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

Phenotypic switching mechanisms determine the structure of cell migration into extracellular matrix under the 'go-or-grow' hypothesis

A fundamental feature of collective cell migration is phenotypic heterogeneity which, for example, influences tumour progression and relapse. While current mathematical models often consider discrete phenotypic structuring of the cell population, in-line with the 'go-or-grow' hypothesis (Hatzikirou et al., 2012; Stepien et al., 2018), they regularly overlook the role that the environment may play in determining the cells' phenotype during migration. Comparing a previously studied volume-filling model for a homogeneous population of generalist cells that can proliferate, move and degrade extracellular matrix (ECM) (Crossley et al., 2023) to a novel model for a heterogeneous population comprising two distinct sub-populations of specialist cells that can either move and degrade ECM or proliferate, this study explores how different hypothetical phenotypic switching mechanisms affect the speed and structure of the invading cell populations. Through a continuum model derived from its individual-based counterpart, insights into the influence of the ECM and the impact of phenotypic switching on migrating cell populations emerge. Notably, specialist cell populations that cannot switch phenotype show reduced invasiveness compared to generalist cell populations, while implementing different forms of switching significantly alters the structure of migrating cell fronts. This key result suggests that the structure of an invading cell population could be used to infer the underlying mechanisms governing phenotypic switching.

Mathematical modeling of brain metastases growth and response to therapies: A review

Brain metastases (BMs) are the most common intracranial tumor type and a significant health concern, affecting approximately 10% to 30% of all oncological patients. Although significant progress is being made, many aspects of the metastatic process to the brain and the growth of the resulting lesions are still not well understood. There is a need for an improved understanding of the growth dynamics and the response to treatment of these tumors. Mathematical models have been proven valuable for drawing inferences and making predictions in different fields of cancer research, but few mathematical works have considered BMs. This comprehensive review aims to establish a unified platform and contribute to fostering emerging efforts dedicated to enhancing our mathematical understanding of this intricate and challenging disease. We focus on the progress made in the initial stages of mathematical modeling research regarding BMs and the significant insights gained from such studies. We also explore the vital role of mathematical modeling in predicting treatment outcomes and enhancing the quality of clinical decision-making for patients facing BMs.

Ras suppression potentiates rear actomyosin contractility-driven cell polarization and migration

Ras has been extensively studied as a promoter of cell proliferation, whereas few studies have explored its role in migration. To investigate the direct and immediate effects of Ras activity on cell motility or polarity, we focused on RasGAPs, C2GAPB in Dictyostelium amoebae and RASAL3 in HL-60 neutrophils and macrophages. In both cellular systems, optically recruiting the respective RasGAP to the cell front extinguished pre-existing protrusions and changed migration direction. However, when these respective RasGAPs were recruited uniformly to the membrane, cells polarized and moved more rapidly, whereas targeting to the back exaggerated these effects. These unexpected outcomes of attenuating Ras activity naturally had strong, context-dependent consequences for chemotaxis. The RasGAP-mediated polarization depended critically on myosin II activity and commenced with contraction at the cell rear, followed by sustained mTORC2-dependent actin polymerization at the front. These experimental results were captured by computational simulations in which Ras levels control front- and back-promoting feedback loops. The discovery that inhibiting Ras activity can produce counterintuitive effects on cell migration has important implications for future drug-design strategies targeting oncogenic Ras.

LINC01138 expresses two novel isoforms and functions as a repressive factor in glioma cells

Objective

The objective of this study is to investigate the aggressive infiltration of glioblastoma into adjacent brain tissue, considering its challenging prognosis. Initially classified as an intergenic non-coding RNA, we aim to elucidate the functional implications of LINC01138 in glioblastoma.

Method

Glioma grading was performed utilizing H&E staining, which unveiled distinct nuclear morphology in high-grade gliomas. The downregulation of LINC01138 in glioma tissues was corroborated through qRT-PCR and gel electrophoresis, concurrently identifying two previously unrecognized LINC01138 isoforms. Expression profiling of all four LINC01138 isoforms was executed in glioma cell lines (A172, SHG-44, U251, U87-MG). The impact of LINC01138 overexpression in U87-MG and U251 cells was evaluated for cell proliferation, migration, and invasion through cell counting, CCK-8 analysis, and Transwell assays. Furthermore, the suppression of LINC01138 in SHG-44 cells substantiated its involvement in fostering tumor malignancy. Transcriptome sequencing revealed the inhibitory influence of LINC01138 on IGF1 expression. These findings contribute to an enriched comprehension of glioma biology by exploring the engagement of LINC01138 through diverse methodologies, thereby elucidating its potential therapeutic significance.

Results

Our investigation elucidates the intricate involvement of LINC01138 in gliomas. High-grade gliomas are characterized by elevated cell density and distinctive nuclear features. LINC01138 demonstrates a substantial downregulation in glioma tissues, with the identification of two novel isoforms. The expression of all four LINC01138 isoforms is notably diminished in both glioma tissues and cell lines. Elevated expression of LINC01138 demonstrates inhibitory effects on tumor cell proliferation, migration, and invasion, while its downregulation exacerbates malignancy. The regulatory function of LINC01138 as a repressor of IGF1 expression was elucidated through transcriptome sequencing.

Conclusion

The LINC01138 isoforms display notable tumor-suppressive effects, suggesting a promising potential for impeding glioma progression.

Phenotype divergence and cooperation in isogenic multicellularity and in cancer

We discuss the mathematical modelling of two of the main mechanisms that pushed forward the emergence of multicellularity: phenotype divergence in cell differentiation and between-cell cooperation. In line with the atavistic theory of cancer, this disease being specific of multicellular animals, we set special emphasis on how both mechanisms appear to be reversed, however not totally impaired, rather hijacked, in tumour cell populations. Two settings are considered: the completely innovating, tinkering, situation of the emergence of multicellularity in the evolution of species, which we assume to be constrained by external pressure on the cell populations, and the completely planned-in the body plan-situation of the physiological construction of a developing multicellular animal from the zygote, or of bet hedging in tumours, assumed to be of clonal formation, although the body plan is largely-but not completely-lost in its constituting cells. We show how cancer impacts these two settings and we sketch mathematical models for them. We present here our contribution to the question at stake with a background from biology, from mathematics and from philosophy of science.

Cancer cells forgo translating mRNA transcribed from genes of nonspecialized tasks

The coupling of transcription and translation enables prokaryotes to regulate mRNA stability and reduce nonfunctional transcripts. Eukaryotes evolved other means to perform these functions. Here, we quantify the disparity between gene expression and protein levels and attempt to explain its origins. We collected publicly available simultaneous measurements of gene expression, protein level, division rate, and growth inhibition of breast cancer cells under drug perturbation. We used the cell lines as entities with shared origin, different evolutionary trajectories, and cancer hallmarks to define tasks subject to specializing and trading-off. We observed varying average mRNA and protein correlation across cell lines, and it was consistently higher for the gene products in the cancer hallmarks. The enrichment of hallmark gene products signifies the resources invested in it as a task. Enrichment based on mRNA or protein abundance corresponds to the relative resources dedicated to transcription and translation. The differences in gene- and protein-based enrichment correlated with nominal division rates but not growth inhibition under drug perturbations. Comparing the range of enrichment scores of the hallmarks within each cell signifies the resources dedicated to each. Cells appear to have a wider range of enrichment in protein synthesis relative to gene transcription. The difference and range of enrichment of the hallmark genes and proteins correlated with cell division and inhibition in response to drug treatments. We posit that cancer cells may express the genes coding for seemingly nonspecialized tasks but do not translate them to the corresponding proteins. This trade-off may cost the cells under normal conditions but confer benefits during stress.

