Computational and systems oncology最新文献

{"title":"SITH: An R package for visualizing and analyzing a spatial model of intratumor heterogeneity","authors":"Phillip B. Nicol,&nbsp;Dániel L. Barabási,&nbsp;Kevin R. Coombes,&nbsp;Amir Asiaee","doi":"10.1002/cso2.1033","DOIUrl":"10.1002/cso2.1033","url":null,"abstract":"<p>Cancer progression, including the development of intratumor heterogeneity, is inherently a spatial process. Mathematical models of tumor evolution may be a useful starting point for understanding the patterns of heterogeneity that can emerge in the presence of spatial growth. A commonly studied spatial growth model assumes that tumor cells occupy sites on a lattice and replicate into neighboring sites. Our R package <i>SITH</i> provides a convenient interface for exploring this model. Our efficient simulation algorithm allows for users to generate 3D tumors with millions of cells in under a minute. For the distribution of mutations throughout the tumor, <i>SITH</i> provides interactive graphics and summary plots. Additionally, <i>SITH</i> can produce synthetic bulk and single-cell DNA-seq datasets by sampling from the simulated tumor. A streamlined application programming interface (API) makes <i>SITH</i> a useful tool for investigating the relationship between spatial growth and intratumor heterogeneity. <i>SITH</i> is a part of <span>CRAN</span> and can be installed by running <span>install.packages(“SITH”)</span> from the R console. See https://CRAN.R-project.org/package=SITH for the user manual and package vignette.</p>","PeriodicalId":72658,"journal":{"name":"Computational and systems oncology","volume":"2 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9374116/pdf/nihms-1801946.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9560891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multiscale modeling of tumor adaption and invasion following anti-angiogenic therapy","authors":"Colin G. Cess,&nbsp;Stacey D. Finley","doi":"10.1002/cso2.1032","DOIUrl":"10.1002/cso2.1032","url":null,"abstract":"<p>In order to promote continued growth, a tumor must recruit new blood vessels, a process known as tumor angiogenesis. Many therapies have been tested that aim to inhibit tumor angiogenesis, with the goal of starving the tumor of nutrients and preventing tumor growth. However, many of these therapies have been unsuccessful and can paradoxically further tumor development by leading to increased local tumor invasion and metastasis. In this study, we use agent-based modeling to examine how hypoxic and acidic conditions following anti-angiogenic therapy can influence tumor development. Under these conditions, we find that cancer cells experience a phenotypic shift to a state of higher survival and invasive capability, spreading further away from the tumor into the surrounding tissue. Although anti-angiogenic therapy alone promotes tumor cell adaptation and invasiveness, we find that augmenting chemotherapy with anti-angiogenic therapy improves chemotherapeutic response and delays the time it takes for the tumor to regrow. Overall, we use computational modeling to explain the behavior of tumor cells in response to anti-angiogenic treatment in the dynamic tumor microenvironment.</p>","PeriodicalId":72658,"journal":{"name":"Computational and systems oncology","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cso2.1032","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48554292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multicellular mechanochemical hybrid cellular Potts model of tissue formation during epithelial-mesenchymal transition","authors":"Shreyas U. Hirway,&nbsp;Christopher A. Lemmon,&nbsp;Seth H. Weinberg","doi":"10.1002/cso2.1031","DOIUrl":"10.1002/cso2.1031","url":null,"abstract":"<p>Epithelial-mesenchymal transition (EMT) is the transdifferentiation of epithelial cells to a mesenchymal phenotype, in which cells lose epithelial-like cell–cell adhesions and gain mesenchymal-like enhanced contractility and mobility. EMT is crucial for tissue regeneration and is also implicated in pathological conditions, such as cancer metastasis. Prior work has shown that transforming growth factor-<math>\u0000 <mi>β</mi></math>1 (TGF-<math>\u0000 <mi>β</mi></math>1) is a potent inducer of this biological process. In this study, we develop a computational model coupling mechanical and biochemical signaling in a multicellular tissue undergoing EMT. Specifically, we utilize a recently developed formulation that integrates a multicellular cellular Potts model (CPM), a lattice-based stochastic model governing cell movement; a first moment of area model, governing cellular traction and junctional forces; a finite element model, which defines extracellular matrix (ECM) substrate strains; an intracellular signaling TGF-<math>\u0000 <mi>β</mi></math>1-mediated EMT model that governs cellular phenotype; and an extracellular signaling component governing ECM and TGF-<math>\u0000 <mi>β</mi></math>1 signaling. In this study, we modeled the spatial cellular patterns that occur in tissue and the ECM during EMT. Our model predicts that EMT often initially occurs at a tissue boundary due to mechanochemical coupling, which results in transdifferentiation to progress inwards toward the center. Variation in model parameters demonstrated conditions enhancing and suppressing EMT, especially to drive EMT in the absence of TGF-<math>\u0000 <mi>β</mi></math>1 and inhibit EMT in the presence of TGF-<math>\u0000 <mi>β</mi></math>1. Specifically, enhancing the mechanochemical feedback typically promoted EMT, whereas greater assembled ECM degradation suppressed EMT. Simulated scratch test experiments illustrate that ECM composition can impact closure directly through EMT signaling. In conclusion, we integrated mechanical, biochemical, and extracellular signaling networks in a novel hybrid computational model to reproduce tissue formation dynamics of EMT.