Capturing cancer evolution using genetically engineered mouse models (GEMMs)

A systematic review on the culture methods and applications of 3D tumoroids for cancer research and personalized medicine

Cancer is a highly heterogeneous disease, and thus treatment responses vary greatly between patients. To improve therapy efficacy and outcome for cancer patients, more representative and patient-specific preclinical models are needed. Organoids and tumoroids are 3D cell culture models that typically retain the genetic and epigenetic characteristics, as well as the morphology, of their tissue of origin. Thus, they can be used to understand the underlying mechanisms of cancer initiation, progression, and metastasis in a more physiological setting. Additionally, co-culture methods of tumoroids and cancer-associated cells can help to understand the interplay between a tumor and its tumor microenvironment. In recent years, tumoroids have already helped to refine treatments and to identify new targets for cancer therapy. Advanced culturing systems such as chip-based fluidic devices and bioprinting methods in combination with tumoroids have been used for high-throughput applications for personalized medicine. Even though organoid and tumoroid models are complex in vitro systems, validation of results in vivo is still the common practice. Here, we describe how both animal- and human-derived tumoroids have helped to identify novel vulnerabilities for cancer treatment in recent years, and how they are currently used for precision medicine.

A prime editor mouse to model a broad spectrum of somatic mutations in vivo

Genetically engineered mouse models only capture a small fraction of the genetic lesions that drive human cancer. Current CRISPR-Cas9 models can expand this fraction but are limited by their reliance on error-prone DNA repair. Here we develop a system for in vivo prime editing by encoding a Cre-inducible prime editor in the mouse germline. This model allows rapid, precise engineering of a wide range of mutations in cell lines and organoids derived from primary tissues, including a clinically relevant Kras mutation associated with drug resistance and Trp53 hotspot mutations commonly observed in pancreatic cancer. With this system, we demonstrate somatic prime editing in vivo using lipid nanoparticles, and we model lung and pancreatic cancer through viral delivery of prime editing guide RNAs or orthotopic transplantation of prime-edited organoids. We believe that this approach will accelerate functional studies of cancer-associated mutations and complex genetic combinations that are challenging to construct with traditional models.

Methods to study xenografted human cancer in genetically diverse mice

Xenografting human cancer tissues into mice to test new cures against cancers is critical for understanding and treating the disease. However, only a few inbred strains of mice are used to study cancers, and derivatives of mainly one strain, mostly NOD/ShiLtJ, are used for therapy efficacy studies. As it has been demonstrated when human cancer cell lines or patient-derived tissues (PDX) are xenografted into mice, the neoplastic cells are human but the supporting cells that comprise the tumor (the stroma) are from the mouse. Therefore, results of studies of xenografted tissues are influenced by the host strain. We previously published that when the same neoplastic cells are xenografted into different mouse strains, the pattern of tumor growth, histology of the tumor, number of immune cells infiltrating the tumor, and types of circulating cytokines differ depending on the strain. Therefore, to better comprehend the behavior of cancer in vivo, one must xenograft multiple mouse strains. Here we describe and report a series of methods that we used to reveal the genes and proteins expressed when the same cancer cell line, MDA-MB-231, is xenografted in different hosts. First, using proteomic analysis, we show how to use the same cell line in vivo to reveal the protein changes in the neoplastic cell that help it adapt to its host. Then, we show how different hosts respond molecularly to the same cell line. We also find that using multiple strains can reveal a more suitable host than those traditionally used for a "difficult to xenograft" PDX. In addition, using complex trait genetics, we illustrate a feasible method for uncovering the alleles of the host that support tumor growth. Finally, we demonstrate that Diversity Outbred mice, the epitome of a model of mouse-strain genetic diversity, can be xenografted with human cell lines or PDX using 2-deoxy-D-glucose treatment.

Preclinical liver cancer models in the context of immunoprecision therapy: Application and perspectives

Hepatocellular carcinoma (HCC), ranking as the third leading cause of cancer-related mortality globally, continues to pose challenges in achieving optimal treatment outcomes. The complex nature of HCC, characterized by high spatiotemporal heterogeneity, invasive potential, and drug resistance, presents difficulties in its research. Consequently, an in-depth understanding and accurate simulation of the immune microenvironment of HCC are of paramount importance. This article comprehensively explores the application of preclinical models in HCC research, encompassing cell line models, patient-derived xenograft mouse models, genetically engineered mouse models, chemically induced models, humanized mouse models, organoid models, and microfluidic chip-based patient derived organotypic spheroids models. Each model possesses its distinct advantages and limitations in replicating the biological behavior and immune microenvironment of HCC. By scrutinizing the limitations of existing models, this paper aims to propel the development of next-generation cancer models, enabling more precise emulation of HCC characteristics. This will, in turn, facilitate the optimization of treatment strategies, drug efficacy prediction, and safety assessments, ultimately contributing to the realization of personalized and precision therapies. Additionally, this article also provides insights into future trends and challenges in the fields of tumor biology and preclinical research.

