Cancer models in preclinical research:A chronicle review of advancement in effective cancer research

Cancer is a major stress for public well-being and is the most dreadful disease.The models used in the discovery of cancer treatment are continuously changing and extending toward advanced preclinical studies.Cancer models are either naturally existing or artificially prepared experimental systems that show similar features with human tumors though the heterogeneous nature of the tumor is very familiar.The choice of the most fitting model to best reflect the given tumor system is one of the real difficulties for cancer examination.Therefore,vast studies have been conducted on the cancer models for developing a better understanding of cancer invasion,progression,and early detection.These models give an insight into cancer etiology,molecular basis,host tumor interaction,the role of microenvironment,and tumor heterogeneity in tumor metastasis.These models are also used to predict novel can-cer markers,targeted therapies,and are extremely helpful in drug development.In this review,the potential of cancer models to be used as a platform for drug screening and therapeutic discoveries are highlighted.Although none of the cancer models is regarded as ideal because each is associated with essential caveats that restraint its application yet by bridging the gap between preliminary cancer research and transla-tional medicine.However,they promise a brighter future for cancer treatment.

An Efficient Technique for One-Dimensional Fractional Diffusion EquationModel for Cancer Tumor

This study intends to examine the analytical solutions to the resulting one-dimensional differential equation of acancer tumor model in the frame of time-fractional order with the Caputo-fractional operator employing a highlyefficient methodology called the q-homotopy analysis transform method.So,the preferred approach effectivelyfound the analytic series solution of the proposed model.The procured outcomes of the present frameworkdemonstrated that this method is authentic for obtaining solutions to a time-fractional-order cancer model.Theresults achieved graphically specify that the concerned paradigm is dependent on arbitrary order and parametersand also disclose the competence of the proposed algorithm.

Dynamics of Advantageous Mutant Spread in Spatial Death-Birth and Birth-Death Moran Models

The spread of an advantageous mutation through a population is of fundamental interest in population genetics. While the classical Moran model is formulated for a well-mixed population, it has long been recognized that in real-world applications, the population usually has an explicit spatial structure which can significantly influence the dynamics. In the context of cancer initiation in epithelial tissue, several recent works have analyzed the dynamics of advantageous mutant spread on integer lattices, using the biased voter model from particle systems theory. In this spatial version of the Moran model, individuals first reproduce according to their fitness and then replace a neighboring individual. From a biological standpoint, the opposite dynamics, where individuals first die and are then replaced by a neighboring individual according to its fitness, are equally relevant. Here, we investigate this death-birth analogue of the biased voter model. We construct the process mathematically, derive the associated dual process, establish bounds on the survival probability of a single mutant, and prove that the process has an asymptotic shape. We also briefly discuss alternative birth-death and death-birth dynamics, depending on how the mutant fitness advantage affects the dynamics. We show that birth-death and death-birth formulations of the biased voter model are equivalent when fitness affects the former event of each update of the model, whereas the birth-death model is fundamentally different from the death-birth model when fitness affects the latter event.

Well-Posedness and a Finite Difference Approximation for a Mathematical Model of HPV-Induced Cervical Cancer

We present a first-order finite difference scheme for approximating solutions of a mathematical model of cervical cancer induced by the human papillomavirus (HPV), which consists of four nonlinear partial differential equations and a nonlinear first-order ordinary differential equation. The scheme is analyzed and used to provide an existence-uniqueness result. Numerical simulations are performed in order to demonstrate the first-order rate of convergence. A sensitivity analysis was done in order to compare the effects of two drug types, those that increase the death rate of HPV-infected cells, and those that increase the death rate of the precancerous cell population. The model predicts that treatments that affect the precancerous cell population by directly increasing the corresponding death rate are far more effective than those that increase the death rate of HPV-infected cells.

Design of Computer Methods for the Solution of Cervical Cancer Epidemic Model

Nonlinear modelling has a significant role in different disciplines of sciences such as behavioral,social,physical and biological sciences.The structural properties are also needed for such types of disciplines,as dynamical consistency,positivity and boundedness are the major requirements of the models in these fields.One more thing,this type of nonlinear model has no explicit solutions.For the sake of comparison its computation will be done by using different computational techniques.Regrettably,the aforementioned structural properties have not been restored in the existing computational techniques in literature.Therefore,the construction of structural preserving computational techniques are needed.The nonlinearmodel for cervical cancer is constructed by parametric perturbation technique.Well-known computer methods are considered for the computation of cervical cancer dynamics.The well-known existing methods in literature are Euler Maruyama,Euler and Runge Kutta.Nonstandard finite difference method or Implicitly driven explicit method is first time considered for aforesaid model under the assumptions given byMickens in a stochastic way.Unfortunately,the aforementioned existing methods did not reinstate structural properties of cervical cancer dynamics in the human population.Our plannedmethod is structural preserving and a powerful tool for all nonlinear models of biomedical engineering problems.We have verified that existing computational methods do not preserve dynamical properties.But,the implicitly driven explicit method is a good device for dynamical properties.In the support of assertions,convergence analysis of implicitly driven explicit method is presented.

