Molecular profiling of patient-derived breast cancer xenografts

The roles of patient-derived xenograft models and artificial intelligence toward precision medicine

Patient-derived xenografts (PDX) involve transplanting patient cells or tissues into immunodeficient mice, offering superior disease models compared with cell line xenografts and genetically engineered mice. In contrast to traditional cell-line xenografts and genetically engineered mice, PDX models harbor the molecular and biologic features from the original patient tumor and are generationally stable. This high fidelity makes PDX models particularly suitable for preclinical and coclinical drug testing, therefore better predicting therapeutic efficacy. Although PDX models are becoming more useful, the several factors influencing their reliability and predictive power are not well understood. Several existing studies have looked into the possibility that PDX models could be important in enhancing our knowledge with regard to tumor genetics, biomarker discovery, and personalized medicine; however, a number of problems still need to be addressed, such as the high cost and time-consuming processes involved, together with the variability in tumor take rates. This review addresses these gaps by detailing the methodologies to generate PDX models, their application in cancer research, and their advantages over other models. Further, it elaborates on how artificial intelligence and machine learning were incorporated into PDX studies to fast-track therapeutic evaluation. This review is an overview of the progress that has been done so far in using PDX models for cancer research and shows their potential to be further improved in improving our understanding of oncogenesis.

Evaluation of Combined Chemotherapy and Genomic-Driven Targeted Therapy in Patient-Derived Xenografts Identifies New Therapeutic Approaches in Squamous Non-Small-Cell Lung Cancer Patients

The combination of chemotherapy and targeted therapy has been validated in non-small-cell lung cancer (NSCLC) patients with EGFR mutations. We therefore investigated whether this type of combined approach could be more widely used by targeting other genetic alterations present in NSCLC. PDXs were generated from patients with NSCLC adenocarcinomas (ADCs) and squamous-cell carcinomas (SCCs). Targeted NGS analyses identified various molecular abnormalities in the MAPK and PI3K pathways and in the cell cycle process in our PDX panel. The antitumor efficacy of targeted therapies alone or in combination with chemotherapy was then tested in vivo. We observed that trametinib, BKM120, AZD2014 and palbociclib increased the efficacy of each chemotherapy in SCC PDXs, in contrast to a non-insignificant or slight improvement in ADCs. Furthermore, we observed high efficacy of trametinib in KRAS-, HRAS- and NRAS-mutated tumors (ADCs and SCCs), suggesting that the MEK inhibitor may be useful in a wider population of NSCLC patients, not just those with KRAS-mutated ADCs. Our results suggest that the detection of pathogenic variants by NGS should be performed in all NSCLCs, and particularly in SCCs, to offer patients a more effective combination of chemotherapy and targeted therapy.

MerTK Drives Proliferation and Metastatic Potential in Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is characterized by the absence of the estrogen receptor, progesterone receptor, and receptor tyrosine kinase HER2 expression. Due to the limited number of FDA-approved targeted therapies for TNBC, there is an ongoing need to understand the molecular underpinnings of TNBC for the development of novel combinatorial treatment strategies. This study evaluated the role of the MerTK receptor tyrosine kinase on proliferation and invasion/metastatic potential in TNBC. Immunohistochemical analysis demonstrated MerTK expression in 58% of patient-derived TNBC xenografts. The stable overexpression of MerTK in human TNBC cell lines induced an increase in proliferation rates, robust in vivo tumor growth, heightened migration/invasion potential, and enhanced lung metastases. NanoString nCounter analysis of MerTK-overexpressing SUM102 cells (SUM102-MerTK) revealed upregulation of several signaling pathways, which ultimately drive cell cycle progression, reduce apoptosis, and enhance cell survival. Proteomic profiling indicated increased endoglin (ENG) production in SUM102-MerTK clones, suggesting that MerTK creates a conducive environment for increased proliferative and metastatic activity via elevated ENG expression. To determine ENG's role in increasing proliferation and/or metastatic potential, we knocked out ENG in a SUM102-MerTK clone with CRISPR technology. Although this ENG knockout clone exhibited similar in vivo growth to the parental SUM102-MerTK clone, lung metastasis numbers were significantly decreased ~4-fold, indicating that MerTK enhances invasion and metastasis through ENG. Our data suggest that MerTK regulates a unique proliferative signature in TNBC, promoting robust tumor growth and increased metastatic potential through ENG upregulation. Targeting MerTK and ENG simultaneously may provide a novel therapeutic approach for TNBC patients.

