An integrated computational/experimental model of tumor invasion

Gradient-induced instability in tumour spheroids unveils the impact of microenvironmental nutrient changes

Tumours often display invasive behaviours that induce fingering, branching and fragmentation processes. The phenomenon, known as diffusional instability, is driven by differential cell proliferation, migration, and death due to the presence of metabolite and catabolite concentration gradients. An understanding of the intricate dynamics of this spatially heterogeneous process plays a key role in the investigation of tumour growth and invasion. In this study, we developed an in vitro tumour invasion assay to investigate cell invasiveness in tumour spheroids under a chemotactic stimulus. Our method, employing tumour spheroids seeded in a 3D collagen gel within a microfluidic chemotaxis chamber, focuses on the role of diffusive gradients. Using Time-Lapse Microscopy, the dynamic evolution of tumour spheroids was monitored in real-time, providing a comprehensive view of the morphological changes and cell migration patterns under different chemotactic conditions. Specifically, we explored the impact of fetal bovine serum (FBS) gradients on the behaviour of CT26 mouse colon carcinoma cells and compared the effects of varying FBS concentrations to two isotropic control conditions. Furthermore, a finite element in silico model was developed to quantify the diffusive flow of nutrients in the chemotaxis chamber and obtain a detailed understanding of tumour dynamics. Our findings reveal that the presence of a chemotactic gradient significantly influences tumour invasiveness, with higher concentrations of nutrients associated with increased cancer growth and cell migration.

Bioengineering Human Upper Respiratory Mucosa: A Systematic Review of the State of the Art of Cell Culture Techniques

BACKGROUND

The upper respiratory mucosa plays a crucial role in both the physical integrity and immunological function of the respiratory tract. However, in certain situations such as infections, trauma, or surgery, it might sustain damage. Tissue engineering, a field of regenerative medicine, has found applications in various medical fields including but not limited to plastic surgery, ophthalmology, and urology. However, its application to the respiratory system remains somewhat difficult due to the complex morphology and histology of the upper respiratory tract. To date, a culture protocol for producing a handleable, well-differentiated nasal mucosa has yet to be developed. The objective of this review is to describe the current state of research pertaining to cell culture techniques used for producing autologous healthy human upper respiratory cells and mucosal tissues, as well as describe its clinical applications.

METHODS

A search of the relevant literature was carried out with no time restriction across Embase, Cochrane, PubMed, and Medline Ovid databases. Keywords related to "respiratory mucosa" and "culture techniques of the human airway" were the focus of the search strategy for this review. The risk of bias in retained studies was assessed using the Joanna Briggs Institute's (JBI) critical appraisal tools for qualitative research. A narrative synthesis of our results was then conducted.

RESULTS

A total of 33 studies were included in this review, and thirteen of these focused solely on developing a cell culture protocol without further use. The rest of the studies used their own developed protocol for various applications such as cystic fibrosis, pharmacological, and viral research. One study was able to develop a promising model for nasal mucosa that could be employed as a replacement in nasotracheal reconstructive surgery.

CONCLUSIONS

This systematic review extensively explored the current state of research regarding cell culture techniques for producing tissue-engineered nasal mucosa. Bioengineering the nasal mucosa holds great potential for clinical use. However, further research on mechanical properties is essential, as the comparison of engineered tissues is currently focused on morphology rather than comprehensive mechanical assessments.

Estimation of the injection criteria for magnetic hyperthermia therapy based on tumor morphology

Intratumoral multi-injection strategy enhances the efficacy of magnetic nanoparticle hyperthermia therapy (MNPH). In this study, criteria for the selection of injections and their location depending on the tumor shape/geometry are developed. The developed strategy is based on the thermal dosimetry results of different invasive 3D tumor models during MNPH simulation. MNPH simulations are conducted on physical tumor tissue models encased within healthy tissue. The tumor shapes are geometrically divided into a central tumor region containing maximum tumor volume and a peripheral tumor portion protruding in any random direction. The concepts of core and invasive radius are used to geometrically divide the tumor volume. Primary & secondary injections are used to inject MNP fluid into these respective tumor regions based on the invasiveness of the tumor. The optimization strategy is devised based on the zone of influence of primary & secondary injection. Results indicate that the zone of influence of secondary injection lies between 0.7 and 0.8 times the radial distance between the center of the tumor core and branch node point (extreme far endpoint on the invasive tumor surface). Additionally, the multi-injection strategy is more effective when the protrusion volume exceeds10%of the total volume. The proposed algorithm is used to devise multi-injection strategies for arbitrarily shaped tumors and will assist in pre-planning magnetic nanoparticle hyperthermia therapy.

Modeling free tumor growth: Discrete, continuum, and hybrid approaches to interpreting cancer development

Tumor growth dynamics serve as a critical aspect of understanding cancer progression and treatment response to mitigate one of the most pressing challenges in healthcare. The in silico approach to understanding tumor behavior computationally provides an efficient, cost-effective alternative to wet-lab examinations and are adaptable to different environmental conditions, time scales, and unique patient parameters. As a result, this paper explored modeling of free tumor growth in cancer, surveying contemporary literature on continuum, discrete, and hybrid approaches. Factors like predictive power and high-resolution simulation competed against drawbacks like simulation load and parameter feasibility in these models. Understanding tumor behavior in different scenarios and contexts became the first step in advancing cancer research and revolutionizing clinical outcomes.

Cancer cell sedimentation in 3D cultures reveals active migration regulated by self-generated gradients and adhesion sites

Cell sedimentation in 3D hydrogel cultures refers to the vertical migration of cells towards the bottom of the space. Understanding this poorly examined phenomenon may allow us to design better protocols to prevent it, as well as provide insights into the mechanobiology of cancer development. We conducted a multiscale experimental and mathematical examination of 3D cancer growth in triple negative breast cancer cells. Migration was examined in the presence and absence of Paclitaxel, in high and low adhesion environments and in the presence of fibroblasts. The observed behaviour was modeled by hypothesizing active migration due to self-generated chemotactic gradients. Our results did not reject this hypothesis, whereby migration was likely to be regulated by the MAPK and TGF-β pathways. The mathematical model enabled us to describe the experimental data in absence (normalized error<40%) and presence of Paclitaxel (normalized error<10%), suggesting inhibition of random motion and advection in the latter case. Inhibition of sedimentation in low adhesion and co-culture experiments further supported the conclusion that cells actively migrated downwards due to the presence of signals produced by cells already attached to the adhesive glass surface.

Bladder cancer: therapeutic challenges and role of 3D cell culture systems in the screening of novel cancer therapeutics

Bladder cancer (BC) is the sixth most common worldwide urologic malignancy associated with elevated morbidity and mortality rates if not well treated. The muscle-invasive form of BC develops in about 25% of patients. Moreover, according to estimates, 50% of patients with invasive BC experience fatal metastatic relapses. Currently, resistance to drug-based therapy is the major tumble to BC treatment. The three-dimensional (3D) cell cultures are clearly more relevant not only as a novel evolving gadget in drug screening but also as a bearable therapeutic for different diseases. In this review, various subtypes of BC and mechanisms of drug resistance to the commonly used anticancer therapies are discussed. We also summarize the key lineaments of the latest cell-based assays utilizing 3D cell culture systems and their impact on understanding the pathophysiology of BC. Such knowledge could ultimately help to address the most efficient BC treatment.