Stability analysis of a fractional-order cancer model with chaotic dynamics

In this paper,we develop a three-dimensional fractional-order cancer model.The proposed model involves the interaction among tumor cells,healthy tissue cells and activated effector cells.The detailed analysis of the equilibrium points is studied.Also,the existence and uniqueness of the solution are investigated.The fractional derivative is considered in the Caputo sense.Numerical simulations are performed to illustrate the effectiveness of the obtained theoretical results.The outcome of the study reveals that the order of the fractional derivative has a significant effect on the dynamic process.Further,the calculated Lyapunov exponents give the existence of chaotic behavior of the proposed model.Also,it is observed from the obtained results that decrease in fractional-order p increases the chaotic behavior of the model.

Tissue-specific cancer stem/progenitor cells:Therapeutic implications

Surgical resection,chemotherapy,and radiation are the standard therapeutic modalities for treating cancer.These approaches are intended to target the more mature and rapidly dividing cancer cells.However,they spare the relatively quiescent and intrinsically resistant cancer stem cells(CSCs)subpopulation residing within the tumor tissue.Thus,a temporary eradication is achieved and the tumor bulk tends to revert supported by CSCs'resistant features.Based on their unique expression profile,the identification,isolation,and selective targeting of CSCs hold great promise for challenging treatment failure and reducing the risk of cancer recurrence.Yet,targeting CSCs is limited mainly by the irrelevance of the utilized cancer models.A new era of targeted and personalized anti-cancer therapies has been developed with cancer patient-derived organoids(PDOs)as a tool for establishing pre-clinical tumor models.Herein,we discuss the updated and presently available tissue-specific CSC markers in five highly occurring solid tumors.Additionally,we highlight the advantage and relevance of the threedimensional PDOs culture model as a platform for modeling cancer,evaluating the efficacy of CSC-based therapeutics,and predicting drug response in cancer patients.

Individualized prediction of perineural invasion in colorectal cancer: development and validation of a radiomics prediction model

Objective： To develop and validate a radiomics prediction model for individualized prediction of perineural invasion（PNI） in colorectal cancer（CRC）.Methods： After computed tomography（CT） radiomics features extraction, a radiomics signature was constructed in derivation cohort（346 CRC patients）. A prediction model was developed to integrate the radiomics signature and clinical candidate predictors [age, sex, tumor location, and carcinoembryonic antigen（CEA） level]. Apparent prediction performance was assessed. After internal validation, independent temporal validation（separate from the cohort used to build the model） was then conducted in 217 CRC patients. The final model was converted to an easy-to-use nomogram.Results： The developed radiomics nomogram that integrated the radiomics signature and CEA level showed good calibration and discrimination performance [Harrell＇s concordance index（c-index）： 0.817; 95% confidence interval（95% CI）： 0.811–0.823]. Application of the nomogram in validation cohort gave a comparable calibration and discrimination（c-index： 0.803; 95% CI： 0.794–0.812）.Conclusions： Integrating the radiomics signature and CEA level into a radiomics prediction model enables easy and effective risk assessment of PNI in CRC. This stratification of patients according to their PNI status may provide a basis for individualized auxiliary treatment.

Mouse models of colorectal cancer

Colorectal cancer is one of the most common malignancies in the world. Many mouse models have been developed to evaluate features of colorectal cancer in humans. These can be grouped into genetically-engineered, chemically-induced, and inoculated models. However, none recapitulates all of the characteristics of human colorectal cancer. It is critical to use a specific mouse model to address a particular research question. Here, we review commonly used mouse models for human colorectal cancer.

A new exploration on the creation of grafted breast cancer model for MA891 cells in TA2 mice

Animal experimental systems are particularly useful for the study of human breast cancer. An ideal model shoulcl be easy to use, closely mimicking human physiopathology and has a stable tumor morbidity. The cell line MA891 was established from a spontaneous TA2 mouse mammary carcinoma by Cancer Institute of Chinese Academy of Medical Sciences. 3 Some researches indicated that MA891 had a very low immunogenecity and maintained a high metastatic potential in vivo. So it has been used as a better grafted mouse tumor model for studying cancer physiopathology and metastasis in human for years. However, about the biological characteristic and the histopathologic feature of this model there has been a lack of investigations.

Establishment of an orthotopic pancreatic cancer mouse model: Cells suspended and injected in Matrigel

AIM: To establish an orthotopic mouse model of pancreatic cancer that mimics the pathological features of exocrine pancreatic adenocarcinoma.

