'Go or grow': the key to the emergence of invasion in tumour progression?

Modelling glioma progression, mass effect and intracranial pressure in patient anatomy

Increased intracranial pressure is the source of most critical symptoms in patients with glioma, and often the main cause of death. Clinical interventions could benefit from non-invasive estimates of the pressure distribution in the patient's parenchyma provided by computational models. However, existing glioma models do not simulate the pressure distribution and they rely on a large number of model parameters, which complicates their calibration from available patient data. Here we present a novel model for glioma growth, pressure distribution and corresponding brain deformation. The distinct feature of our approach is that the pressure is directly derived from tumour dynamics and patient-specific anatomy, providing non-invasive insights into the patient's state. The model predictions allow estimation of critical conditions such as intracranial hypertension, brain midline shift or neurological and cognitive impairments. A diffuse-domain formalism is employed to allow for efficient numerical implementation of the model in the patient-specific brain anatomy. The model is tested on synthetic and clinical cases. To facilitate clinical deployment, a high-performance computing implementation of the model has been publicly released.

Multiomics Analysis of Spatially Distinct Stromal Cells Reveals Tumor-Induced O-Glycosylation of the CDK4-pRB Axis in Fibroblasts at the Invasive Tumor Edge

The invasive leading edge represents a potential gateway for tumor metastasis. The role of fibroblasts from the tumor edge in promoting cancer invasion and metastasis has not been comprehensively elucidated. We hypothesize that cross-talk between tumor and stromal cells within the tumor microenvironment results in activation of key biological pathways depending on their position in the tumor (edge vs. core). Here we highlight phenotypic differences between tumor-adjacent-fibroblasts (TAF) from the invasive edge and tumor core fibroblasts from the tumor core, established from human lung adenocarcinomas. A multiomics approach that includes genomics, proteomics, and O-glycoproteomics was used to characterize cross-talk between TAFs and cancer cells. These analyses showed that O-glycosylation, an essential posttranslational modification resulting from sugar metabolism, alters key biological pathways including the cyclin-dependent kinase 4 (CDK4) and phosphorylated retinoblastoma protein axis in the stroma and indirectly modulates proinvasive features of cancer cells. In summary, the O-glycoproteome represents a new consideration for important biological processes involved in tumor-stroma cross-talk and a potential avenue to improve the anticancer efficacy of CDK4 inhibitors.

SIGNIFICANCE

A multiomics analysis of spatially distinct fibroblasts establishes the importance of the stromal O-glycoproteome in tumor-stroma interactions at the leading edge and provides potential strategies to improve cancer treatment. See related commentary by De Wever, p. 537.

Inducing Biomechanical Heterogeneity in Brain Tumor Modeling by MR Elastography: Effects on Tumor Growth, Vascular Density and Delivery of Therapeutics

Simple Summary

Biomechanical forces aggravate brain tumor progression. In this study, magnetic resonance elastography (MRE) is employed to extract tissue biomechanical properties from five glioblastoma patients and a healthy subject, and data are incorporated in a mathematical model that simulates tumor growth. Mathematical modeling enables further understanding of glioblastoma development and allows patient-specific predictions for tumor vascularity and delivery of drugs. Incorporating MRE data results in a more realistic intratumoral distribution of mechanical stress and anisotropic tumor growth and a better description of subsequent events that are closely related to the development of stresses, including heterogeneity of the tumor vasculature and intrapatient variations in tumor perfusion and delivery of drugs.

Abstract

The purpose of this study is to develop a methodology that incorporates a more accurate assessment of tissue mechanical properties compared to current mathematical modeling by use of biomechanical data from magnetic resonance elastography. The elastography data were derived from five glioblastoma patients and a healthy subject and used in a model that simulates tumor growth, vascular changes due to mechanical stresses and delivery of therapeutic agents. The model investigates the effect of tumor-specific biomechanical properties on tumor anisotropic growth, vascular density heterogeneity and chemotherapy delivery. The results showed that including elastography data provides a more realistic distribution of the mechanical stresses in the tumor and induces anisotropic tumor growth. Solid stress distribution differs among patients, which, in turn, induces a distinct functional vascular density distribution—owing to the compression of tumor vessels—and intratumoral drug distribution for each patient. In conclusion, incorporating elastography data results in a more accurate calculation of intratumoral mechanical stresses and enables a better mathematical description of subsequent events, such as the heterogeneous development of the tumor vasculature and intrapatient variations in tumor perfusion and delivery of drugs.

Periostin in Angiogenesis and Inflammation in CRC-A Preliminary Observational Study

Background and Objectives: To assess the periostin level and the concentrations of pro-inflammatory cytokines: TNFα, IFN-γ, IL-1β and IL-17 in tumor and marginal tissues of CRC and to investigate the influence of periostin on angiogenesis by MVD (microvessel density) and concentration of VEGF-A in relation to clinicopathological parameters of patients. Materials and Methods: The study used 47 samples of tumor and margin tissues derived from CRC patients. To determinate the concentration of periostin, VEGF-A, TNFα, IFNγ, IL-1β and IL-17, we used the commercially available enzyme- linked immunosorbent assay kit. MVD was assessed on CD34-stained specimens. The MVD and budding were assessed using a light microscope Results: We found significantly higher concentrations of periostin, VEGF-A, IFN-γ, IL-1 β, IL-17 and TNFα in the tumor samples compared with surgical tissue margins. The tumor concentrations of periostin were correlated with tumor levels of VEGF-A, IFN-γ, IL-1β and TNFα. We observed significant correlation between margin periostin and VEGF-A, IFN-γ, IL-17 and TNFα in tumor and margin specimens. Additionally, we found a significantly negative correlation between periostin tumor concentration and microvessel density at the invasive front. Tumor periostin levels were also correlated positively with tumor budding. Conclusions: Periostin activity may be associated with pro-inflammatory cytokine levels: TNFα, IFN-γ, IL-1β and IL-17. Our results also suggest the role of periostin in angiogenesis in CRC and its upregulation in poorly vascularized tumors. Further research on the regulations between periostin and cytokines are necessary to understand the interactions between tumor and immune tumor microenvironment, which could be helpful in the development of new targeted therapy.

A nomogram model based on clinical markers for predicting malignancy of ovarian tumors

OBJECTIVE

The aim of this study was to build a nomogram based on clinical markers for predicting the malignancy of ovarian tumors (OTs).

METHOD

A total of 1,268 patients diagnosed with OTs that were surgically removed between October 2017 and May 2019 were enrolled. Clinical markers such as post-menopausal status, body mass index (BMI), serum human epididymis protein 4 (HE4) value, cancer antigen 125 (CA125) value, Risk of Ovarian Malignancy Algorithm (ROMA) index, course of disease, patient-generated subjective global assessment (PG-SGA) score, ascites, and locations and features of masses were recorded and analyzed (p 0.05). Significant variables were further selected using multivariate logistic regression analysis and were included in the decision curve analysis (DCA) used to assess the value of the nomogram model for predicting OT malignancy.

RESULT

The significant variables included post-menopausal status, BMI, HE4 value, CA125 value, ROMA index, course of disease, PG-SGA score, ascites, and features and locations of masses (p 0.05). The ROMA index, BMI (≥ 26), unclear/blurred mass boundary (on magnetic resonance imaging [MRI]/computed tomography [CT]), mass detection (on MRI/CT), and mass size and features (on type B ultrasound [BUS]) were screened out for multivariate logistic regression analysis to assess the value of the nomogram model for predicting OT malignant risk (p 0.05). The DCA revealed that the net benefit of the nomogram's calculation model was superior to that of the CA125 value, HE4 value, and ROMA index for predicting OT malignancy.

CONCLUSION

We successfully tailored a nomogram model based on selected clinical markers which showed superior prognostic predictive accuracy compared with the use of the CA125, HE4, or ROMA index (that combines both HE and CA125 values) for predicting the malignancy of OT patients.