Cell repelling agar@paper interface assisted probing of the tumor spheroids infiltrating natural killer cells

Scaffold-based culture is one of the effective methods to resemble three-dimensional (3D) cells model in vitro. An agar@lens paper hybrid scaffold was prepared by one-pot dip-coating. The lens paper's cellulose fiber networks are the scaffold's backbone. The agar gel seized the gaps between the fibrous structures that can improve the paper scaffold's optical transparency and prevent cells from spreading on the scaffold. The SEM and light microscope images showed that the agar gel on the bottom of the paper and the cellulose fiber of the paper formed micro-well structures. Without staining, the cells growing on the agar@paper scaffold can be directly observed under a light microscope. Cells aggregated between the cellulose fibers and formed spheroids within 24 h. The cell spheroids can be non-enzymatically retrieved from the agar@paper scaffold because of the cell-repelling property of agar. The agar@paper scaffold was applied for co-culturing tumor cells (MDA-MB-231, DU 145) and natural killer cells (NKs, NK-92). Using the agar@paper scaffolds, the tumor-infiltrating NKs can be separated from floating NKs that did not attack the tumor spheroids. The effect of NKs infiltrating on tumor spheroids size was characterized. The results showed that NKs attacking the spheroids grown on agar@paper scaffold can be readily tracked because of the improved optical transparency. Higher NKs: tumor cells ratio resulted in a high percentage of tumor infiltrating NKs. The separated NKs can be further tested to reveal their biological characteristics. Both agar and lens paper is accessible for most biological labs, highlighting the potential of agar@paper scaffold in 3D culture.

A Mathematical Modelling Study of Chemotactic Dynamics in Cell Cultures: The Impact of Spatio-temporal Heterogeneity

As motivated by studies of cellular motility driven by spatiotemporal chemotactic gradients in microdevices, we develop a framework for constructing approximate analytical solutions for the location, speed and cellular densities for cell chemotaxis waves in heterogeneous fields of chemoattractant from the underlying partial differential equation models. In particular, such chemotactic waves are not in general translationally invariant travelling waves, but possess a spatial variation that evolves in time, and may even oscillate back and forth in time, according to the details of the chemotactic gradients. The analytical framework exploits the observation that unbiased cellular diffusive flux is typically small compared to chemotactic fluxes and is first developed and validated for a range of exemplar scenarios. The framework is subsequently applied to more complex models considering the chemoattractant dynamics under more general settings, potentially including those of relevance for representing pathophysiology scenarios in microdevice studies. In particular, even though solutions cannot be constructed in all cases, a wide variety of scenarios can be considered analytically, firstly providing global insight into the important mechanisms and features of cell motility in complex spatiotemporal fields of chemoattractant. Such analytical solutions also provide a means of rapid evaluation of model predictions, with the prospect of application in computationally demanding investigations relating theoretical models and experimental observation, such as Bayesian parameter estimation.

Morphological instability of solid tumors in a nutrient-deficient environment

A phenomenological reaction-diffusion model that includes a nutrient-regulated growth rate of tumor cells is proposed to investigate the morphological instability of solid tumors during the avascular growth. We find that the surface instability could be induced more easily when tumor cells are placed in a harsher nutrient-deficient environment, while the instability is suppressed for tumor cells in a nutrient-rich environment due to the nutrient-regulated proliferation. In addition, the surface instability is shown to be influenced by the growth moving speed of tumor rims. Our analysis reveals that a larger growth movement of the tumor front results in a closer proximity of tumor cells to a nutrient-rich region, which tends to inhibit the surface instability. A nourished length that represents the proximity is defined to illustrate its close relation to the surface instability.

Evaluation of Lung Cancer Patient Response to First-Line Chemotherapy by Integration of Tumor Core Biopsy Metabolomics with Multiscale Modeling

The standard of care for intermediate (Stage II) and advanced (Stages III and IV) non-small cell lung cancer (NSCLC) involves chemotherapy with taxane/platinum derivatives, with or without radiation. Ideally, patients would be screened a priori to allow non-responders to be initially treated with second-line therapies. This evaluation is non-trivial, however, since tumors behave as complex multiscale systems. To address this need, this study employs a multiscale modeling approach to evaluate first-line chemotherapy response of individual patient tumors based on metabolomic analysis of tumor core biopsies obtained during routine clinical evaluation. Model parameters were calculated for a patient cohort as a function of these metabolomic profiles, previously obtained from high-resolution 2DLC-MS/MS analysis. Evaluation metrics were defined to classify patients as Disease-Control (DC) [encompassing complete-response (CR), partial-response (PR), and stable-disease (SD)] and Progressive-Disease (PD) following first-line chemotherapy. Response was simulated for each patient and compared to actual response. The results show that patient classifications were significantly separated from each other, and also when grouped as DC vs. PD and as CR/PR vs. SD/PD, by fraction of initial tumor radius metric at 6 days post simulated bolus drug injection. This study shows that patient first-line chemotherapy response can in principle be evaluated from multiscale modeling integrated with tumor tissue metabolomic data, offering a first step towards individualized lung cancer treatment prognosis.

Data-driven spatio-temporal modelling of glioblastoma

Mathematical oncology provides unique and invaluable insights into tumour growth on both the microscopic and macroscopic levels. This review presents state-of-the-art modelling techniques and focuses on their role in understanding glioblastoma, a malignant form of brain cancer. For each approach, we summarize the scope, drawbacks and assets. We highlight the potential clinical applications of each modelling technique and discuss the connections between the mathematical models and the molecular and imaging data used to inform them. By doing so, we aim to prime cancer researchers with current and emerging computational tools for understanding tumour progression. By providing an in-depth picture of the different modelling techniques, we also aim to assist researchers who seek to build and develop their own models and the associated inference frameworks. Our article thus strikes a unique balance. On the one hand, we provide a comprehensive overview of the available modelling techniques and their applications, including key mathematical expressions. On the other hand, the content is accessible to mathematicians and biomedical scientists alike to accommodate the interdisciplinary nature of cancer research.

