Development and validation of a circulating tumor DNA-based optimization-prediction model for short-term postoperative recurrence of endometrial cancer

BACKGROUND Endometrial cancer(EC)is a common gynecological malignancy that typically requires prompt surgical intervention;however,the advantage of surgical management is limited by the high postoperative recurrence rates and adverse outcomes.Previous studies have highlighted the prognostic potential of circulating tumor DNA(ctDNA)monitoring for minimal residual disease in patients with EC.AIM To develop and validate an optimized ctDNA-based model for predicting shortterm postoperative EC recurrence.METHODS We retrospectively analyzed 294 EC patients treated surgically from 2015-2019 to devise a short-term recurrence prediction model,which was validated on 143 EC patients operated between 2020 and 2021.Prognostic factors were identified using univariate Cox,Lasso,and multivariate Cox regressions.A nomogram was created to predict the 1,1.5,and 2-year recurrence-free survival(RFS).Model performance was assessed via receiver operating characteristic(ROC),calibration,and decision curve analyses(DCA),leading to a recurrence risk stratification system.RESULTS Based on the regression analysis and the nomogram created,patients with postoperative ctDNA-negativity,postoperative carcinoembryonic antigen 125(CA125)levels of<19 U/mL,and grade G1 tumors had improved RFS after surgery.The nomogram’s efficacy for recurrence prediction was confirmed through ROC analysis,calibration curves,and DCA methods,highlighting its high accuracy and clinical utility.Furthermore,using the nomogram,the patients were successfully classified into three risk subgroups.CONCLUSION The nomogram accurately predicted RFS after EC surgery at 1,1.5,and 2 years.This model will help clinicians personalize treatments,stratify risks,and enhance clinical outcomes for patients with EC.

A model of five genes of tumor microenvironment predicts prognosis in Cholangiocarcinoma

Background:Cholangiocarcinoma(CCA)is highly malignant and has a poor prognosis has a high malignant degree and poor prognosis.The purpose of this study is to develop a new prognostic model based on genes related to the tumor microenvironment(TME).Methods:Derived from the discerned differentially expressed genes within The Cancer Genome Atlas(TCGA)dataset,this investigation employed the methodology of weighted gene co-expression network analysis(WGCNA)to ascertain gene co-expressed modules intricately linked to the Tumor Microenvironment(TME)among Cholangiocarcinoma(CCA)patients.The genes associated with prognosis,as identified through Cox regression analysis,were employed in the formulation of a predictive model.This model underwent validation,leading to the development of a risk score formula and nomogram.Concurrently,we validated the model’s reliability using data from CCA patients in the Gene Expression Omnibus(GEO)database(accession:GSE107943).Results:6139 DEGs were divided into 10 co-expressed gene modules using WGCNA.Among these,two modules(blue module with 832 genes and brown module with 1379 genes)showed high correlation with the TME.Five prognostic genes(BNIP3,COL4A3,SPRED3,CEBPB,PLOD2)were identified through Cox regression analysis,and a prognostic model and risk score formula were developed based on these genes.Risk score formula:Risk score=BNIP3×1.70520-COL4A3×2.39815+SPRED3×1.17936+CEBPB×0.40456+PLOD2×0.24785.Kaplan-Meier survival analysis revealed that the survival probabilities of the low-risk group were significantly higher than those of the high-risk group.Furthermore,the related evaluation indexes suggested that the model exhibited strong predictive ability.Conclusion:The prognostic model,based on five TME-related genes(BNIP3,COL4A3,SPRED3,CEBPB,PLOD2),could accurately assess the prognosis of CCA patients to aid in guiding clinical decisions.

Modeling One Dimensional Two-Cell Model with Tumor Interaction Using Krylov Subspace Methods

A brain tumor occurs when abnormal cells grow, sometimes very rapidly, into an abnormal mass of tissue. The tumor can infect normal tissue, so there is an interaction between healthy and infected cell. The aim of this paper is to propose some efficient and accurate numerical methods for the computational solution of one-dimensional continuous basic models for the growth and control of brain tumors. After computing the analytical solution, we construct approximations of the solution to the problem using a standard second order finite difference method for space discretization and the Crank-Nicolson method for time discretization. Then, we investigate the convergence behavior of Conjugate gradient and generalized minimum residual as Krylov subspace methods to solve the tridiagonal toeplitz matrix system derived.

