Models of Models: A Translational Route for Cancer Treatment and Drug Development

Survey and perspective on verification, validation, and uncertainty quantification of digital twins for precision medicine

Digital twins in precision medicine provide tailored health recommendations by simulating patient-specific trajectories and interventions. We examine the critical role of Verification, Validation, and Uncertainty Quantification (VVUQ) for digital twins in ensuring safety and efficacy, with examples in cardiology and oncology. We highlight challenges and opportunities for developing personalized trial methodologies, validation metrics, and standardizing VVUQ processes. VVUQ frameworks are essential for integrating digital twins into clinical practice.

Bridging the gap between tumor and disease: Innovating cancer and glioma models

Recent advances in cancer biology and therapeutics have underscored the importance of preclinical models in understanding and treating cancer. Nevertheless, current models often fail to capture the complexity and patient-specific nature of human tumors, particularly gliomas. This review examines the strengths and weaknesses of such models, highlighting the need for a new generation of models. Emphasizing the critical role of the tumor microenvironment, tumor, and patient heterogeneity, we propose integrating our advanced understanding of glioma biology with innovative bioengineering and AI technologies to create more clinically relevant, patient-specific models. These innovations are essential for improving therapeutic development and patient outcomes.

Ubiquitin specific peptidase (USP37) mediated effects in microscaffold-encapsulated cells: a comprehensive study on growth, proliferation and EMT

Though significant advances have been made in developing therapeutic strategies for cancer, suitable in vitro models for mechanistically identifying relevant drug targets and understanding disease progression are still lacking. Most studies are generally performed using two-dimensional (2D) models, since these models can be readily established and allow high throughput assays. However, these models have also been reported as the reason for unreliable pre-clinical information. To avoid this discrepancy, three-dimensional (3D) cell culture models have been established and have demonstrated the potential to provide alternative ways to study tissue behavior. However, most of these models first require optimization and cell cultures with a certain density, thus adding a prepping step in the platform before it can be used for any studies. This limits their use in studies where the fundamental understanding of biological processes must be carried out in a short time frame. In this study, we developed a 3D cell culture system that tests a less explored cancer therapeutic target-the deubiquitinating enzyme ubiquitin specific peptidase 37 (USP37)-in different cancer cell lines using sensitive carbon dot pH nanosensors, which provides a rapid model for studies compared to the parallel model available commercially. This enzyme is found to be elevated in different cancers and has been reported to play a role in cell cycle regulation, oncogenesis and metastasis. However, the confirmation of the role of USP37 downregulation in cellular proliferation via appropriate in vitro 3D models has not been demonstrated. To establish the applicability of the developed 3D platform in studying such oncogenes, classical 2D models have been used in this study for identifying the role of USP37 in tumor progression and metastasis. The data clearly suggests that this ingeniously developed 3D cell culture system is a better alternative to 2D models to study the growth and migration of different cancer cell lines on depletion of oncogenic proteins like USP37 and its effect on epithelial-mesenchymal transition (EMT) markers, and it can further be targeted as a viable therapeutic option.

Emerging Preclinical Applications of Humanized Mouse Models in the Discovery and Validation of Novel Immunotherapeutics and Their Mechanisms of Action for Improved Cancer Treatment

Cancer therapeutics have undergone immense research over the past decade. While chemotherapies remain the mainstay treatments for many cancers, the advent of new molecular techniques has opened doors for more targeted modalities towards cancer cells. Although immune checkpoint inhibitors (ICIs) have demonstrated therapeutic efficacy in treating cancer, adverse side effects related to excessive inflammation are often reported. There is a lack of clinically relevant animal models to probe the human immune response towards ICI-based interventions. Humanized mouse models have emerged as valuable tools for pre-clinical research to evaluate the efficacy and safety of immunotherapy. This review focuses on the establishment of humanized mouse models, highlighting the challenges and recent advances in these models for targeted drug discovery and the validation of therapeutic strategies in cancer treatment. Furthermore, the potential of these models in the process of uncovering novel disease mechanisms is discussed.