Evaluation of innate and adaptive immune system interactions in the tumor microenvironment via a 3D continuum model

Immune cells in the tumor microenvironment (TME) are known to affect tumor growth, vascularization, and extracellular matrix (ECM) deposition. Marked interest in system-scale analysis of immune species interactions within the TME has encouraged progress in modeling tumor-immune interactions in silico. Due to the computational cost of simulating these intricate interactions, models have typically been constrained to representing a limited number of immune species. To expand the capability for system-scale analysis, this study develops a three-dimensional continuum mixture model of tumor-immune interactions to simulate multiple immune species in the TME. Building upon a recent distributed computing implementation that enables efficient solution of such mixture models, major immune species including monocytes, macrophages, natural killer cells, dendritic cells, neutrophils, myeloid-derived suppressor cells (MDSC), cytotoxic, helper, regulatory T-cells, and effector and regulatory B-cells and their interactions are represented in this novel implementation. Immune species extravasate from blood vasculature, undergo chemotaxis toward regions of high chemokine concentration, and influence the TME in proportion to locally defined levels of stimulation. The immune species contribute to the production of angiogenic and tumor growth factors, promotion of myofibroblast deposition of ECM, upregulation of angiogenesis, and elimination of living and dead tumor species. The results show that this modeling approach offers the capability for quantitative insight into the modulation of tumor growth by diverse immune-tumor interactions and immune-driven TME effects. In particular, MDSC-mediated effects on tumor-associated immune species' activation levels, volume fraction, and influence on the TME are explored. Longer term, linking of the model parameters to particular patient tumor information could simulate cancer-specific immune responses and move toward a more comprehensive evaluation of immunotherapeutic strategies.

Shape and texture analyses based on conventional MRI for the preoperative prediction of the aggressiveness of pituitary adenomas

OBJECTIVES

Pituitary adenomas can exhibit aggressive behavior, characterized by rapid growth, resistance to conventional treatment, and early recurrence. This study aims to evaluate the clinical value of shape-related features combined with textural features based on conventional MRI in evaluating the aggressiveness of pituitary adenomas and develop the best diagnostic model.

METHODS

Two hundred forty-six pituitary adenoma patients (84 aggressive, 162 non-aggressive) who underwent preoperative MRI were retrospectively reviewed. The patients were divided into training (n = 193) and testing (n = 53) sets. Clinical information, shape-related, and textural features extracted from the tumor volume on contrast-enhanced T1-weighted images (CE-T1WI), were compared between aggressive and non-aggressive groups. Variables with significant differences were enrolled into Pearson's correlation analysis to weaken multicollinearity. Logistic regression models based on the selected features were constructed to predict tumor aggressiveness under fivefold cross-validation.

RESULTS

Sixty-five imaging features, including five shape-related and sixty textural features, were extracted from volumetric CE-T1WI. Forty-seven features were significantly different between aggressive and non-aggressive groups (all p values < 0.05). After feature selection, four features (SHAPE_Sphericity, SHAPE_Compacity, DISCRETIZED_Q3, and DISCRETIZED_Kurtosis) were put into logistic regression analysis. Based on the combination of these features and Knosp grade, the model yielded an area under the curve value of 0.935, with a sensitivity of 94.4% and a specificity of 82.9%, to discriminate between aggressive and non-aggressive pituitary adenomas in the testing set.

CONCLUSION

The radiomic model based on tumor shape and textural features study from CE-T1WI might potentially assist in the preoperative aggressiveness diagnosis of pituitary adenomas.

KEY POINTS

• Pituitary adenomas with aggressive behavior exhibit rapid growth, resistance to conventional treatment, and early recurrence despite gross resection and may require multiline treatments. • Shape-related features and texture features based on CE-T1WI were significantly correlated with the Ki-67 labeling index, mitotic count, and p53 expression, and the proposed model achieved a favorable prediction of the aggressiveness of PAs with an AUC value of 0.935. • The prediction model might provide valuable guidance for individualized treatment in patients with PAs.

The impact of tumor associated macrophages on tumor biology under the lens of mathematical modelling: A review

In this article, we review the role of mathematical modelling to elucidate the impact of tumor-associated macrophages (TAMs) in tumor progression and therapy design. We first outline the biology of TAMs, and its current application in tumor therapies, and their experimental methods that provide insights into tumor cell-macrophage interactions. We then focus on the mechanistic mathematical models describing the role of macrophages as drug carriers, the impact of macrophage polarized activation on tumor growth, and the role of tumor microenvironment (TME) parameters on the tumor-macrophage interactions. This review aims to identify the synergies between biological and mathematical approaches that allow us to translate knowledge on fundamental TAMs biology in addressing current clinical challenges.

Three-dimensional (3D) cell culture: a valuable step in advancing treatments for human hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the fifth most common malignant cancer and the third most frequent cause of tumour-related mortality worldwide. Currently, several surgical and medical therapeutic strategies are available for HCCs; however, the interaction between neoplastic cells and non-neoplastic stromal cells within the tumour microenvironment (TME) results in strong therapeutic resistance of HCCs to conventional treatment. Therefore, the development of novel treatments is urgently needed to improve the survival of patients with HCC. The first step in developing efficient chemotherapeutic drugs is the establishment of an appropriate system for studying complex tumour culture and microenvironment interactions. Three-dimensional (3D) culture model might be a crucial bridge between in vivo and in vitro due to its ability to mimic the naturally complicated in vivo TME compared to conventional two-dimensional (2D) cultures. In this review, we shed light on various established 3D culture models of HCC and their role in the investigation of tumour-TME interactions and HCC-related therapeutic resistance.

Tumor growth towards lower extracellular matrix conductivity regions under Darcy's Law and steady morphology

We study a classic Darcy's law model for tumor cell motion with inhomogeneous and isotropic conductivity. The tumor cells are assumed to be a constant density fluid flowing through porous extracellular matrix (ECM). The ECM is assumed to be rigid and motionless with constant porosity. One and two dimensional simulations show that the tumor mass grows from high to low conductivity regions when the tumor morphology is steady. In the one-dimensional case, we proved that when the tumor size is steady, the tumor grows towards lower conductivity regions. We conclude that this phenomenon is produced by the coupling of a special inward flow pattern in the steady tumor and Darcy's law which gives faster flow speed in higher conductivity regions.

Modeling of Tumor Growth with Input from Patient-Specific Metabolomic Data

Advances in omic technologies have provided insight into cancer progression and treatment response. However, the nonlinear characteristics of cancer growth present a challenge to bridge from the molecular- to the tissue-scale, as tumor behavior cannot be encapsulated by the sum of the individual molecular details gleaned experimentally. Mathematical modeling and computational simulation have been traditionally employed to facilitate analysis of nonlinear systems. In this study, for the first time tumor metabolomic data are linked via mathematical modeling to the tumor tissue-scale behavior, showing the capability to mechanistically simulate cancer progression personalized to omic information obtainable from patient tumor core biopsy analysis. Generally, a higher degree of metabolic dysregulation has been correlated with more aggressive tumor behavior. Accordingly, key parameters influenced by metabolomic data in this model include tumor proliferation, vascularization, aggressiveness, lactic acid production, monocyte infiltration and macrophage polarization, and drug effect. The model enables evaluating interactions of interest between these parameters which drive tumor growth based on the metabolomic data. The results show that the model can group patients consistently with the clinically observed outcomes of response/non-response to chemotherapy. This modeling approach provides a first step towards evaluation of tumor growth based on tumor-specific metabolomic data.