Role of cancer stem cell ecosystem on breast cancer metastasis and related mouse models

Breast cancer metastasis is responsible for most breast cancer-related deaths and is influenced by many factors within the tumor ecosystem,including tumor cells and microenvironment.Breast cancer stem cells(BCSCs)constitute a small population of cancer cells with unique characteristics,including their capacity for self-renewal and differentiation.Studies have shown that BCSCs not only drive tumorigenesis but also play a crucial role in promoting metastasis in breast cancer.The tumor microenvironment(TME),composed of stromal cells,immune cells,blood vessel cells,fibroblasts,and microbes in proximity to cancer cells,is increasingly recognized for its crosstalk with BCSCs and role in BCSC survival,growth,and dissemination,thereby influencing metastatic ability.Hence,a thorough understanding of BCSCs and the TME is critical for unraveling the mechanisms underlying breast cancer metastasis.In this review,we summarize current knowledge on the roles of BCSCs and the TME in breast cancer metastasis,as well as the underlying regulatory mechanisms.Furthermore,we provide an overview of relevant mouse models used to study breast cancer metastasis,as well as treatment strategies and clinical trials addressing BCSC-TME interactions during metastasis.Overall,this study provides valuable insights for the development of effective therapeutic strategies to reduce breast cancer metastasis.

3D bioprinting of in vitro porous hepatoma models:establishment,evaluation,and anticancer drug testing

Traditional tumor models do not tend to accurately simulate tumor growth in vitro or enable personalized treatment and are particularly unable to discover more beneficial targeted drugs.To address this,this study describes the use of threedimensional(3D)bioprinting technology to construct a 3D model with human hepatocarcinoma SMMC-7721 cells(3DP-7721)by combining gelatin methacrylate(GelMA)and poly(ethylene oxide)(PEO)as two immiscible aqueous phases to form a bioink and innovatively applying fluorescent carbon quantum dots for long-term tracking of cells.The GelMA(10%,mass fraction)and PEO(1.6%,mass fraction)hydrogel with 3:1 volume ratio offered distinct pore-forming characteristics,satisfactorymechanical properties,and biocompatibility for the creation of the 3DP-7721 model.Immunofluorescence analysis and quantitative real-time fluorescence polymerase chain reaction(PCR)were used to evaluate the biological properties of the model.Compared with the two-dimensional culture cell model(2D-7721)and the 3D mixed culture cell model(3DM-7721),3DP-7721 significantly improved the proliferation of cells and expression of tumor-related proteins and genes.Moreover,we evaluated the differences between the three culture models and the effectiveness of antitumor drugs in the three models and discovered that the efficacy of antitumor drugs varied because of significant differences in resistance proteins and genes between the three models.In addition,the comparison of tumor formation in the three models found that the cells cultured by the 3DP-7721 model had strong tumorigenicity in nude mice.Immunohistochemical evaluation of the levels of biochemical indicators related to the formation of solid tumors showed that the 3DP-7721 model group exhibited pathological characteristics of malignant tumors,the generated solid tumors were similar to actual tumors,and the deterioration was higher.This research therefore acts as a foundation for the application of 3DP-7721 models in drug development research.

Simple Linear Model of Tumor Growth in a Changing Environment

In an environment that is neither static nor in equilibrium, but is dynamic and changing, the kinetics of the reactions that cause the growth of a tumor, which depend on the state of the evolving environment, cannot be parametrized in terms of constant rates. We propose a simple model for describing the growth on an untreated tumor in such environments, which is characterized by a minimal number of parameters and is generalizable to include the effects of various types of therapies. In the simplest version that we consider here, it consists of a linear equation with a time-dependent growth rate, which we interpret as the coupling of the system with a dynamic environment. A complete solution is given in terms of the integral of the growth rate. The essential features of the general solution are illustrated with a few examples, and comparison is made with the models that have been proposed to describe recent data.