</p>","PeriodicalId":72658,"journal":{"name":"Computational and systems oncology","volume":"1 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cso2.1031","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46509810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dose-dependent mathematical modeling of interferon-\u0000 α-treatment for personalized treatment of myeloproliferative neoplasms","authors":"Rasmus K. Pedersen,&nbsp;Morten Andersen,&nbsp;Trine A. Knudsen,&nbsp;Vibe Skov,&nbsp;Lasse Kjær,&nbsp;Hans C. Hasselbalch,&nbsp;Johnny T. Ottesen","doi":"10.1002/cso2.1030","DOIUrl":"10.1002/cso2.1030","url":null,"abstract":"<p>Long-term treatment with interferon-alfa (IFN) can reduce the disease burden of patients diagnosed with myeloproliferative neoplasms (MPNs). Determining individual patient responses to IFN therapy may allow for efficient personalized treatment, reducing both drop-out and disease burden. A mathematical model describing hematopoietic stem cells and the immune system is suggested. Considering the bone marrow and the blood allows for modeling disease dynamics both in the absence and presence of IFN treatment. Through comprehensive modeling of the effects of IFN, the model was related to individualized patient-data consisting of longitudinal hematologic and molecular measurements. Treatment responses were modeled on a population level, allowing for personalized predictions from a single pretreatment data point. Personalized fits were found to agree well with data for individual patients. This allowed for a quantitative description of the treatment response, yielding a mechanistic interpretation of differences from patient to patient. The treatment responses of individual patients were combined and a formulation of treatment responses on the population level was described and simulated. Based on pretreatment data and the actual treatment scheduling, the population-level response was found to predict the treatment response of particular patients accurately over a five-year period. Mechanism-based modeling of treatment effects demonstrates that hematologic and molecular observable quantities can be predicted on the level of individual patients. Personalized patient-fits suggest that the effect of IFN treatment can be quantified and interpreted through mathematical modeling, despite variation in hematologic and molecular responses between patients. Mathematical modeling suggests that in general both hematologic and molecular markers must be considered to avoid early relapse. Furthermore, personalized model-fits provide quantitative measures of the hematologic and molecular responses, determining when treatment-cessation is appropriate. Proof-of-concept population-level modeling of treatment responses from pretreatment data successfully predicted clinical measures for a 5-year period. We believe that this approach could have direct clinical relevance, offering expert guidance for clinical decisions about IFN treatment of MPN patients.</p>","PeriodicalId":72658,"journal":{"name":"Computational and systems oncology","volume":"1 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cso2.1030","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41806259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Individual cell-based modeling of tumor cell plasticity-induced immune escape after CAR-T therapy","authors":"Can Zhang,&nbsp;Changrong Shao,&nbsp;Xiaopei Jiao,&nbsp;Yue Bai,&nbsp;Miao Li,&nbsp;Hanping Shi,&nbsp;Jinzhi Lei,&nbsp;Xiaosong Zhong","doi":"10.1002/cso2.1029","DOIUrl":"10.1002/cso2.1029","url":null,"abstract":"<p>Chimeric antigen receptor (CAR) therapy targeting CD19 is an effective treatment for refractory B cell malignancies, especially B-cell acute lymphoblastic leukemia (B-ALL). The majority of patients achieve a complete response following a single infusion of CD19-targeted CAR-modified T cells (CAR-19 T cells); however, many patients suffer relapse after therapy, and the underlying mechanism remains unclear. To better understand the mechanism of tumor relapse, we developed an individual cell-based computational model based on major assumptions of the tumor cells heterogeneity and plasticity as well as the heterogeneous responses to CAR-T treatment. Model simulations reproduced the process of tumor relapse and predicted that cell plasticity induced by CAR-T stress can lead to tumor relapse in B-ALL. Model predictions were in agreement with experimental results of applying the second-generation CAR-T cells to mice injected with NALM-6-GL leukemic cells, in which 60% of the mice relapse within 3 months, relapsed tumors retained CD19 expression but exhibited a subpopulation of cells with high level CD34 transcription. The computational model suggests that the experimental data are compatible with a CAR-T cell-induced transition of tumor cells to hematopoietic stem-like cells and myeloid-like cells, which are resistant to the treatment. The proposed computational model framework was successfully developed to recapitulate the individual evolutionary dynamics and potentially allows to predict the outcomes of CAR-T treatment through model simulation based on early-stage observations of tumor burden and tumor cells analysis.