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

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

The molecular mechanism of chronic stress affecting the occurrence and development of breast cancer and potential drug therapy

According to the 2020 data released by the International Agency for Research on Cancer, breast cancer has surpassed lung cancer as the world's most newly diagnosed first-time cancer. Compared with patients with other types of cancer, those with breast cancer experience greater mental stress and more severe psychological impacts because of the life-threatening diagnosis, physical changes, treatment side effects, and family and social life dysfunctions. These usually manifest as anxiety, depression, nervousness, and insomnia, all of which elicit stress responses. Particularly under chronic stress, the continuous release of neurotransmitters from the neuroendocrine system can have a highly profound impact on the occurrence and prognosis of breast cancer. However, because of the complex mechanisms underlying chronic stress and the variability in individual tolerance, evidence of the role of chronic stress in the occurrence and evolution of breast cancer remains unclear. This article reviewed previous research on the correlation between chronic stress and the occurrence and development of breast cancer, particularly the molecular mechanism through which chronic stress promotes breast cancer via neurotransmitters secreted by the nervous system. We also review the progress in the development of potential drugs or blockers for the treatment of breast cancer by targeting the neuroendocrine system.

Cooperation between melanoma cell states promotes metastasis through heterotypic cluster formation

Melanomas can have multiple coexisting cell states, including proliferative (PRO) versus invasive (INV) subpopulations that represent a "go or grow" trade-off; however, how these populations interact is poorly understood. Using a combination of zebrafish modeling and analysis of patient samples, we show that INV and PRO cells form spatially structured heterotypic clusters and cooperate in the seeding of metastasis, maintaining cell state heterogeneity. INV cells adhere tightly to each other and form clusters with a rim of PRO cells. Intravital imaging demonstrated cooperation in which INV cells facilitate dissemination of less metastatic PRO cells. We identified the TFAP2 neural crest transcription factor as a master regulator of clustering and PRO/INV states. Isolation of clusters from patients with metastatic melanoma revealed a subset with heterotypic PRO-INV clusters. Our data suggest a framework for the co-existence of these two divergent cell populations, in which heterotypic clusters promote metastasis via cell-cell cooperation.

Hypoxia triggers collective aerotactic migration in Dictyostelium discoideum

Using a self-generated hypoxic assay, we show that the amoeba Dictyostelium discoideum displays a remarkable collective aerotactic behavior. When a cell colony is covered, cells quickly consume the available oxygen (O₂) and form a dense ring moving outwards at constant speed and density. To decipher this collective process, we combined two technological developments: porphyrin-based O₂ -sensing films and microfluidic O₂ gradient generators. We showed that Dictyostelium cells exhibit aerotactic and aerokinetic response in a low range of O₂ concentration indicative of a very efficient detection mechanism. Cell behaviors under self-generated or imposed O₂ gradients were modeled using an in silico cellular Potts model built on experimental observations. This computational model was complemented with a parsimonious ‘Go or Grow’ partial differential equation (PDE) model. In both models, we found that the collective migration of a dense ring can be explained by the interplay between cell division and the modulation of aerotaxis.

Improving personalized tumor growth predictions using a Bayesian combination of mechanistic modeling and machine learning

BACKGROUND

In clinical practice, a plethora of medical examinations are conducted to assess the state of a patient's pathology producing a variety of clinical data. However, investigation of these data faces two major challenges. Firstly, we lack the knowledge of the mechanisms involved in regulating these data variables, and secondly, data collection is sparse in time since it relies on patient's clinical presentation. The former limits the predictive accuracy of clinical outcomes for any mechanistic model. The latter restrains any machine learning algorithm to accurately infer the corresponding disease dynamics.

METHODS

Here, we propose a novel method, based on the Bayesian coupling of mathematical modeling and machine learning, aiming at improving individualized predictions by addressing the aforementioned challenges.

RESULTS

We evaluate the proposed method on a synthetic dataset for brain tumor growth and analyze its performance in predicting two relevant clinical outputs. The method results in improved predictions in almost all simulated patients, especially for those with a late clinical presentation (>95% patients show improvements compared to standard mathematical modeling). In addition, we test the methodology in two additional settings dealing with real patient cohorts. In both cases, namely cancer growth in chronic lymphocytic leukemia and ovarian cancer, predictions show excellent agreement with reported clinical outcomes (around 60% reduction of mean squared error).

CONCLUSIONS

We show that the combination of machine learning and mathematical modeling approaches can lead to accurate predictions of clinical outputs in the context of data sparsity and limited knowledge of disease mechanisms.

Long-term migratory velocity measurements of single glioma cells using microfluidics

Microfluidic platforms enabling single-cell measurements notably contribute to the identification and observation of rare cancer cells that are involved in tumor invasion. Most aggressive, invasive, and heterogeneous glioblastoma cells cause incurable primary brain tumors. Infiltrating gliomas of a brain tumor microenvironment have been intensively studied using conventional assays. Still, quantitative, simple, and precise tools are required for long-term, steady-state migratory-velocity measurements of single glioma cells. To measure long-term velocity changes and investigate the heterogeneity of glioma cells under different growth conditions, we developed a microfluidic platform. We cultured U87 glioma cells in the microfluidic device using either regular growth medium or conditional medium composed of 50% basal medium and 50% macrophage-depleted medium. We microscopically monitored the behavior of 40 glioma cells for 5 days. Using acquired images, we calculated cellular circularity and determined the migratory velocities of glioma cells from 60 h to 120 h. The mean migratory velocity values of the glioma cells were 1.513 μm h^-1 in the basal medium and 3.246 μm h^-1 in the conditional medium. The circularity values of the glioma cells decreased from 0.20-0.25 to 0.15-0.20 when cultured in the conditional medium. Here, we clearly showed that the glioma cells lost their circularity and increased their steady-state velocities; in other words, they adopted an invasive glioma phenotype in the presence of macrophage-depleted medium. Besides, the heterogeneity of the circularity and the velocity of glioma cells were enhanced in the conditional medium.

The Plasticity of Circulating Tumor Cells in Ovarian Cancer During Platinum-containing Chemotherapy

BACKGROUND

Circulating tumor cells (CTCs) are a potential source of metastases and relapses. The data on the ovarian cancer (OC) CTCs molecular characteristics are limited.

OBJECTIVE

To assess the TGFβ, CXCL2, VEGFA and ERCC1 expression in two OC CTC subpopulations before and during chemotherapy (CT), and its relation to clinical characteristics.

METHODS

Two CTCs subpopulations (EpCAM+CK18+E-cadherin+; EpCAM+CK18+Vimentin+) were enriched using immunomagnetic separation before treatment and after 3 cycles of platinum-containing CT. Expression of mRNA was assessed using RT-qPCR.

RESULTS

The study included 31 I-IV stage OC patients. During CT, TGFβ levels increased in both fractions (p=0.054) compared with the initial levels. ERCC1 expression in E-cadherin+ CTCs was higher during neoadjuvant than adjuvant CT (p=0.004). CXCL2 level in E-cadherin+ CTCs increased (p=0.038) during neoadjuvant CT compared with the initial. TGF-β expression in vimentin+ CTCs during CT was negatively correlated to disease stage (p=0.003). Principal component analysis before CT revealed a component combining VEGFA, TGFβ, CXCL2, and a component with ERCC1 and VEGFA; during CT, component 1 contained ERCC1 and VEGFA, component 2 - TGFβ and CXCL2 in both fractions. Increased ERCC1 expression in E-cadherin+ CTCs during CT was associated with decreased progression-free survival (PFS) (HR 1.11 (95% CI 1.03-1.21, p=0.009) in multivariate analysis.

CONCLUSION

EpCAM+ OC CTCs are phenotypically heterogeneous, which may reflect variability in their metastatic potential. CT changes the molecular characteristics of CTCs. Expression of TGFβ in EpCAM+ CTCs increases during CT. High ERCC1 expression in EpCAM+CK18+E-cadherin+ CTCs during CT is associated with decreased PFS in OC.