Phenotypic analysis with trans-recombination-based genetic mosaic models

Mosaicism refers to the presence of genetically distinct cell populations in an individual derived from a single zygote, which occurs during the process of development, aging, and genetic diseases. To date, a variety of genetically engineered mosaic analysis models have been established and widely used in studying gene function at exceptional cellular and spatiotemporal resolution, leading to many ground-breaking discoveries. Mosaic analysis with a repressible cellular marker and mosaic analysis with double markers are genetic mosaic analysis models based on trans-recombination. These models can generate sibling cells of distinct genotypes in the same animal and simultaneously label them with different colors. As a result, they offer a powerful approach for lineage tracing and studying the behavior of individual mutant cells in a wildtype environment, which is particularly useful for determining whether gene function is cell autonomous or nonautonomous. Here, we present a comprehensive review on the establishment and applications of mosaic analysis with a repressible cellular marker and mosaic analysis with double marker systems. Leveraging the capabilities of these mosaic models for phenotypic analysis will facilitate new discoveries on the cellular and molecular mechanisms of development and disease.

Advances in glioma models using in vivo electroporation to highjack neurodevelopmental processes

Glioma is the most prevalent type of neurological malignancies. Despite decades of efforts in neurosurgery, chemotherapy and radiation therapy, glioma remains one of the most treatment-resistant brain tumors with unfavorable outcomes. Recent progresses in genomic and epigenetic profiling have revealed new concepts of genetic events involved in the etiology of gliomas in humans, meanwhile, revolutionary technologies in gene editing and delivery allows to code these genetic "events" in animals to genetically engineer glioma models. This approach models the initiation and progression of gliomas in a natural microenvironment with an intact immune system and facilitates probing therapeutic strategies. In this review, we focus on recent advances in in vivo electroporation-based glioma modeling and outline the established genetically engineered glioma models (GEGMs).

Socs1-knockout in skin-resident CD4+ T cells in a protracted contact-allergic reaction results in an autonomous skin inflammation with features of early-stage mycosis fungoides

Recent detailed genomic analysis of mycosis fungoides (MF) identified suppressor of cytokine signaling 1 (SOCS1), an inhibitor of JAK/STAT signaling, as one of the frequently deleted tumor suppressors in MF, and one-copy deletion of SOCS1 was confirmed in early-stage MF lesions. To better understand the functional role of SOCS1 in the genesis of MF, we used a genetically engineered mouse model emulating heterozygous SOCS1 loss in skin resident CD4+ T cells. In these mice an experimentally induced contact-allergic reaction was maintained for 20 weeks. Ten weeks after discontinuing contact-allergic challenges, only the skin with locally one-copy deletion of Socs1 in CD4+ T cells still showed high numbers of CD3+/CD4+ Socs1 k.o. cells in the dermis (p ＜ 0.0001) with prevalent Stat3 activation (p ＜0.001). And in one out of 9 mice, this had progressed to far more dramatic increases, including the thickened epidermis, and with an explosive growth of Socs1 k.o. T cells in circulation; indicative of cutaneous lymphoma. Hence, we show that Socs1 mono-allelic loss in CD4+ T cells locally in protractedly inflamed skin results in autonomous skin inflammation with features of early-stage MF.