Early changes of hepatic hemodynamics measured by functional CT perfusion in a rabbit model of liver tumor

BACKGROUND:Early detection and treatment of hepatocellular carcinoma is crucial to improving the patients’ survival.The hemodynamic changes caused by tumors can be serially measured using CT perfusion.In this study,we used a CT perfusion technique to demonstrate the changes of hepatic hemodynamics in early tumor growth,as a proof-of-concept study for human early hepatocellular carcinoma.METHODS:VX2 tumors were implanted in the liver of ten New Zealand rabbits.CT perfusion scans were made 1 week(early) and 2 weeks(late) after tumor implantation.Ten normal rabbits served as controls.CT perfusion parameters were obtained at the tumor rim,normal tissue surrounding the tumor,and control liver;the parameters were hepatic blood flow,hepatic blood volume,mean transit time,permeability of capillary vessel surface,hepatic arterial index,hepatic arterial perfusion and hepatic portal perfusion.Microvessel density and vascular endothelial growth factor were correlated.RESULTS:At the tumor rim,compared to the controls,hepatic blood flow,hepatic blood volume,permeability of capillary vessel surface,hepatic arterial index,and hepatic arterial perfusion increased,while mean transit time and hepatic portal perfusion decreased on both early and late scans(P<0.05).Hepatic arterial index increased(135%,P<0.05),combined with a sharp increase in hepatic arterial perfusion(182%,P<0.05) and a marked decrease in hepatic portal perfusion(-76%,P<0.05) at 2 weeks rather than at 1 week(P<0.05).Microvessel density and vascular endothelial growth factor showed significant linear correlations with hepatic blood flow,permeability of capillary vessel surface and hepatic arterial index,but not with hepatic blood volume or mean transit time.CONCLUSION:The CT perfusion technique demonstrated early changes of hepatic hemodynamics in this tumor model as proof-of-concept for early hepatocellular carcinoma detection in humans.

Hepatocellular carcinoma mouse models:Hepatitis B virusassociatedhepatocarcinogenesis and haploinsufficienttumor suppressor genes

The multifactorial and multistage pathogenesis of hepatocellular carcinoma(HCC)has fascinated a wide spectrum of scientists for decades.While a number of major risk factors have been identified,their mechanistic roles in hepatocarcinogenesis still need to be elucidated.Many tumor suppressor genes(TSGs)have been identified as being involved in HCC.These TSGs can be classified into two groups depending on the situation with respect to allelic mutation/loss in the tumors:the recessive TSGs with two required mutated alleles and the haploinsufficient TSGs with one required mutated allele.Hepatitis B virus(HBV)is one of the most important risk factors associated with HCC.Although mice cannot be infected with HBV due to the narrow host range of HBV and the lack of a proper receptor,one advantage of mouse models for HBV/HCC research is the numerous and powerfulgenetic tools that help investigate the phenotypic effects of viral proteins and allow the dissection of the dose-dependent action of TSGs.Here,we mainly focus on the application of mouse models in relation to HBV-associated HCC and on TSGs that act either in a recessive or in a haploinsufficient manner.Discoveries obtained using mouse models will have a great impact on HCC translational medicine.

Effects of Lvy noise and immune delay on the extinction behavior in a tumor growth model

The combined effects of Ltvy noise and immune delay on the extinction behavior in a tumor growth model are explored, The extinction probability of tumor with certain density is measured by exit probability. The expression of the exit probability is obtained using the Taylor expansion and the infinitesimal generator theory. Based on numerical calculations, it is found that the immune delay facilitates tumor extinction when the stability index α〈 1, but inhibits tumor extinction when the stability index α 〉 1. Moreover, larger stability index and smaller noise intensity are in favor of the extinction for tumor with low density. While for tumor with high density, the stability index and the noise intensity should be reduced to promote tumor extinction.

3D tumor model biofabrication

Animal models have been extensively used in cancer pathology studies and drug discovery.These models,however,fail to reflect the complex human tumor microenvironment and do not allow for high-throughput drug screening in more human-like physiological conditions.Three-dimensional(3D)cancer models present an alternative to automated high-throughput cancer drug discovery and oncology.In this review,we highlight recent technology innovations in building 3D tumor models that simulate the complex human tumor microenvironment and responses of patients to treatment.We discussed various biofabrication technologies,including 3D bioprinting techniques developed for characterizing tumor progression,metastasis,and response to treatment.