Structured tracking of alcohol reinforcement (STAR) for basic and translational alcohol research

There is inherent tension between methodologies developed to address basic research questions in model species and those intended for preclinical to clinical translation: basic investigations require flexibility of experimental design as hypotheses are rapidly tested and revised, whereas preclinical models emphasize standardized protocols and specific outcome measures. This dichotomy is particularly relevant in alcohol research, which spans a diverse range of basic sciences in addition to intensive efforts towards understanding the pathophysiology of alcohol use disorder (AUD). To advance these goals there is a great need for approaches that facilitate synergy across basic and translational areas of nonhuman alcohol research. In male and female mice, we establish a modular alcohol reinforcement paradigm: Structured Tracking of Alcohol Reinforcement (STAR). STAR provides a robust platform for quantitative assessment of AUD-relevant behavioral domains within a flexible framework that allows direct crosstalk between translational and mechanistically oriented studies. To achieve cross-study integration, despite disparate task parameters, a straightforward multivariate phenotyping analysis is used to classify subjects based on propensity for heightened alcohol consumption and insensitivity to punishment. Combining STAR with extant preclinical alcohol models, we delineate longitudinal phenotype dynamics and reveal putative neuro-biomarkers of heightened alcohol use vulnerability via neurochemical profiling of cortical and brainstem tissues. Together, STAR allows quantification of time-resolved biobehavioral processes essential for basic research questions simultaneous with longitudinal phenotyping of clinically relevant outcomes, thereby providing a framework to facilitate cohesion and translation in alcohol research.

Targeting Wnt/tenascin C-mediated cross talk between pancreatic cancer cells and stellate cells via activation of the metastasis suppressor NDRG1

A major barrier to successful pancreatic cancer (PC) treatment is the surrounding stroma, which secretes growth factors/cytokines that promote PC progression. Wnt and tenascin C (TnC) are key ligands secreted by stromal pancreatic stellate cells (PSCs) that then act on PC cells in a paracrine manner to activate the oncogenic β-catenin and YAP/TAZ signaling pathways. Therefore, therapies targeting oncogenic Wnt/TnC cross talk between PC cells and PSCs constitute a promising new therapeutic approach for PC treatment. The metastasis suppressor N-myc downstream-regulated gene-1 (NDRG1) inhibits tumor progression and metastasis in numerous cancers, including PC. We demonstrate herein that targeting NDRG1 using the clinically trialed anticancer agent di-2-pyridylketone-4-cyclohexyl-4-methyl-3-thiosemicarbazone (DpC) inhibited Wnt/TnC-mediated interactions between PC cells and the surrounding PSCs. Mechanistically, NDRG1 and DpC markedly inhibit secretion of Wnt3a and TnC by PSCs, while also attenuating Wnt/β-catenin and YAP/TAZ activation and downstream signaling in PC cells. This antioncogenic activity was mediated by direct inhibition of β-catenin and YAP/TAZ nuclear localization and by increasing the Wnt inhibitor, DKK1. Expression of NDRG1 also inhibited transforming growth factor (TGF)-β secretion by PC cells, a key mechanism by which PC cells activate PSCs. Using an in vivo orthotopic PC mouse model, we show DpC downregulated β-catenin, TnC, and YAP/TAZ, while potently increasing NDRG1 expression in PC tumors. We conclude that NDRG1 and DpC inhibit Wnt/TnC-mediated interactions between PC cells and PSCs. These results further illuminate the antioncogenic mechanism of NDRG1 and the potential of targeting this metastasis suppressor to overcome the oncogenic effects of the PC-PSC interaction.

Cancer models in preclinical research: A chronicle review of advancement in effective cancer research

Cancer is a major stress for public well-being and is the most dreadful disease. The models used in the discovery of cancer treatment are continuously changing and extending toward advanced preclinical studies. Cancer models are either naturally existing or artificially prepared experimental systems that show similar features with human tumors though the heterogeneous nature of the tumor is very familiar. The choice of the most fitting model to best reflect the given tumor system is one of the real difficulties for cancer examination. Therefore, vast studies have been conducted on the cancer models for developing a better understanding of cancer invasion, progression, and early detection. These models give an insight into cancer etiology, molecular basis, host tumor interaction, the role of microenvironment, and tumor heterogeneity in tumor metastasis. These models are also used to predict novel cancer markers, targeted therapies, and are extremely helpful in drug development. In this review, the potential of cancer models to be used as a platform for drug screening and therapeutic discoveries are highlighted. Although none of the cancer models is regarded as ideal because each is associated with essential caveats that restraint its application yet by bridging the gap between preliminary cancer research and translational medicine. However, they promise a brighter future for cancer treatment.

