Proposal of a hybrid approach for tumor progression and tumor-induced angiogenesis

A general modeling framework for quantitative tracking, accurate prediction of ICU, and assessing vaccination for COVID-19 in Chile

Background

One of the main lessons of the COVID-19 pandemic is that we must prepare to face another pandemic like it. Consequently, this article aims to develop a general framework consisting of epidemiological modeling and a practical identifiability approach to assess combined vaccination and non-pharmaceutical intervention (NPI) strategies for the dynamics of any transmissible disease.

Materials and methods

Epidemiological modeling of the present work relies on delay differential equations describing time variation and transitions between suitable compartments. The practical identifiability approach relies on parameter optimization, a parametric bootstrap technique, and data processing. We implemented a careful parameter optimization algorithm by searching for suitable initialization according to each processed dataset. In addition, we implemented a parametric bootstrap technique to accurately predict the ICU curve trend in the medium term and assess vaccination.

Results

We show the framework's calibration capabilities for several processed COVID-19 datasets of different regions of Chile. We found a unique range of parameters that works well for every dataset and provides overall numerical stability and convergence for parameter optimization. Consequently, the framework produces outstanding results concerning quantitative tracking of COVID-19 dynamics. In addition, it allows us to accurately predict the ICU curve trend in the medium term and assess vaccination. Finally, it is reproducible since we provide open-source codes that consider parameter initialization standardized for every dataset.

Conclusion

This work attempts to implement a holistic and general modeling framework for quantitative tracking of the dynamics of any transmissible disease, focusing on accurately predicting the ICU curve trend in the medium term and assessing vaccination. The scientific community could adapt it to evaluate the impact of combined vaccination and NPIs strategies for COVID-19 or any transmissible disease in any country and help visualize the potential effects of implemented plans by policymakers. In future work, we want to improve the computational cost of the parametric bootstrap technique or use another more efficient technique. The aim would be to reconstruct epidemiological curves to predict the combined NPIs and vaccination policies' impact on the ICU curve trend in real-time, providing scientific evidence to help anticipate policymakers' decisions.

Non-Invasive and Real-Time Monitoring of the Breast Cancer Metastasis Degree via Metabolomics

Breast cancer (BC) is a serious threat to women's health and metastasis is the major cause of BC-associated mortality. Various techniques are currently used to preoperatively describe the metastatic status of tumors, based on which a comprehensive treatment protocol was determined. However, accurately staging a tumor before surgery remains a challenge, which may lead to the miss of optimal treatment options. More severely, the failure to detect and remove occult micrometastases often causes tumor recurrences. There is an urgent need to develop a more precise and non-invasive strategy for the detection of the tumor metastasis in lymph nodes and distant organs. Based on the facts that tumor metastasis is closely related to the primary tumor microenvironment (TME) evolutions and that metabolomics profiling of the circulatory system can precisely reflect subtle changes within TME, we suppose whether metabolomic technology can be used to achieve non-invasive and real-time monitoring of BC metastatic status. In this study, the metastasis status of BC mouse models with different tumor-bearing times was firstly depicted to mimic clinical anatomic TNM staging system. Metabolomic profiling together with metastasis-related changes in TME among tumor-bearing mice with different metastatic status was conducted. A range of differential metabolites reflecting tumor metastatic states were screened and in vivo experiments proved that two main metastasis-driving factors in TME, TGF-β and hypoxia, were closely related to the regular changes of these metabolites. The differential metabolites level changes were also preliminarily confirmed in a limited number of clinical BC samples. Metabolite lysoPC (16:0) was found to be useful for clinical N stage diagnosis and the possible cause of its changes was analyzed by bioinformatics techniques.

Forecasting COVID-19 Chile' second outbreak by a generalized SIR model with constant time delays and a fitted positivity rate

The COVID-19 disease has forced countries to make a considerable collaborative effort between scientists and governments to provide indicators to suitable follow-up the pandemic's consequences. Mathematical modeling plays a crucial role in quantifying indicators describing diverse aspects of the pandemic. Consequently, this work aims to develop a clear, efficient, and reproducible methodology for parameter optimization, whose implementation is illustrated using data from three representative regions from Chile and a suitable generalized SIR model together with a fitted positivity rate. Our results reproduce the general trend of the infected's curve, distinguishing the reported and real cases. Finally, our methodology is robust, and it allows us to forecast a second outbreak of COVID-19 and the infection fatality rate of COVID-19 qualitatively according to the reported dead cases.

