Redefining the relevance of established cancer cell lines to the study of mechanisms of clinical anti-cancer drug resistance

Preclinical models for bladder cancer therapy research

PURPOSE OF REVIEW

Bladder cancer (BC) is a highly heterogenous disease comprising tumours of various molecular subtypes and histologic variants. This heterogeneity represents a major challenge for the development of novel therapeutics. Preclinical models that closely mimic in vivo tumours and reflect their diverse biology are indispensable for the identification of therapies with specific activity in various BC subtypes. In this review, we summarize efforts and progress made in this context during the last 24 months.

RECENT FINDINGS

In recent years, one main focus was laid on the development of patient-derived BC models. Patient-derived organoids (PDOs) and patient-derived xenografts (PDXs) were demonstrated to widely recapitulate the molecular and histopathological characteristics, as well as the drug response profiles of the corresponding tumours of origin. These models, thus, represent promising tools for drug development and personalized medicine. Besides PDXs, syngenic in vivo models are of growing importance. Since these models are generated using immunocompetent hosts, they can, amongst others, be used to develop novel immunotherapeutics and to evaluate the impact of the immune system on drug response and resistance.

SUMMARY

In the past two years, various in vivo and in vitro models closely recapitulating the biology and heterogeneity of human bladder tumours were developed.

Mouse Models for Head and Neck Squamous Cell Carcinoma

The prognosis and survival rate of head and neck squamous cell carcinoma (HNSCC) have remained unchanged for years, and the pathogenesis of HNSCC is still not fully understood, necessitating further research. An ideal animal model that accurately replicates the complex microenvironment of HNSCC is urgently needed. Among all the animal models for preclinical cancer research, tumor-bearing mouse models are the best known and widely used due to their high similarity to humans. Currently, mouse models for HNSCC can be broadly categorized into chemical-induced models, genetically engineered mouse models (GEMMs), and transplanted mouse models, each with its distinct advantages and limitations. In chemical-induced models, the carcinogen spontaneously initiates tumor formation through a multistep process. The resemblance of this model to human carcinogenesis renders it an ideal preclinical platform for studying HNSCC initiation and progression from precancerous lesions. The major drawback is that these models are time-consuming and, like human cancer, unpredictable in terms of timing, location, and number of lesions. GEMMs involve transgenic and knockout mice with gene modifications, leading to malignant transformation within a tumor microenvironment that recapitulates tumorigenesis in vivo, including their interaction with the immune system. However, most HNSCC GEMMs exhibit low tumor incidence and limited prognostic significance when translated to clinical studies. Transplanted mouse models are the most widely used in cancer research due to their consistency, availability, and efficiency. Based on the donor and recipient species matching, transplanted mouse models can be divided into xenografts and syngeneic models. In the latter, transplanted cells and host are from the same strain, making syngeneic models relevant to study functional immune system. In this review, we provide a comprehensive summary of the characteristics, establishment methods, and potential applications of these different HNSCC mouse models, aiming to assist researchers in choosing suitable animal models for their research.

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.

Recent advances in breast cancer cell line research

Breast cancer, a formidable global health challenge, needs continuous translational research to understand the complexity of mechanisms and improve therapeutic and diagnostic strategies. Breast cancer cell lines are of paramount importance as they significantly contribute to the initial stage of research to understand cancer biology. This review provides insights into targeted therapies and immunotherapies that have emerged using in vitro models and microbiome analysis. It focuses on therapeutic development using cell lines and the limitations of tumor heterogeneity and microenvironment. We explore the evolving landscape of breast cancer cell lines from two-dimensional (2-D) cultures to patient-derived xenograft (PDX) models advancing both fundamental and translational research. Patient-derived xenografts, cell line-derived xenografts (CDX), three-dimensional (3-D) cultures, organoids, and circulating tumor cells (CTC) models provide promising alternatives that capture the intricacies of the tumor microenvironment. This review bridges the gap between traditional cell lines and newer developments exploring the therapeutic and diagnostic advancements and needs for cell lines to expedite the progress in breast cancer research and treatment.

Orthotopic and metastatic tumour models in preclinical cancer research

Mouse models of disease play a pivotal role at all stages of cancer drug development. Cell-line derived subcutaneous tumour models are predominant in early drug discovery, but there is growing recognition of the importance of the more complex orthotopic and metastatic tumour models for understanding both target biology in the correct tissue context, and the impact of the tumour microenvironment and the immune system in responses to treatment. The aim of this review is to highlight the value that orthotopic and metastatic models bring to the study of tumour biology and drug development while pointing out those models that are most likely to be encountered in the literature. Important developments in orthotopic models, such as the increasing use of early passage patient material (PDXs, organoids) and humanised mouse models are discussed, as these approaches have the potential to increase the predictive value of preclinical studies, and ultimately improve the success rate of anticancer drugs in clinical trials.

Microphysiological systems as models for immunologically 'cold' tumors

The tumor microenvironment (TME) is a diverse milieu of cells including cancerous and non-cancerous cells such as fibroblasts, pericytes, endothelial cells and immune cells. The intricate cellular interactions within the TME hold a central role in shaping the dynamics of cancer progression, influencing pivotal aspects such as tumor initiation, growth, invasion, response to therapeutic interventions, and the emergence of drug resistance. In immunologically 'cold' tumors, the TME is marked by a scarcity of infiltrating immune cells, limited antigen presentation in the absence of potent immune-stimulating signals, and an abundance of immunosuppressive factors. While strategies targeting the TME as a therapeutic avenue in 'cold' tumors have emerged, there is a pressing need for novel approaches that faithfully replicate the complex cellular and non-cellular interactions in order to develop targeted therapies that can effectively stimulate immune responses and improve therapeutic outcomes in patients. Microfluidic devices offer distinct advantages over traditional in vitro 3D co-culture models and in vivo animal models, as they better recapitulate key characteristics of the TME and allow for precise, controlled insights into the dynamic interplay between various immune, stromal and cancerous cell types at any timepoint. This review aims to underscore the pivotal role of microfluidic systems in advancing our understanding of the TME and presents current microfluidic model systems that aim to dissect tumor-stromal, tumor-immune and immune-stromal cellular interactions in various 'cold' tumors. Understanding the intricacies of the TME in 'cold' tumors is crucial for devising effective targeted therapies to reinvigorate immune responses and overcome the challenges of current immunotherapy approaches.

NMR Metabolomics of Primary Ovarian Cancer Cells in Comparison to Established Cisplatin-Resistant and -Sensitive Cell Lines

Cancer cell lines are frequently used in metabolomics, such as in vitro tumor models. In particular, A2780 cells are commonly used as a model for ovarian cancer to evaluate the effects of drug treatment. Here, we compare the NMR metabolomics profiles of A2780 and cisplatin-resistant A2780 cells with those of cells derived from 10 patients with high-grade serous ovarian carcinoma (collected during primary cytoreduction before any chemotherapeutic treatment). Our analysis reveals a substantial similarity among all primary cells but significant differences between them and both A2780 and cisplatin-resistant A2780 cells. Notably, the patient-derived cells are closer to the resistant A2780 cells when considering the exo-metabolome, whereas they are essentially equidistant from A2780 and A2780-resistant cells in terms of the endo-metabolome. This behavior results from dissimilarities in the levels of several metabolites attributable to the differential modulation of underlying biochemical pathways. The patient-derived cells are those with the most pronounced glycolytic phenotype, whereas A2780-resistant cells mainly diverge from the others due to alterations in a few specific metabolites already known as markers of resistance.

