Computational and systems oncology最新文献

{"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}

{"title":"A validated mathematical model of FGFR3-mediated tumor growth reveals pathways to harness the benefits of combination targeted therapy and immunotherapy in bladder cancer","authors":"Kamaldeen Okuneye,&nbsp;Daniel Bergman,&nbsp;Jeffrey C. Bloodworth,&nbsp;Alexander T. Pearson,&nbsp;Randy F. Sweis,&nbsp;Trachette L. Jackson","doi":"10.1002/cso2.1019","DOIUrl":"10.1002/cso2.1019","url":null,"abstract":"<p>Bladder cancer is a common malignancy with over 80,000 estimated new cases and nearly 18,000 deaths per year in the United States alone. Therapeutic options for metastatic bladder cancer had not evolved much for nearly four decades, until recently, when five immune checkpoint inhibitors were approved by the U.S. Food and Drug Administration (FDA). Despite the activity of these drugs in some patients, the objective response rate for each is less than 25%. At the same time, fibroblast growth factor receptors (FGFRs) have been attractive drug targets for a variety of cancers, and in 2019 the FDA approved the first therapy targeted against FGFR3 for bladder cancer. Given the excitement around these new receptor tyrosine kinase and immune checkpoint targeted strategies, and the challenges they each may face on their own, emerging data suggest that combining these treatment options could lead to improved therapeutic outcomes. In this paper, we develop a mathematical model for FGFR3-mediated tumor growth and use it to investigate the impact of the combined administration of a small molecule inhibitor of FGFR3 and a monoclonal antibody against the PD-1/PD-L1 immune checkpoint. The model is carefully calibrated and validated with experimental data before survival benefits, and dosing schedules are explored. Predictions of the model suggest that FGFR3 mutation reduces the effectiveness of anti-PD-L1 therapy, that there are regions of parameter space where each monotherapy can outperform the other, and that pretreatment with anti-PD-L1 therapy always results in greater tumor reduction even when anti-FGFR3 therapy is the more effective monotherapy.</p>","PeriodicalId":72658,"journal":{"name":"Computational and systems oncology","volume":"1 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/cso2.1019","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39898438","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":"Biomechanical modelling of cancer: Agent-based force-based models of solid tumours within the context of the tumour microenvironment","authors":"Cicely K. Macnamara","doi":"10.1002/cso2.1018","DOIUrl":"10.1002/cso2.1018","url":null,"abstract":"<p>Once cancer is initiated, with normal cells mutated into malignant ones, a solid tumour grows, develops and spreads within its microenvironment invading the local tissue; the disease progresses and the cancer cells migrate around the body leading to metastasis, the formation of distant secondary tumours. Interactions between the tumour and its microenvironment drive this cascade of events which have devastating, if not fatal, consequences for the human host/patient. Among these interactions, biomechanical interactions are a vital component. In this review paper, key biomechanical relationships are discussed through a presentation of modelling efforts by the mathematical and computational oncology community. The main focus is directed, naturally, towards lattice-free agent-based, force-based models of solid tumour growth and development. In such models, interactions between pairs of cancer cells (as well as between cells and other structures of the tumour microenvironment) are governed by forces. These forces are ones of repulsion and adhesion, and are typically modelled via either an extended Hertz model of contact mechanics or using Johnson–Kendal–Roberts theory, both of which are discussed here. The role of the extracellular matrix in determining disease progression is outlined along with important cell-vessel interactions which combined together account for a great proportion of Hanahan and Weinberg's <i>Hallmarks of Cancer</i>.</p>","PeriodicalId":72658,"journal":{"name":"Computational and systems oncology","volume":"1 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/cso2.1018","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42339163","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":"Boolean dynamic modeling of cancer signaling networks: Prognosis, progression, and therapeutics","authors":"Shubhank Sherekar,&nbsp;Ganesh A. Viswanathan","doi":"10.1002/cso2.1017","DOIUrl":"10.1002/cso2.1017","url":null,"abstract":"<p>Cancer is a multifactorial disease. Aberrant functioning of the underlying complex signaling network that orchestrates cellular response to external or internal cues governs incidence, progression, and recurrence of cancer. Detailed understanding of cancer's etiology can offer useful insights into arriving at novel therapeutic and disease management strategies. Such an understanding for most cancers is currently limited due to unavailability of a predictive large-scale, integrated signaling model accounting for all tumor orchestrating factors. We suggest that the potential of Boolean dynamic (BD) modeling approaches, though qualitative, can be harnessed for developing holistic models capturing multi-scale, multi-cellular signaling processes involved in cancer incidence and progression. We believe that constraining such an integrated BD model with variety of omics data at different scales from laboratory and clinical settings could offer deeper