A phenotype-structured model to reproduce the avascular growth of a tumor and its interaction with the surrounding environment

We here propose a one-dimensional spatially explicit phenotype-structured model to analyze selected aspects of avascular tumor progression. In particular, our approach distinguishes viable and necrotic cell fractions. The metabolically active part of the disease is, in turn, differentiated according to a continuous trait, that identifies cell variants with different degrees of motility and proliferation potential. A parabolic partial differential equation (PDE) then governs the spatio-temporal evolution of the phenotypic distribution of active cells within the host tissue. In this respect, active tumor agents are allowed to duplicate, move upon haptotactic and pressure stimuli, and eventually undergo necrosis. The mutual influence between the emerging malignancy and its environment (in terms of molecular landscape) is implemented by coupling the evolution law of the viable tumor mass with a parabolic PDE for oxygen kinetics and a differential equation that accounts for local consumption of extracellular matrix (ECM) elements. The resulting numerical realizations reproduce tumor growth and invasion in a number scenarios that differ for cell properties (i.e., individual migratory behavior, duplication and mutation potential) and environmental conditions (i.e., level of tissue oxygenation and homogeneity in the initial matrix profile). In particular, our simulations show that, in all cases, more mobile cell variants occupy the front edge of the tumor, whereas more proliferative clones are selected at the more internal regions. A necrotic core constantly occupies the bulk of the mass due to nutrient deprivation. This work may eventually suggest some biomedical strategies to partially reduce tumor aggressiveness, i.e., to enhance necrosis of malignant tissue and to promote the presence of more proliferative cell phenotypes over more invasive ones.

Comparative study between discrete and continuum models for the evolution of competing phenotype-structured cell populations in dynamical environments

Deterministic continuum models formulated as nonlocal partial differential equations for the evolutionary dynamics of populations structured by phenotypic traits have been used recently to address open questions concerning the adaptation of asexual species to periodically fluctuating environmental conditions. These models are usually defined on the basis of population-scale phenomenological assumptions and cannot capture adaptive phenomena that are driven by stochastic variability in the evolutionary paths of single individuals. In light of these considerations, in this paper we develop a stochastic individual-based model for the coevolution of two competing phenotype-structured cell populations that are exposed to time-varying nutrient levels and undergo spontaneous, heritable phenotypic changes with different probabilities. Here, the evolution of every cell is described by a set of rules that result in a discrete-time branching random walk on the space of phenotypic states, and nutrient levels are governed by a difference equation in which a sink term models nutrient consumption by the cells. We formally show that the deterministic continuum counterpart of this model comprises a system of nonlocal partial differential equations for the cell population density functions coupled with an ordinary differential equation for the nutrient concentration. We compare the individual-based model and its continuum analog, focusing on scenarios whereby the predictions of the two models differ. The results obtained clarify the conditions under which significant differences between the two models can emerge due to bottleneck effects that bring about both lower regularity of the density functions of the two populations and more pronounced demographic stochasticity. In particular, bottleneck effects emerge in the presence of lower probabilities of phenotypic variation and are more apparent when the two populations are characterized by lower fitness initial mean phenotypes and smaller initial levels of phenotypic heterogeneity. The emergence of these effects, and thus the agreement between the two modeling approaches, is also dependent on the initial proportions of the two populations. As an illustrative example, we demonstrate the implications of these results in the context of the mathematical modeling of the early stage of metastatic colonization of distant organs.

Multiple Cell Signalling Pathways of Human Proinsulin C-Peptide in Vasculopathy Protection

A major hallmark of diabetes is a constant high blood glucose level (hyperglycaemia), resulting in endothelial dysfunction. Transient or prolonged hyperglycemia can cause diabetic vasculopathy, a secondary systemic damage. C-Peptide is a product of cleavage of proinsulin by a serine protease that occurs within the pancreatic β-cells, being secreted in similar amounts as insulin. The biological activity of human C-peptide is instrumental in the prevention of diabetic neuropathy, nephropathy and other vascular complications. The main feature of type 1 diabetes mellitus is the lack of insulin and of C-peptide, but the progressive β-cell loss is also observed in later stage of type 2 diabetes mellitus. C-peptide has multifaceted effects in animals and diabetic patients due to the activation of multiple cell signalling pathways, highlighting p38 mitogen-activated protein kinase and extracellular signal-regulated kinase ½, Akt, as well as endothelial nitric oxide production. Recent works highlight the role of C-peptide in the prevention and amelioration of diabetes and also in organ-specific complications. Benefits of C-peptide in microangiopathy and vasculopathy have been shown through conservation of vascular function, and also in the prevention of endothelial cell death, microvascular permeability, neointima formation, and in vascular inflammation. Improvement of microvascular blood flow by replacing a physiological amount of C-peptide, in several tissues of diabetic animals and humans, mainly in nerve tissue, myocardium, skeletal muscle, and kidney has been described. A review of the multiple cell signalling pathways of human proinsulin C-peptide in vasculopathy protection is proposed, where the approaches to move beyond the state of the art in the development of innovative and effective therapeutic options of diabetic neuropathy and nephropathy are discussed.