Enhanced epithelial-mesenchymal transition signatures are linked with adverse tumor microenvironment, angiogenesis and worse survival in gastric cancer

Epithelial-mesenchymal transition (EMT) is a crucial mechanism that facilitates cancer cell metastasis. Despite its importance, the clinical significance of EMT in gastric cancer (GC) patients has yet to be clearly demonstrated. For gauging the extent of EMT in GC, we employed gene set variation analysis to score 807 patient samples from two large cohorts: TCGA and GSE84437. In both cohorts, EMT high GC showed a significant association with worse overall survival (hazard ratio (HR) = 1.74, p = 0.011 and HR = 2.01, p < 0.001, respectively). This association was stronger when considering the EMT signature score compared to the individual expressions of EMT-related genes (CDH1, CDH2, VIM, and FN1). While the EMT signature level did not differ among various cancers, high EMT signature specifically correlated with survival in GC alone. Mucinous and diffuse histological types exhibited higher EMT levels compared to others (p < 0.001), and the EMT signature level was correlated with tumor depth and AJCC stage (all p < 0.001). Interestingly, the EMT score was an independent factor for overall and disease-specific survival (multivariate; p = 0.006 and 0.032, respectively). EMT high GC displayed a lower fraction of Th1 cells and a higher fraction of dendritic cells, M1 macrophages and several stromal cells. EMT high GC exhibited an inverse correlation with cell proliferation-related gene sets. While they significantly enriched multiple pro-cancerous gene sets, such as TGF-β signaling, hypoxia, and angiogenesis. The presence of EMT signature in a bulk tumor was linked to TGF-β signaling, hypoxia, and angiogenesis, and was also associated with poorer survival outcomes in GC patients.

Selected aspects of avascular tumor growth reproduced by a hybrid model of cell dynamics and chemical kinetics

We here propose a hybrid computational framework to reproduce and analyze aspects of the avascular progression of a generic solid tumor. Our method first employs an individual-based approach to represent the population of tumor cells, which are distinguished in viable and necrotic agents. The active part of the disease is in turn differentiated according to a set of metabolic states. We then describe the spatio-temporal evolution of the concentration of oxygen and of tumor-secreted proteolytic enzymes using partial differential equations (PDEs). A differential equation finally governs the local degradation of the extracellular matrix (ECM) by the malignant mass. Numerical realizations of the model are run to reproduce tumor growth and invasion in a number scenarios that differ for cell properties (adhesiveness, duplication potential, proteolytic activity) and/or environmental conditions (level of tissue oxygenation and matrix density pattern). In particular, our simulations suggest that tumor aggressiveness, in terms of invasive depth and extension of necrotic tissue, can be reduced by (i) stable cell-cell contact interactions, (ii) poor tendency of malignant agents to chemotactically move upon oxygen gradients, and (iii) presence of an overdense matrix, if coupled by a disrupted proteolytic activity of the disease.

Current Status and Future Perspective in Glioma Invasion Research

Glioblastoma (GBM) is the most malignant brain tumor in adults and shows an extremely poor prognosis, with a median survival of 15 months [...].

UDP-6-glucose dehydrogenase in hormonally responsive breast cancers

Survival for metastatic breast cancer is low and thus, continued efforts to treat and prevent metastatic progression are critical. Estrogen is shown to promote aggressive phenotypes in multiple cancer models irrespective of estrogen receptor (ER) status. Similarly, UDP-Glucose 6-dehydrogenase (UGDH) a ubiquitously expressed enzyme involved in extracellular matrix precursors, as well as hormone processing increases migratory and invasive properties in cancer models. While the role of UGDH in cellular migration is defined, how it intersects with and impacts hormone signaling pathways associated with tumor progression in metastatic breast cancer has not been explored. Here we demonstrate that UGDH knockdown blunts estrogen-induced tumorigenic phenotypes (migration and colony formation) in ER+ and ER- breast cancer in vitro. Knockdown of UGDH also inhibits extravasation of ER- breast cancer ex vivo, primary tumor growth and animal survival in vivo in both ER+ and ER- breast cancer. We also use single cell RNA-sequencing to demonstrate that our findings translate to a human breast cancer clinical specimen. Our findings support the role of estrogen and UGDH in breast cancer progression provide a foundation for future studies to evaluate the role of UGDH in therapeutic resistance to improve outcomes and survival for breast cancer patients.

Functional transcriptional signatures for tumor-type-agnostic phenotype prediction

Cancer transcriptional patterns exhibit both shared and unique features across diverse cancer types, but whether these patterns are sufficient to characterize the full breadth of tumor phenotype heterogeneity remains an open question. We hypothesized that cancer transcriptional diversity mirrors patterns in normal tissues optimized for distinct functional tasks. Starting with normal tissue transcriptomic profiles, we use non-negative matrix factorization to derive six distinct transcriptomic phenotypes, called archetypes, which combine to describe both normal tissue patterns and variations across a broad spectrum of malignancies. We show that differential enrichment of these signatures correlates with key tumor characteristics, including overall patient survival and drug sensitivity, independent of clinically actionable DNA alterations. Additionally, we show that in HR+/HER2- breast cancers, metastatic tumors adopt transcriptomic signatures consistent with the invaded tissue. Broadly, our findings suggest that cancer often arrogates normal tissue transcriptomic characteristics as a component of both malignant progression and drug response. This quantitative framework provides a strategy for connecting the diversity of cancer phenotypes and could potentially help manage individual patients.

Recent Trends in Nanocarrier-Based Drug Delivery System for Prostate Cancer

Prostate cancer remains a significant global health concern, requiring innovative approaches for improved therapeutic outcomes. In recent years, nanoparticle-based drug delivery systems have emerged as promising strategies to address the limitations of conventional cancer chemotherapy. The key trends include utilizing nanoparticles for enhancing drug delivery to prostate cancer cells. Nanoparticles have some advantages such as improved drug solubility, prolonged circulation time, and targeted delivery of drugs. Encapsulation of chemotherapeutic agents within nanoparticles allows for controlled release kinetics, reducing systemic toxicity while maintaining therapeutic efficacy. Additionally, site-specific accumulation within the prostate tumor microenvironment is made possible by the functionalization of nanocarrier with targeted ligands, improving therapeutic effectiveness. This article highlights the basics of prostate cancer, statistics of prostate cancer, mechanism of multidrug resistance, targeting approach, and different types of nanocarrier used for the treatment of prostate cancer. It also includes the applications of nanocarriers for the treatment of prostate cancer and clinical trial studies to validate the safety and efficacy of the innovative drug delivery systems. The article focused on developing nanocarrier-based drug delivery systems, with the goal of translating these advancements into clinical applications in the future.

