In silico experimentation of glioma microenvironment development and anti-tumor therapy

Cancer-on-a-chip for precision cancer medicine

Many cancer therapies fail in clinical trials despite showing potent efficacy in preclinical studies. One of the key reasons is the adopted preclinical models cannot recapitulate the complex tumor microenvironment (TME) and reflect the heterogeneity and patient specificity in human cancer. Cancer-on-a-chip (CoC) microphysiological systems can closely mimic the complex anatomical features and microenvironment interactions in an actual tumor, enabling more accurate disease modeling and therapy testing. This review article concisely summarizes and highlights the state-of-the-art progresses in CoC development for modeling critical TME compartments including the tumor vasculature, stromal and immune niche, as well as its applications in therapying screening. Current dilemma in cancer therapy development demonstrates that future preclinical models should reflect patient specific pathophysiology and heterogeneity with high accuracy and enable high-throughput screening for anticancer drug discovery and development. Therefore, CoC should be evolved as well. We explore future directions and discuss the pathway to develop the next generation of CoC models for precision cancer medicine, such as patient-derived chip, organoids-on-a-chip, and multi-organs-on-a-chip with high fidelity. We also discuss how the integration of sensors and microenvironmental control modules can provide a more comprehensive investigation of disease mechanisms and therapies. Next, we outline the roadmap of future standardization and translation of CoC technology toward real-world applications in pharmaceutical development and clinical settings for precision cancer medicine and the practical challenges and ethical concerns. Finally, we overview how applying advanced artificial intelligence tools and computational models could exploit CoC-derived data and augment the analytical ability of CoC.

Computational modeling of tumor invasion from limited and diverse data in Glioblastoma

For diseases with high morbidity rates such as Glioblastoma Multiforme, the prognostic and treatment planning pipeline requires a comprehensive analysis of imaging, clinical, and molecular data. Many mutations have been shown to correlate strongly with the median survival rate and response to therapy of patients. Studies have demonstrated that these mutations manifest as specific visual biomarkers in tumor imaging modalities such as MRI. To minimize the number of invasive procedures on a patient and for the overall resource optimization for the prognostic and treatment planning process, the correlation of imaging and molecular features has garnered much interest. While the tumor mass is the most significant feature, the impacted tissue surrounding the tumor is also a significant biomarker contributing to the visual manifestation of mutations - which has not been studied as extensively. The pattern of tumor growth impacts the surrounding tissue accordingly, which is a reflection of tumor properties as well. Modeling how the tumor growth impacts the surrounding tissue can reveal important information about the patterns of tumor enhancement, which in turn has significant diagnostic and prognostic value. This paper presents the first work to automate the computational modeling of the impacted tissue surrounding the tumor using generative deep learning. The paper isolates and quantifies the impact of the Tumor Invasion (TI) on surrounding tissue based on change in mutation status, subsequently assessing its prognostic value. Furthermore, a TI Generative Adversarial Network (TI-GAN) is proposed to model the tumor invasion properties. Extensive qualitative and quantitative analyses, cross-dataset testing, and radiologist blind tests are carried out to demonstrate that TI-GAN can realistically model the tumor invasion under practical challenges of medical datasets such as limited data and high intra-class heterogeneity.

Biosensor-Enhanced Organ-on-a-Chip Models for Investigating Glioblastoma Tumor Microenvironment Dynamics