Prediction of tumor biological characteristics in different colorectal cancer liver metastasis animal models using^(18)F-FDG and^(18)F-FLT

Background: Positron emission tomography(PET) is a noninvasive method to characterize different metabolic activities of tumors, providing information for staging, prognosis, and therapeutic response of patients with cancer. The aim of this study was to evaluate the feasibility of18F-fludeoxyglucose(18F-FDG) and 3’-deoxy-3’-18F-fluorothymidine(18F-FLT) PET in predicting tumor biological characteristics of colorectal cancer liver metastasis.Methods: The uptake rate of18F-FDG and18F-FLT in SW480 and SW620 cells was measured via an in vitro cell uptake assay. The region of interest was drawn over the tumor and liver to calculate the maximum standardized uptake value ratio(tumor/liver) from PET images in liver metastasis model. The correlation between tracer uptake in liver metastases and VEGF, Ki67 and CD44 expression was evaluated by linear regression.Results: Compared to SW620 tumor-bearing mice, SW480 tumor-bearing mice presented a higher rate of liver metastases. The uptake rate of18F-FDG in SW480 and SW620 cells was 6.07% ± 1.19% and2.82% ± 0.15%, respectively(t = 4.69, P = 0.04); that of18F-FLT was 24.81% ± 0.45% and 15.57% ± 0.66%, respectively(t = 19.99, P < 0.001). Micro-PET scan showed that all parameters of FLT were significantly higher in SW480 tumors than those in SW620 tumors. A moderate relationship was detected between metastases in the liver and18F-FLT uptake in primary tumors(r = 0.73, P = 0.0019).18F-FLT uptake was also positively correlated with the expression of CD44 in liver metastases(r = 0.81, P = 0.0049).Conclusions: The uptake of18F-FLT in metastatic tumor reflects different biological behaviors of colon cancer cells.18F-FLT can be used to evaluate the metastatic potential of colorectal cancer in nude mice.

The construction of in vitro tumormodels based on 3D bioprinting

Cancer is characterized by a high fatality rate,complex molecular mechanism,and costly therapies.The microenvironment of a tumor consists of multiple biochemical cues and the interaction between tumor cells,stromal cells,and extracellular matrix plays a key role in tumor initiation,development,angiogenesis,invasion and metastasis.To better understand the biological features of tumor and reveal the critical factors of therapeutic treatments against cancer,it is of great significance to build in vitro tumor models that could recapitulate the stages of tumor progression and mimic tumor behaviors in vivo for efficient and patient-specific drug screening and biological studies.Since conventional tissue engineering methods of constructing tumor models always fail to simulate the later stages of tumor development due to the lack of ability to build complex structures and angiogenesis potential,three-dimensional(3D)bioprinting techniques have gradually found its applications in tumor microenvironment modeling with accurate composition and well-organized spatial distribution of tumor-related cells and extracellular components in the past decades.The capabilities of building tumor models with a large range of scale,complex structures,multiple biomaterials and vascular network with high resolution and throughput make 3D bioprinting become a versatile platform in bio-manufacturing aswell as inmedical research.In this review,wewill focus on 3D bioprinting strategies,design of bioinks,current 3D bioprinted tumor models in vitro classified with their structures and propose future perspectives.

Advances in prostate cancer research models:From transgenic mice to tumor xenografting models

The identification of the origin and molecular characteristics of prostate cancer(PCa)has crucial implications for personalized treatment.The development of effective treatments for PCa has been limited;however,the recent establishment of several transgenicmouse lines and/or xenografting models is better reflecting the disease in vivo.With appropriate models,valuable tools for elucidating the functions of specific genes have gone deep into prostate development and carcinogenesis.In the present review,we summarize a number of important PCa research models established in our laboratories(PSA-Cre-ERT2/PTEN transgenic mouse models,AP-OX model,tissue recombination-xenografting models and PDX models),which represent advances of translational models from transgenic mouse lines to human tumor xenografting.Better understanding of the developments of these models will offer new insights into tumor progression and may help explain the functional significance of genetic variations in PCa.Additionally,this understanding could lead to new modes for curing PCa based on their particular biological phenotypes.

