Accelerating glioblastoma drug discovery: Convergence of patient-derived models, genome editing and phenotypic screening

Developing Bottom-Up Induced Pluripotent Stem Cell Derived Solid Tumor Models Using Precision Genome Editing Technologies

Advances in genome and tissue engineering have spurred significant progress and opportunity for innovation in cancer modeling. Human induced pluripotent stem cells (iPSCs) are an established and powerful tool to study cellular processes in the context of disease-specific genetic backgrounds; however, their application to cancer has been limited by the resistance of many transformed cells to undergo successful reprogramming. Here, we review the status of human iPSC modeling of solid tumors in the context of genetic engineering, including how base and prime editing can be incorporated into "bottom-up" cancer modeling, a term we coined for iPSC-based cancer models using genetic engineering to induce transformation. This approach circumvents the need to reprogram cancer cells while allowing for dissection of the genetic mechanisms underlying transformation, progression, and metastasis with a high degree of precision and control. We also discuss the strengths and limitations of respective engineering approaches and outline experimental considerations for establishing future models.

Analytic approaches to clinical validation of results from preclinical models of glioblastoma: A systematic review

INTRODUCTION

Analytic approaches to clinical validation of results from preclinical models are important in assessment of their relevance to human disease. This systematic review examined consistency in reporting of glioblastoma cohorts from The Cancer Genome Atlas (TCGA) or Chinese Glioma Genome Atlas (CGGA) and assessed whether studies included patient characteristics in their survival analyses.

METHODS

We searched Embase and Medline on 02Feb21 for studies using preclinical models of glioblastoma published after Jan2008 that used data from TCGA or CGGA to validate the association between at least one molecular marker and overall survival in adult patients with glioblastoma. Main data items included cohort characteristics, statistical significance of the survival analysis, and model covariates.

RESULTS

There were 58 eligible studies from 1,751 non-duplicate records investigating 126 individual molecular markers. In 14 studies published between 2017 and 2020 using TCGA RNA microarray data that should have the same cohort, the median number of patients was 464.5 (interquartile range 220.5-525). Of the 15 molecular markers that underwent more than one univariable or multivariable survival analyses, five had discrepancies between studies. Covariates used in the 17 studies that used multivariable survival analyses were age (76.5%), pre-operative functional status (35.3%), sex (29.4%) MGMT promoter methylation (29.4%), radiotherapy (23.5%), chemotherapy (17.6%), IDH mutation (17.6%) and extent of resection (5.9%).

CONCLUSION

Preclinical glioblastoma studies that used TCGA for validation did not provide sufficient information about their cohort selection and there were inconsistent results. Transparency in reporting and the use of analytic approaches that adjust for clinical variables can improve the reproducibility between studies.

Epigenetics of glioblastoma multiforme: From molecular mechanisms to therapeutic approaches

Glioblastoma multiforme (GBM) is the most common form of brain cancer and one of the most aggressive cancers found in humans. Most of the signs and symptoms of GBM can be mild and slowly aggravated, although other symptoms might demonstrate it as an acute ailment. However, the precise mechanisms of the development of GBM remain unknown. Due to the improvement of molecular pathology, current researches have reported that glioma progression is strongly connected with different types of epigenetic phenomena, such as histone modifications, DNA methylation, chromatin remodeling, and aberrant microRNA. Furthermore, the genes and the proteins that control these alterations have become novel targets for treating glioma because of the reversibility of epigenetic modifications. In some cases, gene mutations including P16, TP53, and EGFR, have been observed in GBM. In contrast, monosomies, including removals of chromosome 10, particularly q23 and q25-26, are considered the standard markers for determining the development and aggressiveness of GBM. Recently, amid the epigenetic therapies, histone deacetylase inhibitors (HDACIs) and DNA methyltransferase inhibitors have been used for treating tumors, either single or combined. Specifically, HDACIs are served as a good choice and deliver a novel pathway to treat GBM. In this review, we focus on the epigenetics of GBM and the consequence of its mutations. We also highlight various treatment approaches, namely gene editing, epigenetic drugs, and microRNAs to combat GBM.