Bioengineered Cystinotic Kidney Tubules Recapitulate a Nephropathic Phenotype

Nephropathic cystinosis is a rare and severe disease caused by disruptions in the CTNS gene. Cystinosis is characterized by lysosomal cystine accumulation, vesicle trafficking impairment, oxidative stress, and apoptosis. Additionally, cystinotic patients exhibit weakening and leakage of the proximal tubular segment of the nephrons, leading to renal Fanconi syndrome and kidney failure early in life. Current in vitro cystinotic models cannot recapitulate all clinical features of the disease which limits their translational value. Therefore, the development of novel, complex in vitro models that better mimic the disease and exhibit characteristics not compatible with 2-dimensional cell culture is of crucial importance for novel therapies development. In this study, we developed a 3-dimensional bioengineered model of nephropathic cystinosis by culturing conditionally immortalized proximal tubule epithelial cells (ciPTECs) on hollow fiber membranes (HFM). Cystinotic kidney tubules showed lysosomal cystine accumulation, increased autophagy and vesicle trafficking deterioration, the impairment of several metabolic pathways, and the disruption of the epithelial monolayer tightness as compared to control kidney tubules. In particular, the loss of monolayer organization and leakage could be mimicked with the use of the cystinotic kidney tubules, which has not been possible before, using the standard 2-dimensional cell culture. Overall, bioengineered cystinotic kidney tubules recapitulate better the nephropathic phenotype at a molecular, structural, and functional proximal tubule level compared to 2-dimensional cell cultures.

Multicellular Ovarian Cancer Model for Evaluation of Nanovector Delivery in Ascites and Metastatic Environments

A novel multicellular model composed of epithelial ovarian cancer and fibroblast cells was developed as an in vitro platform to evaluate nanovector delivery and ultimately aid the development of targeted therapies. We hypothesized that the inclusion of peptide-based scaffold (PuraMatrix) in the spheroid matrix, to represent in vivo tumor microenvironment alterations along with metastatic site conditions, would enhance spheroid cell growth and migration and alter nanovector transport. The model was evaluated by comparing the growth and migration of ovarian cancer cells exposed to stromal cell activation and tissue hypoxia. Fibroblast activation was achieved via the TGF-β1 mediated pathway and tissue hypoxia via 3D spheroids incubated in hypoxia. Surface-modified nanovector transport was assessed via fluorescence and confocal microscopy. Consistent with previous in vivo observations in ascites and at distal metastases, spheroids exposed to activated stromal microenvironment were denser, more contractile and with more migratory cells than nonactivated counterparts. The hypoxic conditions resulted in negative radial spheroid growth over 5 d compared to a radial increase in normoxia. Nanovector penetration attenuated in PuraMatrix regardless of surface modification due to a denser environment. This platform may serve to evaluate nanovector transport based on ovarian ascites and metastatic environments, and longer term, it provide a means to evaluate nanotherapeutic efficacy.

Engineering Tools for Regulating Hypoxia in Tumour Models

Major advances in the field of genomic technologies have led to an improvement in cancer diagnosis, classification and prognostication. However, many cancers remain incurable due to the development of drug resistance, minimal residual disease (MRD) and disease relapse, highlighting an incomplete understanding of the mechanisms underlying these processes. In recent years, the impact of non-genetic factors on neoplastic transformations has increasingly been acknowledged, and growing evidence suggests that low oxygen (O2 ) levels (ie hypoxia) in the tumour microenvironment play a critical role in the development and treatment of cancer. As a result, there is a growing need to develop research tools capable of reproducing physiologically relevant O2 conditions encountered by cancer cells in their natural environments in order to gain in-depth insight into tumour cell metabolism and function. In this review, the authors highlight the importance of hypoxia in the pathogenesis of malignant diseases and provide an overview of novel engineering tools that have the potential to further drive this evolving, yet technically challenging, field of cancer research.

Hot or cold: Bioengineering immune contextures into in vitro patient-derived tumor models

In the past decade, immune checkpoint inhibitors (ICI) have proven to be tremendously effective for a subset of cancer patients. However, it is difficult to predict the response of individual patients and efforts are now directed at understanding the mechanisms of ICI resistance. Current models of patient tumors poorly recapitulate the immune contexture, which describe immune parameters that are associated with patient survival. In this Review, we discuss parameters that influence the induction of different immune contextures found within tumors and how engineering strategies may be leveraged to recapitulate these contextures to develop the next generation of immune-competent patient-derived in vitro models.

Organoids in Tissue Transplantation

Improvements in stem cell-based research and genetic modification tools enable stem cell-based tissue regeneration applications in clinical therapies. Although inadequate cell numbers in culture, invasive isolation procedures, and poor survival rates after transplantation remain as major challenges, cell-based therapies are useful tools for tissue regeneration.Organoids hold a great promise for tissue regeneration, organ and disease modeling, drug testing, development, and genetic profiling studies. Establishment of 3D cell culture systems eliminates the disadvantages of 2D models in terms of cell adaptation and tissue structure and function. Organoids possess the capacity to mimic the specific features of tissue architecture, cell-type composition, and the functionality of real organs while preserving the advantages of simplified and easily accessible cell culture models. Thus, organoid technology might emerge as an alternative to cell and tissue transplantation. Although transplantation of various organoids in animal models has been demonstrated, liöitations related to vascularized structure formation, cell viability and functionality remain as obstacles in organoid-based transplantation therapies. Clinical applications of organoid-based transplantations might be possible in the near future, when limitations related to cell viability and tissue integration are solved. In this review, the literature was analyzed and discussed to explore the current status of organoid-based transplantation studies.

PML Differentially Regulates Growth and Invasion in Brain Cancer

Glioblastoma is the most malignant brain tumor among adults. Despite multimodality treatment, it remains incurable, mainly because of its extensive heterogeneity and infiltration in the brain parenchyma. Recent evidence indicates dysregulation of the expression of the Promyelocytic Leukemia Protein (PML) in primary Glioblastoma samples. PML is implicated in various ways in cancer biology. In the brain, PML participates in the physiological migration of the neural progenitor cells, which have been hypothesized to serve as the cell of origin of Glioblastoma. The role of PML in Glioblastoma progression has recently gained attention due to its controversial effects in overall Glioblastoma evolution. In this work, we studied the role of PML in Glioblastoma pathophysiology using the U87MG cell line. We genetically modified the cells to conditionally overexpress the PML isoform IV and we focused on its dual role in tumor growth and invasive capacity. Furthermore, we targeted a PML action mediator, the Enhancer of Zeste Homolog 2 (EZH2), via the inhibitory drug DZNeP. We present a combined in vitro–in silico approach, that utilizes both 2D and 3D cultures and cancer-predictive computational algorithms, in order to differentiate and interpret the observed biological results. Our overall findings indicate that PML regulates growth and invasion through distinct cellular mechanisms. In particular, PML overexpression suppresses cell proliferation, while it maintains the invasive capacity of the U87MG Glioblastoma cells and, upon inhibition of the PML-EZH2 pathway, the invasion is drastically eliminated. Our in silico simulations suggest that the underlying mechanism of PML-driven Glioblastoma physiology regulates invasion by differential modulation of the cell-to-cell adhesive and diffusive capacity of the cells. Elucidating further the role of PML in Glioblastoma biology could set PML as a potential molecular biomarker of the tumor progression and its mediated pathway as a therapeutic target, aiming at inhibiting cell growth and potentially clonal evolution regarding their proliferative and/or invasive phenotype within the heterogeneous tumor mass.