Patient-Derived Microphysiological Systems for Precision Medicine

Patient-derived microphysiological systems (P-MPS) have emerged as powerful tools in precision medicine that provide valuable insight into individual patient characteristics. This review discusses the development of P-MPS as an integration of patient-derived samples, including patient-derived cells, organoids, and induced pluripotent stem cells, into well-defined MPSs. Emphasizing the necessity of P-MPS development, its significance as a nonclinical assessment approach that bridges the gap between traditional in vitro models and clinical outcomes is highlighted. Additionally, guidance is provided for engineering approaches to develop microfluidic devices and high-content analysis for P-MPSs, enabling high biological relevance and high-throughput experimentation. The practical implications of the P-MPS are further examined by exploring the clinically relevant outcomes obtained from various types of patient-derived samples. The construction and analysis of these diverse samples within the P-MPS have resulted in physiologically relevant data, paving the way for the development of personalized treatment strategies. This study describes the significance of the P-MPS in precision medicine, as well as its unique capacity to offer valuable insights into individual patient characteristics.

The progressive trend of modeling and drug screening systems of breast cancer bone metastasis

Bone metastasis is considered as a considerable challenge for breast cancer patients. Various in vitro and in vivo models have been developed to examine this occurrence. In vitro models are employed to simulate the intricate tumor microenvironment, investigate the interplay between cells and their adjacent microenvironment, and evaluate the effectiveness of therapeutic interventions for tumors. The endeavor to replicate the latency period of bone metastasis in animal models has presented a challenge, primarily due to the necessity of primary tumor removal and the presence of multiple potential metastatic sites.The utilization of novel bone metastasis models, including three-dimensional (3D) models, has been proposed as a promising approach to overcome the constraints associated with conventional 2D and animal models. However, existing 3D models are limited by various factors, such as irregular cellular proliferation, autofluorescence, and changes in genetic and epigenetic expression. The imperative for the advancement of future applications of 3D models lies in their standardization and automation. The utilization of artificial intelligence exhibits the capability to predict cellular behavior through the examination of substrate materials' chemical composition, geometry, and mechanical performance. The implementation of these algorithms possesses the capability to predict the progression and proliferation of cancer. This paper reviewed the mechanisms of bone metastasis following primary breast cancer. Current models of breast cancer bone metastasis, along with their challenges, as well as the future perspectives of using these models for translational drug development, were discussed.

Azoxymethane/Dextran Sodium Sulfate (AOM/DSS) Model of Colorectal Cancer

Recent progress in developing new vaccination strategies against cancer requires the production of complex and reliable animal models reflecting the complexity of the tumors with their microenvironment. Mice can be considered a good source due to low cost and ease of being genetically modified, inoculated with tumor cell lines or treated by chemicals to induce different cancers. Despite significant limitations in modeling human cancer complexity, preclinical trials conducted in mice can efficiently contribute to understand molecular mechanisms of cancer, to closely resemble and follow carcinogenesis steps impossible to study into humans, and to test new anticancer therapies. In this chapter, we generally describe the different mouse models developed for cancer vaccines' preclinical trials. A particular focus is dedicated to a chemically-induced colorectal cancer model in use in our laboratories.

Assessing Protein Expression in Patient-Derived Xenografts Using Western Blotting

The use of patient-derived xenografts (PDXs) in cancer research is increasing due to their ability to closely mimic the features of patient tumors. The ability to quickly and robustly measure protein expression levels in these tissues is a key methodology required in a broad range of experimental designs. Western blotting (WB) is a cost effective and simple tool that is highly specific and sensitive for detecting and quantifying individual proteins, posttranslational modifications and aberrant signaling pathways. Here, we described a method to assess protein expression in PDX tissues using WB to detect proteins involved in cell growth signaling pathways.

