Computational and systems oncology最新文献

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.

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 in vivo 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.

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.

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.

Mathematical modeling in cancer is enjoying a rapid expansion [1]. For collegial discussion across disciplines, many—if not all of us—have used the aphorism that “All models are wrong, but some are useful” [2]. This has been a convenient approach to justify and communicate the praxis of modeling. This is to suggest that the usefulness of a model is not measured by the accuracy of representation but how well it supports the generation, testing, and refinement of hypotheses. A key insight is not to focus on the model as an outcome, but to consider the modeling process and simulated model predictions as “ways of thinking” about complex nonlinear dynamical systems [3]. Here, we discuss the convoluted interpretation of models being wrong in the arena of predictive modeling.

“All models are wrong, but some are useful” emphasizes the value of abstraction in order to gain insight. While abstraction clearly implies misrepresentation, it allows to explicitly define model assumptions and interpret model results within these limitations – Truth emerges more readily from error than from confusion [4]. It is thus the process of modeling and the discussions about model assumptions that are often considered most valuable in interdisciplinary research. They provide a way of thinking about complex systems and mechanisms underlying observations. Abstractions are being made in cancer biology for every experiment in each laboratory around the world. In vitro cell lines or in vivo mouse experiments are abstractions of complex adaptive evolving human cancers in the complex adaptive dynamic environment called the patient. These "wet lab" experiments akin to "dry lab" mathematical models offer confirmation or refutation of hypotheses and results, which have to be prospectively evaluated in clinical trials before conclusions can be generalized beyond the abstracted assumptions. The key for any model—mathematical, biological, or clinical—to succeed is an iterative cycle of data-driven modeling and model-driven experimentation [5, 6]. The value of such an effort lies in the insights about mechanisms that can then be attributed to the considered variables [7]. With simplified representations of a system one can learn about the emergence of general patterns, like the occurrence of oscillations, bistability, or chaos [8-10].

In this context, Alan Turing framed the purpose of a mathematical model in his seminal paper about “The chemical basis of morphogenesis” [11] with “This model will be a simplification and an idealization, and consequently a falsification. It is to be hoped that the features retained for discussion are those of greatest importance in the present state of knowledge.” For many mathematical biology models that are built to explore, test, and generate hypotheses about emerging dynamics, this remains tru

Introduction. How proteomes differ between normal tissue and tumor microenvironments is an important question for cancer biochemistry. Methods. More than 250 datasets for differentially expressed (up- and downregulated) proteins compiled from the literature were analyzed to calculate the stoichiometric hydration state, which represents the number of water molecules in theoretical mass-balance reactions to form the proteins from a set of basis species. Results. The analysis shows increased stoichiometric hydration state of differentially expressed proteins in cancer compared to normal tissue. In contrast, experiments with different cell types grown in 3D compared to monolayer culture, or exposed to hyperosmotic conditions under high salt or high glucose, cause proteomes to “dry out” as measured by decreased stoichiometric hydration state of the differentially expressed proteins. Conclusion. These findings reveal a basic physicochemical link between proteome composition and water content, which is elevated in many tumors and proliferating cells.