Ex vivo discovery of synergistic drug combinations for hematologic malignancies

Combination therapies have improved outcomes for patients with acute myeloid leukemia (AML). However, these patients still have poor overall survival. Although many combination therapies are identified with high-throughput screening (HTS), these approaches are constrained to disease models that can be grown in large volumes (e.g., immortalized cell lines), which have limited translational utility. To identify more effective and personalized treatments, we need better strategies for screening and exploring potential combination therapies. Our objective was to develop an HTS platform for identifying effective combination therapies with highly translatable ex vivo disease models that use size-limited, primary samples from patients with leukemia (AML and myelodysplastic syndrome). We developed a system, ComboFlow, that comprises three main components: MiniFlow, ComboPooler, and AutoGater. MiniFlow conducts ex vivo drug screening with a miniaturized flow-cytometry assay that uses minimal amounts of patient sample to maximize throughput. ComboPooler incorporates computational methods to design efficient screens of pooled drug combinations. AutoGater is an automated gating classifier for flow cytometry that uses machine learning to rapidly analyze the large datasets generated by the assay. We used ComboFlow to efficiently screen more than 3000 drug combinations across 20 patient samples using only 6 million cells per patient sample. In this screen, ComboFlow identified the known synergistic combination of bortezomib and panobinostat. ComboFlow also identified a novel drug combination, dactinomycin and fludarabine, that synergistically killed leukemic cells in 35 % of AML samples. This combination also had limited effects in normal, hematopoietic progenitors. In conclusion, ComboFlow enables exploration of massive landscapes of drug combinations that were previously inaccessible in ex vivo models. We envision that ComboFlow can be used to discover more effective and personalized combination therapies for cancers amenable to ex vivo models.

Consistency between Primary Uterine Corpus Malignancies and Their Corresponding Patient-Derived Xenograft Models

Patient-derived xenograft (PDX) models retain the characteristics of tumors and are useful tools for personalized therapy and translational research. In this study, we aimed to establish PDX models for uterine corpus malignancies (UC-PDX) and analyze their similarities. Tissue fragments obtained from 92 patients with uterine corpus malignancies were transplanted subcutaneously into immunodeficient mice. Histological and immunohistochemical analyses were performed to compare tumors of patients with PDX tumors. DNA and RNA sequencing were performed to validate the genetic profile. Furthermore, the RNA in extracellular vesicles (EVs) extracted from primary and PDX tumors was analyzed. Among the 92 cases, 52 UC-PDX models were established, with a success rate of 56.5%. The success rate depended on tumor histology and staging. The pathological and immunohistochemical features of primary and PDX tumors were similar. DNA sequencing revealed similarities in gene mutations between the primary and PDX tumors. RNA sequencing showed similarities in gene expressions between primary and PDX tumors. Furthermore, the RNA profiles of the EVs obtained from primary and PDX tumors were similar. As UC-PDX retained the pathological and immunohistochemical features and gene profiles of primary tumors, they may provide a platform for developing personalized medicine and translational research.

Promising preclinical patient-derived organoid (PDO) and xenograft (PDX) models in upper gastrointestinal cancers: progress and challenges

Gastrointestinal (GI) cancers (gastric cancer, oesophageal cancer, liver cancer, colorectal cancer, etc.) are the most common cancers with the highest morbidity and mortality in the world. The therapy for most GI cancers is difficult and is associated with a poor prognosis. In China, upper GI cancers, mainly gastric cancer (GC) and oesophageal cancer (EC), are very common due to Chinese people's characteristics, and more than half of patients are diagnosed with distant metastatic or locally advanced disease. Compared to other solid cancers, such as lung cancer and breast cancer, personalized therapies, especially targeted therapy and immunotherapy, in GC and EC are relatively lacking, leading to poor prognosis. For a long time, most studies were carried out by using in vitro cancer cell lines or in vivo cell line-derived xenograft models, which are unable to reproduce the characteristics of tumours derived from patients, leading to the possible misguidance of subsequent clinical validation. The patient-derived models represented by patient-derived organoid (PDO) and xenograft (PDX) models, known for their high preservation of patient tumour features, have emerged as a very popular platform that has been widely used in numerous studies, especially in the research and development of antitumour drugs and personalized medicine. Herein, based on some of the available published literature, we review the research and application status of PDO and PDX models in GC and EC, as well as detail their future challenges and prospects, to promote their use in basic and translational studies or personalized therapy.

Advances towards the use of gastrointestinal tumor patient-derived organoids as a therapeutic decision-making tool

In December 2022 the US Food and Drug Administration (FDA) removed the requirement that drugs in development must undergo animal testing before clinical evaluation, a declaration that now demands the establishment and verification of ex vivo preclinical models that closely represent tumor complexity and that can predict therapeutic response. Fortunately, the emergence of patient-derived organoid (PDOs) culture has enabled the ex vivo mimicking of the pathophysiology of human tumors with the reassembly of tissue-specific features. These features include histopathological variability, molecular expression profiles, genetic and cellular heterogeneity of parental tissue, and furthermore growing evidence suggests the ability to predict patient therapeutic response. Concentrating on the highly lethal and heterogeneous gastrointestinal (GI) tumors, herein we present the state-of-the-art and the current methodology of PDOs. We highlight the potential additions, improvements and testing required to allow the ex vivo of study the tumor microenvironment, as well as offering commentary on the predictive value of clinical response to treatments such as chemotherapy and immunotherapy.

Preclinical Models of Visceral Sarcomas

Visceral sarcomas are a rare malignant subgroup of soft tissue sarcomas (STSs). STSs, accounting for 1% of all adult tumors, are derived from mesenchymal tissues and exhibit a wide heterogeneity. Their rarity and the high number of histotypes hinder the understanding of tumor development mechanisms and negatively influence clinical outcomes and treatment approaches. Although some STSs (~20%) have identifiable genetic markers, as specific mutations or translocations, most are characterized by complex genomic profiles. Thus, identification of new therapeutic targets and development of personalized therapies are urgent clinical needs. Although cell lines are useful for preclinical investigations, more reliable preclinical models are required to develop and test new potential therapies. Here, we provide an overview of the available in vitro and in vivo models of visceral sarcomas, whose gene signatures are still not well characterized, to highlight current challenges and provide insights for future studies.

Pan-Cancer Drug Response Prediction Using Integrative Principal Component Regression

The pursuit of precision oncology heavily relies on large-scale genomic and pharmacological data garnered from preclinical cancer model systems such as cell lines. While cell lines are instrumental in understanding the interplay between genomic programs and drug response, it well-established that they are not fully representative of patient tumors. Development of integrative methods that can systematically assess the commonalities between patient tumors and cell-lines can help bridge this gap. To this end, we introduce the Integrative Principal Component Regression (iPCR) model which uncovers both joint and model-specific structured variations in the genomic data of cell lines and patient tumors through matrix decompositions. The extracted joint variation is then used to predict patient drug responses based on the pharmacological data from preclinical models. Moreover, the interpretability of our model allows for the identification of key driver genes and pathways associated with the treatment-specific response in patients across multiple cancers. We demonstrate that the outputs of the iPCR model can assist in inferring both model-specific and shared co-expression networks between cell lines and patients. We show that iPCR performs favorably compared to competing approaches in predicting patient drug responses, in both simulation studies and real-world applications, in addition to identifying key genomic drivers of cancer drug responses.