Patient Characteristics Associated with Growth of Patient-Derived Tumor Implants in Mice (Patient-Derived Xenografts)

Background: patient-derived xenografts (PDXs) have defined the field of translational cancer research in recent years, becoming one of the most-used tools in early drug development. The process of establishing cancer models in mice has turned out to be challenging, since little research focuses on evaluating which factors impact engraftment success. We sought to determine the clinical, pathological, or molecular factors which may predict better engraftment rates in PDXs. Methods: between March 2017 and January 2021, tumor samples obtained from patients with primary or metastatic cancer were implanted into athymic nude mice. A full comprehensive evaluation of baseline factors associated with the patients and patients' tumors was performed, with the goal of potentially identifying predictive markers of engraftment. We focused on clinical (patient factors) pathological (patients' tumor samples) and molecular (patients' tumor samples) characteristics, analyzed either by immunohistochemistry (IHC) or next-generation sequencing (NGS), which were associated with the likelihood of final engraftment, as well as with tumor growth rates in xenografts. Results: a total of 585 tumor samples were collected and implanted. Twenty-one failed to engraft, due to lack of malignant cells. Of 564 tumor-positive samples, 187 (33.2%) grew at time of analysis. The study was able to find correlation and predictive value for engraftment for the following: the use of systemic antibiotics by the patient within 2 weeks of sampling (38.1% (72/189) antibiotics- group vs. 30.7% (115/375) no-antibiotics) (p = 0.048), and the administration of systemic steroids to the patients within 2 weeks of sampling (41.5% (34/48) steroids vs. 31.7% (153/329), no-steroids) (p = 0.049). Regarding patient's baseline tests, we found certain markers could help predict final engraftment success: for lactate dehydrogenase (LDH) levels, 34.1% (140/411) of tumors derived from patients with baseline blood LDH levels above the upper limit of normality (ULN) achieved growth, against 30.7% (47/153) with normal LDH (p = 0.047). Histological tumor characteristics, such as grade of differentiation, were also correlated. Grade 1: 25.4% (47/187), grade 2: 34.8% (65/187) and grade 3: 40.1% (75/187) tumors achieved successful growth (p = 0.043), suggesting the higher the grade, the higher the likelihood of success. Similarly, higher ki67 levels were also correlated with better engraftment rates: low (Ki67 < 15%): 8.9% (9/45) achieved growth vs. high (Ki67 ≥ 15%): 31% (35/113) (p: 0.002). Other markers of aggressiveness such as the presence of lymphovascular invasion in tumor sample of origin was also predictive: 42.2% (97/230) with lymphovascular vs. 26.9% (90/334) of samples with no invasion (p = 0.0001). From the molecular standpoint, mismatch-repair-deficient (MMRd) tumors showed better engraftment rates: 62.1% (18/29) achieved growth vs. 40.8% (75/184) of proficient tumors (p = 0.026). A total of 84 PDX were breast models, among which 57.9% (11/19) ER-negative models grew, vs. 15.4% (10/65) of ER-positive models (p = 0.0001), also consonant with ER-negative tumors being more aggressive. BRAFmut cancers are more likely to achieve engraftment during the development of PDX models. Lastly, tumor growth rates during first passages can help establish a cutoff point for the decision-making process during PDX development, since the higher the tumor grades, the higher the likelihood of success. Conclusions: tumors with higher grade and Ki67 protein expression, lymphovascular and/or perineural invasion, with dMMR and are negative for ER expression have a higher probability of achieving growth in the process of PDX development. The use of steroids and/or antibiotics in the patient prior to sampling can also impact the likelihood of success in PDX development. Lastly, establishing a cutoff point for tumor growth rates could guide the decision-making process during PDX development.

Establishment and characterization of a non-gestational choriocarcinoma patient-derived xenograft model

BACKGROUND

Non-gestational choriocarcinoma (NGC) is a rare subtype of malignant germ cell tumour and there is no consensus on its treatment. The lack of suitable preclinical models for NGC is a challenge in drug discovery research. Patient-derived xenograft (PDX) models recapitulate the tumour microenvironment of the original cancer tissue. Therefore, they have received considerable attention for studies on rare cancer. Here, we aimed to establish a PDX model from a patient with recurrent NGC.

METHODS

Fresh NGC tumour tissue was immediately transplanted into a severely immune-deficient mouse (NOD.Cg-Prkdcscid1l2rgtm1Wjl/SzJ) and maintained for more than three in vivo passages. Subsequently, we evaluated the molecular characteristics of the PDX model using immunohistochemistry, polymerase chain reaction, and RNA sequencing. Moreover, the PDX tumours were transplanted into BALB/c nude mice, and we evaluated their sensitivity for cisplatin and methotrexate.