Glioblastoma, an aggressive primary brain tumor, poses a significant challenge owing to its dynamic and intricate tumor microenvironment. This review investigates the innovative integration of biosensor-enhanced organ-on-a-chip (OOC) models as a novel strategy for an in-depth exploration of glioblastoma tumor microenvironment dynamics. In recent years, the transformative approach of incorporating biosensors into OOC platforms has enabled real-time monitoring and analysis of cellular behaviors within a controlled microenvironment. Conventional in vitro and in vivo models exhibit inherent limitations in accurately replicating the complex nature of glioblastoma progression. This review addresses the existing research gap by pioneering the integration of biosensor-enhanced OOC models, providing a comprehensive platform for investigating glioblastoma tumor microenvironment dynamics. The applications of this combined approach in studying glioblastoma dynamics are critically scrutinized, emphasizing its potential to bridge the gap between simplistic models and the intricate in vivo conditions. Furthermore, the article discusses the implications of biosensor-enhanced OOC models in elucidating the dynamic features of the tumor microenvironment, encompassing cell migration, proliferation, and interactions. By furnishing real-time insights, these models significantly contribute to unraveling the complex biology of glioblastoma, thereby influencing the development of more accurate diagnostic and therapeutic strategies.

Tumor-associated microglia and macrophages in glioblastoma: From basic insights to therapeutic opportunities

Glioblastoma (GBM) is the most common and malignant primary brain tumor in adults. Currently, the standard treatment of glioblastoma includes surgery, radiotherapy, and chemotherapy. Despite aggressive treatment, the median survival is only 15 months. GBM progression and therapeutic resistance are the results of the complex interactions between tumor cells and tumor microenvironment (TME). TME consists of several different cell types, such as stromal cells, endothelial cells and immune cells. Although GBM has the immunologically "cold" characteristic with very little lymphocyte infiltration, the TME of GBM can contain more than 30% of tumor-associated microglia and macrophages (TAMs). TAMs can release cytokines and growth factors to promote tumor proliferation, survival and metastasis progression as well as inhibit the function of immune cells. Thus, TAMs are logical therapeutic targets for GBM. In this review, we discussed the characteristics and functions of the TAMs and evaluated the state of the art of TAMs-targeting strategies in GBM. This review helps to understand how TAMs promote GBM progression and summarizes the present therapeutic interventions to target TAMs. It will possibly pave the way for new immune therapeutic avenues for GBM patients.

A perspective on the radiopharmaceutical requirements for imaging and therapy of glioblastoma

Despite numerous clinical trials and pre-clinical developments, the treatment of glioblastoma (GB) remains a challenge. The current survival rate of GB averages one year, even with an optimal standard of care. However, the future promises efficient patient-tailored treatments, including targeted radionuclide therapy (TRT). Advances in radiopharmaceutical development have unlocked the possibility to assess disease at the molecular level allowing individual diagnosis. This leads to the possibility of choosing a tailored, targeted approach for therapeutic modalities. Therapeutic modalities based on radiopharmaceuticals are an exciting development with great potential to promote a personalised approach to medicine. However, an effective targeted radionuclide therapy (TRT) for the treatment of GB entails caveats and requisites. This review provides an overview of existing nuclear imaging and TRT strategies for GB. A critical discussion of the optimal characteristics for new GB targeting therapeutic radiopharmaceuticals and clinical indications are provided. Considerations for target selection are discussed, i.e. specific presence of the target, expression level and pharmacological access to the target, with particular attention to blood-brain barrier crossing. An overview of the most promising radionuclides is given along with a validation of the relevant radiopharmaceuticals and theranostic agents (based on small molecules, peptides and monoclonal antibodies). Moreover, toxicity issues and safety pharmacology aspects will be presented, both in general and for the brain in particular.

P4HA1 Down-Regulation Inhibits Glioma Invasiveness by Promoting M1 Microglia Polarization

Background

Polarization of microglia cells in the glioma microenvironment is closely related to the malignant progression and invasion of gliomas. Prolyl 4-hydroxylase subunit α1 (P4HA1) is the rate-limiting subunit of prolyl 4-hydroxylase (P4H). In previous studies, we showed that P4HA1 could promote the proliferation, migration, and invasion of glioma cells, but the specific mechanisms through which this occurs have not been fully elucidated.

Materials and Methods

Interactions between glioma and microglia cells were analyzed using bioinformatics. Then, co-culture models were used to obtain conditioned media. To characterize microglial cell polarization, we used PCR and immunofluorescence. Proliferation and invasion assays were used to explore the biological behavior of glioma cells affected by microglia. Finally, marker expression was detected using immunohistochemistry in glioblastoma multiform (GBM) specimens.