Applications of organoid technology to brain tumors

Lacking appropriate model impedes basic and preclinical researches of brain tumors. Organoids technology applying on brain tumors enables great recapitulation of the original tumors. Here, we compared brain tumor organoids (BTOs) with common models including cell lines, tumor spheroids, and patient-derived xenografts. Different BTOs can be customized to research objectives and particular brain tumor features. We systematically introduce the establishments and strengths of four different BTOs. BTOs derived from patient somatic cells are suitable for mimicking brain tumors caused by germline mutations and abnormal neurodevelopment, such as the tuberous sclerosis complex. BTOs derived from human pluripotent stem cells with genetic manipulations endow for identifying and understanding the roles of oncogenes and processes of oncogenesis. Brain tumoroids are the most clinically applicable BTOs, which could be generated within clinically relevant timescale and applied for drug screening, immunotherapy testing, biobanking, and investigating brain tumor mechanisms, such as cancer stem cells and therapy resistance. Brain organoids co-cultured with brain tumors (BO-BTs) own the greatest recapitulation of brain tumors. Tumor invasion and interactions between tumor cells and brain components could be greatly explored in this model. BO-BTs also offer a humanized platform for testing the therapeutic efficacy and side effects on neurons in preclinical trials. We also introduce the BTOs establishment fused with other advanced techniques, such as 3D bioprinting. So far, over 11 brain tumor types of BTOs have been established, especially for glioblastoma. We conclude BTOs could be a reliable model to understand brain tumors and develop targeted therapies.

Advances of Osteosarcoma Models for Drug Discovery and Precision Medicine

The management of osteosarcoma (OS) patients presents a significant clinical challenge. Despite progress in conventional and targeted therapies, the survival rate of OS patients remains limited largely due to therapy resistance and the high metastatic potential of the disease. OS models that accurately reflect the fundamental characteristics are vital to the innovation and validation of effective therapies. This review provides an insight into the advances and challenges in OS drug development, focusing on various preclinical models, including cell lines, 3D culture models, murine models, and canine models. The relevance, strengths, and limitations of each model in OS research are explored. In particular, we highlight a range of potential therapeutics identified through these models. These instances of successful drug development represent promising pathways for personalized OS treatment.

The development of a rapid patient-derived xenograft model to predict chemotherapeutic drug sensitivity/resistance in malignant glial tumors

BACKGROUND

High-grade gliomas (HGG) are aggressive brain tumors associated with short median patient survival and limited response to therapies, driving the need to develop tools to improve patient outcomes. Patient-derived xenograft (PDX) models, such as mouse PDX, have emerged as potential Avatar platforms for personalized oncology approaches, but the difficulty for some human grafts to grow successfully and the long time required for mice to develop tumors preclude their use for HGG.

METHODS

We used a rapid and efficient ex-ovo chicken embryo chorioallantoic membrane (CAM) culture system to evaluate the efficacy of oncologic drug options for HGG patients.

RESULTS

Implantation of fresh glioma tissue fragments from 59 of 60 patients, that include difficult-to-grow IDH-mutated samples, successfully established CAM tumor xenografts within 7 days, with a tumor take rate of 98.3%. These xenografts faithfully recapitulate the histological and molecular characteristics of the primary tumor, and the ability of individual fragments to form tumors was predictive of poor patient prognosis. Treatment of drug-sensitive or drug-resistant xenografts indicates that the CAM-glioma assay enables testing tumor sensitivity to temozolomide and carboplatin at doses consistent with those administered to patients. In a proof-of-concept study involving 14 HGG patients, we observed a correlation of 100% between the CAM xenograft response to temozolomide or carboplatin and the clinical response of patients.

CONCLUSION

The CAM-glioma model is a fast and reliable assay that has the potential to serve as a complementary model to drug discovery and a real-time Avatar platform to predict the best treatment for HGG patients.

Preclinical tumor mouse models for studying esophageal cancer

Preclinical models are extensively employed in cancer research because they can be manipulated in terms of their environment, genome, molecular biology, organ systems, and physical activity to mimic human behavior and conditions. The progress made in in vivo cancer research has resulted in significant advancements, enabling the creation of spontaneous, metastatic, and humanized mouse models. Most recently, the remarkable and extensive developments in genetic engineering, particularly the utilization of CRISPR/Cas9, transposable elements, epigenome modifications, and liquid biopsies, have further facilitated the design and development of numerous mouse models for studying cancer. In this review, we have elucidated the production and usage of current mouse models, such as xenografts, chemical-induced models, and genetically engineered mouse models (GEMMs), for studying esophageal cancer. Additionally, we have briefly discussed various gene-editing tools that could potentially be employed in the future to create mouse models specifically for esophageal cancer research.

New mechanisms of multidrug resistance: an introduction to the Cancer Drug Resistance special collection

Cancer Drug Resistance publishes contributions to understanding the biology and consequences of mechanisms that interfere with successful treatment of cancer. Since virtually all patients who die of metastatic cancer have multidrug-resistant tumors, improved treatment will require an understanding of the mechanisms of resistance to design therapies that circumvent these mechanisms, exploit these mechanisms, or inactivate these multidrug resistance mechanisms. One example of a resistance mechanism is the expression of ATP-binding cassette efflux pumps, but unfortunately, inhibition of these transporters has not proved to be the solution to overcome multidrug resistance in cancer. Other mechanisms that confer multidrug resistance, and the confluence of multiple different mechanisms (multifactorial multidrug resistance) have been identified, and it is the goal of this Special Collection to expand this catalog of potential multidrug resistance mechanisms, to explore novel ways to overcome resistance, and to present thoughtful reviews on the problem of multidrug resistance in cancer.

On-chip modeling of tumor evolution: Advances, challenges and opportunities

Tumor evolution is the accumulation of various tumor cell behaviors from tumorigenesis to tumor metastasis and is regulated by the tumor microenvironment (TME). However, the mechanism of solid tumor progression has not been completely elucidated, and thus, the development of tumor therapy is still limited. Recently, Tumor chips constructed by culturing tumor cells and stromal cells on microfluidic chips have demonstrated great potential in modeling solid tumors and visualizing tumor cell behaviors to exploit tumor progression. Herein, we review the methods of developing engineered solid tumors on microfluidic chips in terms of tumor types, cell resources and patterns, the extracellular matrix and the components of the TME, and summarize the recent advances of microfluidic chips in demonstrating tumor cell behaviors, including proliferation, epithelial-to-mesenchymal transition, migration, intravasation, extravasation and immune escape of tumor cells. We also outline the combination of tumor organoids and microfluidic chips to elaborate tumor organoid-on-a-chip platforms, as well as the practical limitations that must be overcome.

Three-Dimensional Cell Cultures: The Bridge between In Vitro and In Vivo Models

Although historically, the traditional bidimensional in vitro cell system has been widely used in research, providing much fundamental information regarding cellular functions and signaling pathways as well as nuclear activities, the simplicity of this system does not fully reflect the heterogeneity and complexity of the in vivo systems. From this arises the need to use animals for experimental research and in vivo testing. Nevertheless, animal use in experimentation presents various aspects of complexity, such as ethical issues, which led Russell and Burch in 1959 to formulate the 3R (Replacement, Reduction, and Refinement) principle, underlying the urgent need to introduce non-animal-based methods in research. Considering this, three-dimensional (3D) models emerged in the scientific community as a bridge between in vitro and in vivo models, allowing for the achievement of cell differentiation and complexity while avoiding the use of animals in experimental research. The purpose of this review is to provide a general overview of the most common methods to establish 3D cell culture and to discuss their promising applications. Three-dimensional cell cultures have been employed as models to study both organ physiology and diseases; moreover, they represent a valuable tool for studying many aspects of cancer. Finally, the possibility of using 3D models for drug screening and regenerative medicine paves the way for the development of new therapeutic opportunities for many diseases.