Peritumoral brain zone in glioblastoma: biological, clinical and mechanical features

Glioblastoma is a highly aggressive and invasive tumor that affects the central nervous system (CNS). With a five-year survival rate of only 6.9% and a median survival time of eight months, it has the lowest survival rate among CNS tumors. Its treatment consists of surgical resection, subsequent fractionated radiotherapy and concomitant and adjuvant chemotherapy with temozolomide. Despite the implementation of clinical interventions, recurrence is a common occurrence, with over 80% of cases arising at the edge of the resection cavity a few months after treatment. The high recurrence rate and location of glioblastoma indicate the need for a better understanding of the peritumor brain zone (PBZ). In this review, we first describe the main radiological, cellular, molecular and biomechanical tissue features of PBZ; and subsequently, we discuss its current clinical management, potential local therapeutic approaches and future prospects.

Modelling microtube driven invasion of glioma

Malignant gliomas are notoriously invasive, a major impediment against their successful treatment. This invasive growth has motivated the use of predictive partial differential equation models, formulated at varying levels of detail, and including (i) "proliferation-infiltration" models, (ii) "go-or-grow" models, and (iii) anisotropic diffusion models. Often, these models use macroscopic observations of a diffuse tumour interface to motivate a phenomenological description of invasion, rather than performing a detailed and mechanistic modelling of glioma cell invasion processes. Here we close this gap. Based on experiments that support an important role played by long cellular protrusions, termed tumour microtubes, we formulate a new model for microtube-driven glioma invasion. In particular, we model a population of tumour cells that extend tissue-infiltrating microtubes. Mitosis leads to new nuclei that migrate along the microtubes and settle elsewhere. A combination of steady state analysis and numerical simulation is employed to show that the model can predict an expanding tumour, with travelling wave solutions led by microtube dynamics. A sequence of scaling arguments allows us reduce the detailed model into simpler formulations, including models falling into each of the general classes (i), (ii), and (iii) above. This analysis allows us to clearly identify the assumptions under which these various models can be a posteriori justified in the context of microtube-driven glioma invasion. Numerical simulations are used to compare the various model classes and we discuss their advantages and disadvantages.

Multiomic screening of invasive GBM cells reveals targetable transsulfuration pathway alterations

While the poor prognosis of glioblastoma arises from the invasion of a subset of tumor cells, little is known of the metabolic alterations within these cells that fuel invasion. We integrated spatially addressable hydrogel biomaterial platforms, patient site-directed biopsies, and multiomics analyses to define metabolic drivers of invasive glioblastoma cells. Metabolomics and lipidomics revealed elevations in the redox buffers cystathionine, hexosylceramides, and glucosyl ceramides in the invasive front of both hydrogel-cultured tumors and patient site-directed biopsies, with immunofluorescence indicating elevated reactive oxygen species (ROS) markers in invasive cells. Transcriptomics confirmed upregulation of ROS-producing and response genes at the invasive front in both hydrogel models and patient tumors. Among oncologic ROS, H2O2 specifically promoted glioblastoma invasion in 3D hydrogel spheroid cultures. A CRISPR metabolic gene screen revealed cystathionine γ-lyase (CTH), which converts cystathionine to the nonessential amino acid cysteine in the transsulfuration pathway, to be essential for glioblastoma invasion. Correspondingly, supplementing CTH knockdown cells with exogenous cysteine rescued invasion. Pharmacologic CTH inhibition suppressed glioblastoma invasion, while CTH knockdown slowed glioblastoma invasion in vivo. Our studies highlight the importance of ROS metabolism in invasive glioblastoma cells and support further exploration of the transsulfuration pathway as a mechanistic and therapeutic target.

Heat-shock protein 90α protects NME1 against degradation and suppresses metastasis of breast cancer

BACKGROUND

NME1 has been exploited as a potential translational target for decades. Substantial efforts have been made to upregulate the expression of NME1 and restore its anti-metastasis function in metastatic cancer.

METHODS

Cycloheximide (CHX) chase assay was used to measure the steady-state protein stability of NME1 and HSP90α. The NME1-associating proteins were identified by immunoprecipitation combined with mass spectrometric analysis. Gene knockdown and overexpression were employed to examine the impact of HSP90AA1 on intracellular NME1 degradation. The motility and invasiveness of breast cancer cells were examined in vitro using wound healing and transwell invasion assays. The orthotopic spontaneous metastasis and intra-venous experimental metastasis assays were used to test the formation of metastasis in vivo, respectively.

RESULTS

HSP90α interacts with NME1 and increases NME1 lifetime by impeding its ubiquitin-proteasome-mediated degradation. HSP90α overexpression significantly inhibits the metastatic potential of breast cancer cells in vitro and in vivo. A novel cell-permeable peptide, OPT22 successfully mimics the HSP90α function and prolongs the life span of endogenous NME1, resulting in reduced metastasis of breast cancer.

CONCLUSION

These results not only reveal a new mechanism of NME1 degradation but also pave the way for the development of new and effective approaches to metastatic cancer therapy.

Microenvironmental stiffness induces metabolic reprogramming in glioblastoma

The mechanical properties of solid tumors influence tumor cell phenotype and the ability to invade surrounding tissues. Using bioengineered scaffolds to provide a matrix microenvironment for patient-derived glioblastoma (GBM) spheroids, this study demonstrates that a soft, brain-like matrix induces GBM cells to shift to a glycolysis-weighted metabolic state, which supports invasive behavior. We first show that orthotopic murine GBM tumors are stiffer than peritumoral brain tissues, but tumor stiffness is heterogeneous where tumor edges are softer than the tumor core. We then developed 3D scaffolds with μ-compressive moduli resembling either stiffer tumor core or softer peritumoral brain tissue. We demonstrate that the softer matrix microenvironment induces a shift in GBM cell metabolism toward glycolysis, which manifests in lower proliferation rate and increased migration activities. Finally, we show that these mechanical cues are transduced from the matrix via CD44 and integrin receptors to induce metabolic and phenotypic changes in cancer cells.

A Narrative Review on CD44's Role in Glioblastoma Invasion, Proliferation, and Tumor Recurrence

High invasiveness is a characteristic of glioblastoma (GBM), making radical resection almost impossible, and thus, resulting in a tumor with inevitable recurrence. GBM recurrence may be caused by glioma stem-like cells (GSCs) that survive many kinds of therapy. GSCs with high expression levels of CD44 are highly invasive and resistant to radio-chemotherapy. CD44 is a multifunctional molecule that promotes the invasion and proliferation of tumor cells via various signaling pathways. Among these, paired pathways reciprocally activate invasion and proliferation under different hypoxic conditions. Severe hypoxia (0.5-2.5% O2) upregulates hypoxia-inducible factor (HIF)-1α, which then activates target genes, including CD44, TGF-β, and cMET, all of which are related to tumor migration and invasion. In contrast, moderate hypoxia (2.5-5% O2) upregulates HIF-2α, which activates target genes, such as vascular endothelial growth factor (VEGF)/VEGFR2, cMYC, and cyclin D1. All these genes are related to tumor proliferation. Oxygen environments around GBM can change before and after tumor resection. Before resection, the oxygen concentration at the tumor periphery is severely hypoxic. In the reparative stage after resection, the resection cavity shows moderate hypoxia. These observations suggest that upregulated CD44 under severe hypoxia may promote the migration and invasion of tumor cells. Conversely, when tumor resection leads to moderate hypoxia, upregulated HIF-2α activates HIF-2α target genes. The phenotypic transition regulated by CD44, leading to a dichotomy between invasion and proliferation according to hypoxic conditions, may play a crucial role in GBM recurrence.