Integrating Quantitative Assays with Biologically Based Mathematical Modeling for Predictive Oncology

We provide an overview on the use of biological assays to calibrate and initialize mechanism-based models of cancer phenomena. Although artificial intelligence methods currently dominate the landscape in computational oncology, mathematical models that seek to explicitly incorporate biological mechanisms into their formalism are of increasing interest. These models can guide experimental design and provide insights into the underlying mechanisms of cancer progression. Historically, these models have included a myriad of parameters that have been difficult to quantify in biologically relevant systems, limiting their practical insights. Recently, however, there has been much interest calibrating biologically based models with the quantitative measurements available from (for example) RNA sequencing, time-resolved microscopy, and in vivo imaging. In this contribution, we summarize how a variety of experimental methods quantify tumor characteristics from the molecular to tissue scales and describe how such data can be directly integrated with mechanism-based models to improve predictions of tumor growth and treatment response.

Cell – extracellular matrix interaction in glioma growth. In silico model

The study aims to investigate the role of viscoelastic interactions between cells and extracellular matrix (ECM) in avascular tumor growth. Computer simulations of glioma multicellular tumor spheroid (MTS) growth are being carried out for various conditions. The calculations are based on a continuous model, which simulates oxygen transport into MTS; transitions between three cell phenotypes, cell transport, conditioned by hydrostatic forces in cell–ECM composite system, cell motility and cell adhesion. Visco-elastic cell aggregation and elastic ECM scaffold represent two compressible constituents of the composite. Cell–ECM interactions form a Transition Layer on the spheroid surface, where mechanical characteristics of tumor undergo rapid transition. This layer facilitates tumor progression to a great extent. The study demonstrates strong effects of ECM stiffness, mechanical deformations of the matrix and cell–cell adhesion on tumor progression. The simulations show in particular that at certain, rather high degrees of matrix stiffness a formation of distant multicellular clusters takes place, while at further increase of ECM stiffness subtumors do not form. The model also illustrates to what extent mere mechanical properties of cell–ECM system may contribute into variations of glioma invasion scenarios.

Indirect Contributions to Tumor Dynamics in the First Stage of the Avascular Phase

A continuum model for tumor invasion in a two-dimensional spatial domain based on the interaction of the urokinase plasminogen activation system with a model for cancer cell dynamics is proposed. The arising system of partial differential equations is numerically solved using the finite element method. We simulated a portion of biological tissue imposing no flux boundary conditions. We monitored the cancer cell dynamics, as well the degradation of an extra cellular matrix representative, vitronectin, and the evolution of a specific degrading enzyme, plasmin, inside the biological tissue. The computations were parameterized as a function of the indirect cell proliferation induced by a plasminogen activator inhibitor binding to vitronectin and of the indirect plasmin deactivation due to the plasminogen activator inhibitor binding to the urokinase plasminogen activator. Their role during the cancer dynamical evolution was identified, together with a possible marker helping the mapping of the cancer invasive front. Our results indicate that indirect cancer cell proliferation biases the speed of the tumor invasive front as well as the heterogeneity of the cancer cell clustering and networking, as it ultimately acts on the proteolytic activity supporting cancer formation. Because of the initial conditions imposed, the numerical solutions of the model show a symmetrical dynamical evolution of heterogeneities inside the simulated domain. Moreover, an increase of up to about 12% in the invasion speed was observed, increasing the rate of indirect cancer cell proliferation, while increasing the plasmin deactivation rate inhibits heterogeneities and networking. As cancer cell proliferation causes vitronectin consumption and plasmin formation, the intensities of the concentration maps of both vitronectin and plasmin are superimposable to the cancer cell concentration maps. The qualitative imprinting that cancer cells leave on the extra cellular matrix during the time evolution as well their activity area is identified, framing the numerical results in the context of a methodology aimed at diagnostic and therapeutic improvement.

Innovative Human Three-Dimensional Tissue-Engineered Models as an Alternative to Animal Testing

Animal testing has long been used in science to study complex biological phenomena that cannot be investigated using two-dimensional cell cultures in plastic dishes. With time, it appeared that more differences could exist between animal models and even more when translated to human patients. Innovative models became essential to develop more accurate knowledge. Tissue engineering provides some of those models, but it mostly relies on the use of prefabricated scaffolds on which cells are seeded. The self-assembly protocol has recently produced organ-specific human-derived three-dimensional models without the need for exogenous material. This strategy will help to achieve the 3R principles.

3D-bioprinted all-inclusive bioanalytical platforms for cell studies

Innovative drug screening platforms should improve the discovery of novel and personalized cancer treatment. Common models such as animals and 2D cell cultures lack the proper recapitulation of organ structure and environment. Thus, a new generation of platforms must consist of cell models that accurately mimic the cells’ microenvironment, along with flexibly prototyped cell handling structures that represent the human environment. Here, we adapted the 3D-bioprinting technology to develop multiple all-inclusive high throughputs and customized organ-on-a-chip-like platforms along with printed 3D-cell structures. Such platforms are potentially capable of performing 3D cell model analysis and cell-therapeutic response studies. We illustrated spherical and rectangular geometries of bio-printed 3D human colon cancer cell constructs. We also demonstrated the utility of directly 3D-bioprinting and rapidly prototyping of PDMS-based microfluidic cell handling arrays in different geometries. Besides, we successfully monitored the post-viability of the 3D-cell constructs for seven days. Furthermore, to mimic the human environment more closely, we integrated a 3D-bioprinted perfused drug screening microfluidics platform. Platform’s channels subject cell constructs to physiological fluid flow, while its concave well array hold and perfused 3D-cell constructs. The bio-applicability of PDMS-based arrays was also demonstrated by performing cancer cell-therapeutic response studies.