A Comparative in Silico Analysis of CD24's Prognostic Value in Human and Canine Prostate Cancer

CD24 is a cell surface molecule anchored by glycosyl-phosphatidyl-inositol and expressed by different human cancers, including prostate cancer (PC). Some studies have demonstrated that CD24 expression is associated with poor patient outcome; however, few studies have investigated CD24 expression in spontaneous animal models of human PC, such as canine PC. This study aimed to evaluate the expression of CD24 in human PC using the in silico analysis of the data obtained from The Cancer Genome Atlas (TCGA) and comparing it with the previously published prostatic canine transcriptome data. In addition, CD24 expression was confirmed by immunohistochemistry in an independent cohort of canine prostatic samples and its prognostic significance assessed. The systematic review identified 10 publications fitting with the inclusion criteria of this study. Of the 10 manuscripts, 5 demonstrated a direct correlation between CD24 overexpression and patient prognoses. CD24 expression was also associated with PSA relapse (2/5) and tumor progression (1/5). However, the in silico analysis did not validate CD24 as a prognostic factor of human PC. Regarding canine PC, 10 out of 30 normal prostates and 27 out of 40 PC samples were positive for CD24. As in humans, there was no association with overall survival. Overall, our results demonstrated a significant CD24 overexpression in human and canine prostate cancer, although its prognostic value may be questionable. However, tumors overexpressing CD24 may be a reliable model for new target therapies and dogs could be used of a unique preclinical model for these studies.

Assessing the interactions between radiotherapy and antitumour immunity

Immunotherapy, specifically the introduction of immune checkpoint inhibitors, has transformed the treatment of cancer, enabling long-term tumour control even in individuals with advanced-stage disease. Unfortunately, only a small subset of patients show a response to currently available immunotherapies. Despite a growing consensus that combining immune checkpoint inhibitors with radiotherapy can increase response rates, this approach might be limited by the development of persistent radiation-induced immunosuppression. The ultimate goal of combining immunotherapy with radiotherapy is to induce a shift from an ineffective, pre-existing immune response to a long-lasting, therapy-induced immune response at all sites of disease. To achieve this goal and enable the adaptation and monitoring of individualized treatment approaches, assessment of the dynamic changes in the immune system at the patient level is essential. In this Review, we summarize the available clinical data, including forthcoming methods to assess the immune response to radiotherapy at the patient level, ranging from serum biomarkers to imaging techniques that enable investigation of immune cell dynamics in patients. Furthermore, we discuss modelling approaches that have been developed to predict the interaction of immunotherapy with radiotherapy, and highlight how they could be combined with biomarkers of antitumour immunity to optimize radiotherapy regimens and maximize their synergy with immunotherapy.

Development of a MEL Cell-Derived Allograft Mouse Model for Cancer Research

Murine erythroleukemia (MEL) cells are often employed as a model to dissect mechanisms of erythropoiesis and erythroleukemia in vitro. Here, an allograft model using MEL cells resulting in splenomegaly was established to develop a diagnostic model for isolation/quantification of metastatic cells, anti-cancer drug screening, and evaluation of the tumorigenic or metastatic potentials of molecules in vivo. In this animal model, circulating MEL cells from the blood stream were successfully isolated and quantified with an additional in vitro cultivation step. In terms of the molecular-pathological analysis, we were able to successfully evaluate the functional discrimination between methyl-CpG-binding domain 2 (Mbd2) and p66α in erythroid differentiation, and tumorigenic potential in spleen and blood stream of allograft model mice. In addition, we found that the number of circulating MEL cells in anti-cancer drug-treated mice was dose-dependently decreased. Our data demonstrate that the newly established allograft model is useful to dissect erythroleukemia pathologies and non-invasively provides valuable means for isolation of metastatic cells, screening of anti-cancer drugs, and evaluation of the tumorigenic potentials.

Tumor Drug Penetration Measurements Could Be the Neglected Piece of the Personalized Cancer Treatment Puzzle

Precision medicine aims to use patient genomic, epigenomic, specific drug dose, and other data to define disease patterns that may potentially lead to an improved treatment outcome. Personalized dosing regimens based on tumor drug penetration can play a critical role in this approach. State-of-the-art techniques to measure tumor drug penetration focus on systemic exposure, tissue penetration, cellular or molecular engagement, and expression of pharmacological activity. Using in silico methods, this information can be integrated to bridge the gap between the therapeutic regimen and the pharmacological link with clinical outcome. These methodologies are described, and challenges ahead are discussed. Supported by many examples, this review shows how the combination of these techniques provides enhanced patient-specific information on drug accessibility at the tumor tissue level, target binding, and downstream pharmacology. Our vision of how to apply tumor drug penetration measurements offers a roadmap for the clinical implementation of precision dosing.