RESULTS

The PDX tumour maintained the morphological features of NGC. Moreover, Immunohistochemistry revealed that the human chorionic gonadotropin, cytokeratin 7, and EpCAM expression levels were similar to those in the primary tumour. Furthermore, serum human chorionic gonadotropin levels were elevated in both the primary tumour and the PDX models. Additionally, using PCR analysis with species-specific primers, we confirmed that the PDX tumour contained human genes and was derived from human tissue. Moreover, the gene expression profile of the NGC was compared with that of epithelial ovarian cancer samples and cell lines, and 568 dysregulated genes in the NGC were extracted. The expression of the dysregulated genes in PDX was significantly correlated with that in the primary tumour (R2 = 0.873, P < 0.001). Finally, we demonstrated that the PDX tumour was sensitive to cisplatin and methotrexate; therefore, its clinical response to the agents was similar to that of the primary tumour.

CONCLUSIONS

We successfully established a PDX model of NGC, to the best of our knowledge, for the first time. The established PDX retained the molecular and transcriptome characteristics of the primary tumour and can be used to predict drug effects. It may facilitate further research and the development of novel therapeutic agents for NGC.

Lung cancer organoids: models for preclinical research and precision medicine

Lung cancer is a malignancy with high incidence and mortality rates globally, and it has a 5-year survival rate of only 10%-20%. The significant heterogeneity in clinical presentation, histological features, multi-omics findings, and drug sensitivity among different lung cancer patients necessitate the development of personalized treatment strategies. The current precision medicine for lung cancer, primarily based on pathological and genomic multi-omics testing, fails to meet the needs of patients with clinically refractory lung cancer. Lung cancer organoids (LCOs) are derived from tumor cells within tumor tissues and are generated through three-dimensional tissue culture, enabling them to faithfully recapitulate in vivo tumor characteristics and heterogeneity. The establishment of a series of LCOs biobanks offers promising platforms for efficient screening and identification of novel targets for anti-tumor drug discovery. Moreover, LCOs provide supplementary decision-making factors to enhance the current precision medicine for lung cancer, thereby addressing the limitations associated with pathology-guided approaches in managing refractory lung cancer. This article presents a comprehensive review on the construction methods and potential applications of LCOs in both preclinical and clinical research. It highlights the significance of LCOs in biomarker exploration, drug resistance investigation, target identification, clinical precision drug screening, as well as microfluidic technology-based high-throughput drug screening strategies. Additionally, it discusses the current limitations and future prospects of this field.

Progress in building clinically relevant patient-derived tumor xenograft models for cancer research

Patient-derived tumor xenograft (PDX) models, a method involving the surgical extraction of tumor tissues from cancer patients and subsequent transplantation into immunodeficient mice, have emerged as a pivotal approach in translational research, particularly in advancing precision medicine. As the first stage of PDX development, the patient-derived orthotopic xenograft (PDOX) models implant tumor tissue in mice in the corresponding anatomical locations of the patient. The PDOX models have several advantages, including high fidelity to the original tumor, heightened drug sensitivity, and an elevated rate of successful transplantation. However, the PDOX models present significant challenges, requiring advanced surgical techniques and resource-intensive imaging technologies, which limit its application. And then, the humanized mouse models, as well as the zebrafish models, were developed. Humanized mouse models contain a human immune environment resembling the tumor and immune system interplay. The humanized mouse models are a hot topic in PDX model research. Regarding zebrafish patient-derived tumor xenografts (zPDX) and patient-derived organoids (PDO) as promising models for studying cancer and drug discovery, zPDX models are used to transplant tumors into zebrafish as novel personalized medical animal models with the advantage of reducing patient waiting time. PDO models provide a cost-effective approach for drug testing that replicates the in vivo environment and preserves important tumor-related information for patients. The present review highlights the functional characteristics of each new phase of PDX and provides insights into the challenges and prospective developments in this rapidly evolving field.

Decellularized ECM hydrogels: prior use considerations, applications, and opportunities in tissue engineering and biofabrication