Inhibition of the MYC-Regulated Glutaminase Metabolic Axis Is an Effective Synthetic Lethal Approach for Treating Chemoresistant Ovarian Cancers

Amplification and overexpression of the MYC oncogene in tumor cells, including ovarian cancer cells, correlates with poor responses to chemotherapy. As MYC is not directly targetable, we have analyzed molecular pathways downstream of MYC to identify potential therapeutic targets. Here we report that ovarian cancer cells overexpressing glutaminase (GLS), a target of MYC and a key enzyme in glutaminolysis, are intrinsically resistant to platinum-based chemotherapy and are enriched with intracellular antioxidant glutathione. Deprivation of glutamine by glutamine-withdrawal, GLS knockdown, or exposure to the GLS inhibitor CB-839 resulted in robust induction of reactive oxygen species in high GLS-expressing but not in low GLS-expressing ovarian cancer cells. Treatment with CB-839 rendered GLS^high cells vulnerable to the poly(ADP-ribose) polymerase (PARP) inhibitor, olaparib, and prolonged survival in tumor-bearing mice. These findings suggest consideration of applying a combined therapy of GLS inhibitor and PARP inhibitor to treat chemoresistant ovarian cancers, especially those with high GLS expression. SIGNIFICANCE: Targeting glutaminase disturbs redox homeostasis and nucleotide synthesis and causes replication stress in cancer cells, representing an exploitable vulnerability for the development of effective therapeutics. GRAPHICAL ABSTRACT: http://cancerres.aacrjournals.org/content/canres/80/20/4514/F1.large.jpg.

Analysis of influencing factors of no/low response to preoperative concurrent chemoradiotherapy in locally advanced rectal cancer

The aim of this study is to investigate the influencing factors associated with no/low response to preoperative concurrent chemoradiotherapy (CCRT) for locally advanced rectal cancer (LARC) patients. A total of 79 patients were included in this prospective study. Fifteen factors that might affect the resistance to CCRT were included in this logistic regression analysis, these factors include the general clinical data of patients, the expression status of tumor stem cell marker CD44v6 and the volumetric imaging parameters of primary tumor lesions. We found that the no/low response status to preoperative CCRT was positively correlated with the real tumor volume (RTV), the total surface area of tumor (TSA), and CD44v6 expression, whereas negatively correlated with the tumor compactness (TC). According to the results of logistic regression analysis, two formulas that could predict whether or not no/low response to preoperative CCRT were established. The Area Under Curve (AUC) of the two formulas and those significant measurement data (RTV, TC, TSA) were 0.900, 0.858, 0.771, 0.754, 0.859, the sensitivity were 95.8%, 79.17%, 62.50%, 95.83%, 62.5%, the specificity were 70.9%, 74.55%, 83.64%,47.27%, 96.36%, the positive predictive values were 58.96%, 57.58%, 62.51%,44.23%, 88.23%, the negative predictive values were 97.48%, 89.13%, 83.64%, 96.29%, and 85.48%, respectively.

Tumor volume and sphericity as predictors of local control after stereotactic radiosurgery for limited number (1-4) brain metastases from nonsmall cell lung cancer

AIM

This study aims to evaluate the usage of brain metastases (BM) tumor volume and sphericity as prognostic factors in local control (LC) after stereotactic radiosurgery (SRS) for limited number (1-4) BM from nonsmall cell lung cancer (NSCLC).

METHODS

We retrospectively reviewed 80 patients, with 141 BM, who were treated with SRS from 2012 to 2017. Local failure was defined as an increase in lesion size after SRS. LC and overall survival (OS) were estimated using Kaplan-Meier method. The Cox proportional hazards model was used for univariate and multivariate analysis.

RESULTS

The median clinical and radiographic follow-up was 11.2 and 9.0 months, respectively. The median BM tumor volume was 0.31 cm³ (0.01-21.64 cm³ ) and the median tumor sphericity was 0.76 (0.39-0.95). The median LC of the entire cohort was 28.8 months. LC rate at last follow-up was achieved in 84.4% of patients (35.5% CR, 35.5% PR, and 13.5% SD). LC was 83.8% at 1 year and 56.3% at 2 years. On multivariate analysis, only sphericity (P < .001) and volume (P = .004) were found to be a strong predictor for LC. The median OS of the entire cohort was 24.1 months. On multivariate analysis, only GPA score was found to be a predictor for OS.

CONCLUSION

BM tumor sphericity and volume were found to be strong predictors for LC. Tumor sphericity and volume should be taken into consideration when treating patients with BM and when designing future prospective studies and developing prognostic indices.

Nonlinear simulation of an elastic tumor-host interface

We develop a computational method for simulating the nonlinear dynamics of an elastic tumor-host interface. This work is motivated by the recent linear stability analysis of a two-phase tumor model with an elastic membrane interface in 2D [47]. Unlike the classic tumor model with surface tension, the elastic interface condition is numerically challenging due to the 4th order derivative from the Helfrich bending energy. Here we are interested in exploring the nonlinear interface dynamics in a sharp interface framework. We consider a curvature dependent bending rigidity (curvature weakening [22]) to investigate metastasis patterns such as chains or fingers that invade the host environment. We solve the nutrient field and the Stokes flow field using a spectrally accurate boundary integral method, and update the interface using a nonstiff semi-implicit approach. Numerical results suggest curvature weakening promotes the development of branching patterns instead of encapsulated morphologies in a long period of time. For non-weakened bending rigidity, we are able to find self-similar shrinking morphologies based on marginally stable value of the apoptosis rate.

Volumetric imaging parameters are significant for predicting the pathological complete response of preoperative concurrent chemoradiotherapy in local advanced rectal cancer

Preoperative concurrent chemoradiotherapy (CCRT) as the standard treatment for locally advanced rectal cancer (LARC) has been widely used in clinic. Its efficiency influences the prognosis and the selection of subsequent treatment. The current criteria for evaluating the prognosis of patients with extremely sensitive preoperative CCRT include the clinical complete remission response (cCR) and pathological complete response (pCR), but those with cCR may not necessarily achieve pCR, and the pCR can be confirmed only after surgery. Some scholars believe that patients with pCR after CCRT can be categorized as 'watch and wait'. Therefore, it is extremely important to find a way to predict the pCR status of patients before therapy. In this study, we examined the expression of stem cell markers and obtained direct and derivative volumetric imaging parameters before treatment. Subsequently, these factors and the general clinical data were adopted into a regression model, and the correlation between them and the pCR was analyzed. We found that the pCR of LARC was positively correlated with tumor compactness (TC), whereas it was negatively correlated with approximate tumor volume (ATV), real tumor volume (RTV), total surface area of the tumor (TSA) and tumor maximum longitudinal length (TML). In these meaningful predictors, the positive predictive values and the negative predictive values of TC were 74.73% and 94.61%, respectively. Compared with other possible predictors, TC is the most encouraging predictor of pCR. Our findings provide a way for clinicians to predict the sensitivity of preoperative CCRT and will help to select individualized treatment options for LARC patients.