Modeling the Tumor Microenvironment and Cancer Immunotherapy in Next-Generation Humanized Mice

Cancer immunotherapy has brought significant clinical benefits to numerous patients with malignant disease. However, only a fraction of patients experiences complete and durable responses to currently available immunotherapies. This highlights the need for more effective immunotherapies, combination treatments and predictive biomarkers. The molecular properties of a tumor, intratumor heterogeneity and the tumor immune microenvironment decisively shape tumor evolution, metastasis and therapy resistance and are therefore key targets for precision cancer medicine. Humanized mice that support the engraftment of patient-derived tumors and recapitulate the human tumor immune microenvironment of patients represent a promising preclinical model to address fundamental questions in precision immuno-oncology and cancer immunotherapy. In this review, we provide an overview of next-generation humanized mouse models suitable for the establishment and study of patient-derived tumors. Furthermore, we discuss the opportunities and challenges of modeling the tumor immune microenvironment and testing a variety of immunotherapeutic approaches using human immune system mouse models.

Towards an optimal model for gastric cancer peritoneal metastasis: current challenges and future directions

Peritoneal metastasis is a challenging aspect of clinical practice for gastric cancer. Animal models are crucial in understanding molecular mechanisms, assessing drug efficacy, and conducting clinical intervention studies, including those related to gastric cancer peritoneal metastasis. Unlike other xenograft models, peritoneal metastasis models should not only present tumor growth at the transplant site, but also recapitulate tumor cell metastasis in the abdominal cavity. Developing a reliable model of gastric cancer peritoneal metastasis involves several technical aspects, such as the selection of model animals, source of xenograft tumors, technology of transplantation, and dynamic monitoring of the tumor progression. To date, challenges remain in developing a reliable model that can completely recapitulate peritoneal metastasis. Thus, this review aims to summarize the techniques and strategies used to establish animal models of gastric cancer peritoneal metastasis, providing a reference for future model establishment.

Establishment of humanized mice and its application progress in cancer immunotherapy

The current high prevalence of malignant tumors has attracted considerable attention, and treating advanced malignancies is becoming increasingly difficult. Although immunotherapy is a hopeful alternative, it is effective in only a few people. Thus, development of preclinical animal models is needed. Humanized xenotransplantation mouse models can help with selecting treatment protocols, evaluating curative effects and assessing prognosis. This review discusses the establishment of humanized mouse models and their application prospects in cancer immunotherapy to identify tailored therapies for individual patients.

3D bioprinting tumor models mimic the tumor microenvironment for drug screening

Cancer is a severe threat to human life and health and represents the main cause of death globally. Drug therapy is one of the primary means of treating cancer; however, most anticancer medications do not proceed beyond preclinical testing because the conditions of actual human tumors are not effectively mimicked by traditional tumor models. Hence, bionic in vitro tumor models must be developed to screen for anticancer drugs. Three-dimensional (3D) bioprinting technology can produce structures with built-in spatial and chemical complexity and models with accurately controlled structures, a homogeneous size and morphology, less variation across batches, and a more realistic tumor microenvironment (TME). This technology can also rapidly produce such models for high-throughput anticancer medication testing. This review describes 3D bioprinting methods, the use of bioinks in tumor models, and in vitro tumor model design strategies for building complex tumor microenvironment features using biological 3D printing technology. Moreover, the application of 3D bioprinting in vitro tumor models in drug screening is also discussed.

Proteomic Characterization of Acute Myeloid Leukemia for Precision Medicine

Acute myeloid leukemia (AML) is a highly heterogeneous cancer of the hematopoietic system with no cure for most patients. In addition to chemotherapy, treatment options for AML include recently approved therapies that target proteins with roles in AML pathobiology, such as FLT3, BLC2, and IDH1/2. However, due to disease complexity, these therapies produce very diverse responses, and survival rates are still low. Thus, despite considerable advances, there remains a need for therapies that target different aspects of leukemic biology and for associated biomarkers that define patient populations likely to respond to each available therapy. To meet this need, drugs that target different AML vulnerabilities are currently in advanced stages of clinical development. Here, we review proteomics and phosphoproteomics studies that aimed to provide insights into AML biology and clinical disease heterogeneity not attainable with genomic approaches. To place the discussion in context, we first provide an overview of genetic and clinical aspects of the disease, followed by a summary of proteins targeted by compounds that have been approved or are under clinical trials for AML treatment and, if available, the biomarkers that predict responses. We then discuss proteomics and phosphoproteomics studies that provided insights into AML pathogenesis, from which potential biomarkers and drug targets were identified, and studies that aimed to rationalize the use of synergistic drug combinations. When considered as a whole, the evidence summarized here suggests that proteomics and phosphoproteomics approaches can play a crucial role in the development and implementation of precision medicine for AML patients.

Criteria for preclinical models of cholangiocarcinoma: scientific and medical relevance

Cholangiocarcinoma (CCA) is a rare malignancy that develops at any point along the biliary tree. CCA has a poor prognosis, its clinical management remains challenging, and effective treatments are lacking. Therefore, preclinical research is of pivotal importance and necessary to acquire a deeper understanding of CCA and improve therapeutic outcomes. Preclinical research involves developing and managing complementary experimental models, from in vitro assays using primary cells or cell lines cultured in 2D or 3D to in vivo models with engrafted material, chemically induced CCA or genetically engineered models. All are valuable tools with well-defined advantages and limitations. The choice of a preclinical model is guided by the question(s) to be addressed; ideally, results should be recapitulated in independent approaches. In this Consensus Statement, a task force of 45 experts in CCA molecular and cellular biology and clinicians, including pathologists, from ten countries provides recommendations on the minimal criteria for preclinical models to provide a uniform approach. These recommendations are based on two rounds of questionnaires completed by 35 (first round) and 45 (second round) experts to reach a consensus with 13 statements. An agreement was defined when at least 90% of the participants voting anonymously agreed with a statement. The ultimate goal was to transfer basic laboratory research to the clinics through increased disease understanding and to develop clinical biomarkers and innovative therapies for patients with CCA.

Anti-cancer effect of afatinib, dual inhibitor of HER2 and EGFR, on novel mutation HER2 E401G in models of patient-derived cancer

BACKGROUND

Precision medicine with gene panel testing based on next-generation sequencing for patients with cancer is being used increasingly in clinical practice. HER2, which encodes the human epidermal growth factor receptor 2 (HER2), is a potentially important driver gene. However, therapeutic strategies aimed at mutations in the HER2 extracellular domain have not been clarified. We therefore investigated the effect of EGFR co-targeted therapy with HER2 on patient-derived cancer models with the HER2 extracellular domain mutation E401G, based on our previous findings that this mutation has an epidermal growth factor receptor (EGFR)-mediated activation mechanism.

METHODS

We generated a xenograft (PDX) and a cancer tissue-originated spheroid (CTOS) from a patient's cancer containing an amplified HER2 E401G mutation. With these platforms, we compared the efficacy of afatinib, a tyrosine kinase inhibitor having anti-HER2 and anti-EGFR activity, with two other therapeutic options: lapatinib, which has similar properties but weaker EGFR inhibition, and trastuzumab plus pertuzumab, for which evidence exists of treatment efficacy against cancers with wild-type HER2 amplification. Similar experiments were also performed with H2170, a cell line with wild-type HER2 amplification, to contrast the characteristics of these drug's efficacies against HER2 E401G.