Organoids: approaches and utility in cancer research

ABSTRACT

Organoids are three-dimensional cellular structures with self-organizing and self-differentiation capacities. They faithfully recapitulate structures and functions of in vivo organs as represented by functionality and microstructural definitions. Heterogeneity in in vitro disease modeling is one of the main reasons for anti-cancer therapy failures. Establishing a powerful model to represent tumor heterogeneity is crucial for elucidating tumor biology and developing effective therapeutic strategies. Tumor organoids can retain the original tumor heterogeneity and are commonly used to mimic the cancer microenvironment when co-cultured with fibroblasts and immune cells; therefore, considerable effort has been made recently to promote the use of this new technology from basic research to clinical studies in tumors. In combination with gene editing technology and microfluidic chip systems, engineered tumor organoids show promising abilities to recapitulate tumorigenesis and metastasis. In many studies, the responses of tumor organoids to various drugs have shown a positive correlation with patient responses. Owing to these consistent responses and personalized characteristics with patient data, tumor organoids show excellent potential for preclinical research. Here, we summarize the properties of different tumor models and review their current state and progress in tumor organoids. We further discuss the substantial challenges and prospects in the rapidly developing tumor organoid field.

A C57BL/6J Fancg-KO Mouse Model Generated by CRISPR/Cas9 Partially Captures the Human Phenotype

Fanconi anemia (FA) develops due to a mutation in one of the FANC genes that are involved in the repair of interstrand crosslinks (ICLs). FANCG, a member of the FA core complex, is essential for ICL repair. Previous FANCG-deficient mouse models were generated with drug-based selection cassettes in mixed mice backgrounds, leading to a disparity in the interpretation of genotype-related phenotype. We created a Fancg-KO (KO) mouse model using CRISPR/Cas9 to exclude these confounders. The entire Fancg locus was targeted and maintained on the immunological well-characterized C57BL/6J background. The intercrossing of heterozygous mice resulted in sub-Mendelian numbers of homozygous mice, suggesting the loss of FANCG can be embryonically lethal. KO mice displayed infertility and hypogonadism, but no other developmental problems. Bone marrow analysis revealed a defect in various hematopoietic stem and progenitor subsets with a bias towards myelopoiesis. Cell lines derived from Fancg-KO mice were hypersensitive to the crosslinking agents cisplatin and Mitomycin C, and Fancg-KO mouse embryonic fibroblasts (MEFs) displayed increased γ-H2AX upon cisplatin treatment. The reconstitution of these MEFs with Fancg cDNA corrected for the ICL hypersensitivity. This project provides a new, genetically, and immunologically well-defined Fancg-KO mouse model for further in vivo and in vitro studies on FANCG and ICL repair.

Recent Advancement in Breast Cancer Research: Insights from Model Organisms-Mouse Models to Zebrafish

Animal models have been utilized for decades to investigate the causes of human diseases and provide platforms for testing novel therapies. Indeed, breakthrough advances in genetically engineered mouse (GEM) models and xenograft transplantation technologies have dramatically benefited in elucidating the mechanisms underlying the pathogenesis of multiple diseases, including cancer. The currently available GEM models have been employed to assess specific genetic changes that underlay many features of carcinogenesis, including variations in tumor cell proliferation, apoptosis, invasion, metastasis, angiogenesis, and drug resistance. In addition, mice models render it easier to locate tumor biomarkers for the recognition, prognosis, and surveillance of cancer progression and recurrence. Furthermore, the patient-derived xenograft (PDX) model, which involves the direct surgical transfer of fresh human tumor samples to immunodeficient mice, has contributed significantly to advancing the field of drug discovery and therapeutics. Here, we provide a synopsis of mouse and zebrafish models used in cancer research as well as an interdisciplinary 'Team Medicine' approach that has not only accelerated our understanding of varied aspects of carcinogenesis but has also been instrumental in developing novel therapeutic strategies.

Synergistic effects of combined immunotherapy strategies in a model of multifocal hepatocellular carcinoma

Immune checkpoint-inhibitor combinations are the best therapeutic option for advanced hepatocellular carcinoma (HCC) patients, but improvements in efficacy are needed to improve response rates. We develop a multifocal HCC model to test immunotherapies by introducing c-myc using hydrodynamic gene transfer along with CRISPR-Cas9-mediated disruption of p53 in mouse hepatocytes. Additionally, induced co-expression of luciferase, EGFP, and the melanosomal antigen gp100 facilitates studies on the underlying immunological mechanisms. We show that treatment of the mice with a combination of anti-CTLA-4 + anti-PD1 mAbs results in partial clearance of the tumor with an improvement in survival. However, the addition of either recombinant IL-2 or an anti-CD137 mAb markedly improves both outcomes in these mice. Combining tumor-specific adoptive T cell therapy to the aCTLA-4/aPD1/rIL2 or aCTLA-4/aPD1/aCD137 regimens enhances efficacy in a synergistic manner. As shown by multiplex tissue immunofluorescence and intravital microscopy, combined immunotherapy treatments enhance T cell infiltration and the intratumoral performance of T lymphocytes.

