Computational and systems oncology最新文献

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.

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.

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.

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 CTNNB1 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.

As an example, we analyzed the spread of mutations in the genes APC and CTNNB1, 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.

The in silico 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.

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.

Motivation: 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.

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.

Results: In cases where the number of studied alterations is small ($��<�30�$), 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.

Availability: OncoBN is implemented in R and is available at https://github.com/phillipnicol/OncoBN.

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.

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.

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 Hallmarks of Cancer.

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. In silico 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.

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.