RESULTS

We confirmed that PDX and CTOS retained morphological and immunohistochemical characteristics and HER2 gene profiles of the original tumor. In both PDX and CTOS, afatinib reduced tumor size more than lapatinib or trastuzumab plus pertuzumab. In addition, afatinib treatment resulted in a statistically significant reduction in HER2 copy number at the end of treatment. On the other hand, in H2170 xenografts with wild-type HER2 amplification, trastuzumab plus pertuzumab was most effective.

CONCLUSIONS

Afatinib, a dual inhibitor of HER2 and EGFR, showed a promising effect on cancers with amplified HER2 E401G, which have an EGFR-mediated activation mechanism. Analysis of the activation mechanisms of mutations and development of therapeutic strategies based on those mechanisms are critical in precision medicine for cancer patients.

An in vitro model to monitor natural killer cell effector functions against breast cancer cells derived from human tumor tissue

Adoptive natural killer (NK) cell-based immunotherapy poses a promising treatment approach in cancer. Despite minimal toxicities associated with NK cell infusion, the potential of NK cell therapy is inhibited by the immunosuppressive tumor microenvironment (TME). Multiple approaches to improve anti-cancer NK cell effector functions are being investigated. While much of this preclinical research is currently performed with commercially available tumor cell lines, this approach lacks the influence of the TME and heterogeneity of the primary tumor in patients. Here, we describe a comprehensive protocol for NK cell cytotoxicity- and degranulation assays against tumor cells derived from primary breast cancer tissue. Treatments to boost NK cell anti-tumor effector functions can be implemented in this model. Moreover, by using culture supernatants in follow up assays or by including additional cell types in the co-culture system, other NK cell effector mechanisms that further orchestrate innate and adaptive immunity could be studied.

Patient-derived models: Advanced tools for precision medicine in neuroblastoma

Neuroblastoma is a childhood cancer derived from the sympathetic nervous system. High-risk neuroblastoma patients have a poor overall survival and account for ~15% of childhood cancer deaths. There is thus a need for clinically relevant and authentic models of neuroblastoma that closely resemble the human disease to further interrogate underlying mechanisms and to develop novel therapeutic strategies. Here we review recent developments in patient-derived neuroblastoma xenograft models and in vitro cultures. These models can be used to decipher mechanisms of metastasis and treatment resistance, for drug screening, and preclinical drug testing. Patient-derived neuroblastoma models may also provide useful information about clonal evolution, phenotypic plasticity, and cell states in relation to neuroblastoma progression. We summarize current opportunities for, but also barriers to, future model development and application. Integration of patient-derived models with patient data holds promise for the development of precision medicine treatment strategies for children with high-risk neuroblastoma.

Antioxidant and Anti-Inflammatory Effects of 6,3',4´- and 7,3´,4´-Trihydroxyflavone on 2D and 3D RAW264.7 Models

Dietary flavones 6,3´,4´-trihydroxyflavone (6,3´,4´-HOFL) and 7,3´,4´-trihydroxyflavone (7,3´,4´-HOFL) showed preliminary antioxidant and anti-inflammatory activities in a two-dimensional (2D) cell culture model. However, their action mechanisms remain unclear, and the anti-inflammatory activities have not been studied in a reliable three-dimensional (3D) cell model. Therefore, in the current study, the antioxidant potency was examined by their scavenging ability of cellular reactive oxygen species. Anti-inflammatory activities were examined via their inhibitory effects on inflammatory mediators in vitro on 2D and 3D macrophage models, and their mechanisms were determined through transcriptome. In the 3D macrophages, two flavones were less bioactive than they were in 2D macrophages, but they both significantly suppressed the overexpression of proinflammatory mediators in two cell models. The divergent position of the hydroxyl group on the A ring resulted in activity differences. Compared to 6,3´,4´-HOFL, 7,3´,4´-HOFL showed lower activity on NO, IL-1β suppression, and c-Src binding (IC₅₀: 12.0 and 20.9 µM) but higher ROS-scavenging capacity (IC₅₀: 3.20 and 2.71 µM) and less cytotoxicity. In addition to the IL-17 and TNF pathways of 6,3´,4´-HOFL, 7,3´,4´-HOFL also exerted anti-inflammatory activity through JAK-STAT, as indicated by the RNA-sequencing results. Two flavones showed prominent antioxidant and anti-inflammatory activities on 2D and 3D models.

Application status and future prospects of the PDX model in lung cancer

Lung cancer is one of the most prevalent, fatal, and highly heterogeneous diseases that, seriously threaten human health. Lung cancer is primarily caused by the aberrant expression of multiple genes in the cells. Lung cancer treatment options include surgery, radiation, chemotherapy, targeted therapy, and immunotherapy. In recent decades, significant progress has been made in developing therapeutic agents for lung cancer as well as a biomarker for its early diagnosis. Nonetheless, the alternative applications of traditional pre-clinical models (cell line models) for diagnosis and prognosis prediction are constrained by several factors, including the lack of microenvironment components necessary to affect cancer biology and drug response, and the differences between laboratory and clinical results. The leading reason is that substantial shifts accrued to cell biological behaviors, such as cell proliferative, metastatic, invasive, and gene expression capabilities of different cancer cells after decades of growing indefinitely in vitro. Moreover, the introduction of individualized treatment has prompted the development of appropriate experimental models. In recent years, preclinical research on lung cancer has primarily relied on the patient-derived tumor xenograft (PDX) model. The PDX provides stable models with recapitulate characteristics of the parental tumor such as the histopathology and genetic blueprint. Additionally, PDXs offer valuable models for efficacy screening of new cancer drugs, thus, advancing the understanding of tumor biology. Concurrently, with the heightened interest in the PDX models, potential shortcomings have gradually emerged. This review summarizes the significant advantages of PDXs over the previous models, their benefits, potential future uses and interrogating open issues.

Analogous comparison unravels heightened antiviral defense and boosted viral infection upon immunosuppression in bat organoids

Horseshoe bats host numerous SARS-related coronaviruses without overt disease signs. Bat intestinal organoids, a unique model of bat intestinal epithelium, allow direct comparison with human intestinal organoids. We sought to unravel the cellular mechanism(s) underlying bat tolerance of coronaviruses by comparing the innate immunity in bat and human organoids. We optimized the culture medium, which enabled a consecutive passage of bat intestinal organoids for over one year. Basal expression levels of IFNs and IFN-stimulated genes were higher in bat organoids than in their human counterparts. Notably, bat organoids mounted a more rapid, robust and prolonged antiviral defense than human organoids upon Poly(I:C) stimulation. TLR3 and RLR might be the conserved pathways mediating antiviral response in bat and human intestinal organoids. The susceptibility of bat organoids to a bat coronavirus CoV-HKU4, but resistance to EV-71, an enterovirus of exclusive human origin, indicated that bat organoids adequately recapitulated the authentic susceptibility of bats to certain viruses. Importantly, TLR3/RLR inhibition in bat organoids significantly boosted viral growth in the early phase after SARS-CoV-2 or CoV-HKU4 infection. Collectively, the higher basal expression of antiviral genes, especially more rapid and robust induction of innate immune response, empowered bat cells to curtail virus propagation in the early phase of infection.