Effects of Copper Chelation on BRAFV600E Positive Colon Carcinoma Cells

High affinity copper binding to mitogen-activated protein kinase kinase 1 (MAP2K1, also known as MEK1) allosterically promotes the kinase activity of MEK1/2 on extracellular signal regulated kinases 1 and 2 (ERK1/2). Consequently, copper-dependent activation of the mitogen-activated (MAP) kinase pathway has a role in promoting tumor growth. Conversely, copper chelation may represent a possible therapeutic approach for a specific subset of tumors characterized by activating mutations in the serine/threonine protein kinase V-Raf Murine Sarcoma Viral Oncogene Homolog B1 (BRAF), such as the V600E, occurring within the kinase domain (BRAF^V600E). Tetrathiomolybdate (TM) is a specific copper chelating agent currently used for the treatment of Wilson’s disease and in preclinical studies for the management of metastatic cancers owing to its anti-angiogenic and anti-inflammatory properties. We evaluated in vitro and in vivo the effects of copper depletion achieved by pharmacological treatment with TM in human colorectal cells bearing the BRAF^V600E mutation in comparison with BRAF wild type cells. We provide evidence that selective copper chelation differentially affects proliferation, survival and migration of colon cancer cells bearing the BRAF^V600E mutation compared to BRAF^wt acting via differential phosphorylation levels of ERK1/2. Moreover, tetrathiomolybdate treatment was also effective in reducing the clonogenic potential of colon cancer BRAF^V600E cells resistant to BRAF pharmacological inhibition. In conclusion, these results support further assessment of copper chelation therapy as an adjuvant therapy for inhibiting the progression of colon cancers containing the BRAF^V600E mutation.

Evolutionary Multiobjective Clustering and Its Applications to Patient Stratification

Patient stratification has a major role in enabling efficient and personalized medicine. An important task in patient stratification is to discover disease subtypes for effective treatment. To achieve this goal, the research on clustering algorithms for patient stratification has brought attention from both academia and medical community over the past decades. However, existing clustering algorithms suffer from realistic restrictions such as experimental noises, high dimensionality, and poor interpretability. In particular, the existing clustering algorithms usually determine clustering quality using only one internal evaluation function. Unfortunately, it is obvious that one internal evaluation function is hard to be fitted and robust for all datasets. Therefore, in this paper, a novel multiobjective framework called multiobjective clustering algorithm by fast search and find of density peaks is proposed to address those limitations altogether. In the proposed framework, a parameter candidate population is evolved under multiple objectives to select features and evaluate clustering densities automatically. To guide the multiobjective evolution, five cluster validity indices including compactness, separation, Calinski-Harabasz index, Davies-Bouldin index, and Dunn index, are chosen as the objective functions, capturing multiple characteristics of the evolving clusters. Multiobjective differential evolution algorithm based on decomposition is adopted to optimize those five objective functions simultaneously. To demonstrate its effectiveness, extensive experiments have been conducted, comparing the proposed algorithm with 45 algorithms including nine state-of-the-art clustering algorithms, five multiobjective evolutionary algorithms, and 31 baseline algorithms under different objective subsets on 94 datasets featuring 35 real patient stratification datasets, 55 synthetic datasets based on a real human transcription regulation network model, and four other medical datasets. The numerical results reveal that the proposed algorithm can achieve better or competitive solutions than the others. Besides, time complexity analysis, convergence analysis, and parameter analysis are conducted to demonstrate the robustness of the proposed algorithm from different perspectives.

Cancer-on-a-chip for Drug Screening

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The oncology pharmaceutical research spent a shocking amount of money on target validation and drug optimization in preclinical models because many oncology drugs fail during clinical trial phase III. One of the most important reasons for oncology drug failures in clinical trials may due to the poor predictive tool of existing preclinical models. Therefore, in cancer research and personalized medicine field, it is critical to improve the effectiveness of preclinical predictions of the drug response of patients to therapies and to reduce costly failures in clinical trials. Three dimensional (3D) tumor models combine micro-manufacturing technologies mimic critical physiologic parameters present in vivo, including complex multicellular architecture with multicellular arrangement and extracellular matrix deposition, packed 3D structures with cell–cell interactions, such as tight junctions, barriers to mass transport of drugs, nutrients and other factors, which are similar to in vivo tumor tissues. These systems provide a solution to mimic the physiological environment for improving predictive accuracy in oncology drug discovery.

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his review gives an overview of the innovations, development and limitations of different types of tumor-like construction techniques such as self-assemble spheroid formation, spheroids formation by micro-manufacturing technologies, micro-dissected tumor tissues and tumor organoid. Combination of 3D tumor-like construction and microfluidic techniques to achieve tumor on a chip for in vitro tumor environment modeling and drug screening were all included. Eventually, developmental directions and technical challenges in the research field are also discussed. We believe tumor on chip models have provided better sufficient clinical predictive power and will bridge the gap between proof-of-concept studies and a wider implementation within the oncology drug development for pathophysiological applications.

Efficacy of Surface-Modified PLGA Nanoparticles as a Function of Cervical Cancer Type

PURPOSE

Hypovascularization of cervical tumors, coupled with intrinsic and acquired drug resistance, has contributed to marginal therapeutic outcomes by hindering chemotherapeutic transport and efficacy. Recently, the heterogeneous penetration and distribution of cell penetrating peptide (CPP, here MPG) and polyethylene glycol (PEG) modified poly(lactic-co-glycolic acid) (PLGA) nanoparticles (NPs) were evaluated as a function of tumor type and morphology in cervical cancer spheroids modeling hypovascularized tumor nodules. Building upon this work, this study investigates the efficacy imparted by surface-modified Doxorubicin-loaded NPs transported into hypovascularized tissue.

METHODS

NP efficacy was measured in HeLa, CaSki, and SiHa cells. NP internalization and association, and associated cell viability, were determined in monolayer and spheroid models.

RESULTS

MPG and PEG-NP co-treatment was most efficacious in HeLa cells, while PEG NPs were most efficacious in CaSki cells. NP surface-modifications were unable to improve efficacy, relative to unmodified NPs, in SiHa cells.

CONCLUSIONS

The results highlight the dependence of efficacy on tumor type and the associated microenvironment. The results further relate previous NP transport studies to efficacy, as a function of surface-modification and cell type. Longer-term, this information may help guide the design of NP-mediated strategies to maximize efficacy based on patient-specific cervical tumor origin and characteristics.

Towards control of cellular decision-making networks in the epithelial-to-mesenchymal transition

We present the epithelial-to-mesenchymal transition (EMT) from two perspectives: experimental/technological and theoretical. We review the state of the current understanding of the regulatory networks that underlie EMT in three physiological contexts: embryonic development, wound healing, and metastasis. We describe the existing experimental systems and manipulations used to better understand the molecular participants and factors that influence EMT and metastasis. We review the mathematical models of the regulatory networks involved in EMT, with a particular emphasis on the network motifs (such as coupled feedback loops) that can generate intermediate hybrid states between the epithelial and mesenchymal states. Ultimately, the understanding gained about these networks should be translated into methods to control phenotypic outcomes, especially in the context of cancer therapeutic strategies. We present emerging theories of how to drive the dynamics of a network toward a desired dynamical attractor (e.g. an epithelial cell state) and emerging synthetic biology technologies to monitor and control the state of cells.

