Computational and systems oncology最新文献

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.

Ever since the war on cancer was declared in 1971, there has been an explosion in our understanding of this diverse group of diseases. The application of molecular genetics and molecular biology technologies have enabled a deep understanding of the genetic, epigenetic, signaling cascades, survival pathways, and invasive mechanisms that underlie the cancer phenotype [1, 2]. Concomitantly this has translated in the development of ever more effective and safe medications that work through different mechanisms of action and target fundamental aspects of the biology of the tumor. The paradigm has been chronic myeloid leukemia where the discovery of the Philadelphia chromosome [3] ultimately led to the identification of the BCR-ABL oncogene and the development of tyrosine kinase inhibitors such as imatinib, nilotinib, dasatinib, and so on led to rapid, deep, and long-lasting remissions in this disease [4-6]. Another success story has been acute promyelocytic leukemia with the vast majority of patients now being cured of the disease without the need for any classical chemotherapy [7].

The rapid development of deep sequencing technologies has enabled the discovery of multiple mutations and a deeper understanding of the complex “structure” of the tumor as being composed of multiple subclones that are competing with each other for resources [8-15]. The subclones are being selected for or against by therapy [16]. Principles from evolutionary biology have been applied to understand the dynamics of how these clones change in time [16, 17]. It appears that in the absence of therapy, neutral evolution is very important for the development of the tumor [18], but in the presence of therapy, the potential fitness advantage of resistant clones dominates. The identification of a specific tumor sequence also enables monitoring of patients using simple blood tests (liquid biopsy) [19] for the presence of disease and its burden and perhaps will be used in the future to screen people for premalignant or early malignant processes.

Naturally over the years, a major focus has been on the tumor cells themselves leading to major advances in understanding of signaling pathways that are critical for tumor cell replication, growth, survival, and cell cycle regulation. This leads to the discovery of important pathways such as the Janus kinases/signal transducer and activator of transcription proteins (JAK/STAT), phosphoinositide-3 (PI-3) kinase, Protein kinase B (AKT), and receptor tyrosine kinases (RTK) [1, 2]. All of this knowledge has been translated into effective therapies for a wide variety of tumors including myeloproliferative neoplasms, hepatocellular carcinoma, renal cell carcinoma, nonsmall cell lung cancer, and so on. The discovery of potent anti-apoptotic mechanisms that are overexpressed in tumor cells has led to th