3D cancer models: One step closer to in vitro human studies

Cancer immunotherapy is the great breakthrough in cancer treatment as it displayed prolonged progression-free survival over conventional therapies, yet, to date, in only a minority of patients. In order to broad cancer immunotherapy clinical applicability some roadblocks need to be overcome, first among all the lack of preclinical models that faithfully depict the local tumor microenvironment (TME), which is known to dramatically affect disease onset, progression and response to therapy. In this review, we provide the reader with a detailed overview of current 3D models developed to mimick the complexity and the dynamics of the TME, with a focus on understanding why the TME is a major target in anticancer therapy. We highlight the advantages and translational potentials of tumor spheroids, organoids and immune Tumor-on-a-Chip models in disease modeling and therapeutic response, while outlining pending challenges and limitations. Thinking forward, we focus on the possibility to integrate the know-hows of micro-engineers, cancer immunologists, pharmaceutical researchers and bioinformaticians to meet the needs of cancer researchers and clinicians interested in using these platforms with high fidelity for patient-tailored disease modeling and drug discovery.

Naturally occurring canine sarcomas: Bridging the gap from mouse models to human patients through cross-disciplinary research partnerships

Fueled by support from the National Cancer Institute's "Cancer Moonshot" program, the past few years have witnessed a renewed interest in the canine spontaneous cancer model as an invaluable resource in translational oncology research. Increasingly, there is awareness that pet dogs with cancer provide an accessible bridge to improving the efficiency of cancer drug discovery and clinical therapeutic development. Canine tumors share many biological, genetic, and histologic features with their human tumor counterparts, and most importantly, retain the complexities of naturally occurring drug resistance, metastasis, and tumor-host immune interactions, all of which are difficult to recapitulate in induced or genetically engineered murine tumor models. The utility of canine models has been particularly apparent in sarcoma research, where the increased incidence of sarcomas in dogs as compared to people has facilitated comparative research resulting in treatment advances benefitting both species. Although there is an increasing awareness of the advantages in using spontaneous canine sarcoma models for research, these models remain underutilized, in part due to a lack of more permanent institutional and cross-institutional infrastructure to support partnerships between veterinary and human clinician-scientists. In this review, we provide an updated overview of historical and current applications of spontaneously occurring canine tumor models in sarcoma research, with particular attention to knowledge gaps, limitations, and growth opportunities within these applications. Furthermore, we propose considerations for working within existing veterinary translational and comparative oncology research infrastructures to maximize the benefit of partnerships between veterinary and human biomedical researchers within and across institutions to improve the utility and application of spontaneous canine sarcomas in translational oncology research.

Advances in the Application of Preclinical Models in Photodynamic Therapy for Tumor: A Narrative Review

Photodynamic therapy (PDT) is a non-invasive laser light local treatment that has been utilized in the management of a wide variety of solid tumors. Moreover, the evaluation of efficacy, adverse reactions, the development of new photosensitizers and the latest therapeutic regimens are inseparable from the preliminary exploration in preclinical studies. Therefore, our aim was to better comprehend the characteristics and limitations of these models and to provide a reference for related research.

METHODS

We searched the databases, including PubMed, Web of Science and Scopus for the past 25 years of original research articles on the feasibility of PDT in tumor treatment based on preclinical experiments and animal models. We provided insights into inclusion and exclusion criteria and ultimately selected 40 articles for data synthesis.

RESULTS

After summarizing and comparing the methods and results of these studies, the experimental model selection map was drawn. There are 7 main preclinical models, which are used for different research objectives according to their characteristics.

CONCLUSIONS

Based on this narrative review, preclinical experimental models are crucial to the development and promotion of PDT for tumors. The traditional animal models have some limitations, and the emergence of organoids may be a promising new insight.

Predictive validity in drug discovery: what it is, why it matters and how to improve it

Successful drug discovery is like finding oases of safety and efficacy in chemical and biological deserts. Screens in disease models, and other decision tools used in drug research and development (R&D), point towards oases when they score therapeutic candidates in a way that correlates with clinical utility in humans. Otherwise, they probably lead in the wrong direction. This line of thought can be quantified by using decision theory, in which 'predictive validity' is the correlation coefficient between the output of a decision tool and clinical utility across therapeutic candidates. Analyses based on this approach reveal that the detectability of good candidates is extremely sensitive to predictive validity, because the deserts are big and oases small. Both history and decision theory suggest that predictive validity is under-managed in drug R&D, not least because it is so hard to measure before projects succeed or fail later in the process. This article explains the influence of predictive validity on R&D productivity and discusses methods to evaluate and improve it, with the aim of supporting the application of more effective decision tools and catalysing investment in their creation.