Osteosarcoma

Osteosarcoma is the most common primary malignant tumour of the bone. Osteosarcoma incidence is bimodal, peaking at 18 and 60 years of age, and is slightly more common in males. The key pathophysiological mechanism involves several possible genetic drivers of disease linked to bone formation, causing malignant progression and metastasis. While there have been significant improvements in the outcome of patients with localized disease, with event-free survival outcomes exceeding 60%, in patients with metastatic disease, event-free survival outcomes remain poor at less than 30%. The suspicion of osteosarcoma based on radiographs still requires pathological evaluation of a bone biopsy specimen for definitive diagnosis and CT imaging of the chest should be performed to identify lung nodules. So far, population-based screening and surveillance strategies have not been implemented due to the rarity of osteosarcoma and the lack of reliable markers. Current screening focuses only on groups at high risk such as patients with genetic cancer predisposition syndromes. Management of osteosarcoma requires a multidisciplinary team of paediatric and medical oncologists, orthopaedic and general surgeons, pathologists, radiologists and specialist nurses. Survivors of osteosarcoma require specialized medical follow-up, as curative treatment consisting of chemotherapy and surgery has long-term adverse effects, which also affect the quality of life of patients. The development of osteosarcoma model systems and related research as well as the evaluation of new treatment approaches are ongoing to improve disease outcomes, especially for patients with metastases.

Precision oncology using ex vivo technology: a step towards individualised cancer care?

Despite advances in cancer genomics and the increased use of genomic medicine, metastatic cancer is still mostly an incurable and fatal disease. With diminishing returns from traditional drug discovery strategies, and high clinical failure rates, more emphasis is being placed on alternative drug discovery platforms, such as ex vivo approaches. Ex vivo approaches aim to embed biological relevance and inter-patient variability at an earlier stage of drug discovery, and to offer more precise treatment stratification for patients. However, these techniques also have a high potential to offer personalised therapies to patients, complementing and enhancing genomic medicine. Although an array of approaches are available to researchers, only a minority of techniques have made it through to direct patient treatment within robust clinical trials. Within this review, we discuss the current challenges to ex vivo approaches within clinical practice and summarise the contemporary literature which has directed patient treatment. Finally, we map out how ex vivo approaches could transition from a small-scale, predominantly research based technology to a robust and validated predictive tool. In future, these pre-clinical approaches may be integrated into clinical cancer pathways to assist in the personalisation of therapy choices and to hopefully improve patient experiences and outcomes.

Establishment and characterization of chemotherapy-enriched sphere-forming cells with stemness phenotypes as a new cell line (BAG50) of gastric carcinoma

Gastric cancer is a malignancy with a high mortality rate worldwide. Cancer stem cells (CSCs) are a small subpopulation of tumor cells that possess the tumor-initiating ability, self-renewal capacity, and high resistance to conventional therapies. Due to the diversity and complexity of human tumors, new cell lines are urgently needed to supply clinically and physiologically relevant cancer models. Here, we report establishing a novel cell line (BAG₅₀) with stemness properties. Chemotherapy-enriched sphere-forming cells with CSC properties isolated from a patient with GC were cultured in a serum-containing medium and passaged for up to 51 passages. The colony-forming ability and tumor-forming capacity of BAG₅₀ cells were evaluated in vitro and in vivo. mRNA upregulation of stemness-related transcriptional factors using real-time PCR as well as expression of CSC markers using flow cytometry was investigated. Finally, STR profiling and chromosome studies were performed. BAG₅₀ cells formed floating spheroid colonies in a serum-free medium. Subcutaneous injection of these cells generated xenograft tumors in nude mice. Pluripotency markers (SOX-2, OCT4, and Cripto-1) in them were upregulated compared with normal gastric cells. The majority of them expressed CSC markers of CD44, CD54, and EpCAM, and stemness marker of oct-4. STR profiling showed a unique DNA fingerprint. Karyotype also demonstrated multiple aneuploidies and chromosomal translocations. We suggested that the highly tumorigenic BAG₅₀ cell line with stem cell-like phenotypes may provide a valuable in vitro tool to support new diagnostic, prognostic, and predictive biomarkers as well as the development of more effective treatment strategies.

Harnessing preclinical models for the interrogation of ovarian cancer

Ovarian cancer (OC) is a heterogeneous malignancy with various etiology, histopathology, and biological feature. Despite accumulating understanding of OC in the post-genomic era, the preclinical knowledge still undergoes limited translation from bench to beside, and the prognosis of ovarian cancer has remained dismal over the past 30 years. Henceforth, reliable preclinical model systems are warranted to bridge the gap between laboratory experiments and clinical practice. In this review, we discuss the status quo of ovarian cancer preclinical models which includes conventional cell line models, patient-derived xenografts (PDXs), patient-derived organoids (PDOs), patient-derived explants (PDEs), and genetically engineered mouse models (GEMMs). Each model has its own strengths and drawbacks. We focus on the potentials and challenges of using these valuable tools, either alone or in combination, to interrogate critical issues with OC.

Large-scale transcriptomic analysis of coding and non-coding pathological biomarkers, associated with the tumor immune microenvironment of thyroid cancer and potential target therapy exploration

Papillary thyroid carcinoma (PTC) is the most prevalent endocrine malignancy with a steadily increasing global incidence in recent decades. The pathogenesis of PTC is poorly understood, and the present diagnostic protocols are deficient. Thus, identifying novel prognostic biomarkers to improve our understanding of the mechanisms of pathogenesis, diagnosis, and designing therapeutic strategies for PTC is crucial. In this study, we integrated 27 PTC transcriptomic datasets and identified overlapping differentially expressed genes (DEGs) and differentially expressed microRNAs, collectively known as thyroid tumor-enriched proteins (TTEPs), and TTEmiRs, respectively. Our integrated bioinformatics analysis revealed that TTEPs were associated with tumor stages, poor surgical outcomes, distant metastasis, and worse prognoses in PTC cohorts. In addition, TTEPs were found to be associated with tumor immune infiltrating cells and immunosuppressive phenotypes of PTC. Enrichment analysis suggested the association of TTEPs with epithelial-to-mesenchymal transition (EMT), cell-matrix remodeling, and transcriptional dysregulation, while the TTEmiRs (miR-146b-5p and miR-21-5p) were associated with the modulation of the immune response, EMT, migration, cellular proliferation, and stemness. Molecular docking simulations were performed to evaluate binding affinities between TTEPs and antrocinnamomin, antcin, and antrocin, the bioactive compounds from one of the most reputable Taiwan indigenous medicinal plants (Antrodia camphorata). Our results revealed that antcin exhibited higher binding efficacies toward FN1, ETV5, and NRCAM, whereas antrocin demonstrated the least. Among the targets, fibronectin (FN1) demonstrated high ligandability potential for the compounds whereas NRCAM demonstrated the least. Collectively, our results hinted at the potential of antcin for targeting TTEPs. In conclusion, this comprehensive bioinformatics analysis strongly suggested that TTEPs and TTEmiRs could be used as potential diagnostic biomarker signatures and be exploited as potential targets for therapeutics development.

Computational estimation of quality and clinical relevance of cancer cell lines

Immortal cancer cell lines (CCLs) are the most widely used system for investigating cancer biology and for the preclinical development of oncology therapies. Pharmacogenomic and genome‐wide editing screenings have facilitated the discovery of clinically relevant gene–drug interactions and novel therapeutic targets via large panels of extensively characterised CCLs. However, tailoring pharmacological strategies in a precision medicine context requires bridging the existing gaps between tumours and in vitro models. Indeed, intrinsic limitations of CCLs such as misidentification, the absence of tumour microenvironment and genetic drift have highlighted the need to identify the most faithful CCLs for each primary tumour while addressing their heterogeneity, with the development of new models where necessary. Here, we discuss the most significant limitations of CCLs in representing patient features, and we review computational methods aiming at systematically evaluating the suitability of CCLs as tumour proxies and identifying the best patient representative in vitro models. Additionally, we provide an overview of the applications of these methods to more complex models and discuss future machine‐learning‐based directions that could resolve some of the arising discrepancies.