Results

Knockdown of P4HA1 resulted in reduced chemotaxis of microglia toward GBM cells and increased polarization of microglia toward the M1 phenotype. The changed microglial polarization state, in turn, inhibited the proliferation and invasion of GBM cells. Moreover, in GBM tissue specimens, the P4HA1 expression level is negatively correlated with that of the CD86 microglia M1-specific marker.

Conclusion

Our results show that P4HA1 promotes immunosuppressive microenvironment formation by cross-talk between GBM and microglia cells and indirectly increases the aggressiveness of GBM.

Revealing and Harnessing Tumour-Associated Microglia/Macrophage Heterogeneity in Glioblastoma

Abstract: Cancer heterogeneity and progression are subject to complex interactions between neoplastic cells and their microenvironment, including the immune system. Although glioblastomas (GBMs) are classified as 'cold tumours' with very little lymphocyte infiltration, they can contain up to 30-40% of tumour-associated macrophages, reported to contribute to a supportive microenvironment that facilitates tumour proliferation, survival and migration. In GBM, tumour-associated macrophages comprise either resident parenchymal microglia, perivascular macrophages or peripheral monocyte-derived cells. They are recruited by GBMs and in turn release growth factors and cytokines that affect the tumour. Notably, tumour-associated microglia/macrophages (TAMs) acquire different expression programs, which shape the tumour microenvironment and contribute to GBM molecular subtyping. Further, emerging evidence highlights that TAM programs may adapt to specific tumour features and landscapes. Here, we review key evidence describing TAM transcriptional and functional heterogeneity in GBM. We propose that unravelling the intricate complexity and diversity of the myeloid compartment as well as understanding how different TAM subsets may affect tumour progression will possibly pave the way to new immune therapeutic avenues for GBM patients.

In silico disease model: from simple networks to complex diseases

In this chapter we consider in silico modeling of diseases starting from some simple to some complex (and mathematical) concepts. Examples and applications of in silico modeling for some important categories of diseases (such as for cancers, infectious diseases, and neuronal diseases) are also given.

Single-Cell Protein Secretion Detection and Profiling

Secreted proteins play important roles in mediating various biological processes such as cell-cell communication, differentiation, migration, and homeostasis at the population or tissue level. Here, we review bioanalytical technologies and devices for detecting protein secretions from single cells. We begin by discussing conventional approaches followed by detailing the latest advances in microengineered systems for detecting single-cell protein secretions with an emphasis on multiplex measurement. These platforms include droplet microfluidics, micro-/nanowell-based assays, and microchamber-based assays, among which the advantages and limitations are compared. Microscale systems also enable the tracking of protein secretion dynamics in single cells, further empowering the study of the cell-cell communication network. Looking forward, we discuss the remaining challenges and future opportunities that will transform basic research of cellular secretion functions at the systems level and the clinical applications for immune monitoring and cancer treatment.

CD90 determined two subpopulations of glioma-associated mesenchymal stem cells with different roles in tumour progression

Human glioma-associated mesenchymal stem cells (gbMSCs) are the stromal cell components that contribute to the tumourigenesis of malignant gliomas. Recent studies have shown that gbMSCs consist of two distinct subpopulations (CD90⁺ and CD90⁻ gbMSCs). However, the different roles in glioma progression have not been expounded. In this study, we found that the different roles of gbMSCs in glioma progression were associated with CD90 expression. CD90^high gbMSCs significantly drove glioma progression mainly by increasing proliferation, migration and adhesion, where as CD90^low gbMSCs contributed to glioma progression chiefly through the transition to pericytes and stimulation of vascular formation via vascular endothelial cells. Furthermore, discrepancies in long non-coding RNAs and mRNAs expression were verified in these two gbMSC subpopulations, and the potential underlying molecular mechanism was discussed. Our data confirm for the first time that CD90^high and CD90^low gbMSCs play different roles in human glioma progression. These results provide new insights into the possible future use of strategies targeting gbMSC subpopulations in glioma patients.

