lubbock, Erola Pairo-Castineira and Abdelkader Essafi
Cancers 2020, 12(10), 2823; https://doi.org/10.3390/cancers12102823 - 30 Sep 2020
Cited by 3 | Viewed by 4129
Abstract
Cell identity is governed by gene expression, regulated by transcription factor (TF) binding at cis-regulatory modules. Decoding the relationship between TF binding patterns and gene regulation is nontrivial, remaining a fundamental limitation in understanding cell decision-making. We developed the NetNC software to predict [...] Read more.
Cell identity is governed by gene expression, regulated by transcription factor (TF) binding at cis-regulatory modules. Decoding the relationship between TF binding patterns and gene regulation is nontrivial, remaining a fundamental limitation in understanding cell decision-making. We developed the NetNC software to predict functionally active regulation of TF targets; demonstrated on nine datasets for the TFs Snail, Twist, and modENCODE Highly Occupied Target (HOT) regions. Snail and Twist are canonical drivers of epithelial to mesenchymal transition (EMT), a cell programme important in development, tumour progression and fibrosis. Predicted “neutral” (non-functional) TF binding always accounted for the majority (50% to 95%) of candidate target genes from statistically significant peaks and HOT regions had higher functional binding than most of the Snail and Twist datasets examined. Our results illuminated conserved gene networks that control epithelial plasticity in development and disease. We identified new gene functions and network modules including crosstalk with notch signalling and regulation of chromatin organisation, evidencing networks that reshape Waddington’s epigenetic landscape during epithelial remodelling. Expression of orthologous functional TF targets discriminated breast cancer molecular subtypes and predicted novel tumour biology, with implications for precision medicine. Predicted invasion roles were validated using a tractable cell model, supporting our approach. Full article
(This article belongs to the Special Issue Cancer Modeling and Network Biology)
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26 pages, 4272 KiB  
Article
Comprehensive Map of the Regulated Cell Death Signaling Network: A Powerful Analytical Tool for Studying Diseases
by Jean-Marie Ravel, L. Cristobal Monraz Gomez, Nicolas Sompairac, Laurence Calzone, Boris Zhivotovsky, Guido Kroemer, Emmanuel Barillot, Andrei Zinovyev and Inna Kuperstein
Cancers 2020, 12(4), 990; https://doi.org/10.3390/cancers12040990 - 17 Apr 2020
Cited by 4 | Viewed by 4930
Abstract
The processes leading to, or avoiding cell death are widely studied, because of their frequent perturbation in various diseases. Cell death occurs in three highly interconnected steps: Initiation, signaling and execution. We used a systems biology approach to gather information about all known [...] Read more.
The processes leading to, or avoiding cell death are widely studied, because of their frequent perturbation in various diseases. Cell death occurs in three highly interconnected steps: Initiation, signaling and execution. We used a systems biology approach to gather information about all known modes of regulated cell death (RCD). Based on the experimental data retrieved from literature by manual curation, we graphically depicted the biological processes involved in RCD in the form of a seamless comprehensive signaling network map. The molecular mechanisms of each RCD mode are represented in detail. The RCD network map is divided into 26 functional modules that can be visualized contextually in the whole seamless network, as well as in individual diagrams. The resource is freely available and accessible via several web platforms for map navigation, data integration, and analysis. The RCD network map was employed for interpreting the functional differences in cell death regulation between Alzheimer’s disease and non-small cell lung cancer based on gene expression data that allowed emphasizing the molecular mechanisms underlying the inverse comorbidity between the two pathologies. In addition, the map was used for the analysis of genomic and transcriptomic data from ovarian cancer patients that provided RCD map-based signatures of four distinct tumor subtypes and highlighted the difference in regulations of cell death molecular mechanisms. Full article
(This article belongs to the Special Issue Cancer Modeling and Network Biology)
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22 pages, 2757 KiB  
Article
Biphasic Mathematical Model of Cell–Drug Interaction That Separates Target-Specific and Off-Target Inhibition and Suggests Potent Targeted Drug Combinations for Multi-Driver Colorectal Cancer Cells
by Jinyan Shen, Li Li, Tao Yang, Paul S. Cohen and Gongqin Sun
Cancers 2020, 12(2), 436; https://doi.org/10.3390/cancers12020436 - 13 Feb 2020
Cited by 15 | Viewed by 3176
Abstract
Quantifying the response of cancer cells to a drug, and understanding the mechanistic basis of the response, are the cornerstones for anti-cancer drug discovery. Classical single target-based IC50 measurements are inadequate at describing cancer cell responses to targeted drugs. In this study, [...] Read more.