Game-theoretical description of the go-or-grow dichotomy in tumor development for various settings and parameter constellations

A medically important feature of several types of tumors is their ability to "decide" between staying at a primary site in the body or leaving it and forming metastases. The present theoretical study aims to provide a better understanding of the ultimate reasons for this so-called "go-or-grow" dichotomy. To that end, we use game theory, which has proven to be useful in analyzing the competition between tumors and healthy tissues or among different tumor cells. We begin by determining the game types in the Basanta-Hatzikirou-Deutsch model, depending on the parameter values. Thereafter, we suggest and analyze five modified variants of the model. For example, in the basic model, the deadlock game, Prisoner's Dilemma, and hawk-dove game can occur. The modified versions lead to several additional game types, such as battle of the sexes, route-choice, and stag-hunt games. For some game types, all cells are predicted to stay on their original site ("grow phenotype"), while for other types, only a certain fraction stay and the other cells migrate away ("go phenotype"). If the nutrient supply at a distant site is high, all the cells are predicted to go. We discuss our predictions in terms of the pros and cons of caloric restriction and limitations of the supply of vitamins or methionine. Our results may help devise treatments to prevent metastasis.

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.

Uptake of H-ferritin by Glioblastoma stem cells and its impact on their invasion capacity

PURPOSE

Iron acquisition is key to maintaining cell survival and function. Cancer cells in general are considered to have an insatiable iron need. Iron delivery via the transferrin/transferrin receptor pathway has been the canonical iron uptake mechanism. Recently, however, our laboratory and others have explored the ability of ferritin, particularly the H-subunit, to deliver iron to a variety of cell types. Here, we investigate whether Glioblastoma (GBM) initiating cells (GICs), a small population of stem-like cells, are known for their iron addiction and invasive nature acquire exogenous ferritin, as a source of iron. We further assess the functional impact of ferritin uptake on the invasion capacity of the GICs.

METHODS

To establish that H-ferritin can bind to human GBM, tissue-binding assays were performed on samples collected at the time of surgery. To interrogate the functional consequences of H-ferritin uptake, we utilized two patient-derived GIC lines. We further describe H-ferritin's impact on GIC invasion capacity using a 3D invasion assay.

RESULTS

H-ferritin bound to human GBM tissue at the amount of binding was influenced by sex. GIC lines showed uptake of H-ferritin protein via transferrin receptor. FTH1 uptake correlated with a significant decrease in the invasion capacity of the cells. H-ferritin uptake was associated with a significant decrease in the invasion-related protein Rap1A.

CONCLUSION

These findings indicate that extracellular H-ferritin participates in iron acquisition to GBMs and patient-derived GICs. The functional significance of the increased iron delivery by H-ferritin is a decreased invasion capacity of GICs potentially via reduction of Rap1A protein levels.

Ras-mediated homeostatic control of front-back signaling dictates cell polarity

Studies in the model systems, Dictyostelium amoebae and HL-60 neutrophils, have shown that local Ras activity directly regulates cell motility or polarity. Localized Ras activation on the membrane is spatiotemporally regulated by its activators, RasGEFs, and inhibitors, RasGAPs, which might be expected to create a stable 'front' and 'back', respectively, in migrating cells. Focusing on C2GAPB in amoebae and RASAL3 in neutrophils, we investigated how Ras activity along the cortex controls polarity. Since existing gene knockout and overexpression studies can be circumvented, we chose optogenetic approaches to assess the immediate, local effects of these Ras regulators on the cell cortex. In both cellular systems, optically targeting the respective RasGAPs to the cell front extinguished existing protrusions and changed the direction of migration, as might be expected. However, when the expression of C2GAPB was induced globally, amoebae polarized within hours. Furthermore, within minutes of globally recruiting either C2GAPB in amoebae or RASAL3 in neutrophils, each cell type polarized and moved more rapidly. Targeting the RasGAPs to the cell backs exaggerated these effects on migration and polarity. Overall, in both cell types, RasGAP-mediated polarization was brought about by increased actomyosin contractility at the back and sustained, localized F-actin polymerization at the front. These experimental results were accurately captured by computational simulations in which Ras levels control front and back feedback loops. The discovery that context-dependent Ras activity on the cell cortex has counterintuitive, unanticipated effects on cell polarity can have important implications for future drug-design strategies targeting oncogenic Ras.

Modelling cell adaptation using internal variables: Accounting for cell plasticity in continuum mathematical biology

Cellular adaptation is the ability of cells to change in response to different stimuli and environmental conditions. It occurs via phenotypic plasticity, that is, changes in gene expression derived from changes in the physiological environment. This phenomenon is important in many biological processes, in particular in cancer evolution and its treatment. Therefore, it is crucial to understand the mechanisms behind it. Specifically, the emergence of the cancer stem cell phenotype, showing enhanced proliferation and invasion rates, is an essential process in tumour progression. We present a mathematical framework to simulate phenotypic heterogeneity in different cell populations as a result of their interaction with chemical species in their microenvironment, through a continuum model using the well-known concept of internal variables to model cell phenotype. The resulting model, derived from conservation laws, incorporates the relationship between the phenotype and the history of the stimuli to which cells have been subjected, together with the inheritance of that phenotype. To illustrate the model capabilities, it is particularised for glioblastoma adaptation to hypoxia. A parametric analysis is carried out to investigate the impact of each model parameter regulating cellular adaptation, showing that it permits reproducing different trends reported in the scientific literature. The framework can be easily adapted to any particular problem of cell plasticity, with the main limitation of having enough cells to allow working with continuum variables. With appropriate calibration and validation, it could be useful for exploring the underlying processes of cellular adaptation, as well as for proposing favourable/unfavourable conditions or treatments.

Data driven modeling of pseudopalisade pattern formation

Pseudopalisading is an interesting phenomenon where cancer cells arrange themselves to form a dense garland-like pattern. Unlike the palisade structure, a similar type of pattern first observed in schwannomas by pathologist J.J. Verocay (Wippold et al. in AJNR Am J Neuroradiol 27(10):2037-2041, 2006), pseudopalisades are less organized and associated with a necrotic region at their core. These structures are mainly found in glioblastoma (GBM), a grade IV brain tumor, and provide a way to assess the aggressiveness of the tumor. Identification of the exact bio-mechanism responsible for the formation of pseudopalisades is a difficult task, mainly because pseudopalisades seem to be a consequence of complex nonlinear dynamics within the tumor. In this paper we propose a data-driven methodology to gain insight into the formation of different types of pseudopalisade structures. To this end, we start from a state of the art macroscopic model for the dynamics of GBM, that is coupled with the dynamics of extracellular pH, and formulate a terminal value optimal control problem. Thus, given a specific, observed pseudopalisade pattern, we determine the evolution of parameters (bio-mechanisms) that are responsible for its emergence. Random histological images exhibiting pseudopalisade-like structures are chosen to serve as target pattern. Having identified the optimal model parameters that generate the specified target pattern, we then formulate two different types of pattern counteracting ansatzes in order to determine possible ways to impair or obstruct the process of pseudopalisade formation. This provides the basis for designing active or live control of malignant GBM. Furthermore, we also provide a simple, yet insightful, mechanism to synthesize new pseudopalisade patterns by linearly combining the optimal model parameters responsible for generating different known target patterns. This particularly provides a hint that complex pseudopalisade patterns could be synthesized by a linear combination of parameters responsible for generating simple patterns. Going even further, we ask ourselves if complex therapy approaches can be conceived, such that some linear combination thereof is able to reverse or disrupt simple pseudopalisade patterns; this is investigated with the help of numerical simulations.