Similarity on neural stem cells and brain tumor stem cells in transgenic brain tumor mouse models

Although it is believed that glioma is derived from brain tumor stem cells, the source and molecular signal pathways of these cells are still unclear. In this study, we used stable doxycycline-inducible transgenic mouse brain tumor models （c-myc/SV40Tag＋/Tet-on＋） to explore the malignant trans- formation potential of neural stem cells by observing the differences of neural stem cells and brain tumor stem cells in the tumor models. Results showed that chromosome instability occurred in brain tumor stem cells. The numbers of cytolysosomes and autophagosomes in brain tumor stem cells and induced neural stem cells were lower and the proliferative activity was obviously stronger than that in normal neural stem cells. Normal neural stem cells could differentiate into glial fibrillary acidic protein-positive and microtubule associated protein-2-positive cells, which were also negative for nestin. However, glial fibrillary acidic protein/nestin, microtubule associated protein-2/nestin, and glial fibrillary acidic protein/microtubule associated protein-2 double-positive cells were found in induced neural stem cells and brain tumor stem cells. Results indicate that induced neural stem cells are similar to brain tumor stem cells, and are possibly the source of brain tumor stem cells.

Non-Linear Mathematical Model of the Interaction between Tumor and Oncolytic Viruses

A mathematical modeling of tumor therapy with oncolytic viruses is discussed. The model consists of two coupled, deterministic differential equations allowing for cell reproduction and death, and cell infection. The model is one of the conceptual mathematical models of tumor growth that treat a tumor as a dynamic society of interacting cells. In this paper, we obtain an approximate analytical expression of uninfected and infected cell population by solving the non-linear equations using Homotopy analysis method (HAM). Furthermore, the results are compared with the numerical simulation of the problem using Matlab program. The obtained results are valid for the whole solution domain.

A Semi-automatic method for segmentation and 3D modeling of glioma tumors from brain MRI

This work presents an efficient method for volume rendering of glioma tumors from segmented 2D MRI Datasets with user interactive control, by replacing manual segmentation required in the state of art methods. The most common primary brain tumors are gliomas, evolving from the cerebral supportive cells. For clinical follow-up, the evaluation of the preoperative tumor volume is essential. Tumor portions were automatically segmented from 2D MR images using morphological filtering techniques. These segmented tumor slices were propagated and modeled with the software package. The 3D modeled tumor consists of gray level values of the original image with exact tumor boundary. Axial slices of FLAIR and T2 weighted images were used for extracting tumors. Volumetric assessment of tumor volume with manual segmentation of its outlines is a time-consuming process and is prone to error. These defects are overcome in this method. Authors verified the performance of our method on several sets of MRI scans. The 3D modeling was also done using segmented 2D slices with the help of medical software package called 3D DOCTOR for verification purposes. The results were validated with the ground truth models by the Radiologist.

Clinical Analysis of Primary Tracheobronchial Tumors in Children and Evaluation of the Predicting Models for Mucoepidermoid Carcinoma

Objective:To determine the clinical characteristics and prognosis of primary tracheobronchial tumors(PTTs)in children,and to explore the most common tumor identification methods.Methods:The medical records of children with PTTs who were hospitalized at the Children's Hospital of Chongqing Medical University from January 1995 to January 2020 were reviewed retrospectively.The clinical features,imaging,treatments,and outcomes of these patients were statistically analyzed.Machine learning techniques such as Gaussian na?ve Bayes,support vector machine(SVM)and decision tree models were used to identify mucoepidermoid carcinoma(ME).Results:A total of 16 children were hospitalized with PTTs during the study period.This included 5(31.3%)children with ME,3(18.8%)children with inflammatory myofibroblastic tumors(IMT),2 children(12.5%)with sarcomas,2(12.5%)children with papillomatosis and 1 child(6.3%)each with carcinoid carcinoma,adenoid cystic carcinoma(ACC),hemangioma,and schwannoma,respectively.ME was the most common tumor type and amongst the 3 ME recognition methods,the SVM model showed the best performance.The main clinical symptoms of PPTs were cough(81.3%),breathlessness(50%),wheezing(43.8%),progressive dyspnea(37.5%),hemoptysis(37.5%),and fever(25%).Of the 16 patients,7 were treated with surgery,8 underwent bronchoscopic tumor resection,and 1 child died.Of the 11 other children,3 experienced recurrence,and the last 8 remained disease-free.No deaths were observed during the follow-up period.Conclusion:PTT are very rare in children and the highest percentage of cases is due to ME.The SVM model was highly accurate in identifying ME.Chest CT and bronchoscopy can effectively diagnose PTTs.Surgery and bronchoscopic intervention can both achieve good clinical results and the prognosis of the 11 children that were followed up was good.