Parameter estimation and mathematical modeling for the quantitative description of therapy failure due to drug resistance in gastrointestinal stromal tumor metastasis to the liver

In this work we develop a general mathematical model and devise a practical identifiability approach for gastrointestinal stromal tumor (GIST) metastasis to the liver, with the aim of quantitatively describing therapy failure due to drug resistance. To this end, we have modeled metastatic growth and therapy failure produced by resistance to two standard treatments based on tyrosine kinase inhibitors (Imatinib and Sunitinib) that have been observed clinically in patients with GIST metastasis to the liver. The parameter identification problem is difficult to solve, since there are no general results on this issue for models based on ordinary differential equations (ODE) like the ones studied here. We propose a general modeling framework based on ODE for GIST metastatic growth and therapy failure due to drug resistance and analyzed five different model variants, using medical image observations (CT scans) from patients that exhibit drug resistance. The associated parameter estimation problem was solved using the Nelder-Mead simplex algorithm, by adding a regularization term to the objective function to address model instability, and assessing the agreement of either an absolute or proportional error in the objective function. We compared the goodness of fit to data for the proposed model variants, as well as evaluated both error forms in order to improve parameter estimation results. From the model variants analyzed, we identified the one that provides the best fit to all the available patient data sets, as well as the best assumption in computing the objective function (absolute or proportional error). This is the first work that reports mathematical models capable of capturing and quantitatively describing therapy failure due to drug resistance based on clinical images in a patient-specific manner.

The Antitumor Activities of Marsdenia tenacissima

Marsdenia tenacissima (MT), a traditional Chinese herbal medicine, has long been used for thousands of years to treat asthma, tracheitis, rheumatism, etc. An increasing number of recent studies have focused on the antitumor effects of MT. The effects of MT on cancer are the result of various activated signaling pathways and inhibiting factors and the high expression levels of regulatory proteins. MT can inhibit different cancer types including non-small cell lung cancer (NSCLC), malignant tumors, hepatic carcinoma, and so on. This article mainly focuses on the activities and mechanisms of MT. In addition, the efficacy and toxicity of MT are also discussed. Further studies of MT are required for improved medicinal utilization.

Monitoring of tumor vascular normalization: the key points from basic research to clinical application

Tumor vascular normalization alleviates hypoxia in the tumor microenvironment, reduces the degree of malignancy, and increases the efficacy of traditional therapy. However, the time window for vascular normalization is narrow; therefore, how to determine the initial and final points of the time window accurately is a key factor in combination therapy. At present, the gold standard for detecting the normalization of tumor blood vessels is histological staining, including tumor perfusion, microvessel density (MVD), vascular morphology, and permeability. However, this detection method is almost unrepeatable in the same individual and does not dynamically monitor the trend of the time window; therefore, finding a relatively simple and specific monitoring index has important clinical significance. Imaging has long been used to assess changes in tumor blood vessels and tumor changes caused by the oxygen environment in clinical practice; some preclinical and clinical research studies demonstrate the feasibility to assess vascular changes, and some new methods were in preclinical research. In this review, we update the most recent insights of evaluating tumor vascular normalization.

Association between expression of nuclear receptor co-activator 5 protein and prognosis in postoperative patients with osteosarcoma

The aim of the present study was to investigate the association between the expression of nuclear receptor co-activator 5 protein (NCOA5) and the prognosis of postoperative patients with osteosarcoma. Human osteosarcoma samples were collected from 145 patients and normal bone tissues were collected from 100 individuals as controls. Immunohistochemistry (IHC) and reverse transcription-polymerase chain reaction (RT-PCR) were employed to measure the levels of NCOA5 protein in cases of human osteosarcoma. The results from the RT-PCR analysis demonstrated that the positive rate of NCOA5 mRNA expression in human osteosarcoma was 17.24% (25/145). The positive rate in normal bone tissues was 84.00% (84/100), which was significantly higher compared with that of human osteosarcoma tissues (χ²=33.166; P<0.001). IHC staining indicated that the positive rate of NCOA5 protein in the osteosarcoma samples was 26.21% (38/145). The positive rate in normal bone tissues was 82.00% (82/100), which was significantly increased compared with that of human osteosarcoma tissues (χ²=28.166; P<0.001). NCOA5 mRNA and protein expression levels were consistent in human osteosarcoma tissues, and were lower than in control tissues. The expression of NCOA5 was low in human osteosarcoma tissues, while it was high in normal bone tissues. These low NCOA5 expression levels were associated with postoperative survival of human osteosarcoma.