The NF-Y splicing signature controls hybrid EMT and ECM-related pathways to promote aggressiveness of colon cancer

Aberrant splicing events are associated with colorectal cancer (CRC) and provide new opportunities for tumor diagnosis and treatment. The expression of the splice variants of NF-YA, the DNA binding subunit of the transcription factor NF-Y, is deregulated in multiple cancer types compared to healthy tissues. NF-YAs and NF-YAl isoforms differ in the transactivation domain, which may result in distinct transcriptional programs. In this study, we demonstrated that the NF-YAl transcript is higher in aggressive mesenchymal CRCs and predicts shorter patients' survival. In 2D and 3D conditions, CRC cells overexpressing NF-YAl (NF-YAl^high) exhibit reduced cell proliferation, rapid single cell amoeboid-like migration, and form irregular spheroids with poor cell-to-cell adhesion. Compared to NF-YAs^high, NF-YAl^high cells show changes in the transcription of genes involved in epithelial-mesenchymal transition, extracellular matrix and cell adhesion. NF-YAl and NF-YAs bind similarly to the promoter of the E-cadherin gene, but oppositely regulate its transcription. The increased metastatic potential of NF-YAl^high cells in vivo was confirmed in zebrafish xenografts. These results suggest that the NF-YAl splice variant could be a new CRC prognostic factor and that splice-switching strategies may reduce metastatic CRC progression.

Estimation of Parameter Distributions for Reaction-Diffusion Equations with Competition using Aggregate Spatiotemporal Data

Reaction-diffusion equations have been used to model a wide range of biological phenomenon related to population spread and proliferation from ecology to cancer. It is commonly assumed that individuals in a population have homogeneous diffusion and growth rates; however, this assumption can be inaccurate when the population is intrinsically divided into many distinct subpopulations that compete with each other. In previous work, the task of inferring the degree of phenotypic heterogeneity between subpopulations from total population density has been performed within a framework that combines parameter distribution estimation with reaction-diffusion models. Here, we extend this approach so that it is compatible with reaction-diffusion models that include competition between subpopulations. We use a reaction-diffusion model of glioblastoma multiforme, an aggressive type of brain cancer, to test our approach on simulated data that are similar to measurements that could be collected in practice. We use Prokhorov metric framework and convert the reaction-diffusion model to a random differential equation model to estimate joint distributions of diffusion and growth rates among heterogeneous subpopulations. We then compare the new random differential equation model performance against other partial differential equation models' performance. We find that the random differential equation is more capable at predicting the cell density compared to other models while being more time efficient. Finally, we use k-means clustering to predict the number of subpopulations based on the recovered distributions.

Evaluation of entropy and fractal dimension as biomarkers for tumor growth and treatment response using cellular automata

Cell-based models provide a helpful approach for simulating complex systems that exhibit adaptive, resilient qualities, such as cancer. Their focus on individual cell interactions makes them a particularly appropriate strategy to study cancer therapies' effects, which are often designed to disrupt single-cell dynamics. In this work, we propose them as viable methods for studying the time evolution of cancer imaging biomarkers (IBM). We propose a cellular automata model for tumor growth and three different therapies: chemotherapy, radiotherapy, and immunotherapy, following well-established modeling procedures documented in the literature. The model generates a sequence of tumor images, from which a time series of two biomarkers: entropy and fractal dimension, is obtained. Our model shows that the fractal dimension increased faster at the onset of cancer cell dissemination. At the same time, entropy was more responsive to changes induced in the tumor by the different therapy modalities. These observations suggest that the prognostic value of the proposed biomarkers could vary considerably with time. Thus, it is essential to assess their use at different stages of cancer and for different imaging modalities. Another observation derived from the results was that both biomarkers varied slowly when the applied therapy attacked cancer cells scattered along the automatons' area, leaving multiple independent clusters of cells at the end of the treatment. Thus, patterns of change of simulated biomarkers time series could reflect on essential qualities of the spatial action of a given cancer intervention.

Probabilistic cellular automata modelling of intercellular interactions in airways: complex pattern formation in patients with chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is a highly prevalent lung disease characterized by chronic inflammation and tissue remodeling possibly induced by unusual interactions between fibrocytes and CD8+ T lymphocytes in the peribronchial area. To investigate this phenomenon, we developed a probabilistic cellular automata type model where the two types of cells follow simple local interaction rules taking into account cell death, proliferation, migration and infiltration. We conducted a rigorous mathematical analysis using multiscale experimental data obtained in control and disease conditions to estimate the model's parameters accurately. The simulation of the model is straightforward to implement, and two distinct patterns emerged that we can analyse quantitatively. In particular, we show that the change in fibrocyte density in the COPD condition is mainly the consequence of their infiltration into the lung during exacerbations, suggesting possible explanations for experimental observations in normal and COPD tissue. Our integrated approach that combines a probabilistic cellular automata model and experimental findings will provide further insights into COPD in future studies.

A stochastic hierarchical model for low grade glioma evolution

A stochastic hierarchical model for the evolution of low grade gliomas is proposed. Starting with the description of cell motion using a piecewise diffusion Markov process (PDifMP) at the cellular level, we derive an equation for the density of the transition probability of this Markov process based on the generalised Fokker-Planck equation. Then, a macroscopic model is derived via parabolic limit and Hilbert expansions in the moment equations. After setting up the model, we perform several numerical tests to study the role of the local characteristics and the extended generator of the PDifMP in the process of tumour progression. The main aim focuses on understanding how the variations of the jump rate function of this process at the microscopic scale and the diffusion coefficient at the macroscopic scale are related to the diffusive behaviour of the glioma cells and to the onset of malignancy, i.e., the transition from low-grade to high-grade gliomas.

Glioblastoma Microenvironment and Invasiveness: New Insights and Therapeutic Targets

Glioblastoma (GBM) is the most common and malignant primary brain cancer in adults. Without treatment the mean patient survival is approximately 6 months, which can be extended to 15 months with the use of multimodal therapies. The low effectiveness of GBM therapies is mainly due to the tumor infiltration into the healthy brain tissue, which depends on GBM cells' interaction with the tumor microenvironment (TME). The interaction of GBM cells with the TME involves cellular components such as stem-like cells, glia, endothelial cells, and non-cellular components such as the extracellular matrix, enhanced hypoxia, and soluble factors such as adenosine, which promote GBM's invasiveness. However, here we highlight the role of 3D patient-derived glioblastoma organoids cultures as a new platform for study of the modeling of TME and invasiveness. In this review, the mechanisms involved in GBM-microenvironment interaction are described and discussed, proposing potential prognosis biomarkers and new therapeutic targets.

Mitochondria in cancer: clean windmills or stressed tinkerers?