Human pluripotent stem cell-derived models and drug screening in CNS precision medicine

Development of effective therapeutics for neurological disorders has historically been challenging partly because of lack of accurate model systems in which to investigate disease etiology and test new therapeutics at the preclinical stage. Human stem cells, particularly patient-derived induced pluripotent stem cells (iPSCs) upon differentiation, have the ability to recapitulate aspects of disease pathophysiology and are increasingly recognized as robust scalable systems for drug discovery. We review advances in deriving cellular models of human central nervous system (CNS) disorders using iPSCs along with strategies for investigating disease-relevant phenotypes, translatable biomarkers, and therapeutic targets. Given their potential to identify novel therapeutic targets and leads, we focus on phenotype-based, small-molecule screens employing human stem cell-derived models. Integrated efforts to assemble patient iPSC-derived cell models with deeply annotated clinicopathological data, along with molecular and drug-response signatures, may aid in the stratification of patients, diagnostics, and clinical trial success, shifting translational science and precision medicine approaches. A number of remaining challenges, including the optimization of cost-effective, large-scale culture of iPSC-derived cell types, incorporation of aging into neuronal models, as well as robustness and automation of phenotypic assays to support quantitative drug efficacy, toxicity, and metabolism testing workflows, are covered. Continued advancement of the field is expected to help fully humanize the process of CNS drug discovery.

Experimental models and tools to tackle glioblastoma

Glioblastoma multiforme (GBM) is one of the deadliest human cancers. Despite increasing knowledge of the genetic and epigenetic changes that underlie tumour initiation and growth, the prognosis for GBM patients remains dismal. Genome analysis has failed to lead to success in the clinic. Fresh approaches are needed that can stimulate new discoveries across all levels: cell-intrinsic mechanisms (transcriptional/epigenetic and metabolic), cell-cell signalling, niche and microenvironment, systemic signals, immune regulation, and tissue-level physical forces. GBMs are inherently extremely challenging: tumour detection occurs too late, and cells infiltrate widely, hiding in quiescent states behind the blood-brain barrier. The complexity of the brain tissue also provides varied and complex microenvironments that direct cancer cell fates. Phenotypic heterogeneity is therefore superimposed onto pervasive genetic heterogeneity. Despite this bleak outlook, there are reasons for optimism. A myriad of complementary, and increasingly sophisticated, experimental approaches can now be used across the research pipeline, from simple reductionist models devised to delineate molecular and cellular mechanisms, to complex animal models required for preclinical testing of new therapeutic approaches. No single model can cover the breadth of unresolved questions. This Review therefore aims to guide investigators in choosing the right model for their question. We also discuss the recent convergence of two key technologies: human stem cell and cancer stem cell culture, as well as CRISPR/Cas tools for precise genome manipulations. New functional genetic approaches in tailored models will likely fuel new discoveries, new target identification and new therapeutic strategies to tackle GBM.

Genome Editing in Human Neural Stem and Progenitor Cells

Experimental tools for precise manipulation of mammalian genomes enable reverse genetic approaches to explore biology and disease. Powerful genome editing technologies built upon designer nucleases, such as CRISPR/Cas9, have recently emerged. Parallel progress has been made in methodologies for the expansion and differentiation of human pluripotent and tissue stem cells. Together these innovations provide a remarkable new toolbox for human cellular genetics and are opening up vast opportunities for discoveries and applications across the breadth of life sciences research. In this chapter, we review the emergence of genome editing technologies and how these are being deployed in studies of human neurobiology, neurological disease, and neuro-oncology. We focus our discussion on CRISPR/Cas9 and its application in studies of human neural stem and progenitor cells.

A Critical Appraisal of the Quality of Glioma Imaging Guidelines Using the AGREE II Tool: A EuroAIM Initiative

Background: Following the EuroAIM initiative to assess the quality of medical imaging guidelines by using the Appraisal of Guidelines for Research and Evaluation (AGREE) II instrument, we aimed to evaluate the quality of the current imaging guidelines in patients with gliomas.

Methods: A literature search was conducted to identify eligible imaging guidelines considered in the management of adult patients with gliomas. The selected guidelines were evaluated using the AGREE II instrument by four independent appraisers. The agreement among the four appraisers was estimated using the intraclass correlation coefficient (ICC) analysis.

Results: Seven guidelines were selected for the appraisal. Six out of the seven guidelines showed an average level of quality with only one showing a low quality. The highest scores were found in Domain 1 “Scope and purpose” (mean score = 81.2%) and Domain 4 “Clarity of presentation” (mean score = 77.6%). The remaining domains showed a low level of quality and, in particular, Domain 5 “Applicability” was the most critical with a mean score of 41.7%, mainly related to a minor attention to barriers and facilitators as well as costs and resources implications of applying the guidelines. The ICC analysis showed a very good agreement among the four appraisers with ICC values ranging from 0.907 to 0.993.