insights into causal mechanisms governing the disease leading to better prognosis. We review the recent literature employing different BD modeling strategies to model variety of cancer signaling programs leading to identification of cancer-specific prognostic markers such as SMAD proteins, which may also serve as early predictors of tumor cells hijacking the epithelial-mesenchymal plasticity program. <i>In silico</i> simulations of BD models of different cancer signaling networks combined with attractor landscape analysis and validated with experimental data predicted the nature of short- and long-term response of standard targeted therapeutic agents such as Nutlin-3, a small molecule inhibitor for p53-MDM2 interaction. BD simulations also offered a mechanistic view of emerging resistance to drugs such as Trastuzumab for HER+ breast cancer, analysis of which suggested new combination therapies to circumvent them. We believe future improvements in BD modeling techniques, and tools can lead to development of a comprehensive platform that can drive holistic approaches toward better decision-making in the clinical settings, and thereby help identify novel therapeutic strategies for improved cancer treatment at personalised levels.</p>","PeriodicalId":72658,"journal":{"name":"Computational and systems oncology","volume":"1 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/cso2.1017","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43754769","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 immune infiltration analysis of pediatric high-grade gliomas (pHGGs) reveals differences in immunosuppression and prognosis by tumor location","authors":"Cavan P. Bailey,&nbsp;Ruiping Wang,&nbsp;Mary Figueroa,&nbsp;Shaojun Zhang,&nbsp;Linghua Wang,&nbsp;Joya Chandra","doi":"10.1002/cso2.1016","DOIUrl":"10.1002/cso2.1016","url":null,"abstract":"<p>Immunotherapy for cancer has moved from pre-clinical hypothesis to successful clinical application in the past 15 years. However, not all cancers have shown response rates in clinical trials for these new agents. igh-grade gliomas, in particular, have proved exceedingly refractory to immunotherapy. In adult patients, there has been much investigation into these failures, and researchers have concluded that an immunosuppressive microenvironment combined with low mutational burden renders adult glioblastomas “immune cold.” Pediatric cancer patients develop gliomas at a higher rate per malignancy than adults, and their brain tumors bear even fewer mutations. These tumors can also develop in more diverse locations in the brain, beyond the cerebral hemispheres seen in adults, including in the brainstem where critical motor functions are controlled. While adult brain tumor immune infiltration has been extensively profiled from surgical resections, this is not possible for brainstem tumors that can only be sampled at autopsy. Given these limitations, there is a dearth of information on immune cells and their therapeutic and prognostic impact in pediatric high-grade gliomas (pHGGs), including hemispheric tumors in addition to brainstem. In this report, we use computational methods to examine immune infiltrate in pHGGs and discover distinct immune patterns between hemispheric and brainstem tumors. In hemispheric tumors, we find positive prognostic associations for regulatory T-cells, memory B-cells, eosinophils, and dendritic cells, but not in brainstem tumors. These differences suggest that immunotherapeutic approaches must be cognizant of pHGG tumor location and tailored for optimum efficacy.</p>","PeriodicalId":72658,"journal":{"name":"Computational and systems oncology","volume":"1 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/cso2.1016","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39581241","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":"Mechanistic insights into the heterogeneous response to anti-VEGF treatment in tumors","authors":"Ding Li,&nbsp;Stacey D. Finley","doi":"10.1002/cso2.1013","DOIUrl":"10.1002/cso2.1013","url":null,"abstract":"<p>Vascular endothelial growth factor (VEGF) is a strong promoter of angiogenesis in tumors, and anti-VEGF treatment, such as a humanized antibody to VEGF, is clinically used as a monotherapy or in combination with chemotherapy to treat cancer patients. However, this approach is not effective in all patients or cancer types. To better understand the heterogeneous responses to anti-VEGF and the synergy between anti-VEGF and other anticancer therapies, we constructed a computational model characterizing angiogenesis-mediated growth of <i>in vivo</i> mouse tumor xenografts. The model captures VEGF-mediated cross-talk between tumor cells and endothelial cells and is able to predict the details of molecular- and cellular-level dynamics. The model predictions of tumor growth in response to anti-VEGF closely match the quantitative measurements from multiple preclinical mouse studies. We applied the model to investigate the effects of VEGF-targeted treatment on tumor cells and endothelial cells. We identified that tumors with lower tumor cell growth rate and higher carrying capacity have a stronger response to anti-VEGF treatment. The predictions indicate that the variation of tumor cell growth rate can be a main reason for the experimentally observed heterogeneous response to anti-VEGF. In addition, our simulation results suggest a new synergy mechanism where anticancer therapy can enhance anti-VEGF simply through reducing the tumor cell growth rate. Overall, this work generates novel insights into the heterogeneous response to anti-VEGF treatment and the synergy of anti-VEGF with other therapies, providing a tool that be further used to test and optimize anticancer therapy.