There is now a consensus that mitochondria are important tumor drivers, sophisticated biological machines that can engender a panoply of key disease traits. How this happens, however, is still mostly elusive. The opinion presented here is that what cancer exploits are not the normal mitochondria of oxygenated and nutrient-replete tissues, but the unfit, damaged, and dysfunctional organelles generated by the hostile environment of tumor growth. These 'ghost' mitochondria survive quality control and thwart cell death to relay multiple comprehensive 'danger signals' of metabolic starvation, cellular stress, and reprogrammed gene expression. The result is a new, treacherous cellular phenotype, proliferatively quiescent but highly motile, that enables tumor cell escape from a threatening environment and colonization of distant, more favorable sites (metastasis).

Traveling wave speed and profile of a "go or grow" glioblastoma multiforme model

Glioblastoma multiforme (GBM) is a fast-growing and deadly brain tumor due to its ability to aggressively invade the nearby brain tissue. A host of mathematical models in the form of reaction-diffusion equations have been formulated and studied in order to assist clinical assessment of GBM growth and its treatment prediction. To better understand the speed of GBM growth and form, we propose a two population reaction-diffusion GBM model based on the 'go or grow' hypothesis. Our model is validated by in vitro data and assumes that tumor cells are more likely to leave and search for better locations when resources are more limited at their current positions. Our findings indicate that the tumor progresses slower than the simpler Fisher model, which is known to overestimate GBM progression. Moreover, we obtain accurate estimations of the traveling wave solution profiles under several plausible GBM cell switching scenarios by applying the approximation method introduced by Canosa.

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.

Multi-omic screening of invasive GBM cells in engineered biomaterials and patient biopsies reveals targetable transsulfuration pathway alterations

While the poor prognosis of glioblastoma arises from the invasion of a subset of tumor cells, little is known of the metabolic alterations within these cells that fuel invasion. We integrated spatially addressable hydrogel biomaterial platforms, patient site-directed biopsies, and multi-omics analyses to define metabolic drivers of invasive glioblastoma cells. Metabolomics and lipidomics revealed elevations in the redox buffers cystathionine, hexosylceramides, and glucosyl ceramides in the invasive front of both hydrogel-cultured tumors and patient site-directed biopsies, with immunofluorescence indicating elevated reactive oxygen species (ROS) markers in invasive cells. Transcriptomics confirmed upregulation of ROS-producing and response genes at the invasive front in both hydrogel models and patient tumors. Amongst oncologic ROS, hydrogen peroxide specifically promoted glioblastoma invasion in 3D hydrogel spheroid cultures. A CRISPR metabolic gene screen revealed cystathionine gamma lyase (CTH), which converts cystathionine to the non-essential amino acid cysteine in the transsulfuration pathway, to be essential for glioblastoma invasion. Correspondingly, supplementing CTH knockdown cells with exogenous cysteine rescued invasion. Pharmacologic CTH inhibition suppressed glioblastoma invasion, while CTH knockdown slowed glioblastoma invasion in vivo. Our studies highlight the importance of ROS metabolism in invasive glioblastoma cells and support further exploration of the transsulfuration pathway as a mechanistic and therapeutic target.

Integration of transcriptomics data into agent-based models of solid tumor metastasis

Recent progress in our understanding of cancer mostly relies on the systematic profiling of patient samples with high-throughput techniques like transcriptomics. With this approach, one can find gene signatures and networks underlying cancer aggressiveness and therapy resistance. However, omics data alone cannot generate insights into the spatiotemporal aspects of tumor progression. Here, multi-level computational modeling is a promising approach that would benefit from protocols to integrate the data generated by the high-throughput profiling of patient samples. We present a computational workflow to integrate transcriptomics data from tumor patients into hybrid, multi-scale cancer models. In the method, we conduct transcriptomics analysis to select key differentially regulated pathways in therapy responders and non-responders and link them to agent-based model parameters. We then determine global and local sensitivity through systematic model simulations that assess the relevance of parameter variations in triggering therapy resistance. We illustrate the methodology with a de novo generated agent-based model accounting for the interplay between tumor and immune cells in a melanoma micrometastasis. The application of the workflow identifies three distinct scenarios of therapy resistance.

Cell signaling activation and extracellular matrix remodeling underpin glioma tumor microenvironment heterogeneity and organization

PURPOSE

Tumor cells thrive by adapting to the signals in their microenvironment. To adapt, cancer cells activate signaling and transcriptional programs and migrate to establish micro-niches, in response to signals from neighboring cells and non-cellular stromal factors. Understanding how the tumor microenvironment evolves during disease progression is crucial to deciphering the mechanisms underlying the functional behavior of cancer cells.

METHODS

Multiplex immunohistochemistry, spatial analysis and histological dyes were used to identify and measure immune cell infiltration, cell signal activation and extracellular matrix deposition in low-grade, high-grade astrocytoma and glioblastoma.

RESULTS

We show that lower grade astrocytoma tissue is largely devoid of infiltrating immune cells and extracellular matrix proteins, while high-grade astrocytoma exhibits abundant immune cell infiltration, activation, and extensive tissue remodeling. Spatial analysis shows that most T-cells are restricted to perivascular regions, but bone marrow-derived macrophages penetrate deep into neoplastic cell-rich regions. The tumor microenvironment is characterized by heterogeneous PI3K, MAPK and CREB signaling, with specific signaling profiles correlating with distinct pathological hallmarks, including angiogenesis, tumor cell density and regions where neoplastic cells border the extracellular matrix. Our results also show that tissue remodeling is important in regulating the architecture of the tumor microenvironment during tumor progression.

CONCLUSION

The tumor microenvironment in malignant astrocytoma, exhibits changes in cell composition, cell signaling activation and extracellular matrix deposition during disease development and that targeting the extracellular matrix, as well as cell signaling activation will be critical to designing personalized therapy.

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.

Effect of the intratumoral microbiota on spatial and cellular heterogeneity in cancer

The tumour-associated microbiota is an intrinsic component of the tumour microenvironment across human cancer types1,2. Intratumoral host-microbiota studies have so far largely relied on bulk tissue analysis1-3, which obscures the spatial distribution and localized effect of the microbiota within tumours. Here, by applying in situ spatial-profiling technologies4 and single-cell RNA sequencing5 to oral squamous cell carcinoma and colorectal cancer, we reveal spatial, cellular and molecular host-microbe interactions. We adapted 10x Visium spatial transcriptomics to determine the identity and in situ location of intratumoral microbial communities within patient tissues. Using GeoMx digital spatial profiling6, we show that bacterial communities populate microniches that are less vascularized, highly immuno‑suppressive and associated with malignant cells with lower levels of Ki-67 as compared to bacteria-negative tumour regions. We developed a single-cell RNA-sequencing method that we name INVADEseq (invasion-adhesion-directed expression sequencing) and, by applying this to patient tumours, identify cell-associated bacteria and the host cells with which they interact, as well as uncovering alterations in transcriptional pathways that are involved in inflammation, metastasis, cell dormancy and DNA repair. Through functional studies, we show that cancer cells that are infected with bacteria invade their surrounding environment as single cells and recruit myeloid cells to bacterial regions. Collectively, our data reveal that the distribution of the microbiota within a tumour is not random; instead, it is highly organized in microniches with immune and epithelial cell functions that promote cancer progression.