</p>","PeriodicalId":72658,"journal":{"name":"Computational and systems oncology","volume":"1 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cso2.1029","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47154224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting time to relapse in acute myeloid leukemia through stochastic modeling of minimal residual disease based on clonality data","authors":"Khanh N. Dinh,&nbsp;Roman Jaksik,&nbsp;Seth J. Corey,&nbsp;Marek Kimmel","doi":"10.1002/cso2.1026","DOIUrl":"10.1002/cso2.1026","url":null,"abstract":"<p>Event-free and overall survival remain poor for patients with acute myeloid leukemia. Chemoresistant clones contributing to relapse arise from minimal residual disease (MRD) or newly acquired mutations. However, the dynamics of clones comprising MRD is poorly understood. We developed a predictive stochastic model, based on a multitype age-dependent Markov branching process, to describe how random events in MRD contribute to the heterogeneity in treatment response. We employed training and validation sets of patients who underwent whole-genome sequencing and for whom mutant clone frequencies at diagnosis and relapse were available. The disease evolution and treatment outcome are subject to stochastic fluctuations. Estimates of malignant clone growth rates, obtained by model fitting, are consistent with published data. Using the estimates from the training set, we developed a function linking MRD and time of relapse with MRD inferred from the model fits to clone frequencies and other data. An independent validation set confirmed our model. In a third dataset, we fitted the model to data at diagnosis and remission and predicted the time to relapse. As a conclusion, given bone marrow genome at diagnosis and MRD at or past remission, the model can predict time to relapse and help guide treatment decisions to mitigate relapse.</p>","PeriodicalId":72658,"journal":{"name":"Computational and systems oncology","volume":"1 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/cso2.1026","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39431799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A computational model for investigating the evolution of colonic crypts during Lynch syndrome carcinogenesis","authors":"Saskia Haupt,&nbsp;Nils Gleim,&nbsp;Aysel Ahadova,&nbsp;Hendrik Bläker,&nbsp;Magnus von Knebel Doeberitz,&nbsp;Matthias Kloor,&nbsp;Vincent Heuveline","doi":"10.1002/cso2.1020","DOIUrl":"https://doi.org/10.1002/cso2.1020","url":null,"abstract":"<p>Lynch syndrome (LS), the most common inherited colorectal cancer (CRC) syndrome, increases the cancer risk in affected individuals. LS is caused by pathogenic germline variants in one of the DNA mismatch repair (MMR) genes, complete inactivation of which causes numerous mutations in affected cells. As CRC is believed to originate in colonic crypts, understanding the intra-crypt dynamics caused by mutational processes is essential for a complete picture of LS CRC and may have significant implications for cancer prevention.</p><p>We propose a computational model describing the evolution of colonic crypts during LS carcinogenesis. Extending existing modeling approaches for the non-Lynch scenario, we incorporated MMR deficiency and implemented recent experimental data demonstrating that somatic <i>CTNNB1</i> mutations are common drivers of LS-associated CRCs, if affecting both alleles of the gene. Further, we simulated the effect of different mutations on the entire crypt, distinguishing non-transforming and transforming mutations.</p><p>As an example, we analyzed the spread of mutations in the genes <i>APC</i> and <i>CTNNB1</i>, which are frequently mutated in LS tumors, as well as of MMR deficiency itself. We quantified each mutation's potential for monoclonal conversion and investigated the influence of the cell location and of stem cell dynamics on mutation spread.</p><p>The <i>in silico</i> experiments underline the importance of stem cell dynamics for the overall crypt evolution. Further, simulating different mutational processes is essential in LS since mutations without survival advantages (the MMR deficiency-inducing second hit) play a key role. The effect of other mutations can be simulated with the proposed model. Our results provide first mathematical clues towards more effective surveillance protocols for LS carriers.