A Mathematical Study of the Influence of Hypoxia and Acidity on the Evolutionary Dynamics of Cancer

Hypoxia and acidity act as environmental stressors promoting selection for cancer cells with a more aggressive phenotype. As a result, a deeper theoretical understanding of the spatio-temporal processes that drive the adaptation of tumour cells to hypoxic and acidic microenvironments may open up new avenues of research in oncology and cancer treatment. We present a mathematical model to study the influence of hypoxia and acidity on the evolutionary dynamics of cancer cells in vascularised tumours. The model is formulated as a system of partial integro-differential equations that describe the phenotypic evolution of cancer cells in response to dynamic variations in the spatial distribution of three abiotic factors that are key players in tumour metabolism: oxygen, glucose and lactate. The results of numerical simulations of a calibrated version of the model based on real data recapitulate the eco-evolutionary spatial dynamics of tumour cells and their adaptation to hypoxic and acidic microenvironments. Moreover, such results demonstrate how nonlinear interactions between tumour cells and abiotic factors can lead to the formation of environmental gradients which select for cells with phenotypic characteristics that vary with distance from intra-tumour blood vessels, thus promoting the emergence of intra-tumour phenotypic heterogeneity. Finally, our theoretical findings reconcile the conclusions of earlier studies by showing that the order in which resistance to hypoxia and resistance to acidity arise in tumours depend on the ways in which oxygen and lactate act as environmental stressors in the evolutionary dynamics of cancer cells.

Non-local multiscale approach for the impact of go or grow hypothesis on tumour-viruses interactions

We propose and study computationally a novel non-local multiscale moving boundary mathematical model for tumour and oncolytic virus (OV) interactions when we consider the go or grow hypothesis for cancer dynamics. This spatio-temporal model focuses on two cancer cell phenotypes that can be infected with the OV or remain uninfected, and which can either move in response to the extracellular-matrix (ECM) density or proliferate. The interactions between cancer cells, those among cancer cells and ECM, and those among cells and OV occur at the macroscale. At the micro-scale, we focus on the interactions between cells and matrix degrading enzymes (MDEs) that impact the movement of tumour boundary. With the help of this multiscale model we explore the impact on tumour invasion patterns of two different assumptions that we consider in regard to cell-cell and cell-matrix interactions. In particular we investigate model dynamics when we assume that cancer cell fluxes are the result of local advection in response to the density of extracellular matrix (ECM), or of non-local advection in response to cell-ECM adhesion. We also investigate the role of the transition rates between mainly-moving and mainly-growing cancer cell sub-populations, as well as the role of virus infection rate and virus replication rate on the overall tumour dynamics.

Predicting cell behaviour parameters from glioblastoma on a chip images. A deep learning approach

The broad possibilities offered by microfluidic devices in relation to massive data monitoring and acquisition open the door to the use of deep learning technologies in a very promising field: cell culture monitoring. In this work, we develop a methodology for parameter identification in cell culture from fluorescence images using Convolutional Neural Networks (CNN). We apply this methodology to the in vitro study of glioblastoma (GBM), the most common, aggressive and lethal primary brain tumour. In particular, the aim is to predict the three parameters defining the go or grow GBM behaviour, which is determinant for the tumour prognosis and response to treatment. The data used to train the network are obtained from a mathematical model, previously validated with in vitro experimental results. The resulting CNN provides remarkably accurate predictions (Pearson's ρ > 0.99 for all the parameters). Besides, it proves to be sound, to filter noise and to generalise. After training and validation with synthetic data, we predict the parameters corresponding to a real image of a microfluidic experiment. The obtained results show good performance of the CNN. The proposed technique may set the first steps towards patient-specific tools, able to predict in real-time the tumour evolution for each particular patient, thanks to a combined in vitro-in silico approach.

Cancer Community Ecology

Here we advocate Cancer Community Ecology as a valuable focus of study in Cancer Biology. We hypothesize that the heterogeneity and characteristics of cancer cells within tumors should vary systematically in space and time and that cancer cells form local ecological communities within tumors. These communities possess limited numbers of species determined by local conditions, with each species in a community possessing predictable traits that enable them to cope with their particular environment and coexist with each other. We start with a discussion of concepts and assumptions that ecologists use to study closely related species. We then discuss the competitive exclusion principle as a means for knowing when two species should not coexist, and as an opening towards understanding how they can. We present the five major categories of mechanisms of coexistence that operate in nature and suggest that the same mechanisms apply towards understanding the diversification and coexistence of cancer cell species. They are: Food-Safety Tradeoffs, Diet Choice, Habitat Selection, Variance Partitioning, and Competition-Colonization Tradeoffs. For each mechanism, we discuss how it works in nature, how it might work in cancers, and its implications for therapy.

Hypoxia-induced phenotypic transition from highly invasive to less invasive tumors in glioma stem-like cells: Significance of CD44 and osteopontin as therapeutic targets in glioblastoma

•

CD44, upregulated by HIF-1α under 1%O₂, induces highly invasive phenotype GSCs.

•

HIF-2α-activated OPN under 5%O₂ promotes less-invasive/proliferative type GSCs.

•

CD44 and OPN knockdowns inhibit in vitro/vivo GSCs invasion and proliferation.

The poor prognosis of glioblastoma multiforme (GBM) is primarily due to highly invasive glioma stem-like cells (GSCs) in tumors. Upon GBM recurrence, GSCs with highly invasive and highly migratory activities must assume a less-motile state and proliferate to regenerate tumor mass. Elucidating the molecular mechanism underlying this transition from a highly invasive phenotype to a less-invasive, proliferative tumor could facilitate the identification of effective molecular targets for treating GBM. Here, we demonstrate that severe hypoxia (1% O₂) upregulates CD44 expression via activation of hypoxia-inducible factor (HIF-1α), inducing GSCs to assume a highly invasive tumor. In contrast, moderate hypoxia (5% O₂) upregulates osteopontin expression via activation of HIF-2α. The upregulated osteopontin inhibits CD44-promoted GSC migration and invasion and stimulates GSC proliferation, inducing GSCs to assume a less-invasive, highly proliferative tumor. These data indicate that the GSC phenotype is determined by interaction between CD44 and osteopontin. The expression of both CD44 and osteopontin is regulated by differential hypoxia levels. We found that CD44 knockdown significantly inhibited GSC migration and invasion both in vitro and in vivo. Mouse brain tumors generated from CD44-knockdown GSCs exhibited diminished invasiveness, and the mice survived significantly longer than control mice. In contrast, siRNA-mediated silencing of the osteopontin gene decreased GSC proliferation. These results suggest that interaction between CD44 and osteopontin plays a key role in tumor progression in GBM; inhibition of both CD44 and osteopontin may represent an effective therapeutic approach for suppressing tumor progression, thus resulting in a better prognosis for patients with GBM.

Testing Multi-Task Cancer Evolution: How Do We Test Ecological Hypotheses in Cancer?

Recently several authors described a family of models, according to which different cancer types and subtypes fall within a space of selective trade-offs between archetypes that maximize the performance of different tasks: cell division, biomass and energy production, lipogenesis, immune interaction, and invasion and tissue remodeling. On this picture, inter- and intratumor heterogeneity can be explained in part as a product of these selective trade-offs in different cancers, at different stages of cancer progression. The aim of this Perspective is to critically assess this approach. I use this case study to consider more generally both the advantages of using ecological models in the context of cancer, and the challenges facing testing of such models.

Dichotomy in Growth and Invasion from Low- to High-Grade Glioma Cellular Variants

Glial dysfunction outraging CNS plasticity and integrity results in one of the most dangerous cancers, namely glioma, featuring little median survival period and high recurrence. The hallmark properties of proliferation, invasion and angiogenesis with the infiltrated macrophages in glioma are expected to be tightly coupled or cross-linked, but not properly related so far. The present study is aimed to find a relationship between this featured quadrangle from lower to higher grades (HG) of post-operative glioma tissues and their invading subsets. Elevated Ki67-associated proliferation in lower grades (LG) was supported with VEGF dependent angiogenic maintenance which found a decrease unlikely in HG. In contrast, MMP 2 and 9-associated invasions augmented high in HG with the dominant presence of CD204⁺ M2 polarized macrophages and a general increase in global DNMT1-associated methylation. Marked differences found in ECM invading cellular subsets of HG showing high proliferative capacity indicating rationally for recurrence, contrasting the nature of gross tumor tissue of the same grade. Thus in LG, the neoplastic lesion is more inclined to its growth while in higher grade more disposed towards tissue wreckage in support with cellular environmental milieu whereas the cellular variants and subsets of invaded cells showed different trends. Therefore, some operational dichotomy or coupling among cellular variants in glioma is active in determining its low- to high-grade transition and aggressive progression.