Preclinical models of epithelial ovarian cancer: practical considerations and challenges for a meaningful application

Despite many improvements in ovarian cancer diagnosis and treatment, until now, conventional chemotherapy and new biological drugs have not been shown to cure the disease, and the overall prognosis remains poor. Over 90% of ovarian malignancies are categorized as epithelial ovarian cancers (EOC), a collection of different types of neoplasms with distinctive disease biology, response to chemotherapy, and outcome. Advances in our understanding of the histopathology and molecular features of EOC subtypes, as well as the cellular origins of these cancers, have given a boost to the development of clinically relevant experimental models. The overall goal of this review is to provide a comprehensive description of the available preclinical investigational approaches aimed at better characterizing disease development and progression and at identifying new therapeutic strategies. Systems discussed comprise monolayer (2D) and three-dimensional (3D) cultures of established and primary cancer cell lines, organoids and patient-derived explants, animal models, including carcinogen-induced, syngeneic, genetically engineered mouse, xenografts, patient-derived xenografts (PDX), humanized PDX, and the zebrafish and the laying hen models. Recent advances in tumour-on-a-chip platforms are also detailed. The critical analysis of strengths and weaknesses of each experimental model will aid in identifying opportunities to optimize their translational value.

In Vivo Modeling of Human Breast Cancer Using Cell Line and Patient-Derived Xenografts

Historically, human breast cancer has been modeled largely in vitro using long-established cell lines primarily in two-dimensional culture, but also in three-dimensional cultures of varying cellular and molecular complexities. A subset of cell line models has also been used in vivo as cell line-derived xenografts (CDX). While outstanding for conducting detailed molecular analysis of regulatory mechanisms that may function in vivo, results of drug response studies using long-established cell lines have largely failed to translate clinically. In an attempt to address this shortcoming, many laboratories have succeeded in developing clinically annotated patient-derived xenograft (PDX) models of human cancers, including breast, in a variety of host systems. While immunocompromised mice are the predominant host, the immunocompromised rat and pig, zebrafish, as well as the chicken egg chorioallantoic membrane (CAM) have also emerged as potential host platforms to help address perceived shortcomings of immunocompromised mice. With any modeling platform, the two main issues to be resolved are criteria for "credentialing" the models as valid models to represent human cancer, and utility with respect to the ability to generate clinically relevant translational research data. Such data are beginning to emerge, particularly with the activities of PDX consortia such as the NCI PDXNet Program, EuroPDX, and the International Breast Cancer Consortium, as well as a host of pharmaceutical companies and contract research organizations (CRO). This review focuses primarily on these important aspects of PDX-related research, with a focus on breast cancer.

Combined Application of Patient-Derived Cells and Biomaterials as 3D In Vitro Tumor Models

Simple Summary

For years, cancer has remained the second leading cause of death in U.S. and Europe even though cancer mortality has decreased, as new advances in medical treatment have made this decrease possible. Chemotherapy has remained the gold standard and “one-size-fits-all” treatment for cancer, yet this approach has lacked precision and, at times, failed. Recent studies attempt to mimic the spatial microenvironment of cancer tissue to better study chemotherapy agents by combining patient-derived cells and three-dimensional (3D) scaffold, bioprinting, spheroid, and hydrogel culturing. This commentary aims to collect and discuss recent findings concerning the combined application of biomaterials with patient-derived cancer cells to better study and test therapies in vitro, that will further personalize and facilitate the treatment of various cancers, and also address the limitation and challenges in developing these 3D models.

Abstract

Although advances have been made in cancer therapy, cancer remains the second leading cause of death in the U.S. and Europe, and thus efforts to continue to study and discover better treatment methods are ongoing. Three-dimensional (3D) tumor models have shown advantages over bi-dimensional (2D) cultures in evaluating the efficacy of chemotherapy. This commentary aims to highlight the potential of combined application of biomaterials with patient-derived cancer cells as a 3D in vitro model for the study and treatment of cancer patients. Five studies were discussed which demonstrate and provided early evidence to create 3D models with accurate microenvironments that are comparable to in vivo tumors. To date, the use of patient-derived cells for a more personalized approach to healthcare in combination with biomaterials to create a 3D tumor is still relatively new and uncommon for application in clinics. Although highly promising, it is important to acknowledge the current limitations and challenges of developing these innovative in vitro models, including the need for biologists and laboratory technicians to become familiar with biomaterial scaffolds, and the effort for bioengineers to create easy-to-handle scaffolds for routine assessment.

Endometrial Cancer Patient-Derived Xenograft Models: A Systematic Review

Background: Because patient-derived xenograft (PDX) models resemble the original tumors, they can be used as platforms to find target agents for precision medicine and to study characteristics of tumor biology such as clonal evolution and microenvironment interactions. The aim of this review was to identify articles on endometrial cancer PDXs (EC-PDXs) and verify the methodology and outcomes. Methods: We used PubMed to research and identify articles on EC-PDX. The data were analyzed descriptively. Results: Post literature review, eight studies were selected for the systematic review. Eighty-five EC-PDXs were established from 173 patients with EC, with a total success rate of 49.1%. A 1–10 mm³ fragment was usually implanted. Fresh-fragment implantation had higher success rates than using overnight-stored or frozen fragments. Primary tumors were successfully established with subcutaneous implantation, but metastasis rarely occurred; orthotopic implantation via minced tumor cell injection was better for metastatic models. The success rate did not correspond to immunodeficiency grades, and PDXs using nude mice reduced costs. The tumor growth period ranged from 2 weeks to 13 months. Similar characteristics were observed between primary tumors and PDXs, including pathological findings, gene mutations, and gene expression. Conclusion: EC-PDXs are promising tools for translational research because they closely resemble the features of tumors in patients and retain molecular and histological features of the disease.

The Patient-Derived Cancer Organoids: Promises and Challenges as Platforms for Cancer Discovery

The cancer burden is rapidly increasing in most countries, and thus, new anticancer drugs for effective cancer therapy must be developed. Cancer model systems that recapitulate the biological processes of human cancers are one of the cores of the drug development process. PDCO has emerged as a unique model that preserves the genetic, physiological, and histologic characteristics of original cancer, including inter- and intratumoral heterogeneities. Due to these advantages, the PCDO model is increasingly investigated for anticancer drug screening and efficacy testing, preclinical patient stratification, and precision medicine for selecting the most effective anticancer therapy for patients. Here, we review the prospects and limitations of PDCO compared to the conventional cancer models. With advances in culture success rates, co-culture systems with the tumor microenvironment, organoid-on-a-chip technology, and automation technology, PDCO will become the most promising model to develop anticancer drugs and precision medicine.

Monitoring and modulation of the tumor microenvironment for enhanced cancer modeling

Cancer treatments utilizing biologic or cytotoxic drugs compose the frontline of therapy, and though gains in treatment efficacy have been persistent in recent decades, much work remains in understanding cancer progression and treatment. Compounding this situation is the low rate of success when translating preclinical drug candidates to the clinic, which raises costs and development timelines. This underperformance is due in part to the poor recapitulation of the tumor microenvironment, a critical component of cancer biology, in cancer model systems. New technologies capable of both accurately observing and manipulating the tumor microenvironment are needed to effectively model cancer response to treatment. In this review, conventional cancer models are summarized, and a primer on emerging techniques for monitoring and modulating the tumor microenvironment is presented and discussed.