</p>","PeriodicalId":72658,"journal":{"name":"Computational and systems oncology","volume":"1 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/cso2.1020","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"137795565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Role of microRNAs in oncogenesis: Insights from computational and systems-level modeling approaches","authors":"Vinodhini Govindaraj,&nbsp;Sandip Kar","doi":"10.1002/cso2.1028","DOIUrl":"10.1002/cso2.1028","url":null,"abstract":"<p>MicroRNAs (miRNAs) often govern the cell fate decision-making events associated with oncogenesis. miRNAs repress the target genes either by degrading the target mRNA or inhibiting the process of translation. However, mathematical and computational modeling of miRNA-mediated target gene regulation in various cellular network motifs indicates that miRNAs play a much more complex role in cellular decision-making events. In this review, we give an overview of the quantitative insights obtained from mathematical modeling of miRNA-mediated gene regulations by highlighting the various factors associated with it that are pivotal in diversifying the cell fate decisions related to oncogenesis. Intriguingly, recent experiments suggest that under certain circumstances, miRNAs can lead to more complex gene regulatory dynamics by causing target gene upregulation. We discuss these modeling approaches that can help in understanding the subtleties of miRNA effects in oncogenesis.</p>","PeriodicalId":72658,"journal":{"name":"Computational and systems oncology","volume":"1 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/cso2.1028","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41889484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Oncogenetic network estimation with disjunctive Bayesian networks","authors":"Phillip B. Nicol,&nbsp;Kevin R. Coombes,&nbsp;Courtney Deaver,&nbsp;Oksana Chkrebtii,&nbsp;Subhadeep Paul,&nbsp;Amanda E. Toland,&nbsp;Amir Asiaee","doi":"10.1002/cso2.1027","DOIUrl":"https://doi.org/10.1002/cso2.1027","url":null,"abstract":"<p><b>Motivation</b>: Cancer is the process of accumulating genetic alterations that confer selective advantages to tumor cells. The order in which aberrations occur is not arbitrary, and inferring the order of events is challenging due to the lack of longitudinal samples from tumors. Moreover, a network model of oncogenesis should capture biological facts such as distinct progression trajectories of cancer subtypes and patterns of mutual exclusivity of alterations in the same pathways.</p><p>In this paper, we present the disjunctive Bayesian network (DBN), a novel oncogenetic model with a phylogenetic interpretation. DBN is expressive enough to capture cancer subtypes' trajectories and mutually exclusive relations between alterations from unstratified data.</p><p><b>Results</b>: In cases where the number of studied alterations is small (<math>\u0000 <mrow>\u0000 <mo>&lt;</mo>\u0000 <mn>30</mn>\u0000 </mrow></math>), we provide an efficient dynamic programming implementation of an exact structure learning method that finds a best DBN in the superexponential search space of networks. In rare cases that the number of alterations is large, we provided an efficient genetic algorithm in our software package, OncoBN. Through numerous synthetic and real data experiments, we show OncoBN's ability in inferring ground truth networks and recovering biologically meaningful progression networks.</p><p><b>Availability</b>: OncoBN is implemented in R and is available at https://github.com/phillipnicol/OncoBN.</p>","PeriodicalId":72658,"journal":{"name":"Computational and systems oncology","volume":"1 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/cso2.1027","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"137559155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational systems-biology approaches for modeling gene networks driving epithelial–mesenchymal transitions","authors":"Ataur Katebi,&nbsp;Daniel Ramirez,&nbsp;Mingyang Lu","doi":"10.1002/cso2.1021","DOIUrl":"10.1002/cso2.1021","url":null,"abstract":"<p>Epithelial–mesenchymal transition (EMT) is an important biological process through which epithelial cells undergo phenotypic transitions to mesenchymal cells by losing cell–cell adhesion and gaining migratory properties that cells use in embryogenesis, wound healing, and cancer metastasis. An important research topic is to identify the underlying gene regulatory networks (GRNs) governing the decision making of EMT and develop predictive models based on the GRNs. The advent of recent genomic technology, such as single-cell RNA sequencing, has opened new opportunities to improve our understanding about the dynamical controls of EMT. In this article, we review three major types of computational and mathematical approaches and methods for inferring and modeling GRNs driving EMT. We emphasize (1) the bottom-up approaches, where GRNs are constructed through literature search; (2) the top-down approaches, where GRNs are derived from genome-wide sequencing data; (3) the combined top-down and bottom-up approaches, where EMT GRNs are constructed and simulated by integrating bioinformatics and mathematical modeling. We discuss the methodologies and applications of each approach and the available resources for these studies.</p>","PeriodicalId":72658,"journal":{"name":"Computational and systems oncology","volume":"1 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/cso2.1021","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39100629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}