Conclusions: The available guidelines on glioma imaging emerged as of average quality according to the AGREE II tool analysis. Based on these results, further efforts should be made in order to involve different professional bodies and stakeholders and increase patient and public involvement in any future guideline drafting as well as to improve the applicability of these guidelines into the clinical practice.

Discovery of Small-Molecule Inhibitors of the HSP90-Calcineurin-NFAT Pathway against Glioblastoma

Glioblastoma (GBM) is among the most common and malignant types of primary brain tumors in adults, with a dismal prognosis. Although alkylating agents such as temozolomide are widely applied as the first-line treatment for GBM, they often cause chemoresistance and remain ineffective with recurrent GBM. Alternative therapeutics against GBM are urgently needed in the clinic. We report herein the discovery of a class of inhibitors (YZ129 and its derivatives) of the calcineurin-NFAT pathway that exhibited potent anti-tumor activity against GBM. YZ129-induced GBM cell-cycle arrest at the G2/M phase promoted apoptosis and inhibited tumor cell proliferation and migration. At the molecular level, YZ129 directly engaged HSP90 to antagonize its chaperoning effect on calcineurin to abrogate NFAT nuclear translocation, and also suppressed other proto-oncogenic pathways including hypoxia, glycolysis, and the PI3K/AKT/mTOR signaling axis. Our data highlight the potential for targeting the cancer-promoting HSP90 chaperone network to treat GBM.

Spinal cord astrocytomas: progresses in experimental and clinical investigations for developing recovery neurobiology-based novel therapies

Spinal cord astrocytomas (SCAs) have discernibly unique signatures in regards to epidemiology, clinical oncological features, genetic markers, pathophysiology, and research and therapeutic challenges. Overall, there are presently very limited clinical management options for high grade SCAs despite progresses made in validating key molecular markers and standardizing tumor classification. The endeavors were aimed to improve diagnosis, therapy design and prognosis assessment, as well as to define more effective oncolytic targets. Efficacious treatment for high grade SCAs still remains an unmet medical demand. This review is therefore focused on research state updates that have been made upon analyzing clinical characteristics, diagnostic classification, genetic and molecular features, tumor initiation cell biology, and current management options for SCAs. Particular emphasis was given to basic and translational research endeavors targeting SCAs, including establishment of experimental models, exploration of unique profiles of SCA stem cell-like tumor survival cells, characterization of special requirements for effective therapeutic delivery into the spinal cord, and development of donor stem cell-based gene-directed enzyme prodrug therapy. We concluded that precise understanding of molecular oncology, tumor survival mechanisms (e.g., drug resistance, metastasis, and cancer stem cells/tumor survival cells), and principles of Recovery Neurobiology can help to create clinically meaningful experimental models of SCAs. Establishment of such systems will expedite the discovery of efficacious therapies that not only kill tumor cells but simultaneously preserve and improve residual neural function.

Mechanical stabilization of proteolytically degradable polyethylene glycol dimethacrylate hydrogels through peptide interaction

Balancing enhancement of neurite extension against loss of matrix support in synthetic hydrogels containing proteolytically degradable and bioactive signaling peptides to optimize tissue formation is difficult. Using a systematic approach, polyethylene glycol hydrogels containing concurrent continuous concentration gradients of the laminin derived bioactive signaling peptide, Ile-Lys-Val-Ala-Val (IKVAV), and collagen derived matrix metalloprotease degradable peptide, GPQGIWGQ, were fabricated and characterized. During proteolytic degradation of the concentration gradient hydrogels, the IKVAV and IWGQ cleavage fragment from GPQGIWGQ were found to interact and stabilize the bulk Young's Modulus of the hydrogel. Further testing of discrete samples containing GPQGIWGQ or its cleavage fragments, GPQG and IWGQ, indicates hydrophobic interactions between the peptides are not necessary for mechanical stabilization of the hydrogel, but changes in the concentration ratio between the peptides tethered in the hydrogel and salts and ions in the swelling solution can affect the stabilization. Encapsulation of human induced pluripotent stem cell derived neural stem cells did not reduce the mechanical properties of the hydrogel over a 14 day neural differentiation culture period, and IKVAV was found to maintain concentration dependent effects on neurite extension and mRNA gene expression of neural cytoskeletal markers, similar to previous studies. As a result, this work has significant implications for the analysis of biological studies in matrices, as the material and mechanical properties of the hydrogel may be unexpectedly temporally changing during culture due to interactions between peptide signaling elements, underscoring the need for greater matrix characterization during the degradation and cell culture.