Feasibility and clinical usefulness of modelling glioblastoma migration in adjuvant radiotherapy

Glioblastoma (GBM) is one of the most common primary brain tumours in adults, with a dismal prognosis despite aggressive multimodality treatment by a combination of surgery and adjuvant radiochemotherapy. A detailed knowledge of the spreading of glioma cells in the brain might allow for more targeted escalated radiotherapy, aiming to reduce locoregional relapse. Recent years have seen the development of a large variety of mathematical modelling approaches to predict glioma migration. The aim of this study is hence to evaluate the clinical applicability of a detailed micro- and meso-scale mathematical model in radiotherapy. First and foremost, a clinical workflow is established, in which the tumour is automatically segmented as input data and then followed in time mathematically based on the diffusion tensor imaging data. The influence of several free model parameters is individually evaluated, then the full model is retrospectively validated for a collective of 3 GBM patients treated at our institution by varying the most important model parameters to achieve optimum agreement with the tumour development during follow-up. Agreement of the model predictions with the real tumour growth as defined by manual contouring based on the follow-up MRI images is analyzed using the dice coefficient. The tumour evolution over 103-212 days follow-up could be predicted by the model with a dice coefficient better than 60% for all three patients. In all cases, the final tumour volume was overestimated by the model by a factor between 1.05 and 1.47. To evaluate the quality of the agreement between the model predictions and the ground truth, we must keep in mind that our gold standard relies on a single observer's (CB) manually-delineated tumour contours. We therefore decided to add a short validation of the stability and reliability of these contours by an inter-observer analysis including three other experienced radiation oncologists from our department. In total, a dice coefficient between 63% and 89% is achieved between the four different observers. Compared with this value, the model predictions (62-66%) perform reasonably well, given the fact that these tumour volumes were created based on the pre-operative segmentation and DTI.

Cancer metastasis as a non-healing wound

Most cancer deaths are caused by metastasis: recurrence of disease by disseminated tumour cells at sites distant from the primary tumour. Large numbers of disseminated tumour cells are released from the primary tumour, even during the early stages of tumour growth. However, only a minority survive as potential seeds for future metastatic outgrowths. These cells must adapt to a relatively inhospitable microenvironment, evade immune surveillance and progress from the micro- to macro-metastatic stage to generate a secondary tumour. A pervasive driver of this transition is chronic inflammatory signalling emanating from tumour cells themselves. These signals can promote migration and engagement of stem and progenitor cell function, events that are also central to a wound healing response. In this review, we revisit the concept of cancer as a non-healing wound, first introduced by Virchow in the 19th century, with a new tumour cell-intrinsic perspective on inflammation and focus on metastasis. Cellular responses to inflammation in both wound healing and metastasis are tightly regulated by crosstalk with the surrounding microenvironment. Targeting or restoring canonical responses to inflammation could represent a novel strategy to prevent the lethal spread of cancer.

Cell Force-Driven Basement Membrane Disruption Fuels EGF- and Stiffness-Induced Invasive Cell Dissemination from Benign Breast Gland Acini

Local basement membrane (BM) disruption marks the initial step of breast cancer invasion. The activation mechanisms of force-driven BM-weakening remain elusive. We studied the mechanical response of MCF10A-derived human breast cell acini with BMs of tuneable maturation to physical and soluble tumour-like extracellular matrix (ECM) cues. Traction force microscopy (TFM) and elastic resonator interference stress microscopy (ERISM) were used to quantify pro-invasive BM stress and protrusive forces. Substrate stiffening and mechanically impaired BM scaffolds induced the invasive transition of benign acini synergistically. Robust BM scaffolds attenuated this invasive response. Additional oncogenic EGFR activation compromised the BMs’ barrier function, fuelling invasion speed and incidence. Mechanistically, EGFR-PI3-Kinase downstream signalling modulated both MMP- and force-driven BM-weakening processes. We show that breast acini form non-proteolytic and BM-piercing filopodia for continuous matrix mechanosensation, which significantly push and pull on the BM and ECM under pro-invasive conditions. Invasion-triggered acini further shear and compress their BM by contractility-based stresses that were significantly increased (3.7-fold) compared to non-invasive conditions. Overall, the highest amplitudes of protrusive and contractile forces accompanied the highest invasiveness. This work provides a mechanistic concept for tumour ECM-induced mechanically misbalanced breast glands fuelling force-driven BM disruption. Finally, this could facilitate early cell dissemination from pre-invasive lesions to metastasize eventually.

Karyotype Aberrations in Action: The Evolution of Cancer Genomes and the Tumor Microenvironment

Cancer is a disease of cellular evolution. For this cellular evolution to take place, a population of cells must contain functional heterogeneity and an assessment of this heterogeneity in the form of natural selection. Cancer cells from advanced malignancies are genomically and functionally very different compared to the healthy cells from which they evolved. Genomic alterations include aneuploidy (numerical and structural changes in chromosome content) and polyploidy (e.g., whole genome doubling), which can have considerable effects on cell physiology and phenotype. Likewise, conditions in the tumor microenvironment are spatially heterogeneous and vastly different than in healthy tissues, resulting in a number of environmental niches that play important roles in driving the evolution of tumor cells. While a number of studies have documented abnormal conditions of the tumor microenvironment and the cellular consequences of aneuploidy and polyploidy, a thorough overview of the interplay between karyotypically abnormal cells and the tissue and tumor microenvironments is not available. Here, we examine the evidence for how this interaction may unfold during tumor evolution. We describe a bidirectional interplay in which aneuploid and polyploid cells alter and shape the microenvironment in which they and their progeny reside; in turn, this microenvironment modulates the rate of genesis for new karyotype aberrations and selects for cells that are most fit under a given condition. We conclude by discussing the importance of this interaction for tumor evolution and the possibility of leveraging our understanding of this interplay for cancer therapy.

Superlinear growth reveals the Allee effect in tumors

Integrating experimental data into ecological models plays a central role in understanding biological mechanisms that drive tumor progression where such knowledge can be used to develop new therapeutic strategies. While the current studies emphasize the role of competition among tumor cells, they fail to explain recently observed superlinear growth dynamics across human tumors. Here we study tumor growth dynamics by developing a model that incorporates evolutionary dynamics inside tumors with tumor-microenvironment interactions. Our results reveal that tumor cells' ability to manipulate the environment and induce angiogenesis drives superlinear growth-a process compatible with the Allee effect. In light of this understanding, our model suggests that, for high-risk tumors that have a higher growth rate, suppressing angiogenesis can be the appropriate therapeutic intervention.

In vitro biomimetic models for glioblastoma-a promising tool for drug response studies

The poor response of glioblastoma to current treatment protocols is a consequence of its intrinsic drug resistance. Resistance to chemotherapy is primarily associated with considerable cellular heterogeneity, and plasticity of glioblastoma cells, alterations in gene expression, presence of specific tumor microenvironment conditions and blood-brain barrier. In an attempt to successfully overcome chemoresistance and better understand the biological behavior of glioblastoma, numerous tri-dimensional (3D) biomimetic models were developed in the past decade. These novel advanced models are able to better recapitulate the spatial organization of glioblastoma in a real time, therefore providing more realistic and reliable evidence to the response of glioblastoma to therapy. Moreover, these models enable the fine-tuning of different tumor microenvironment conditions and facilitate studies on the effects of the tumor microenvironment on glioblastoma chemoresistance. This review outlines current knowledge on the essence of glioblastoma chemoresistance and describes the progress achieved by 3D biomimetic models. Moreover, comprehensive literature assessment regarding the influence of 3D culturing and microenvironment mimicking on glioblastoma gene expression and biological behavior is also provided. The contribution of the blood-brain barrier as well as the blood-tumor barrier to glioblastoma chemoresistance is also reviewed from the perspective of 3D biomimetic models. Finally, the role of mathematical models in predicting 3D glioblastoma behavior and drug response is elaborated. In the future, technological innovations along with mathematical simulations should create reliable 3D biomimetic systems for glioblastoma research that should facilitate the identification and possibly application in preclinical drug testing and precision medicine.