Recurrence risk model for esophageal cancer after radical surgery

Objective：The aim of the present study was to construct a risk assessment model which was tested by disease-free survival （DFS） of esophageal cancer after radical surgery.Methods：A total of 164 consecutive esophageal cancer patients who had undergone radical surgery between January 2005 and December 2006 were retrospectively analyzed.The cutpoint of value at risk （VaR） was inferred by stem-and-leaf plot,as well as by independent-samples t-test for recurrence-free time,further confirmed by crosstab chi-square test,univariate analysis and Cox regression analysis for DFS.Results：The cutpoint of VaR was 0.3 on the basis of our model.The rate of recurrence was 30.3 ％ （30/99）and 52.3％ （34/65） in VaR ＜0.3 and VaR ＞0.3 （chi-square test,x2 =7.984,P=0.005）,respectively.The 1-,3-,and 5-year DFS of esophageal cancer after radical surgery was 70.4％,48.7％,and 45.3％,respectively in VaR ＞≥0.3,whereas 91.5％,75.8％,and 67.3％,respectively in VaR ＜0.3 （Log-rank test,x2 =9.59,P=0.0020）,and further confirmed by Cox regression analysis [hazard ratio =2.10,95 ％ confidence interval （CI）：1.2649-3.4751; P=0.0041].Conclusions：The model could be applied for integrated assessment of recurrence risk after radical surgery for esophageal cancer.

Inhibition of growth and metastasis of triple-negative breast cancer targeted by Traditional Chinese Medicine Tubeimu in orthotopic mice models

Objective： Triple-negative breast cancer（TNBC） is highly invasive and metastatic, which is in urgent need of transformative therapeutics. Tubeimu（TBM）, the rhizome of Bolbostemma paniculatum（Maxim.） Franquet, is one of the Chinese medicinal herbs used for breast diseases since the ancient times. The present study evaluated the efficacy, especially the anti-metastatic effects of the dichloromethane extract of Tubeimu（ETBM） on TNBC orthotopic mouse models and cell lines.Methods： We applied real-time imaging on florescent orthotopic TNBC mice model and tested cell migration and invasion abilities with MDA-MB-231 cell line. Digital gene expression sequencing was performed and Kyoto Encyclopedia of Genes and Genomes（KEGG） analysis applied to explore the pathways influenced by ETBM.Moreover, quantitative real-time polymerase chain reactions（q RT-PCR） and Western blot were delivered to confirm the gene expression changes.Results： ETBM exhibited noticeable control on tumor metastasis and growth of TNBC tumors with no obvious toxicity. In compliance with this, it also showed inhibition of cell migration and invasion in vitro. Its impact on the changed biological behavior in TNBC may be a result of decreased expression of integrin β1（ITGβ1）, integrin β8（ITGβ8） and Rho GTPase activating protein 5（ARHGAP5）, which disabled the focal adhesion pathway and caused change in cell morphology.Conclusions： This study reveals that ETBM has anti-metastatic effects on MDA-MB-231-GFP tumor and may lead to a new therapeutic agent for the integrative treatment of highly invasive TNBC.

Animal models of human colorectal cancer:Current status, uses and limitations

AIM: To make orthotopic colon cancer murine modelsa more clearly understood subject. The orthotopic tumor models have been found to be more relevant in replicating the human disease process as compared to heterotopic models, many techniques for making orthotopic colorectal murine models have been reported.METHODS: We evaluated the current literature for various reported orthotopic colon cancer models to understand their techniques, advantages and limitations. An extensive literature review was performed by searching the National Library of Medicine Database(Pub Med) using Me SH terms animal model; colon cancer; orthotopic model; murine model. Twenty studies related to colon cancer orthotopic xenograft model were evaluated in detail and discussed here. RESULTS: The detailed analysis of all relevant reports on orthotopic model showed tumor take rate between 42%-100%. While models using the enema technique and minimally invasive technique have reported development of tumor from mucosa with tumor take rate between 87%-100% with metastasis in 76%-90%.CONCLUSION: Over the years, the increased understanding of the murine models of human colon cancer has resulted in the development of various models. Each reported model has some limitations. These latest models have opened up new doors for continuing cancer research for not only understanding the colon cancer pathogenesis but also aid in the development of newer chemotherapeutic drugs as they mimic the human disease closely.