A Spatio-Temporal Model of Macrophage-Mediated Drug Resistance in Glioma Immunotherapy

The emergence of drug resistance is often an inevitable obstacle that limits the long-term effectiveness of clinical cancer chemotherapeutics. Although various forms of cancer cell-intrinsic mechanisms of drug resistance have been experimentally revealed, the role and the underlying mechanism of tumor microenvironment in driving the development of acquired drug resistance remain elusive, which significantly impedes effective clinical cancer treatment. Recent experimental studies have revealed a macrophage-mediated drug resistance mechanism in which the tumor microenvironment undergoes adaptation in response to macrophage-targeted colony-stimulating factor-1 receptor (CSF1R) inhibition therapy in gliomas. In this study, we developed a spatio-temporal model to quantitatively describe the interplay between glioma cells and CSF1R inhibitor-targeted macrophages through CSF1 and IGF1 pathways. Our model was used to investigate the evolutionary kinetics of the tumor regrowth and the associated dynamic adaptation of the tumor microenvironment in response to the CSF1R inhibitor treatment. The simulation result obtained using this model was in agreement with the experimental data. The sensitivity analysis revealed the key parameters involved in the model, and their potential impacts on the model behavior were examined. Moreover, we demonstrated that the drug resistance is dose-dependent. In addition, we quantitatively evaluated the effects of combined CSFR inhibition and IGF1 receptor (IGF1R) inhibition with the goal of designing more effective therapies for gliomas. Our study provides quantitative and mechanistic insights into the microenvironmental adaptation mechanisms that operate during macrophage-targeted immunotherapy and has implications for drug dose optimization and the design of more effective combination therapies. Mol Cancer Ther; 17(4); 814-24. ©2018 AACR.

Glioblastoma-associated microglia and macrophages: targets for therapies to improve prognosis

Glioblastoma is the most common and most malignant primary adult human brain tumour. Diagnosis of glioblastoma carries a dismal prognosis. Treatment resistance and tumour recurrence are the result of both cancer cell proliferation and their interaction with the tumour microenvironment. A large proportion of the tumour microenvironment consists of an inflammatory infiltrate predominated by microglia and macrophages, which are thought to be subverted by glioblastoma cells for tumour growth. Thus, glioblastoma-associated microglia and macrophages are logical therapeutic targets. Their emerging roles in glioblastoma progression are reflected in the burgeoning research into therapeutics directed at their modification or elimination. Here, we review the biology of glioblastoma-associated microglia and macrophages, and model systems used to study these cells in vitro and in vivo. We discuss translation of results using these model systems and review recent advances in immunotherapies targeting microglia and macrophages in glioblastoma. Significant challenges remain but medications that affect glioblastoma-associated microglia and macrophages hold considerable promise to improve the prognosis for patients with this disease.

Ultrastructural characterization of the Mitochondria-associated membranes abnormalities in human astrocytomas: Functional and therapeutics implications

Mitochondria-associated membranes (MAMs) are currently considered an intracellular organelle "hot spot" for the intracellular signaling. MAMs are thought to function in cellular energy homeostasis, apoptosis, and calcium signaling. MAM ultrastructure in surgical specimens from human astrocytic neoplasms was studied. Abnormalities in respect to density, length, and width were found. Poorly differentiated glioma like-stem cells deficient in MAM and well-differentiated glioma cells abundant in MAM were observed. This finding could be the structural basis of functional role of MAM linked to some metabolic abnormalities in astrocytic tumors associated to mitochondrial dysfunction and the Warburg effect and their therapeutics implications.