Tissue development, wound healing, pathogenesis, regeneration, and homeostasis rely upon coordinated and dynamic spatial and temporal remodeling of extracellular matrix (ECM) molecules. ECM reorganization and normal physiological tissue function, require the establishment and maintenance of biological, chemical, and mechanical feedback mechanisms directed by cell-matrix interactions. To replicate the physical and biological environment provided by the ECM in vivo, methods have been developed to decellularize and solubilize tissues which yield organ and tissue-specific bioactive hydrogels. While these biomaterials retain several important traits of the native ECM, the decellularizing process, and subsequent sterilization, and solubilization result in fragmented, cleaved, or partially denatured macromolecules. The final product has decreased viscosity, moduli, and yield strength, when compared to the source tissue, limiting the compatibility of isolated decellularized ECM (dECM) hydrogels with fabrication methods such as extrusion bioprinting. This review describes the physical and bioactive characteristics of dECM hydrogels and their role as biomaterials for biofabrication. In this work, critical variables when selecting the appropriate tissue source and extraction methods are identified. Common manual and automated fabrication techniques compatible with dECM hydrogels are described and compared. Fabrication and post-manufacturing challenges presented by the dECM hydrogels decreased mechanical and structural stability are discussed as well as circumvention strategies. We further highlight and provide examples of the use of dECM hydrogels in tissue engineering and their role in fabricating complex in vitro 3D microenvironments.

Polymeric encapsulation of a ruthenium(ii) polypyridyl complex: from synthesis to in vivo studies against high-grade epithelial ovarian cancer

The in vitro to in vivo translation of metal-based cytotoxic drugs has proven to be a significant hurdle in their establishment as effective anti-cancer alternatives. Various nano-delivery systems, such as polymeric nanoparticles, have been explored to address the pharmacokinetic limitations associated with the use of these complexes. However, these systems often suffer from poor stability or involve complex synthetic procedures. To circumvent these problems, we report here a simple, one-pot procedure for the preparation of covalently-attached Ru-polylactide nanoparticles. This methodology relies on the ring-opening polymerization of lactide initiated by a calcium alkoxide derivative formed from calcium bis(trimethylsilyl amide) and a hydroxyl-bearing ruthenium complex. This procedure proceeds with high efficiency (near-quantitative incorporation of Ru in the polymer) and enables the preparation of polymers with varying molecular weights (2000-11000 Da) and high drug loadings (up to 68% w/w). These polymers were formulated as narrowly dispersed nanoparticles (110 nm) that exhibited a slow and predictable release of the ruthenium payload. Unlike standard encapsulation methods routinely used, the release kinetics of these nanoparticles is controlled and may be adjusted on demand, by tuning the size of the polymer chain. In terms of cytotoxicity, the nanoparticles were assessed in the ovarian cancer cell line A2780 and displayed potency comparable to cisplatin and the free drug, in the low micromolar range. Interestingly, the activity was maintained when tested in a cisplatin-resistant cell line, suggesting a possible orthogonal mechanism of action. Additionally, the internalization in tumour cells was found to be significantly higher than the free ruthenium complex (>200 times in some cases), clearly showcasing the added benefit in the drug's cellular permeation and accumulation of the drug. Finally, the in vivo performance was evaluated for the first time in mice. The experiments showed that the intravenously injected nanoparticles were well tolerated and were able to significantly improve the pharmacokinetics and biodistribution of the parent drug. Not only was the nanosystem able to promote an 18-fold increase in tumour accumulation, but it also allowed a considerable reduction of drug accumulation in vital organs, achieving, for example, reduction levels of 90% and 97% in the brain and lungs respectively. In summary, this simple and efficient one-pot procedure enables the generation of stable and predictable nanoparticles capable of improving the cellular penetration and systemic accumulation of the Ru drug in the tumour. Altogether, these results showcase the potential of covalently-loaded ruthenium polylactide nanoparticles and pave the way for its exploitation and application as a viable tool in the treatment of ovarian cancer.

Ferrocifen stealth LNCs and conventional chemotherapy: A promising combination against multidrug-resistant ovarian adenocarcinoma

Ovarian cancer is one of the deadliest epithelial malignancies in women, owing to the multidrug resistance that restricts the success of conventional chemotherapy, carboplatin and paclitaxel. High grade serous ovarian carcinoma can be classified into two subtypes, the chemosensitive High OXPHOS and the Low OXPHOS tumour, less sensitive to chemotherapy. This difference of treatment efficacy could be explained by the redox status of these tumours, High OXPHOS exhibiting a chronic oxidative stress and an accumulation of reactive oxygen species. Ferrocifens, bio-organometallic compounds, are believed to be ROS producers with a good cytotoxicity on ovarian cancer cell lines. The aim of this study was to evaluate the in vivo efficacy of ferrocifen stealth lipid nanocapsules on High and Low OXPHOS ovarian Patient-Derived Xenograft models, alone or in combination to standard chemotherapy. Accordingly, two ferrocifens, P53 and P722, were encapsulated in stealth LNCs. The treatment by stealth P722-LNCs in combination with standard chemotherapy induced, with a concentration eight time lower than in stealth P53-LNCs, similar tumour reduction on a Low OXPHOS model, allowing us to conclude that P722 could be a leading ferrocifen to treat ovarian cancer. This combination of treatments may represent a promising synergistic approach to treat resistant ovarian adenocarcinoma.