Spontaneous xenogeneic GvHD in Wilms'tumor Patient-Derived xenograft models and potential solutions

Severely immunocompromised NOD.Cg-PrkdcIl2rg(NOG)mice are among the ideal animal recipients for generation of human cancer models.Transplantation of human solid tumors having abundant tumor-i nfiltrating lymphocytes(TILs)can induce xenogeneic graft-versus-host disease(xGvHD)following engraftment and expansion of the TILs inside the animal body.Wilms’tumor(WT)has not been recognized as a lymphocyte-predominant tumor.However,3 consecutive generations of NOG mice bearing WT patient-derived xenografts(PDX)xenotransplanted from a single donor showed different degrees of inflammatory symptoms after transplantation before any therapeutic intervention.In the initial generation,dermatitis,auto-amputation of digits,weight loss,lymphadenopathy,hepatitis,and interstitial pneumonitis were observed.Despite antibiotic treatment,no response was noticed,and thus the animals were prematurely euthanized(day 47 posttransplantation).Laboratory and histopathologic evaluations revealed lymphoid infiltrates positively immunostained with anti-human CD3 and CD8 antibodies in the xenografts and primary tumor,whereas no microbial infection or lymphoproliferative disorder was found.Mice of the next generation that lived longer(91 days)developed sclerotic skin changes and more severe pneumonitis.Cutaneous symptoms were milder in the last generation.The xenografts of the last 2 generations also contained TILs,and lacked lymphoproliferative transformation.The systemic immunoinflammatory syndrome in the absence of microbial infection and posttransplant lymphoproliferative disorder was suggestive of xGvHD.While there are few reports of xGvHD in severely immunodeficient mice xenotransplanted from lymphodominant tumor xenografts,this report for the first time documented serial xGvHD in consecutive passages of WT PDX-bearing models and discussed potential solutions to prevent such an undesired complication.

<i>In Vitro</i>Evaluation System of Pharmacokinetics and Irradiation Effect in Boron Neutron Capture Therapy (BNCT) Using Three-Dimensional Artificial Human Tumor Tissue Model

Boron neutron capture therapy (BNCT) is based on the incorporation of boron-containing drugs to cancer cells and the nuclear reaction of 10B atoms by thermal neutron irradiation results in tumor degeneration. For the development of this therapy, currently, long time and high cost consuming experiments using many animals are required. In this study, we constructed a new in vitro evaluation system for BNCT by combination of an artificial tumor tissue model, comprised of normal human dermal-derived fibroblast (NHDF) and human pancreatic cancer cell line BxPC3, and the optical plastic material CR-39 as a solid state nuclear track detector. Administration of boronophenylalanine (10BPA) as a boron-containing drug and neutron irradiation up to 2.52 × 1012 n/cm2 to the control tissue constructed by NHDF (NHDF3D) and BxPC3 cell loaded tissue (NHDF3D/BxPC3) resulted in detection of 1.6 times higher number of α-ray/recoiled Li particle tracks in NHDF3D/BxPC3 in comparison to NHDF3D, demonstrating that putative irradiation damage to cancer cells can be evaluated by this system. On a cellular level, the hit number of α-ray/recoiled Li particle tracks per single BxPC3 cells and NHDF was evaluated as 5.46 and 1.71, respectively. The tumor and normal tissue ratio (T/N ratio) was 3.19, which was corresponded with those of BPA as 2 - 4 that reported in the previous studies. This new in vitro evaluation system may provide a useful tool for a low cost, labor-saving, and non-animal method for the development of new boron-containing drugs or improvement of BNCT conditions.

Existence and Uniqueness of Almost Periodic Solution for a Mathematical Model of Tumor Growth

This article is concerned with a mathematical model of tumor growth governed by 2nd order diffusion equation . The source of mitotic inhibitor is almost periodic and time-dependent within the tissue. The system is set up with the initial condition C(r, 0) = C0(r) and Robin type inhomogeneous boundary condition . Under certain conditions we show that there exists a unique solution for this model which is almost periodic.