Collateral responses to classical cytotoxic chemotherapies are heterogeneous and sensitivities are sparse

Chemotherapy resistance is a major obstacle to curing cancer patients. Combination drug regimens have shown promise as a method to overcome resistance; however, to date only some cancers have been cured with this method. Collateral sensitivity-the phenomenon whereby resistance to one drug is co-occurrent with sensitivity to a second drug-has been gaining traction as a promising new concept to guide rational design of combination regimens. Here we evolved over 100 subclones of the Eµ-Myc; p19^ARF-/- cell line to be resistant to one of four classical chemotherapy agents: doxorubicin, vincristine, paclitaxel, and cisplatin. We then surveyed collateral responses to acquisition of resistance to these agents. Although numerous collateral sensitivities have been documented for antibiotics and targeted cancer therapies, we observed only one collateral sensitivity: half of cell lines that acquired resistance to paclitaxel also acquired a collateral sensitivity to verapamil. However, we found that the mechanism of this collateral sensitivity was unrelated to the mechanism of paclitaxel resistance. Interestingly, we observed heterogeneity in the phenotypic response to acquisition of resistance to most of the drugs we tested, most notably for paclitaxel, suggesting the existence of multiple different states of resistance. Surprisingly, this phenotypic heterogeneity in paclitaxel resistant cell lines was unrelated to transcriptomic heterogeneity among those cell lines. These features of phenotypic and transcriptomic heterogeneity must be taken into account in future studies of treated tumor subclones and in design of chemotherapy combinations.

Preclinical Models of Brain Metastases in Breast Cancer

Breast cancer remains a leading cause of mortality among women worldwide. Brain metastases confer extremely poor prognosis due to a lack of understanding of their specific biology, unique physiologic and anatomic features of the brain, and limited treatment strategies. A major roadblock in advancing the treatment of breast cancer brain metastases (BCBM) is the scarcity of representative experimental preclinical models. Current models are predominantly based on the use of animal xenograft models with immortalized breast cancer cell lines that poorly capture the disease’s heterogeneity. Recent years have witnessed the development of patient-derived in vitro and in vivo breast cancer culturing systems that more closely recapitulate the biology from individual patients. These advances led to the development of modern patient-tissue-based experimental models for BCBM. The success of preclinical models is also based on the imaging technologies used to detect metastases. Advances in animal brain imaging, including cellular MRI and multimodality imaging, allow sensitive and specific detection of brain metastases and monitoring treatment responses. These imaging technologies, together with novel translational breast cancer models based on patient-derived cancer tissues, represent a unique opportunity to advance our understanding of brain metastases biology and develop novel treatment approaches. This review discusses the state-of-the-art knowledge in preclinical models of this disease.

Organoid: Next-Generation Modeling of Cancer Research and Drug Development

Colorectal carcinoma is a highly prevalent and heterogeneous gastrointestinal malignancy. The emergence of organoid technology has provided a new direction for colorectal cancer research. As a novel-type model, organoid has significant advantages compared with conventional tumor research models, characterized with the high success rate of construction and the high matching with the original tumor. These characteristics provide new possibilities to study the mechanism of colorectal carcinogenesis and improve the treatment effects. The present literature would mainly summarize the characteristics of tumor organoids and the up-to-date technique development of patient-derived organoids (PDOs) and application in colorectal cancer.

Identification of a novel gene signature predicting response to first-line chemotherapy in BRCA wild-type high-grade serous ovarian cancer patients

BACKGROUND

High-grade serous ovarian cancer (HGSOC) has poor survival rates due to a combination of diagnosis at advanced stage and disease recurrence as a result of chemotherapy resistance. In BRCA1 (Breast Cancer gene 1) - or BRCA2-wild type (BRCAwt) HGSOC patients, resistance and progressive disease occur earlier and more often than in mutated BRCA. Identification of biomarkers helpful in predicting response to first-line chemotherapy is a challenge to improve BRCAwt HGSOC management.

METHODS

To identify a gene signature that can predict response to first-line chemotherapy, pre-treatment tumor biopsies from a restricted cohort of BRCAwt HGSOC patients were profiled by RNA sequencing (RNA-Seq) technology. Patients were sub-grouped according to platinum-free interval (PFI), into sensitive (PFI > 12 months) and resistant (PFI < 6 months). The gene panel identified by RNA-seq analysis was then tested by high-throughput quantitative real-time PCR (HT RT-qPCR) in a validation cohort, and statistical/bioinformatic methods were used to identify eligible markers and to explore the relevant pathway/gene network enrichments of the identified gene set. Finally, a panel of primary HGSOC cell lines was exploited to uncover cell-autonomous mechanisms of resistance.

RESULTS

RNA-seq identified a 42-gene panel discriminating sensitive and resistant BRCAwt HGSOC patients and pathway analysis pointed to the immune system as a possible driver of chemotherapy response. From the extended cohort analysis of the 42 DEGs (differentially expressed genes), a statistical approach combined with the random forest classifier model generated a ten-gene signature predictive of response to first-line chemotherapy. The ten-gene signature included: CKB (Creatine kinase B), CTNNBL1 (Catenin, beta like 1), GNG11 (G protein subunit gamma 11), IGFBP7 (Insulin-like growth factor-binding protein 7), PLCG2 (Phospholipase C, gamma 2), RNF24 (Ring finger protein 24), SLC15A3 (Solute carrier family 15 member 3), TSPAN31 (Tetraspanin 31), TTI1 (TELO2 interacting protein 1) and UQCC1 (Ubiquinol-cytochrome c reductase complex assembly factor). Cytotoxicity assays, combined with gene-expression analysis in primary HGSOC cell lines, allowed to define CTNNBL1, RNF24, and TTI1 as cell-autonomous contributors to tumor resistance.

CONCLUSIONS

Using machine-learning techniques we have identified a gene signature that could predict response to first-line chemotherapy in BRCAwt HGSOC patients, providing a useful tool towards personalized treatment modalities.

The Molecular Subtype of Adult Acute Lymphoblastic Leukemia Samples Determines the Engraftment Site and Proliferation Kinetics in Patient-Derived Xenograft Models

In acute lymphoblastic leukemia (ALL), conventional cell lines do not recapitulate the clonal diversity and microenvironment. Orthotopic patient-derived xenograft models (PDX) overcome these limitations and mimic the clinical situation, but molecular stability and engraftment patterns have not yet been thoroughly assessed. We herein describe and characterize the PDX generation in NSG mice. In vivo tumor cell proliferation, engraftment and location were monitored by flow cytometry and bioluminescence imaging. Leukemic cells were retransplanted for up to four passages, and comparative analyses of engraftment pattern, cellular morphology and genomic hotspot mutations were conducted. Ninety-four percent of all samples were successfully engrafted, and the xenograft velocity was dependent on the molecular subtype, outcome of the patient and transplantation passage. While BCR::ABL1 blasts were located in the spleen, KMT2A-positive cases had higher frequencies in the bone marrow. Molecular changes appeared in most model systems, with low allele frequency variants lost during primary engraftment. After the initial xenografting, however, the PDX models demonstrated high molecular stability. This protocol for reliable ALL engraftment demonstrates variability in the location and molecular signatures during serial transplantation. Thorough characterization of experimentally used PDX systems is indispensable for the correct analysis and valid data interpretation of preclinical PDX studies.