Quantifying the response of cancer cells to a drug, and understanding the mechanistic basis of the response, are the cornerstones for anti-cancer drug discovery. Classical single target-based IC50 measurements are inadequate at describing cancer cell responses to targeted drugs. In this study, based on an analysis of targeted inhibition of colorectal cancer cell lines, we develop a new biphasic mathematical model that accurately describes the cell–drug response. The model describes the drug response using three kinetic parameters: ratio of target-specific inhibition, F1, potency of target-specific inhibition, Kd1, and potency of off-target toxicity, Kd2. Determination of these kinetic parameters also provides a mechanistic basis for predicting effective combination targeted therapy for multi-driver cancer cells. The experiments confirmed that a combination of inhibitors, each blocking a driver pathway and having a distinct target-specific effect, resulted in a potent and synergistic blockade of cell viability, improving potency over mono-agent treatment by one to two orders of magnitude. We further demonstrate that mono-driver cancer cells represent a special scenario in which F1 becomes nearly 100%, and the drug response becomes monophasic. Application of this model to the responses of >400 cell lines to kinase inhibitor dasatinib revealed that the ratio of biphasic versus monophasic responses is about 4:1. This study develops a new mathematical model of quantifying cancer cell response to targeted therapy, and suggests a new framework for developing rational combination targeted therapy for colorectal and other multi-driver cancers. Full article
(This article belongs to the Special Issue Cancer Modeling and Network Biology)
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23 pages, 6549 KiB  
Article
Novel MicroRNA Biomarkers for Colorectal Cancer Early Diagnosis and 5-Fluorouracil Chemotherapy Resistance but Not Prognosis: A Study from Databases to AI-Assisted Verifications
by Xueli Zhang, Hong Zhang, Bairong Shen and Xiao-Feng Sun
Cancers 2020, 12(2), 341; https://doi.org/10.3390/cancers12020341 - 3 Feb 2020
Cited by 10 | Viewed by 3418
Abstract
Colorectal cancer (CRC) is one of the major causes of cancer death worldwide. In general, early diagnosis for CRC and individual therapy have led to better survival for the cancer patients. Accumulating studies concerning biomarkers have provided positive evidence to improve cancer early [...] Read more.
Colorectal cancer (CRC) is one of the major causes of cancer death worldwide. In general, early diagnosis for CRC and individual therapy have led to better survival for the cancer patients. Accumulating studies concerning biomarkers have provided positive evidence to improve cancer early diagnosis and better therapy. It is, however, still necessary to further investigate the precise biomarkers for cancer early diagnosis and precision therapy and predicting prognosis. In this study, AI-assisted systems with bioinformatics algorithm integrated with microarray and RNA sequencing (RNA-seq) gene expression (GE) data has been approached to predict microRNA (miRNA) biomarkers for early diagnosis of CRC based on the miRNA-messenger RNA (mRNA) interaction network. The relationships between the predicted miRNA biomarkers and other biological components were further analyzed on biological networks. Bayesian meta-analysis of diagnostic test was utilized to verify the diagnostic value of the miRNA candidate biomarkers and the combined multiple biomarkers. Biological function analysis was performed to detect the relationship of candidate miRNA biomarkers and identified biomarkers in pathways. Text mining was used to analyze the relationships of predicted miRNAs and their target genes with 5-fluorouracil (5-FU). Survival analyses were conducted to evaluate the prognostic values of these miRNAs in CRC. According to the number of miRNAs single regulated mRNAs (NSR) and the number of their regulated transcription factor gene percentage (TFP) on the miRNA-mRNA network, there were 12 promising miRNA biomarkers were selected. There were five potential candidate miRNAs (miRNA-186-5p, miRNA-10b-5, miRNA-30e-5p, miRNA-21 and miRNA-30e) were confirmed as CRC diagnostic biomarkers, and two of them (miRNA-21 and miRNA-30e) were previously reported. Furthermore, the combinations of the five candidate miRNAs biomarkers showed better prediction accuracy for CRC early diagnosis than the single miRNA biomarkers. miRNA-10b-5p and miRNA-30e-5p were associated with the 5-FU therapy resistance by targeting the related genes. These miRNAs biomarkers were not statistically associated with CRC prognosis. Full article
(This article belongs to the Special Issue Cancer Modeling and Network Biology)
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Review

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29 pages, 2325 KiB  
Review
Genome Scale Modeling to Study the Metabolic Competition between Cells in the Tumor Microenvironment
by Itziar Frades, Carles Foguet, Marta Cascante and Marcos J. Araúzo-Bravo
Cancers 2021, 13(18), 4609; https://doi.org/10.3390/cancers13184609 - 14 Sep 2021
Cited by 10 | Viewed by 3499
Abstract
The tumor’s physiology emerges from the dynamic interplay of numerous cell types, such as cancer cells, immune cells and stromal cells, within the tumor microenvironment. Immune and cancer cells compete for nutrients within the tumor microenvironment, leading to a metabolic battle between these [...] Read more.