Computational Approaches in Theranostics: Mining and Predicting Cancer Data

The ability to understand the complexity of cancer-related data has been prompted by the applications of (1) computer and data sciences, including data mining, predictive analytics, machine learning, and artificial intelligence, and (2) advances in imaging technology and probe development. Computational modelling and simulation are systematic and cost-effective tools able to identify important temporal/spatial patterns (and relationships), characterize distinct molecular features of cancer states, and address other relevant aspects, including tumor detection and heterogeneity, progression and metastasis, and drug resistance. These approaches have provided invaluable insights for improving the experimental design of therapeutic delivery systems and for increasing the translational value of the results obtained from early and preclinical studies. The big question is: Could cancer theranostics be determined and controlled in silico? This review describes the recent progress in the development of computational models and methods used to facilitate research on the molecular basis of cancer and on the respective diagnosis and optimized treatment, with particular emphasis on the design and optimization of theranostic systems. The current role of computational approaches is providing innovative, incremental, and complementary data-driven solutions for the prediction, simplification, and characterization of cancer and intrinsic mechanisms, and to promote new data-intensive, accurate diagnostics and therapeutics.

Scalable Inference of Ordinary Differential Equation Models of Biochemical Processes

Ordinary differential equation models have become a standard tool for the mechanistic description of biochemical processes. If parameters are inferred from experimental data, such mechanistic models can provide accurate predictions about the behavior of latent variables or the process under new experimental conditions. Complementarily, inference of model structure can be used to identify the most plausible model structure from a set of candidates, and, thus, gain novel biological insight. Several toolboxes can infer model parameters and structure for small- to medium-scale mechanistic models out of the box. However, models for highly multiplexed datasets can require hundreds to thousands of state variables and parameters. For the analysis of such large-scale models, most algorithms require intractably high computation times. This chapter provides an overview of the state-of-the-art methods for parameter and model inference, with an emphasis on scalability.

Development of a Novel 3D Tumor-tissue Invasion Model for High-throughput, High-content Phenotypic Drug Screening

While much progress has been made in the war on cancer, highly invasive cancers such as pancreatic cancer remain difficult to treat and anti-cancer clinical trial success rates remain low. One shortcoming of the drug development process that underlies these problems is the lack of predictive, pathophysiologically relevant preclinical models of invasive tumor phenotypes. While present-day 3D spheroid invasion models more accurately recreate tumor invasion than traditional 2D models, their shortcomings include poor reproducibility and inability to interface with automated, high-throughput systems. To address this gap, a novel 3D tumor-tissue invasion model which supports rapid, reproducible setup and user-definition of tumor and surrounding tissue compartments was developed. High-cell density tumor compartments were created using a custom-designed fabrication system and standardized oligomeric type I collagen to define and modulate ECM physical properties. Pancreatic cancer cell lines used within this model showed expected differential invasive phenotypes. Low-passage, patient-derived pancreatic cancer cells and cancer-associated fibroblasts were used to increase model pathophysiologic relevance, yielding fibroblast-mediated tumor invasion and matrix alignment. Additionally, a proof-of-concept multiplex drug screening assay was applied to highlight this model's ability to interface with automated imaging systems and showcase its potential as a predictive tool for high-throughput, high-content drug screening.

Transgenic Mouse Models in Cancer Research

The use of existing mouse models in cancer research is of utmost importance as they aim to explore the casual link between candidate cancer genes and carcinogenesis as well as to provide models to develop and test new therapies. However, faster progress in translating mouse cancer model research into the clinic has been hampered due to the limitations of these models to better reflect the complexities of human tumors. Traditionally, immunocompetent and immunodeficient mice with syngeneic and xenografted tumors transplanted subcutaneously or orthotopically have been used. These models are still being widely employed for many different types of studies, in part due to their widespread availability and low cost. Other types of mouse models used in cancer research comprise transgenic mice in which oncogenes can be constitutively or conditionally expressed and tumor-suppressor genes silenced using conventional methods, such as retroviral infection, microinjection of DNA constructs, and the so-called "gene-targeted transgene" approach. These traditional transgenic models have been very important in studies of carcinogenesis and tumor pathogenesis, as well as in studies evaluating the development of resistance to therapy. Recently, the clustered regularly interspaced short palindromic repeats (CRISPR)-based genome editing approach has revolutionized the field of mouse cancer models and has had a profound and rapid impact on the development of more effective systems to study human cancers. The CRISPR/Cas9-based transgenic models have the capacity to engineer a wide spectrum of mutations found in human cancers and provide solutions to problems that were previously unsolvable. Recently, humanized mouse xenograft models that accept patient-derived xenografts and CD34+ cells were developed to better mimic tumor heterogeneity, the tumor microenvironment, and cross-talk between the tumor and stromal/immune cells. These features make them extremely valuable models for the evaluation of investigational cancer therapies, specifically new immunotherapies. Taken together, improvements in both the CRISPR/Cas9 system producing more valid mouse models and in the humanized mouse xenograft models resembling complex interactions between the tumor and its environment might represent one of the successful pathways to precise individualized cancer therapy, leading to improved cancer patient survival and quality of life.