Circulating Tumour Cells in Predictive Molecular Pathology: Focus on Drug-Sensitive Assays and 3D Culture

Molecular cytopathology is a rapidly evolving field of cytopathology that provides biological information about the response to personalised therapy and about the prognosis of neoplasms diagnosed on cytological samples. Biomarkers such as circulating tumour cells and circulating tumour DNA are increasingly being evaluated in blood and in other body fluids. Such liquid biopsies are non-invasive, repeatable, and feasible also in patients with severe comorbidities. However, liquid biopsy may be challenging due to a low concentration of biomarkers. In such cases, biomarkers can be detected with highly sensitive molecular techniques, which in turn should be validated and integrated in a complex algorithm that includes tissue-based molecular assessments. The aim of this review is to provide the cytopathologist with practical information that is relevant to daily practice, particularly regarding the emerging role of circulating tumour cells in the field of predictive molecular pathology.

Design Optimization of Tumor Vasculature-Bound Nanoparticles

Nanotherapy may constitute a promising approach to target tumors with anticancer drugs while minimizing systemic toxicity. Computational modeling can enable rapid evaluation of nanoparticle (NP) designs and numerical optimization. Here, an optimization study was performed using an existing tumor model to find NP size and ligand density that maximize tumoral NP accumulation while minimizing tumor size. Optimal NP avidity lies at lower bound of feasible values, suggesting reduced ligand density to prolong NP circulation. For the given set of tumor parameters, optimal NP diameters were 288 nm to maximize NP accumulation and 334 nm to minimize tumor diameter, leading to uniform NP distribution and adequate drug load. Results further show higher dependence of NP biodistribution on the NP design than on tumor morphological parameters. A parametric study with respect to drug potency was performed. The lower the potency of the drug, the bigger the difference is between the maximizer of NP accumulation and the minimizer of tumor size, indicating the existence of a specific drug potency that minimizes the differential between the two optimal solutions. This study shows the feasibility of applying optimization to NP designs to achieve efficacious cancer nanotherapy, and offers a first step towards a quantitative tool to support clinical decision making.

Stability and Roughness of Interfaces in Mechanically Regulated Tissues

Cell division and death can be regulated by the mechanical forces within a tissue. We study the consequences for the stability and roughness of a propagating interface by analyzing a model of mechanically regulated tissue growth in the regime of small driving forces. For an interface driven by homeostatic pressure imbalance or leader-cell motility, long and intermediate-wavelength instabilities arise, depending, respectively, on an effective viscosity of cell number change, and on substrate friction. A further mechanism depends on the strength of directed motility forces acting in the bulk. We analyze the fluctuations of a stable interface subjected to cell-level stochasticity, and find that mechanical feedback can help preserve reproducibility at the tissue scale. Our results elucidate mechanisms that could be important for orderly interface motion in developing tissues.

Modeling three-dimensional invasive solid tumor growth in heterogeneous microenvironment under chemotherapy

A systematic understanding of the evolution and growth dynamics of invasive solid tumors in response to different chemotherapy strategies is crucial for the development of individually optimized oncotherapy. Here, we develop a hybrid three-dimensional (3D) computational model that integrates pharmacokinetic model, continuum diffusion-reaction model and discrete cell automaton model to investigate 3D invasive solid tumor growth in heterogeneous microenvironment under chemotherapy. Specifically, we consider the effects of heterogeneous environment on drug diffusion, tumor growth, invasion and the drug-tumor interaction on individual cell level. We employ the hybrid model to investigate the evolution and growth dynamics of avascular invasive solid tumors under different chemotherapy strategies. Our simulations indicate that constant dosing is generally more effective in suppressing primary tumor growth than periodic dosing, due to the resulting continuous high drug concentration. In highly heterogeneous microenvironment, the malignancy of the tumor is significantly enhanced, leading to inefficiency of chemotherapies. The effects of geometrically-confined microenvironment and non-uniform drug dosing are also investigated. Our computational model, when supplemented with sufficient clinical data, could eventually lead to the development of efficient in silico tools for prognosis and treatment strategy optimization.

Nonlinear studies of tumor morphological stability using a two-fluid flow model

We consider the nonlinear dynamics of an avascular tumor at the tissue scale using a two-fluid flow Stokes model, where the viscosity of the tumor and host microenvironment may be different. The viscosities reflect the combined properties of cell and extracellular matrix mixtures. We perform a linear morphological stability analysis of the tumors, and we investigate the role of nonlinearity using boundary-integral simulations in two dimensions. The tumor is non-necrotic, although cell death may occur through apoptosis. We demonstrate that tumor evolution is regulated by a reduced set of nondimensional parameters that characterize apoptosis, cell-cell/cell-extracellular matrix adhesion, vascularization and the ratio of tumor and host viscosities. A novel reformulation of the equations enables the use of standard boundary integral techniques to solve the equations numerically. Nonlinear simulation results are consistent with linear predictions for nearly circular tumors. As perturbations develop and grow, the linear and nonlinear results deviate and linear theory tends to underpredict the growth of perturbations. Simulations reveal two basic types of tumor shapes, depending on the viscosities of the tumor and microenvironment. When the tumor is more viscous than its environment, the tumors tend to develop invasive fingers and a branched-like structure. As the relative ratio of the tumor and host viscosities decreases, the tumors tend to grow with a more compact shape and develop complex invaginations of healthy regions that may become encapsulated in the tumor interior. Although our model utilizes a simplified description of the tumor and host biomechanics, our results are consistent with experiments in a variety of tumor types that suggest that there is a positive correlation between tumor stiffness and tumor aggressiveness.

The biology and mathematical modelling of glioma invasion: a review

Adult gliomas are aggressive brain tumours associated with low patient survival rates and limited life expectancy. The most important hallmark of this type of tumour is its invasive behaviour, characterized by a markedly phenotypic plasticity, infiltrative tumour morphologies and the ability of malignant progression from low- to high-grade tumour types. Indeed, the widespread infiltration of healthy brain tissue by glioma cells is largely responsible for poor prognosis and the difficulty of finding curative therapies. Meanwhile, mathematical models have been established to analyse potential mechanisms of glioma invasion. In this review, we start with a brief introduction to current biological knowledge about glioma invasion, and then critically review and highlight future challenges for mathematical models of glioma invasion.

2D and 3D cell cultures - a comparison of different types of cancer cell cultures

Cell culture is a widely used in vitro tool for improving our understanding of cell biology, tissue morphology, and mechanisms of diseases, drug action, protein production and the development of tissue engineering. Most research regarding cancer biology is based on experiments using two-dimensional (2D) cell cultures in vitro. However, 2D cultures have many limitations, such as the disturbance of interactions between the cellular and extracellular environments, changes in cell morphology, polarity, and method of division. These disadvantages led to the creation of models which are more closely able to mimic conditions in vivo. One such method is three-dimensional culture (3D). Optimisation of the culture conditions may allow for a better understanding of cancer biology and facilitate the study of biomarkers and targeting therapies. In this review, we compare 2D and 3D cultures in vitro as well as different versions of 3D cultures.