</p>","PeriodicalId":72658,"journal":{"name":"Computational and systems oncology","volume":"1 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/cso2.1013","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49477916","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":"Investigating epithelial-mesenchymal heterogeneity of tumors and circulating tumor cells with transcriptomic analysis and biophysical modeling","authors":"Federico Bocci,&nbsp;Susmita Mandal,&nbsp;Tanishq Tejaswi,&nbsp;Mohit Kumar Jolly","doi":"10.1002/cso2.1015","DOIUrl":"https://doi.org/10.1002/cso2.1015","url":null,"abstract":"<p>Cellular heterogeneity along the epithelial-mesenchymal plasticity (EMP) spectrum is a paramount feature observed in tumors and circulating tumor cells (CTCs). High-throughput techniques now offer unprecedented details on this variability at a single-cell resolution. Yet, there is no current consensus about how EMP in tumors propagates to that in CTCs. To investigate the relationship between EMP-associated heterogeneity of tumors and that of CTCs, we integrated transcriptomic analysis and biophysical modeling. We apply three epithelial-mesenchymal transition (EMT) scoring metrics to multiple tumor samples and CTC datasets from several cancer types. Moreover, we develop a biophysical model that couples EMT-associated phenotypic switching in a primary tumor with cell migration. Finally, we integrate EMT transcriptomic analysis and in silico modeling to evaluate the predictive power of several measurements of tumor aggressiveness, including tumor EMT score, CTC EMT score, fraction of CTC clusters found in circulation, and CTC cluster size distribution. Analysis of high-throughput datasets reveals a pronounced heterogeneity without a well-defined relation between EMT traits in tumors and CTCs. Moreover, mathematical modeling predicts different phases where CTCs can be less, equally, or more mesenchymal than primary tumor depending on the dynamics of phenotypic transition and cell migration. Consistently, various datasets of CTC cluster size distribution from different cancer types are fitted onto different regimes of the model. By further constraining the model with experimental measurements of tumor EMT score, CTC EMT score, and fraction of CTC cluster in bloodstream, we show that none of these assays alone can provide sufficient information to predict the other variables. In conclusion, we propose that the relationship between EMT progression in tumors and CTCs can be variable, and in general, predicting one from the other may not be as straightforward as tacitly assumed.</p>","PeriodicalId":72658,"journal":{"name":"Computational and systems oncology","volume":"1 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/cso2.1015","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"137460925","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":"Relating prostate-specific antigen leakage with vascular tumor growth in a mathematical model of prostate cancer response to androgen deprivation","authors":"Johnna P. Barnaby,&nbsp;Inmaculada C. Sorribes,&nbsp;Harsh Vardhan Jain","doi":"10.1002/cso2.1014","DOIUrl":"10.1002/cso2.1014","url":null,"abstract":"<p>The use of prostate-specific antigen (PSA) as a prognostic indicator for prostate cancer (PCa) patients is controversial, especially since it has been shown to correlate poorly with tumor burden. The poor quality of PSA as a biomarker could be explained by current guidelines not accounting for the mechanism by which it enters circulation. Given that mature blood vessels are relatively impermeable to it, we hypothesize that immature and leaky blood vessels, formed under angiogenic cues in a hypoxic tumor, facilitate PSA extravasation into circulation. To explore our hypothesis, we develop a nonlinear dynamical systems model describing the vascular growth of PCa, that explicitly links PSA leakage into circulation with changes in intra-tumoral oxygen tension and vessel permeability. The model is calibrated versus serum PSA and tumor burden time-courses from a mouse xenograft model of castration resistant PCa response to androgen deprivation. The model recapitulates the experimentally observed and – counterintuitive – phenomenon of increasing tumor burden despite decreasing serum PSA levels. The validated model is then extended to the human scale by incorporating patient-specific parameters and fitting individual PSA time-courses from patients with biochemically failing PCa. Our results highlight the limitations of using time to PSA failure as a clinical indicator of androgen deprivation efficacy. We propose an alternative indicator, namely a treatment efficacy index, for patients with castration resistant disease, to identify who would benefit most from enhanced androgen deprivation. A critical challenge in PCa therapeutics is quantifying the relationship between serum PSA and tumor burden. Our results underscore the potential of mathematical modeling in understanding the limitations of serum PSA as a prognostic indicator. Finally, we provide a means of augmenting PSA time-courses in the diagnostic process, with changes in intra-tumoral vascularity and vascular architecture.</p>","PeriodicalId":72658,"journal":{"name":"Computational and systems oncology","volume":"1 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/cso2.1014","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41629096","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}