Mathematical Models and Numerical Simulation for Dynamic Evolutions of Cancer and Immune Cells

Cancer is a major public health problem worldwide and finding a total cure or eradication of the disease has been the expectations of medical researchers and medical practitioners in the recent times. In this paper, invasion of normal cells by carcinogens is considered. The purpose of the research is to study the dynamic evolutions of cancer and immune cells with the view finding most effective strategic way to control or eradicate cancer growth in human beings. We proposed five growths and mitigate models for benign and malignant cancer which are coupled ordinary differential equations and partial differential equations and Numerical simulations are made for the models. Analytic and Numerical solutions and sensitivity analysis of the models to parameters are obtained. It is found that the benign and malignant cancer cells displayed out of control growth and hence unstable in nature and the immune cells depreciated to the point of immune collapse. By the use of energy function it is established that staving of cancer cells of oxygen or use of drugs are strategic ways of combating cancer disease. Moreover, if the cancer cells are starved of basic nutrients or some basic enzymes inhibited it is expected that similar effect can also be achieved. The starvation of cancer cells should focus on oxygen, nutrients and vital enzymes. However, it is hoped that drugs developers and bioengineers will come up with means to achieve the starvation strategies to combat cancer disease.

A Statistical Model with Non-Linear Effects and Non-Proportional Hazards for Breast Cancer Survival Analysis

The Cox proportional hazard model is being used extensively in oncology in studying the relationship between survival times and prognostic factors. The main question that needs to be addressed with respect to the applicability of the Cox PH model is whether the proportional hazard assumption is met. Failure to justify the subject assumption will lead to misleading results. In addition, identifying the correct functional form of the continuous covariates is an important aspect in the development of a Cox proportional hazard model. The purpose of this study is to develop an extended Cox regression model for breast cancer survival data which takes non-proportional hazards and non-linear effects that exist in prognostic factors into consideration. Non-proportional hazards and non-linear effects are detected using methods based on residuals. An extended Cox model with non-linear effects and time-varying effects is proposed to adjust the Cox proportional hazard model. Age and tumor size were found to have nonlinear effects. Progesterone receptor assay status and age violated the proportional hazard assumption in the Cox model. Quadratic effect of age and progesterone receptor assay status had hazard ratio that changes with time. We have introduced a statistical model to overcome the presence of the proportional hazard assumption violation for the Cox proportional hazard model for breast cancer data. The proposed extended model considers the time varying nature of the hazard ratio and non-linear effects of the covariates. Our improved Cox model gives a better insight on the hazard rates associated with the breast cancer risk factors.

Pancreatic cancer organotypics: High throughput, preclinical models for pharmacological agent evaluation

Pancreatic cancer carries a terrible prognosis,as the fourth most common cause of cancer death in the Western world.There is clearly a need for new therapies to treat this disease.One of the reasons no effective treatment has been developed in the past decade may in part,be explained by the diverse influences exerted by the tumour microenvironment.The tumour stroma cross-talk in pancreatic cancer can influence chemotherapy delivery and response rate.Thus,appropriate preclinical in vitro models which can bridge simple 2D in vitro cell based assays and complex in vivo models are required to understand the biology of pancreatic cancer.Here we discuss the evolution of 3D organotypic models,which recapitulare the morphological and functional features of pancreatic ductal adenocarcinoma(PDAC).Organotypic cultures are a valid high throughput preclinical in vitro model that maybe a useful tool to help establish new therapies for PDAC.A huge advantage of the organotypic model system is that any component of the model can be easily modulated in a short timeframe.This allows new therapies that can target the cancer,the stromal compartment or both to be tested in a model that mirrors the in vivo situation.A major challenge for the future is to expand the cellular composition of the organotypic model to further develop a system that mimics the PDAC environment more precisely.We discuss how this challenge is being met to increase our understanding of this terrible disease and develop novel therapies that can improve the prognosis for patients.

Three-dimensional cell culture systems as an in vitro platform for cancer and stem cell modeling

Three-dimensional(3D)culture systems are becoming increasingly popular due to their ability to mimic tissue-like structures more effectively than the monolayer cultures.In cancer and stem cell research,the natural cell characteristics and architectures are closely mimicked by the 3D cell models.Thus,the 3D cell cultures are promising and suitable systems for various proposes,ranging from disease modeling to drug target identification as well as potential therapeutic substances that may transform our lives.This review provides a comprehensive compendium of recent advancements in culturing cells,in particular cancer and stem cells,using 3D culture techniques.The major approaches highlighted here include cell spheroids,hydrogel embedding,bioreactors,scaffolds,and bioprinting.In addition,the progress of employing 3D cell culture systems as a platform for cancer and stem cell research was addressed,and the prominent studies of 3D cell culture systems were discussed.

Progress in research on the application of nursing models for breast cancer patients during the perioperative period

Nursing models at home and abroad for breast cancer patients during the perioperative period were screened, including eight types of models： the nursing model guided by self-care theory, the plan-do-check-act cycle combined with the four-in-one model, the peer support nursing model, the nursing model guided by transcultural theory, the multidisciplinary cooperative nursing model, the knowledge-attitude-practice nursing model, the safe nursing management model, and the case nursing model. These models were analyzed and described with the aim of providing a reference for the clinical breast surgery nursing staff in China and for promoting the development of nursing in China for breast cancer the perioperative period.