The tumor’s physiology emerges from the dynamic interplay of numerous cell types, such as cancer cells, immune cells and stromal cells, within the tumor microenvironment. Immune and cancer cells compete for nutrients within the tumor microenvironment, leading to a metabolic battle between these cell populations. Tumor cells can reprogram their metabolism to meet the high demand of building blocks and ATP for proliferation, and to gain an advantage over the action of immune cells. The study of the metabolic reprogramming mechanisms underlying cancer requires the quantification of metabolic fluxes which can be estimated at the genome-scale with constraint-based or kinetic modeling. Constraint-based models use a set of linear constraints to simulate steady-state metabolic fluxes, whereas kinetic models can simulate both the transient behavior and steady-state values of cellular fluxes and concentrations. The integration of cell- or tissue-specific data enables the construction of context-specific models that reflect cell-type- or tissue-specific metabolic properties. While the available modeling frameworks enable limited modeling of the metabolic crosstalk between tumor and immune cells in the tumor stroma, future developments will likely involve new hybrid kinetic/stoichiometric formulations. Full article
(This article belongs to the Special Issue Cancer Modeling and Network Biology)
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26 pages, 1304 KiB  
Review
Metabolic Reprogramming of Fibroblasts as Therapeutic Target in Rheumatoid Arthritis and Cancer: Deciphering Key Mechanisms Using Computational Systems Biology Approaches
by Sahar Aghakhani, Naouel Zerrouk and Anna Niarakis
Cancers 2021, 13(1), 35; https://doi.org/10.3390/cancers13010035 - 24 Dec 2020
Cited by 14 | Viewed by 6058
Abstract
Fibroblasts, the most abundant cells in the connective tissue, are key modulators of the extracellular matrix (ECM) composition. These spindle-shaped cells are capable of synthesizing various extracellular matrix proteins and collagen. They also provide the structural framework (stroma) for tissues and play a [...] Read more.
Fibroblasts, the most abundant cells in the connective tissue, are key modulators of the extracellular matrix (ECM) composition. These spindle-shaped cells are capable of synthesizing various extracellular matrix proteins and collagen. They also provide the structural framework (stroma) for tissues and play a pivotal role in the wound healing process. While they are maintainers of the ECM turnover and regulate several physiological processes, they can also undergo transformations responding to certain stimuli and display aggressive phenotypes that contribute to disease pathophysiology. In this review, we focus on the metabolic pathways of glucose and highlight metabolic reprogramming as a critical event that contributes to the transition of fibroblasts from quiescent to activated and aggressive cells. We also cover the emerging evidence that allows us to draw parallels between fibroblasts in autoimmune disorders and more specifically in rheumatoid arthritis and cancer. We link the metabolic changes of fibroblasts to the toxic environment created by the disease condition and discuss how targeting of metabolic reprogramming could be employed in the treatment of such diseases. Lastly, we discuss Systems Biology approaches, and more specifically, computational modeling, as a means to elucidate pathogenetic mechanisms and accelerate the identification of novel therapeutic targets. Full article
(This article belongs to the Special Issue Cancer Modeling and Network Biology)
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33 pages, 2682 KiB  
Review
An Optimal Time for Treatment—Predicting Circadian Time by Machine Learning and Mathematical Modelling
by Janina Hesse, Deeksha Malhan, Müge Yalҫin, Ouda Aboumanify, Alireza Basti and Angela Relógio
Cancers 2020, 12(11), 3103; https://doi.org/10.3390/cancers12113103 - 23 Oct 2020
Cited by 25 | Viewed by 5563
Abstract
Tailoring medical interventions to a particular patient and pathology has been termed personalized medicine. The outcome of cancer treatments is improved when the intervention is timed in accordance with the patient’s internal time. Yet, one challenge of personalized medicine is how to consider [...] Read more.
Tailoring medical interventions to a particular patient and pathology has been termed personalized medicine. The outcome of cancer treatments is improved when the intervention is timed in accordance with the patient’s internal time. Yet, one challenge of personalized medicine is how to consider the biological time of the patient. Prerequisite for this so-called chronotherapy is an accurate characterization of the internal circadian time of the patient. As an alternative to time-consuming measurements in a sleep-laboratory, recent studies in chronobiology predict circadian time by applying machine learning approaches and mathematical modelling to easier accessible observables such as gene expression. Embedding these results into the mathematical dynamics between clock and cancer in mammals, we review the precision of predictions and the potential usage with respect to cancer treatment and discuss whether the patient’s internal time and circadian observables, may provide an additional indication for individualized treatment timing. Besides the health improvement, timing treatment may imply financial advantages, by ameliorating side effects of treatments, thus reducing costs. Summarizing the advances of recent years, this review brings together the current clinical standard for measuring biological time, the general assessment of circadian rhythmicity, the usage of rhythmic variables to predict biological time and models of circadian rhythmicity. Full article
(This article belongs to the Special Issue Cancer Modeling and Network Biology)
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