The Impact of Phenotypic Heterogeneity on Chemotactic Self-Organisation

The capacity to aggregate through chemosensitive movement forms a paradigm of self-organisation, with examples spanning cellular and animal systems. A basic mechanism assumes a phenotypically homogeneous population that secretes its own attractant, with the well known system introduced more than five decades ago by Keller and Segel proving resolutely popular in modelling studies. The typical assumption of population phenotypic homogeneity, however, often lies at odds with the heterogeneity of natural systems, where populations may comprise distinct phenotypes that vary according to their chemotactic ability, attractant secretion, etc. To initiate an understanding into how this diversity can impact on autoaggregation, we propose a simple extension to the classical Keller and Segel model, in which the population is divided into two distinct phenotypes: those performing chemotaxis and those producing attractant. Using a combination of linear stability analysis and numerical simulations, we demonstrate that switching between these phenotypic states alters the capacity of a population to self-aggregate. Further, we show that switching based on the local environment (population density or chemoattractant level) leads to diverse patterning and provides a route through which a population can effectively curb the size and density of an aggregate. We discuss the results in the context of real world examples of chemotactic aggregation, as well as theoretical aspects of the model such as global existence and blow-up of solutions.

Targeting Key Signaling Pathways in Glioblastoma Stem Cells for the Development of Efficient Chemo- and Immunotherapy

Glioblastoma multiforme (GBM) is the most aggressive and most common malignant brain tumor with poor patient survival despite therapeutic intervention. On the cellular level, GBM comprises a rare population of glioblastoma stem cells (GSCs), driving therapeutic resistance, invasion, and recurrence. GSCs have thus come into the focus of therapeutic strategies, although their targeting remains challenging. In the present study, we took advantage of three GSCs-populations recently established in our lab to investigate key signaling pathways and subsequent therapeutic strategies targeting GSCs. We observed that NF-κB, a crucial transcription factor in GBM progression, was expressed in all CD44+/CD133+/Nestin+-GSC-populations. Exposure to TNFα led to activation of NF-κB-RELA and/or NF-κB-c-REL, depending on the GBM type. GSCs further expressed the proto-oncogene MYC family, with MYChigh GSCs being predominantly located in the tumor spheres ("GROW"-state) while NF-κB-RELAhigh GSCs were migrating out of the sphere ("GO"-state). We efficiently targeted GSCs by the pharmacologic inhibition of NF-κB using PTDC/Bortezomib or inhibition of MYC by KJ-Pyr-9, which significantly reduced GSC-viability, even in comparison to the standard chemotherapeutic drug temozolomide. As an additional cell-therapeutic strategy, we showed that NK cells could kill GSCs. Our findings offer new perspectives for developing efficient patient-specific chemo- and immunotherapy against GBM.

Efficient Bayesian inference for stochastic agent-based models

The modelling of many real-world problems relies on computationally heavy simulations of randomly interacting individuals or agents. However, the values of the parameters that underlie the interactions between agents are typically poorly known, and hence they need to be inferred from macroscopic observations of the system. Since statistical inference rests on repeated simulations to sample the parameter space, the high computational expense of these simulations can become a stumbling block. In this paper, we compare two ways to mitigate this issue in a Bayesian setting through the use of machine learning methods: One approach is to construct lightweight surrogate models to substitute the simulations used in inference. Alternatively, one might altogether circumvent the need for Bayesian sampling schemes and directly estimate the posterior distribution. We focus on stochastic simulations that track autonomous agents and present two case studies: tumour growths and the spread of infectious diseases. We demonstrate that good accuracy in inference can be achieved with a relatively small number of simulations, making our machine learning approaches orders of magnitude faster than classical simulation-based methods that rely on sampling the parameter space. However, we find that while some methods generally produce more robust results than others, no algorithm offers a one-size-fits-all solution when attempting to infer model parameters from observations. Instead, one must choose the inference technique with the specific real-world application in mind. The stochastic nature of the considered real-world phenomena poses an additional challenge that can become insurmountable for some approaches. Overall, we find machine learning approaches that create direct inference machines to be promising for real-world applications. We present our findings as general guidelines for modelling practitioners.

Archetype tasks link intratumoral heterogeneity to plasticity and cancer hallmarks in small cell lung cancer

Small cell lung cancer (SCLC) tumors comprise heterogeneous mixtures of cell states, categorized into neuroendocrine (NE) and non-neuroendocrine (non-NE) transcriptional subtypes. NE to non-NE state transitions, fueled by plasticity, likely underlie adaptability to treatment and dismal survival rates. Here, we apply an archetypal analysis to model plasticity by recasting SCLC phenotypic heterogeneity through multi-task evolutionary theory. Cell line and tumor transcriptomics data fit well in a five-dimensional convex polytope whose vertices optimize tasks reminiscent of pulmonary NE cells, the SCLC normal counterparts. These tasks, supported by knowledge and experimental data, include proliferation, slithering, metabolism, secretion, and injury repair, reflecting cancer hallmarks. SCLC subtypes, either at the population or single-cell level, can be positioned in archetypal space by bulk or single-cell transcriptomics, respectively, and characterized as task specialists or multi-task generalists by the distance from archetype vertex signatures. In the archetype space, modeling single-cell plasticity as a Markovian process along an underlying state manifold indicates that task trade-offs, in response to microenvironmental perturbations or treatment, may drive cell plasticity. Stifling phenotypic transitions and plasticity may provide new targets for much-needed translational advances in SCLC. A record of this paper's Transparent Peer Review process is included in the supplemental information.

Decoding the associations between cell functional states in head and neck cancer based on single-cell transcriptome

OBJECTIVES

Systematically identifying cancer cell functional states, especially their associations, is key to understanding the pathogenesis of cancers.

MATERIALS AND METHODS

Here, we systematically identified six cancer-related states, including epithelial-mesenchymal transition (EMT), immune response, epithelial differentiation, stress, G1/S and G2/M phases, in head and neck squamous cell carcinoma (HNSCC) based on single-cell RNA-sequencing (scRNA-seq).

RESULTS AND CONCLUSION

We defined the association patterns between these functional states and found the patterns were correlated with the state activity. Particularly, immune response and EMT were negatively, positively, or non-significantly correlated in samples with the highest immune response activity, the lowest activity of the two states, or with the highest EMT activity, respectively. Combining scRNA-seq data of immune cells and four independent HNSCC cohorts, we found the negative relationship between EMT and immune response was correlated with an activated immune microenvironment and a longer survival, while the non-significant relationship was correlated with an immunosuppressed microenvironment and a poor prognosis. Collectively, our results provide insight into the association patterns between functional states in HNSCC, and may facilitate the elucidation of the interactions between cancer cells and immune system during cancer progression.

Quantification of spatial subclonal interactions enhancing the invasive phenotype of pediatric glioma

Diffuse midline gliomas (DMGs) are highly aggressive, incurable childhood brain tumors. They present a clinical challenge due to many factors, including heterogeneity and diffuse infiltration, complicating disease management. Recent studies have described the existence of subclonal populations that may co-operate to drive pro-tumorigenic processes such as cellular invasion. However, a precise quantification of subclonal interactions is lacking, a problem that extends to other cancers. In this study, we combine spatial computational modeling of cellular interactions during invasion with co-evolution experiments of clonally disassembled patient-derived DMG cells. We design a Bayesian inference framework to quantify spatial subclonal interactions between molecular and phenotypically distinct lineages with different patterns of invasion. We show how this approach could discriminate genuine interactions, where one clone enhanced the invasive phenotype of another, from those apparently only due to the complex dynamics of spatially restricted growth. This study provides a framework for the quantification of subclonal interactions in DMG.