Pro-angiogenic Role of Insulin: From Physiology to Pathology

The underlying molecular mechanisms involve in the regulation of the angiogenic process by insulin are not well understood. In this review article, we aim to describe the role of insulin and insulin receptor activation on the control of angiogenesis and how these mechanisms can be deregulated in human diseases. Functional expression of insulin receptors and their signaling pathways has been described on endothelial cells and pericytes, both of the main cells involved in vessel formation and maturation. Consequently, insulin has been shown to regulate endothelial cell migration, proliferation, and in vitro tubular structure formation through binding to its receptors and activation of intracellular phosphorylation cascades. Furthermore, insulin-mediated pro-angiogenic state is potentiated by generation of vascular growth factors, such as the vascular endothelial growth factor, produced by endothelial cells. Additionally, diseases such as insulin resistance, obesity, diabetes, and cancer may be associated with the deregulation of insulin-mediated angiogenesis. Despite this knowledge, the underlying molecular mechanisms need to be elucidated in order to provide new insights into the role of insulin on angiogenesis.

Marsdeniae tenacissimae extract (MTE) suppresses cell proliferation by attenuating VEGF/VEGFR2 interactions and promotes apoptosis through regulating PKC pathway in human umbilical vein endothelial cells

Marsdeniae tenacissimae extract (MTE), commonly known as Xiao-Ai-Ping in China, is a traditional Chinese herb medicine capable of inhibiting proliferation and metastasis and boosting apoptosis in various cancer cells. However, little is known about the contribution of MTE towards tumor angiogenesis and the underlying mechanism. The present study aimed to evaluate the effects of MTE on the proliferation and apoptosis of human umbilical vein endothelial cells (HUVECs) and the molecular mechanism. 3-(4,5-dimethylthiazol-2-yl)-5(3-carboxymethoxyphenyl)-2-(4-sulfopheny)-2H-tetrazolium, inner salt (MTS) and PI-stained flow cytometry assays revealed that MTE dose-dependently reduced the proliferation of HUVECs by arresting cell cycle at S phase (P < 0.05). Annexin V-FITC/PI-stained flow cytometry confirmed that MTE (160 μL·L^-1) enhanced the apoptosis of HUVECs significantly (P < 0.001). Real-time quantitative RT-PCR and Western blot analyses showed an increase in Bax expression and a sharply decline in Bcl-2 expression; caspase-3 was activated simultaneously in a dose-dependent manner (P < 0.05). Further study observed the dose-dependent down-regulation of vascular endothelial growth factor (VEGF) receptor-2 (VEGFR-2), P2Y6 receptor (P2Y6R), and chemokine (C-C motif) ligand 2 (CCL-2), along with the activation of PKC Δ and up-regulation of p53 in a dose-dependent manner in MTE-treated selected cells (P < 0.05). Collectively, the results from the present study suggested that MTE suppressed the proliferation by attenuating CCL-2-mediated VEGF/VEGFR2 interactions and promoted the apoptosis through PKCΔ-induced p53-dependent mitochondrial pathway in HUVECs, supporting that MTE may be developed as a potent anti-cancer medicine.

How tumour-induced vascular changes alter angiogenesis: Insights from a computational model

A computational model was developed to describe experimentally observed vascular changes induced by the introduction of a tumour on a mouse equipped with a dorsal skinfold chamber. The vascular structure of the host tissue was segmented from in vivo images and transposed into the computational framework. Simulations of tumour-induced vascular changes were performed and include the destabilizing effects of the growth factor VEGF on the integrity of the vessels walls. The integration of those effects, that include alteration of the vessel wall elasticity and wall breaching, were required to realistically reproduce the experimental observations. The model was then used to investigate the importance of the vascular changes for oxygen delivery and tumour development. To that end, we compared simulations obtained with a dynamic vasculature with those obtained with a static one. The results showed that the tumour growth was strongly impeded by the constant vascular changes. More precisely, it is the angiogenic process itself that was affected by vascular changes occurring in bigger upstream vessels and resulting in a less efficient angiogenic network for oxygen delivery. As a consequence, tumour cells are mostly kept in a non-proliferative hypoxic state. Tumour dormancy thus appears as one potential consequence of the intense vascular changes in the host tissue.