Effectiveness of porous silicon nanoparticle treatment at inhibiting the migration of a heterogeneous glioma cell population

BACKGROUND

Approximately 80% of brain tumours are gliomas. Despite treatment, patient mortality remains high due to local metastasis and relapse. It has been shown that transferrin-functionalised porous silicon nanoparticles (Tf@pSiNPs) can inhibit the migration of U87 glioma cells. However, the underlying mechanisms and the effect of glioma cell heterogeneity, which is a hallmark of the disease, on the efficacy of Tf@pSiNPs remains to be addressed.

RESULTS

Here, we observed that Tf@pSiNPs inhibited heterogeneous patient-derived glioma cells' (WK1) migration across small perforations (3 μm) by approximately 30%. A phenotypical characterisation of the migrated subpopulations revealed that the majority of them were nestin and fibroblast growth factor receptor 1 positive, an indication of their cancer stem cell origin. The treatment did not inhibit cell migration across large perforations (8 μm), nor cytoskeleton formation. This is in agreement with our previous observations that cellular-volume regulation is a mediator of Tf@pSiNPs' cell migration inhibition. Since aquaporin 9 (AQP9) is closely linked to cellular-volume regulation, and is highly expressed in glioma, the effect of AQP9 expression on WK1 migration was investigated. We showed that WK1 migration is correlated to the differential expression patterns of AQP9. However, AQP9-silencing did not affect WK1 cell migration across perforations, nor the efficacy of cell migration inhibition mediated by Tf@pSiNPs, suggesting that AQP9 is not a mediator of the inhibition.

CONCLUSION

This in vitro investigation highlights the unique therapeutic potentials of Tf@pSiNPs against glioma cell migration and indicates further optimisations that are required to maximise its therapeutic efficacies.

5-HT7 receptors as a new target for prostate cancer physiopathology and treatment: an experimental study on PC-3 cells and FFPE tissues

Prostate cancer (PCa) is one of the most common types of cancer seen among men worldwide. Previous studies have demonstrated that serotonin regulates cell proliferation, migration, and invasion in vitro; the presence of 5-HT receptors in cancer cells; and the role of serotonin in tumor development. The most recently discovered of these receptors is 5-HT7 but also least characterized receptors of serotonin. The aim of this study is to investigate the existence and possible role of 5-HT7 receptors in healthy and cancerous prostate tissues and also investigate effects of receptor agonists and antagonists on PC-3 cells to evaluate potential therapeutic effects. PC-3 cells were cultured and effects of 5-HT7 receptor agonist (LP-44) and antagonist (SB-269970) were evaluated on these cells. After proliferation analyses, relative expression of apoptotic markers and 5-HT7 receptor mRNA expression levels were determined through real-time PCR. Annexin V-FITC/PI double staining and Hoechst 33258 staining assay methods were applied to determine apoptosis. Additional PCR studies were performed on healthy and cancerous prostate tissue to see existence of receptors in human samples. The viability of PC-3 cells was decreased by SB-269970 after 48 and 72 h of incubation. However, LP-44 increased PC-3 cell proliferation at all time points. In 10^-6 M SB-269970 treated PC-3 cells, there was significant increase in the expression of CAS-3 (4-fold), CAS-9 (2.5-fold), BAX (1.9-fold), and Tp-53 (4.8-fold) gene mRNA levels when compared to non-treated control group. Conversely, there was a significant decrease in NF-κB (2.9-fold) and 5-HT7 receptor (3.6-fold) mRNA expression in cells treated with SB-269970 when compared to control. SB-269970 that antagonized 5-HT7 receptors also induced apoptosis in Annexin V-FITC/PI double staining assay and Hoechst 33258 staining assays when compared with other groups. In human samples, 5-HT7 receptor mRNA expression was approximately 200-fold higher than that of heathy ones. In this study, for the first time, the 5-HT7 receptor antagonist SB-269970 has been shown to inhibit proliferation in PC-3 cells and to be associated with an apoptosis-inducing effect. These results suggest blocking 5-HT7 receptors can be a novel therapeutic target for the treatment of prostate cancer.

Extracellular matrix density regulates the formation of tumour spheroids through cell migration

In this work, we show how the mechanical properties of the cellular microenvironment modulate the growth of tumour spheroids. Based on the composition of the extracellular matrix, its stiffness and architecture can significantly vary, subsequently influencing cell movement and tumour growth. However, it is still unclear exactly how both of these processes are regulated by the matrix composition. Here, we present a centre-based computational model that describes how collagen density, which modulates the steric hindrance properties of the matrix, governs individual cell migration and, consequently, leads to the formation of multicellular clusters of varying size. The model was calibrated using previously published experimental data, replicating a set of experiments in which cells were seeded in collagen matrices of different collagen densities, hence producing distinct mechanical properties. At an initial stage, we tracked individual cell trajectories and speeds. Subsequently, the formation of multicellular clusters was also analysed by quantifying their size. Overall, the results showed that our model could accurately replicate what was previously seen experimentally. Specifically, we showed that cells seeded in matrices with low collagen density tended to migrate more. Accordingly, cells strayed away from their original cluster and thus promoted the formation of small structures. In contrast, we also showed that high collagen densities hindered cell migration and produced multicellular clusters with increased volume. In conclusion, this model not only establishes a relation between matrix density and individual cell migration but also showcases how migration, or its inhibition, modulates tumour growth.

Structure Activity Relationship of Key Heterocyclic Anti-Angiogenic Leads of Promising Potential in the Fight against Cancer

Pathological angiogenesis is a hallmark of cancer; accordingly, a number of anticancer FDA-approved drugs act by inhibiting angiogenesis via different mechanisms. However, the development process of the most potent anti-angiogenics has met various hurdles including redundancy, multiplicity, and development of compensatory mechanisms by which blood vessels are remodeled. Moreover, identification of broad-spectrum anti-angiogenesis targets is proved to be required to enhance the efficacy of the anti-angiogenesis drugs. In this perspective, a proper understanding of the structure activity relationship (SAR) of the recent anti-angiogenics is required. Various anti-angiogenic classes have been developed over the years; among them, the heterocyclic organic compounds come to the fore as the most promising, with several drugs approved by the FDA. In this review, we discuss the structure–activity relationship of some promising potent heterocyclic anti-angiogenic leads. For each lead, a molecular modelling was also carried out in order to correlate its SAR and specificity to the active site. Furthermore, an in silico pharmacokinetics study for some representative leads was presented. Summarizing, new insights for further improvement for each lead have been reviewed.

Advances in Research of Adult Gliomas

Diffuse gliomas are the most frequent brain tumours, representing 75% of all primary malignant brain tumours in adults. Because of their locally aggressive behaviour and the fact that they cannot be cured by current therapies, they represent one of the most devastating cancers. The present review summarises recent advances in our understanding of glioma development and progression by use of various in vitro and in vivo models, as well as more complex techniques including cultures of 3D organoids and organotypic slices. We discuss the progress that has been made in understanding glioma heterogeneity, alteration in gene expression and DNA methylation, as well as advances in various in silico models. Lastly current treatment options and future clinical trials, which aim to improve early diagnosis and disease monitoring, are also discussed.