Mathematical Modeling of Therapy-induced Cancer Drug Resistance: Connecting Cancer Mechanisms to Population Survival Rates

Drug resistance significantly limits the long-term effectiveness of targeted therapeutics for cancer patients. Recent experimental studies have demonstrated that cancer cell heterogeneity and microenvironment adaptations to targeted therapy play important roles in promoting the rapid acquisition of drug resistance and in increasing cancer metastasis. The systematic development of effective therapeutics to overcome drug resistance mechanisms poses a major challenge. In this study, we used a modeling approach to connect cellular mechanisms underlying cancer drug resistance to population-level patient survival. To predict progression-free survival in cancer patients with metastatic melanoma, we developed a set of stochastic differential equations to describe the dynamics of heterogeneous cell populations while taking into account micro-environment adaptations. Clinical data on survival and circulating tumor cell DNA (ctDNA) concentrations were used to confirm the effectiveness of our model. Moreover, our model predicted distinct patterns of dose-dependent synergy when evaluating a combination of BRAF and MEK inhibitors versus a combination of BRAF and PI3K inhibitors. These predictions were consistent with the findings in previously reported studies. The impact of the drug metabolism rate on patient survival was also discussed. The proposed model might facilitate the quantitative evaluation and optimization of combination therapeutics and cancer clinical trial design.

A microchip platform for interrogating tumor-macrophage paracrine signaling at the single-cell level

It is increasingly recognized that infiltrating immune cells contribute to the pathogenesis of a wide range of solid tumors. The paracrine signaling between the tumor and the immune cells alters the functional state of individual tumor cells and, correspondingly, the anticipated response to radiation or chemotherapies, which is of great importance to clinical oncology. Here we present a high-density microchip platform capable of measuring a panel of paracrine signals associated with heterotypic tumor-immune cell interactions in the single-cell, pair-wise manner. The device features a high-content cell capture array of 5000+ sub-nanoliter microchambers for the isolation of single and multi-cell combinations and a multi-plex antibody "barcode" array for multiplexed protein secretion analysis from each microchamber. In this work, we measured a panel of 16 proteins produced from individual glioma cells, individual macrophage cells and varying heterotypic multi-cell combinations of both on the same device. The results show changes of tumor cell functional phenotypes that cannot be explained by an additive effect from isolated single cells and, presumably, can be attributed to the paracrine signaling between macrophage and glioma cells. The protein correlation analysis reveals the key signaling nodes altered by tumor-macrophage communication. This platform enables the novel pair-wise interrogation of heterotypic cell-cell paracrine signaling at the individual cell level with an in-depth analysis of the changing functional phenotypes for different co-culture cell combinations.

Cancer systems biology: embracing complexity to develop better anticancer therapeutic strategies

The transformation of normal cells into cancer cells and maintenance of the malignant state and phenotypes are associated with genetic and epigenetic deregulations, altered cellular signaling responses and aberrant interactions with the microenvironment. These alterations are constantly evolving as tumor cells face changing selective pressures induced by the cells themselves, the microenvironment and drug treatments. Tumors are also complex ecosystems where different, sometime heterogeneous, subclonal tumor populations and a variety of nontumor cells coexist in a constantly evolving manner. The interactions between molecules and between cells that arise as a result of these alterations and ecosystems are even more complex. The cancer research community is increasingly embracing this complexity and adopting a combination of systems biology methods and integrated analyses to understand and predictively model the activity of cancer cells. Systems biology approaches are helping to understand the mechanisms of tumor progression and design more effective cancer therapies. These approaches work in tandem with rapid technological advancements that enable data acquisition on a broader scale, with finer accuracy, higher dimensionality and higher throughput than ever. Using such data, computational and mathematical models help identify key deregulated functions and processes, establish predictive biomarkers and optimize therapeutic strategies. Moving forward, implementing patient-specific computational and mathematical models of cancer will significantly improve the specificity and efficacy of targeted therapy, and will accelerate the adoption of personalized and precision cancer medicine.