Lineage tracing reveals the phylodynamics, plasticity, and paths of tumor evolution

Tumor evolution is driven by the progressive acquisition of genetic and epigenetic alterations that enable uncontrolled growth and expansion to neighboring and distal tissues. The study of phylogenetic relationships between cancer cells provides key insights into these processes. Here, we introduced an evolving lineage-tracing system with a single-cell RNA-seq readout into a mouse model of Kras;Trp53(KP)-driven lung adenocarcinoma and tracked tumor evolution from single-transformed cells to metastatic tumors at unprecedented resolution. We found that the loss of the initial, stable alveolar-type2-like state was accompanied by a transient increase in plasticity. This was followed by the adoption of distinct transcriptional programs that enable rapid expansion and, ultimately, clonal sweep of stable subclones capable of metastasizing. Finally, tumors develop through stereotypical evolutionary trajectories, and perturbing additional tumor suppressors accelerates progression by creating novel trajectories. Our study elucidates the hierarchical nature of tumor evolution and, more broadly, enables in-depth studies of tumor progression.

A state-of-the-art review of stratified medicine in cancer: towards a future precision medicine strategy in cancer

BACKGROUND

Building on the success of targeted therapy in certain well-defined cancer genotypes, three platform studies-NCI-MATCH, LUNG-MAP and The National Lung Matrix Trial (NLMT)-have attempted to discover new genotype-matched therapies for people with cancer.

PATIENTS AND METHODS

We review the outputs from these platform studies. This review led us to propose a series of recommendations and considerations that we hope will inform future precision medicine programmes in cancer.

RESULTS

The three studies collectively screened over 13 000 patients. Across 37 genotype-matched cohorts, there have been 66/875 responders, with an overall response rate of 7.5%. Targeting copy number gain yielded 5/199 responses across nine biomarker-drug matched cohorts, with a response rate of 2.5%.

CONCLUSIONS

The majority of these studies used single-agent targeted therapies. Whilst preclinical data can suggest rational combination treatment to reverse adaptive resistance or block parallel activated pathways, there is an essential need for accurate modelling of the toxicity-activity trade-off of combinations. Agent selection is often suboptimal; dose expansion should only be carried out with agents with clear clinical proof of mechanism and high target selectivity. Targeting copy number change has been disappointing; it is crucial to define the drivers on shared amplicons that include the targeted aberration. Maximising outcomes with currently available targeted therapies requires moving towards a more contextualised stratified medicine acknowledging the criticality of the genomic, transcriptional and immunological context on which the targeted aberration is inscribed. Genomic complexity and instability is likely to be a leading cause of targeted therapy failure in genomically complex cancers. Preclinical models must be developed that more accurately capture the genomic complexity of human disease. The degree of attrition of studies carried out after standard-of-care therapy suggests that serious efforts be made to develop a suite of precision medicine studies in the minimal residual disease setting.

Shifting the focus of zebrafish toward a model of the tumor microenvironment

Cancer cells exist in a complex ecosystem with numerous other cell types in the tumor microenvironment (TME). The composition of this tumor/TME ecosystem will vary at each anatomic site and affects phenotypes such as initiation, metastasis, and drug resistance. A mechanistic understanding of the large number of cell-cell interactions between tumor and TME requires models that allow us to both characterize as well as genetically perturb this complexity. Zebrafish are a model system optimized for this problem, because of the large number of existing cell-type-specific drivers that can label nearly any cell in the TME. These include stromal cells, immune cells, and tissue resident normal cells. These cell-type-specific promoters/enhancers can be used to drive fluorophores to facilitate imaging and also CRISPR cassettes to facilitate perturbations. A major advantage of the zebrafish is the ease by which large numbers of TME cell types can be studied at once, within the same animal. While these features make the zebrafish well suited to investigate the TME, the model has important limitations, which we also discuss. In this review, we describe the existing toolset for studying the TME using zebrafish models of cancer and highlight unique biological insights that can be gained by leveraging this powerful resource.