To Divide or Invade: A Look Behind the Scenes of the Proliferation-Invasion Interplay in the Caenorhabditis elegans Anchor Cell

Cell invasion is defined by the capability of cells to migrate across compartment boundaries established by basement membranes (BMs). The development of complex organs involves regulated cell growth and regrouping of different cell types, which are enabled by controlled cell proliferation and cell invasion. Moreover, when a malignant tumor takes control over the body, cancer cells evolve to become invasive, allowing them to spread to distant sites and form metastases. At the core of the switch between proliferation and invasion are changes in cellular morphology driven by remodeling of the cytoskeleton. Proliferative cells utilize their actomyosin network to assemble a contractile ring during cytokinesis, while invasive cells form actin-rich protrusions, called invadopodia that allow them to breach the BMs. Studies of developmental cell invasion as well as of malignant tumors revealed that cell invasion and proliferation are two mutually exclusive states. In particular, anchor cell (AC) invasion during Caenorhabditis elegans larval development is an excellent model to study the transition from cell proliferation to cell invasion under physiological conditions. This mini-review discusses recent insights from the C. elegans AC invasion model into how G1 cell-cycle arrest is coordinated with the activation of the signaling networks required for BM breaching. Many regulators of the proliferation-invasion network are conserved between C. elegans and mammals. Therefore, the worm may provide important clues to better understand cell invasion and metastasis formation in humans.

Towards understanding the messengers of extracellular space: Computational models of outside-in integrin reaction networks

The interactions between cells and their extracellular matrix (ECM) are critically important for homeostatic control of cell growth, proliferation, differentiation and apoptosis. Transmembrane integrin molecules facilitate the communication between ECM and the cell. Since the characterization of integrins in the late 1980s, there has been great advancement in understanding the function of integrins at different subcellular levels. However, the versatility in molecular pathways integrins are involved in, the high diversity in their interaction partners both outside and inside the cell as well as on the cell membrane and the short lifetime of events happening at the cell-ECM interface make it difficult to elucidate all the details regarding integrin function experimentally. To overcome the experimental challenges and advance the understanding of integrin biology, computational modeling tools have been used extensively. In this review, we summarize the computational models of integrin signaling while we explain the function of integrins at three main subcellular levels (outside the cell, cell membrane, cytosol). We also discuss how these computational modeling efforts can be helpful in other disciplines such as biomaterial design. As such, this review is a didactic modeling summary for biomaterial researchers interested in complementing their experimental work with computational tools or for seasoned computational scientists that would like to advance current in silico integrin models.

Analysis of the Parametric Correlation in Mathematical Modeling of In Vitro Glioblastoma Evolution Using Copulas

Modeling and simulation are essential tools for better understanding complex biological processes, such as cancer evolution. However, the resulting mathematical models are often highly non-linear and include many parameters, which, in many cases, are difficult to estimate and present strong correlations. Therefore, a proper parametric analysis is mandatory. Following a previous work in which we modeled the in vitro evolution of Glioblastoma Multiforme (GBM) under hypoxic conditions, we analyze and solve here the problem found of parametric correlation. With this aim, we develop a methodology based on copulas to approximate the multidimensional probability density function of the correlated parameters. Once the model is defined, we analyze the experimental setting to optimize the utility of each configuration in terms of gathered information. We prove that experimental configurations with oxygen gradient and high cell concentration have the highest utility when we want to separate correlated effects in our experimental design. We demonstrate that copulas are an adequate tool to analyze highly-correlated multiparametric mathematical models such as those appearing in Biology, with the added value of providing key information for the optimal design of experiments, reducing time and cost in in vivo and in vitro experimental campaigns, like those required in microfluidic models of GBM evolution.

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.

Mathematical formulation and parametric analysis of in vitro cell models in microfluidic devices: application to different stages of glioblastoma evolution

In silico models and computer simulation are invaluable tools to better understand complex biological processes such as cancer evolution. However, the complexity of the biological environment, with many cell mechanisms in response to changing physical and chemical external stimuli, makes the associated mathematical models highly non-linear and multiparametric. One of the main problems of these models is the determination of the parameters’ values, which are usually fitted for specific conditions, making the conclusions drawn difficult to generalise. We analyse here an important biological problem: the evolution of hypoxia-driven migratory structures in Glioblastoma Multiforme (GBM), the most aggressive and lethal primary brain tumour. We establish a mathematical model considering the interaction of the tumour cells with oxygen concentration in what is called the go or grow paradigm. We reproduce in this work three different experiments, showing the main GBM structures (pseudopalisade and necrotic core formation), only changing the initial and boundary conditions. We prove that it is possible to obtain versatile mathematical tools which, together with a sound parametric analysis, allow to explain complex biological phenomena. We show the utility of this hybrid “biomimetic in vitro-in silico” platform to help to elucidate the mechanisms involved in cancer processes, to better understand the role of the different phenomena, to test new scientific hypotheses and to design new data-driven experiments.

Crosstalk between microglia and patient-derived glioblastoma cells inhibit invasion in a three-dimensional gelatin hydrogel model

BACKGROUND

Glioblastoma is the most common and deadly form of primary brain cancer, accounting for more than 13,000 new diagnoses annually in the USA alone. Microglia are the innate immune cells within the central nervous system, acting as a front-line defense against injuries and inflammation via a process that involves transformation from a quiescent to an activated phenotype. Crosstalk between GBM cells and microglia represents an important axis to consider in the development of tissue engineering platforms to examine pathophysiological processes underlying GBM progression and therapy.

METHODS

This work used a brain-mimetic hydrogel system to study patient-derived glioblastoma specimens and their interactions with microglia. Here, glioblastoma cells were either cultured alone in 3D hydrogels or in co-culture with microglia in a manner that allowed secretome-based signaling but prevented direct GBM-microglia contact. Patterns of GBM cell invasion were quantified using a three-dimensional spheroid assay. Secretome and transcriptome (via RNAseq) were used to profile the consequences of GBM-microglia interactions.

RESULTS

Microglia displayed an activated phenotype as a result of GBM crosstalk. Three-dimensional migration patterns of patient-derived glioblastoma cells showed invasion was significantly decreased in response to microglia paracrine signaling. Potential molecular mechanisms underlying with this phenotype were identified from bioinformatic analysis of secretome and RNAseq data.

CONCLUSION

The data demonstrate a tissue engineered hydrogel platform can be used to investigate crosstalk between immune cells of the tumor microenvironment related to GBM progression. Such multi-dimensional models may provide valuable insight to inform therapeutic innovations to improve GBM treatment.

Integrating Mathematical Modeling with High-Throughput Imaging Explains How Polyploid Populations Behave in Nutrient-Sparse Environments

Breast cancer progresses in a multistep process from primary tumor growth and stroma invasion to metastasis. Nutrient-limiting environments promote chemotaxis with aggressive morphologies characteristic of invasion. It is unknown how coexisting cells differ in their response to nutrient limitations and how this impacts invasion of the metapopulation as a whole. In this study, we integrate mathematical modeling with microenvironmental perturbation data to investigate invasion in nutrient-limiting environments inhabited by one or two cancer cell subpopulations. Subpopulations were defined by their energy efficiency and chemotactic ability. Invasion distance traveled by a homogeneous population was estimated. For heterogeneous populations, results suggest that an imbalance between nutrient efficacy and chemotactic superiority accelerates invasion. Such imbalance will spatially segregate the two populations and only one type will dominate at the invasion front. Only if these two phenotypes are balanced, the two subpopulations compete for the same space, which decelerates invasion. We investigate ploidy as a candidate biomarker of this phenotypic heterogeneity and discuss its potential to inform the dose of mTOR inhibitors (mTOR-I) that can inhibit chemotaxis just enough to facilitate such competition. SIGNIFICANCE: This study identifies the double-edged sword of high ploidy as a prerequisite to personalize combination therapies with cytotoxic drugs and inhibitors of signal transduction pathways such as mTOR-Is. GRAPHICAL ABSTRACT: http://cancerres.aacrjournals.org/content/canres/80/22/5109/F1.large.jpg.