Microglia and macrophages in malignant gliomas: recent discoveries and implications for promising therapies

Malignant gliomas are the most common primary brain tumors. Their deadliest manifestation, glioblastoma multiforme (GBM), accounts for 15% of all primary brain tumors and is associated with a median survival of only 15 months even after multimodal therapy. There is substantial presence of microglia and macrophages within and surrounding brain tumors. These immune cells acquire an alternatively activated phenotype with potent tumor-tropic functions that contribute to glioma growth and invasion. In this review, we briefly summarize recent data that has been reported on the interaction of microglia/macrophages with brain tumors and discuss potential application of these findings to the development of future antiglioma therapies.

Inter-cellular signaling network reveals a mechanistic transition in tumor microenvironment

We conducted inter-cellular cytokine correlation and network analysis based upon a stochastic population dynamics model that comprises five cell types and fifteen signaling molecules inter-connected through a large number of cell-cell communication pathways. We observed that the signaling molecules are tightly correlated even at very early stages (e.g. the first month) of human glioma, but such correlation rapidly diminishes when tumor grows to a size that can be clinically detected. Further analysis suggests that paracrine is shown to be the dominant force during tumor initiation and priming, while autocrine supersedes it and supports a robust tumor expansion. In correspondence, the cytokine correlation network evolves through an increasing to decreasing complexity. This study indicates a possible mechanistic transition from the microenvironment-controlled, paracrine-based regulatory mechanism to self-sustained rapid progression to fetal malignancy. It also reveals key nodes that are responsible for such transition and can be potentially harnessed for the design of new anti-cancer therapies.

Single-cell protein secretomic signatures as potential correlates to tumor cell lineage evolution and cell-cell interaction

Secreted proteins including cytokines, chemokines, and growth factors represent important functional regulators mediating a range of cellular behavior and cell-cell paracrine/autocrine signaling, e.g., in the immunological system (Rothenberg, 2007), tumor microenvironment (Hanahan and Weinberg, 2011), or stem cell niche (Gnecchi etal., 2008). Detection of these proteins is of great value not only in basic cell biology but also for diagnosis and therapeutic monitoring of human diseases such as cancer. However, due to co-production of multiple effector proteins from a single cell, referred to as polyfunctionality, it is biologically informative to measure a panel of secreted proteins, or secretomic signature, at the level of single cells. Recent evidence further indicates that a genetically identical cell population can give rise to diverse phenotypic differences (Niepel etal., 2009). Non-genetic heterogeneity is also emerging as a potential barrier to accurate monitoring of cellular immunity and effective pharmacological therapies (Cohen etal., 2008; Gascoigne and Taylor, 2008), but can hardly assessed using conventional approaches that do not examine cellular phenotype at the functional level. It is known that cytokines, for example, in the immune system define the effector functions and lineage differentiation of immune cells. In this article, we hypothesize that protein secretion profile may represent a universal measure to identify the definitive correlate in the larger context of cellular functions to dissect cellular heterogeneity and evolutionary lineage relationship in human cancer.

High-throughput secretomic analysis of single cells to assess functional cellular heterogeneity

Secreted proteins dictate a range of cellular functions in human health and disease. Because of the high degree of cellular heterogeneity and, more importantly, polyfunctionality of individual cells, there is an unmet need to simultaneously measure an array of proteins from single cells and to rapidly assay a large number of single cells (more than 1000) in parallel. We describe a simple bioanalytical assay platform consisting of a large array of subnanoliter microchambers integrated with high-density antibody barcode microarrays for highly multiplexed protein detection from over a thousand single cells in parallel. This platform has been tested for both cell lines and complex biological samples such as primary cells from patients. We observed distinct heterogeneity among the single cell secretomic signatures that, for the first time, can be directly correlated to the cells' physical behavior such as migration. Compared to the state-of-the-art protein secretion assay such as ELISpot and emerging microtechnology-enabled assays, our approach offers both high throughput and high multiplicity. It also has a number of clinician-friendly features such as ease of operation, low sample consumption, and standardized data analysis, representing a potentially transformative tool for informative monitoring of cellular function and immunity in patients.