STATEMENT OF SIGNIFICANCE

Greater emulation of the native extracellular matrix is necessary for tissue formation. To achieve this, matrices are becoming more complex, often including multiple bioactive signaling elements. However, peptide signaling in polyethylene glycol matrices and amino acids interactions between peptides can affect hydrogel material and mechanical properties, but are rarely studied. The current study identifies such an interaction between laminin derived peptide, IKVAV, and collagen derived matrix metalloprotease degradable peptide, GPQGIWGQ. Previous studies using these peptides did not identify their interactions' ability to mechanically stabilize the hydrogel during degradation. This work underscores the need for greater matrix characterization and consideration of bioactive signaling element effects temporally on the matrix's material and mechanical properties, as they can contribute to cellular response.

Machine Learning Enables Live Label-Free Phenotypic Screening in Three Dimensions

There is a large amount of information in brightfield images that was previously inaccessible by using traditional microscopy techniques. This information can now be exploited by using machine-learning approaches for both image segmentation and the classification of objects. We have combined these approaches with a label-free assay for growth and differentiation of leukemic colonies, to generate a novel platform for phenotypic drug discovery. Initially, a supervised machine-learning algorithm was used to identify in-focus colonies growing in a three-dimensional (3D) methylcellulose gel. Once identified, unsupervised clustering and principle component analysis of texture-based phenotypic profiles were applied to group similar phenotypes. In a proof-of-concept study, we successfully identified a novel phenotype induced by a compound that is currently in clinical trials for the treatment of leukemia. We believe that our platform will be of great benefit for the utilization of patient-derived 3D cell culture systems for both drug discovery and diagnostic applications.

Opportunities and challenges in phenotypic drug discovery: an industry perspective

Phenotypic drug discovery (PDD) approaches do not rely on knowledge of the identity of a specific drug target or a hypothesis about its role in disease, in contrast to the target-based strategies that have been widely used in the pharmaceutical industry in the past three decades. However, in recent years, there has been a resurgence in interest in PDD approaches based on their potential to address the incompletely understood complexity of diseases and their promise of delivering first-in-class drugs, as well as major advances in the tools for cell-based phenotypic screening. Nevertheless, PDD approaches also have considerable challenges, such as hit validation and target deconvolution. This article focuses on the lessons learned by researchers engaged in PDD in the pharmaceutical industry and considers the impact of 'omics' knowledge in defining a cellular disease phenotype in the era of precision medicine, introducing the concept of a chain of translatability. We particularly aim to identify features and areas in which PDD can best deliver value to drug discovery portfolios and can contribute to the identification and the development of novel medicines, and to illustrate the challenges and uncertainties that are associated with PDD in order to help set realistic expectations with regard to its benefits and costs.

Identification of repaglinide as a therapeutic drug for glioblastoma multiforme

Glioblastoma multiforme (GBM) is a highly aggressive brain tumor with a median survival time of only 14 months after treatment. It is urgent to find new therapeutic drugs that increase survival time of GBM patients. To achieve this goal, we screened differentially expressed genes between long-term and short-term survived GBM patients from Gene Expression Omnibus database and found gene expression signature for the long-term survived GBM patients. The signaling networks of all those differentially expressed genes converged to protein binding, extracellular matrix and tissue development as revealed in BiNGO and Cytoscape. Drug repositioning in Connectivity Map by using the gene expression signature identified repaglinide, a first-line drug for diabetes mellitus, as the most promising novel drug for GBM. In vitro experiments demonstrated that repaglinide significantly inhibited the proliferation and migration of human GBM cells. In vivo experiments demonstrated that repaglinide prominently prolonged the median survival time of mice bearing orthotopic glioma. Mechanistically, repaglinide significantly reduced Bcl-2, Beclin-1 and PD-L1 expression in glioma tissues, indicating that repaglinide may exert its anti-cancer effects via apoptotic, autophagic and immune checkpoint signaling. Taken together, repaglinide is likely to be an effective drug to prolong life span of GBM patients.