Receptor tyrosine kinases activate heterotrimeric G proteins via phosphorylation within the interdomain cleft of Gαi

The molecular mechanisms by which receptor tyrosine kinases (RTKs) and heterotrimeric G proteins, two major signaling hubs in eukaryotes, independently relay signals across the plasma membrane have been extensively characterized. How these hubs cross-talk has been a long-standing question, but answers remain elusive. Using linear ion-trap mass spectrometry in combination with biochemical, cellular, and computational approaches, we unravel a mechanism of activation of heterotrimeric G proteins by RTKs and chart the key steps that mediate such activation. Upon growth factor stimulation, the guanine-nucleotide exchange modulator dissociates Gαi•βγ trimers, scaffolds monomeric Gαi with RTKs, and facilitates the phosphorylation on two tyrosines located within the interdomain cleft of Gαi. Phosphorylation triggers the activation of Gαi and inhibits second messengers (cAMP). Tumor-associated mutants reveal how constitutive activation of this pathway impacts cell's decision to "go" vs. "grow." These insights define a tyrosine-based G protein signaling paradigm and reveal its importance in eukaryotes.

Glucose transporter Glut1 controls diffuse invasion phenotype with perineuronal satellitosis in diffuse glioma microenvironment

Background

Gliomas typically escape surgical resection and recur due to their “diffuse invasion” phenotype, enabling them to infiltrate diffusely into the normal brain parenchyma. Over the past 80 years, studies have revealed 2 key features of the “diffuse invasion” phenotype, designated the Scherer’s secondary structure, and include perineuronal satellitosis (PS) and perivascular satellitosis (PVS). However, the mechanisms are still unknown.

Methods

We established a mouse glioma cell line (IG27) by manipulating the histone H3K27M mutation, frequently harboring in diffuse intrinsic pontine gliomas, that reproduced the diffuse invasion phenotype, PS and PVS, following intracranial transplantation in the mouse brain. Further, to broadly apply the results in this mouse model to human gliomas, we analyzed data from 66 glioma patients.

Results

Increased H3K27 acetylation in IG27 cells activated glucose transporter 1 (Glut1) expression and induced aerobic glycolysis and TCA cycle activation, leading to lactate, acetyl-CoA, and oncometabolite production irrespective of oxygen and glucose levels. Gain- and loss-of-function in vivo experiments demonstrated that Glut1 controls the PS of glioma cells, that is, attachment to and contact with neurons. GLUT1 is also associated with early progression in glioma patients.

Conclusions

Targeting the transporter Glut1 suppresses the unique phenotype, “diffuse invasion” in the diffuse glioma mouse model. This work leads to promising therapeutic and potential useful imaging targets for anti-invasion in human gliomas widely.

The FABP12/PPARγ pathway promotes metastatic transformation by inducing epithelial-to-mesenchymal transition and lipid-derived energy production in prostate cancer cells

Early stage localized prostate cancer (PCa) has an excellent prognosis; however, patient survival drops dramatically when PCa metastasizes. The molecular mechanisms underlying PCa metastasis are complex and remain unclear. Here, we examine the role of a new member of the fatty acid‐binding protein (FABP) family, FABP12, in PCa progression. FABP12 is preferentially amplified and/or overexpressed in metastatic compared to primary tumors from both PCa patients and xenograft animal models. We show that FABP12 concurrently triggers metastatic phenotypes (induced epithelial‐to‐mesenchymal transition (EMT) leading to increased cell motility and invasion) and lipid bioenergetics (increased fatty acid uptake and accumulation, increased ATP production from fatty acid β‐oxidation) in PCa cells, supporting increased reliance on fatty acids for energy production. Mechanistically, we show that FABP12 is a driver of PPARγ activation which, in turn, regulates FABP12's role in lipid metabolism and PCa progression. Our results point to a novel role for a FABP‐PPAR pathway in promoting PCa metastasis through induction of EMT and lipid bioenergetics.

Numerical simulation of the effect of necrosis area in systemic delivery of magnetic nanoparticles in hyperthermia cancer treatment

In a magnetic hyperthermia treatment, malignant cancerous cells are ablated by the heat production of magnetic nanoparticles (MNP) under an external magnetic field. This novel approach is a promising tool to eliminate the tumor cells by a higher temperature inside the tumor microenvironment. MNPs are needed inside the tumor microenvironment to increase the heat, and this could be possible with intravenous drug injection. However, tumors with necrosis regions are more resistant to drug penetration, and this can cause inadequate and non-homogeneous temperature distribution in the tumor. Hence, in this study, we used numerical methods to investigate the Spatio-temporal temperature field distribution in the necrotic tumor and its surrounding tissue. To this end, an intravenous bolus injection is used to simulate the effect of systemic drug delivery in tumors with necrosis region. Results show that the temperature field with the necrosis region with 10% of the tumor radius is more prone to higher temperature values. The hypoxia region is affected by the high temperature despite the necrosis region in the tumor. However, a broader necrosis region impedes drug penetration inside the inner layers of tumors, which leads to a lower heat generation by the MNPs. Results also demonstrate that only 15.5% of MNP concentration distributed to the necrosis with 50% of tumor radius, leading a temperature of 42^∘C in the necrosis region, which is not sufficient for the tumor ablation. Therefore, the temperature distribution is dependant on the sizes of necrosis regions in tumors, and tumors with a larger necrotic region (over 20% of tumor radius) are challenging to treat with hyperthermia treatment. This study could help the future in vitro and in vivo studies of hyperthermia treatment in necrotic tumors.

Emerging role of tumor cell plasticity in modifying therapeutic response

Resistance to cancer therapy is a major barrier to cancer management. Conventional views have proposed that acquisition of resistance may result from genetic mutations. However, accumulating evidence implicates a key role of non-mutational resistance mechanisms underlying drug tolerance, the latter of which is the focus that will be discussed here. Such non-mutational processes are largely driven by tumor cell plasticity, which renders tumor cells insusceptible to the drug-targeted pathway, thereby facilitating the tumor cell survival and growth. The concept of tumor cell plasticity highlights the significance of re-activation of developmental programs that are closely correlated with epithelial-mesenchymal transition, acquisition properties of cancer stem cells, and trans-differentiation potential during drug exposure. From observations in various cancers, this concept provides an opportunity for investigating the nature of anticancer drug resistance. Over the years, our understanding of the emerging role of phenotype switching in modifying therapeutic response has considerably increased. This expanded knowledge of tumor cell plasticity contributes to developing novel therapeutic strategies or combination therapy regimens using available anticancer drugs, which are likely to improve patient outcomes in clinical practice.

Tumour budding in solid cancers

Tumour budding is an emerging prognostic biomarker in colorectal cancer (CRC) and other solid cancers. Tumour buds are usually defined as isolated single cancer cells or clusters of up to four cancer cells located at the invasive tumour front. The prognostic value of tumour budding is now supported by a large body of evidence, whereas the utility of this phenotype as a predictive biomarker remains under investigation. The application of tumour budding indices in clinical practice requires a standardized scoring system that can be tailored to specific tumour types and clinical scenarios. In the context of CRC, tumour budding can be assessed according to the method agreed at the International Tumour Budding Consensus Conference (ITBCC) in 2016. Using the ITBCC scoring system, tumour budding is an independent predictor of lymph node metastasis in patients with pT1 CRC and of unfavourable survival in patients with stage II colon cancer. Regardless of the clinical scenario or tumour type, the assertion that 'the more tumour buds, the worse the clinical outcome' applies. In this Review, we provide an overview of tumour budding in solid cancers, highlighting the molecular and biological aspects of this phenomenon, including its associations with epithelial-mesenchymal transition and features of the tumour microenvironment. We also describe the available evidence demonstrating the value of tumour budding as a biomarker across various solid cancers.