Disruption of cancer cell functions by task-specific drug perturbations

Cancer expands clonally, capitalizing on the variations between growing cells. Cancer cells specialize in one or more functions to gain an advantage. This study examined the prediction that cells would be vulnerable to drugs that perturb their specific tasks. We analyzed the correlation between gene expression and the response to drug perturbations in different cancer cells. Next, we assigned every cancer cell to an archetype based on gene expression. Finally, we calculated the enrichment of the cancer hallmark gene sets in each cell, archetypes, and response to drug treatment. We found that the extremes of gene expression were susceptible to change in response to perturbations. This correlation predicted the growth rate inhibition of breast cancer cells. Cancer hallmarks were enriched differently in the archetypes, and this enrichment predicted the cell’s response to perturbations. We present evidence that specialized cancer cells are sensitive to compounds that perturb their tasks.

miR-218 affects the ECM composition and cell biomechanical properties of glioblastoma cells

Glioblastoma (GBM) is the most common malignant brain tumour. GBM cells have the ability to infiltrate into the surrounding brain tissue, which results in a significant decrease in the patient’s survival rate. Infiltration is a consequence of the low adhesion and high migration of the tumour cells, two features being associated with the highly remodelled extracellular matrix (ECM). In this study, we report that ECM composition is partially regulated at the post‐transcriptional level by miRNA. Particularly, we show that miR‐218, a well‐known miRNA suppressor, is involved in the direct regulation of ECM components, tenascin‐C (TN‐C) and syndecan‐2 (SDC‐2). We demonstrated that the overexpression of miR‐218 reduces the mRNA and protein expression levels of TN‐C and SDC‐2, and subsequently influences biomechanical properties of GBM cells. Atomic force microscopy (AFM) and real‐time migration analysis revealed that miR‐218 overexpression impairs the migration potential and enhances the adhesive properties of cells. AFM analysis followed by F‐actin staining demonstrated that the expression level of miR‐218 has an impact on cell stiffness and cytoskeletal reorganization. Global gene expression analysis showed deregulation of a number of genes involved in tumour cell motility and adhesion or ECM remodelling upon miR‐218 treatment, suggesting further indirect interactions between the cells and ECM. The results demonstrated a direct impact of miR‐218 reduction in GBM tumours on the qualitative ECM content, leading to changes in the rigidity of the ECM and GBM cells being conducive to increased invasiveness of GBM.

Estrogen Receptor-Regulated Gene Signatures in Invasive Breast Cancer Cells and Aggressive Breast Tumors

Simple Summary

Metastatic breast cancer remains a major clinical problem, contributing to significant patient mortality, which is partly due to a lack of understanding around the early changes within the primary tumor. Tumors frequently become more aggressive and less treatable due to the activation of other signaling pathways, and, in ER+ disease, one of these pathways is NFκB. The coactivation of ER and NFκB (via IKKβ) promotes invasion and metastasis, and, here, we identify the signatures that are associated with these phenotypes. These signatures improve our understanding of how ER can drive aggressive disease, and may lead to the identification of key drivers, which could potentially be targeted with future therapies.

Abstract

Most metastatic breast cancers arise from estrogen receptor α (ER)-positive disease, and yet the role of ER in promoting metastasis is unclear. Here, we used an ER+ breast cancer cell line that is highly invasive in an ER- and IKKβ-dependent manner. We defined two ER-regulated gene signatures that are specifically regulated in the subpopulations of invasive cells. The first consists of proliferation-associated genes, which is a known function of ER, which actually suppress rather than enhance invasion. The second signature consists of genes involved in essential biological processes, such as organelle assembly and vesicle trafficking. Importantly, the second subpopulation-specific signature is associated with aggressive disease and poor patient outcome, independently of proliferation. These findings indicate a complex interplay between ER-driven proliferation and invasion, and they define new ER-regulated gene signatures that are predictive of aggressive ER+ breast cancer.

Cancer cell foraging to explain bone-specific metastatic progression

Metastatic cancer is lethal and patients who suffer bone metastases fare especially poorly. Bone-specific metastatic progression in prostate and breast cancers is a highly observed example of organ-specific metastasis, or organotropism. Though research has delineated the sequential steps of the metastatic cascade, the determinants of bone-specific metastasis have remained elusive for decades. Applying fundamental ecological principles to cancer biology models of metastasis provides novel insights into metastatic organotropism. We use critical concepts from foraging theory and movement ecology to propose that observed bone-specific metastasis is the result of habitat selection by foraging cancer cells. Furthermore, we posit that cancer cells can only perform habitat selection if and when they employ a reversible motile foraging strategy. Only a very small percentage of cells in a primary tumor harbor this ability. Therefore, our habitat selection model emphasizes the importance of identifying the rare subset of cancer cells that might exhibit habitat selection, ergo achieve bone-specific metastatic colonization.

Understanding glioblastoma invasion using physically-guided neural networks with internal variables

Microfluidic capacities for both recreating and monitoring cell cultures have opened the door to the use of Data Science and Machine Learning tools for understanding and simulating tumor evolution under controlled conditions. In this work, we show how these techniques could be applied to study Glioblastoma, the deadliest and most frequent primary brain tumor. In particular, we study Glioblastoma invasion using the recent concept of Physically-Guided Neural Networks with Internal Variables (PGNNIV), able to combine data obtained from microfluidic devices and some physical knowledge governing the tumor evolution. The physics is introduced in the network structure by means of a nonlinear advection-diffusion-reaction partial differential equation that models the Glioblastoma evolution. On the other hand, multilayer perceptrons combined with a nodal deconvolution technique are used for learning the go or grow metabolic behavior which characterises the Glioblastoma invasion. The PGNNIV is here trained using synthetic data obtained from in silico tests created under different oxygenation conditions, using a previously validated model. The unravelling capacity of PGNNIV enables discovering complex metabolic processes in a non-parametric way, thus giving explanatory capacity to the networks, and, as a consequence, surpassing the predictive power of any parametric approach and for any kind of stimulus. Besides, the possibility of working, for a particular tumor, with different boundary and initial conditions, permits the use of PGNNIV for defining virtual therapies and for drug design, thus making the first steps towards in silico personalised medicine.

In this work, we apply Physically-Guided Neural Networks with Internal Variables (PGNNIV) to the understanding of the Glioblastoma evolution process. We explain the metabolic changes between the proliferative and migrative activity of Glioblastoma cell cultures by using the go or grow activation functions as a pair of internal variables, whose dependence on the oxygen level is unravelled by some building blocks of the whole PGNNIV. Due to its model-free nature, our method is able to identify different classical mechanistic approaches and to outperform cell culture evolution predictions, as we demonstrate in the paper. Unlike Biologically-Informed Neural Networks we can assimilate data obtained from different boundary conditions and under different external stimuli to simulate the tumor progression under arbitrary conditions. We demonstrate this ability by comparing the predictions with different boundary conditions, resulting in different oxygenation conditions. This flexibility enables the use of our proposed method for personalised medical purposes, as the cell culture metabolic information, for a particular tumor, is encapsulated in a sub-network and may be used for arbitrary in silico tests.

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.