2D and 3D in vitro assays to quantify the invasive behavior of glioblastoma stem cells in response to SDF-1α

Invasion is a hallmark of cancer and therefore in vitro invasion assays are important tools in cancer research. We aimed to describe in vitro 2D transwell assays and 3D spheroid assays to quantitatively determine the invasive behavior of glioblastoma stem cells in response to the chemoattractant SDF-1α. Matrigel was used as a matrix in both assays. We demonstrated quantitatively that SDF-1α increased invasive behavior of glioblastoma stem cells in both assays. We conclude that the 2D transwell invasion assay is easy to perform, fast and less complex whereas the more time-consuming 3D spheroid invasion assay is physiologically closer to the in vivo situation.

Collective invasion of glioma cells through OCT1 signalling and interaction with reactive astrocytes after surgery

Glioblastoma multiforme (GBM) is the most aggressive form of brain cancer with a short median survival time. GBM is characterized by the hallmarks of aggressive proliferation and cellular infiltration of normal brain tissue. miR-451 and its downstream molecules are known to play a pivotal role in regulation of the balance of proliferation and aggressive invasion in response to metabolic stress in the tumour microenvironment (TME). Surgery-induced transition in reactive astrocyte populations can play a significant role in tumour dynamics. In this work, we develop a multi-scale mathematical model of miR-451-LKB1-AMPK-OCT1-mTOR pathway signalling and individual cell dynamics of the tumour and reactive astrocytes after surgery. We show how the effects of fluctuating glucose on tumour cells need to be reprogrammed by taking into account the recent history of glucose variations and an AMPK/miR-451 reciprocal feedback loop. The model shows how variations in glucose availability significantly affect the activity of signalling molecules and, in turn, lead to critical cell migration. The model also predicts that microsurgery of a primary tumour induces phenotypical changes in reactive astrocytes and stem cell-like astrocytes promoting tumour cell proliferation and migration by Cxcl5. Finally, we investigated a new anti-tumour strategy by Cxcl5-targeting drugs. This article is part of the theme issue 'Multi-scale analysis and modelling of collective migration in biological systems'.

The role of tumor-stroma interactions on desmoplasia and tumorigenicity within a microengineered 3D platform

The tumor microenvironment has been demonstrated to play a crucial role in modulating cancer progression. Amongst various cell types within the tumor microenvironment, cancer associated fibroblasts (CAFs) are in abundance, serving to modulate the biophysical properties of the stromal matrix, through excessive deposition of extracellular matrix (ECM) proteins that leads to enhanced tumor progression. There is still a critical need to develop a fundamental framework on the role of tumor-stromal cell interactions on desmoplasia and tumorigenicity. Herein, we developed a 3D microengineered organotypic tumor-stroma model incorporated with breast cancer cells surrounded by CAF-embedded collagen matrix. We further integrated our platform with atomic force microscopy (AFM) to study the dynamic changes in stromal stiffness during active tumor invasion. Our findings primarily demonstrated enhanced tumor progression in the presence of CAFs. Furthermore, we highlighted the crucial role of crosstalk between tumor cells and CAFs on stromal desmoplasia, where we identified the role of tumor-secreted PDGF-AA/-BB on elevated matrix stiffness. Inhibition of the activity of PDGFRs in CAFs led to attenuation of stromal stiffness. Overall, our work presents a well-controlled tumor microenvironment model capable of dissecting specific biophysical and biochemical signaling cues which lead to stromal desmoplasia and tumor progression.

The effects of ephrinB2 signaling on proliferation and invasion in glioblastoma multiforme

The aggressive nature of glioblastoma multiforme (GBM) may be attributed to the dysregulation of pathways driving both proliferation and invasion. EphrinB2, a membrane-bound ligand for some of the Eph receptors, has emerged as a critical target regulating these pathways. In this study, we investigated the role of ephrinB2 in regulating proliferation and invasion in GBM using intracranial and subcutaneous xenograft models. The Cancer Genome Atlas analysis suggested high transcript and low methylation levels of ephrinB2 as poor prognostic indicators in GBM, consistent with its role as an oncogene. EphrinB2 knockdown, however, increased tumor growth, an effect that was reversed by ephrinB2 Fc protein. This was associated with EphB4 receptor activation, consistent with the data showing a significant decrease in tumor growth with ephrinB2 overexpression. Mechanistic analyses showed that ephrinB2 knockdown has anti-invasive but pro-proliferative effects in GBM. EphB4 stimulation following ephrinB2 Fc treatment in ephrinB2 knockdown tumors was shown to impart strong anti-proliferative and anti-invasive effects, which correlated with decrease in PCNA, p-ERK, vimentin, Snail, Fak, and increase in the E-cadherin levels. Overall, our study suggests that ephrinB2 cannot be used as a sole therapeutic target. Concomitant inhibition of ephrinB2 signaling with EphB4 activation is required to achieve maximal therapeutic benefit in GBM.

Exploiting Cancer's Tactics to Make Cancer a Manageable Chronic Disease

The history of modern oncology started around eighty years ago with the introduction of cytotoxic agents such as nitrogen mustard into the clinic, followed by multi-agent chemotherapy protocols. Early success in radiation therapy in Hodgkin lymphoma gave birth to the introduction of radiation therapy into different cancer treatment protocols. Along with better understanding of cancer biology, we developed drugs targeting cancer-related cellular and genetic aberrancies. Discovery of the crucial role of vasculature in maintenance, survival, and growth of a tumor opened the way to the development of anti-angiogenic agents. A better understanding of T-cell regulatory pathways advanced immunotherapy. Awareness of stem-like cancer cells and their role in cancer metastasis and local recurrence led to the development of drugs targeting them. At the same time, sequential and rapidly accelerating advances in imaging and surgical technology have markedly increased our ability to safely remove ≥90% of tumor cells. While we have advanced our ability to kill cells from multiple directions, we have still failed to stop most types of cancer from recurring. Here we analyze the tactics employed in cancer evolution; namely, chromosomal instability (CIN), intra-tumoral heterogeneity (ITH), and cancer-specific metabolism. These tactics govern the resistance to current cancer therapeutics. It is time to focus on maximally delaying the time to recurrence, with drugs that target these fundamental tactics of cancer evolution. Understanding the control of CIN and the optimal state of ITH as the most important tactics in cancer evolution could facilitate the development of improved cancer therapeutic strategies designed to transform cancer into a manageable chronic disease.

Population Dynamics with Threshold Effects Give Rise to a Diverse Family of Allee Effects

The Allee effect describes populations that deviate from logistic growth models and arises in applications including ecology and cell biology. A common justification for incorporating Allee effects into population models is that the population in question has altered growth mechanisms at some critical density, often referred to as a threshold effect. Despite the ubiquitous nature of threshold effects arising in various biological applications, the explicit link between local threshold effects and global Allee effects has not been considered. In this work, we examine a continuum population model that incorporates threshold effects in the local growth mechanisms. We show that this model gives rise to a diverse family of Allee effects, and we provide a comprehensive analysis of which choices of local growth mechanisms give rise to specific Allee effects. Calibrating this model to a recent set of experimental data describing the growth of a population of cancer cells provides an interpretation of the threshold population density and growth mechanisms associated with the population.

Integrated Biophysical Modeling and Image Analysis: Application to Neuro-Oncology

Central nervous system (CNS) tumors come with vastly heterogeneous histologic, molecular, and radiographic landscapes, rendering their precise characterization challenging. The rapidly growing fields of biophysical modeling and radiomics have shown promise in better characterizing the molecular, spatial, and temporal heterogeneity of tumors. Integrative analysis of CNS tumors, including clinically acquired multi-parametric magnetic resonance imaging (mpMRI) and the inverse problem of calibrating biophysical models to mpMRI data, assists in identifying macroscopic quantifiable tumor patterns of invasion and proliferation, potentially leading to improved (a) detection/segmentation of tumor subregions and (b) computer-aided diagnostic/prognostic/predictive modeling. This article presents a summary of (a) biophysical growth modeling and simulation,(b) inverse problems for model calibration, (c) these models' integration with imaging workflows, and (d) their application to clinically relevant studies. We anticipate that such quantitative integrative analysis may even be beneficial in a future revision of the World Health Organization (WHO) classification for CNS tumors, ultimately improving patient survival prospects.