Radiobiology of the FLASH effect

Radiation exposures at ultrahigh dose rates (UHDRs) at several orders of magnitude greater than in current clinical radiotherapy (RT) have been shown to manifest differential radiobiological responses compared to conventional (CONV) dose rates. This has led to studies investigating the application of UHDR for therapeutic advantage (FLASH-RT) that have gained significant interest since the initial discovery in 2014 that demonstrated reduced lung toxicity with equivalent levels of tumor control compared with conventional dose-rate RT. Many subsequent studies have demonstrated the potential protective role of FLASH-RT in normal tissues, yet the underlying molecular and cellular mechanisms of the FLASH effect remain to be fully elucidated. Here, we summarize the current evidence of the FLASH effect and review FLASH-RT studies performed in preclinical models of normal tissue response. To critically examine the underlying biological mechanisms of responses to UHDR radiation exposures, we evaluate in vitro studies performed with normal and tumor cells. Differential responses to UHDR versus CONV irradiation recurrently involve reduced inflammatory processes and differential expression of pro- and anti-inflammatory genes. In addition, frequently reduced levels of DNA damage or misrepair products are seen after UHDR irradiation. So far, it is not clear what signal elicits these differential responses, but there are indications for involvement of reactive species. Different susceptibility to FLASH effects observed between normal and tumor cells may result from altered metabolic and detoxification pathways and/or repair pathways used by tumor cells. We summarize the current theories that may explain the FLASH effect and highlight important research questions that are key to a better mechanistic understanding and, thus, the future implementation of FLASH-RT in the clinic.

Modern Approaches to Testing Drug Sensitivity of Patients' Tumors (Review)

Drug therapy is still one of the basic techniques used to treat cancers of different etiology. However, tumor resistance to drugs is a pressing problem limiting drug treatment efficacy. It is obvious for both modern fundamental and clinical oncology that there is the need for an individual approach to treating cancer taking into account the biological properties of a tumor when prescribing chemo- and targeted therapy. One of the promising strategies is to increase the antitumor therapy efficacy by developing predictive tests, which enable to evaluate the sensitivity of a particular tumor to a specific drug or a drug combination before the treatment initiation and, thus, make individual therapy selection possible.

The present review considers the main approaches to drug sensitivity assessment of patients’ tumors: molecular genetic profiling of tumor cells, and direct efficiency testing of the drugs on tumor cells isolated from surgical or biopsy material. There were analyzed the key directions in research and clinical studies such as: the search for predictive molecular markers, the development of methods to maintain tumor cells or tissue sections viable, i.e. in a condition maximum close to their physiological state, the development of high throughput systems to assess therapy efficiency. Special attention was given to a patient-centered approach to drug therapy in colorectal cancer.

Preclinical tumor organoid models in personalized cancer therapy: Not everyone fits the mold

In contrast to conventional cancer treatment, in personalized cancer medicine each patient receives a specific treatment. The response to therapy, clinical outcomes, and tumor behavior such as metastases, tumor progression, carcinogenesis can be significantly affected by the heterogeneous tumor microenvironment (TME) and interpersonal differences. Therefore, using native tumor microenvironment mimicking models is necessary to improving personalized cancer therapy. Both in vitro 2D cell culture and in vivo animal models poorly recapitulate the heterogeneous tumor (immune) microenvironments of native tumors. The development of 3D culture models, native tumor microenvironment mimicking models, made it possible to evaluate the chemoresistance of tumor tissue and the functionality of drugs in the presence of cell-extracellular matrix and cell-cell interactions in a 3D construction. Various personalized tumor models have been designed to preserving the native tumor microenvironment, including patient-derived tumor xenografts and organoid culture strategies. In this review, we will discuss the patient-derived organoids as a native tumor microenvironment mimicking model in personalized cancer therapy. In addition, we will also review the potential and the limitations of organoid culture systems for predicting patient outcomes and preclinical drug screening. Finally, we will discuss immunotherapy drug screening in tumor organoids by using microfluidic technology.

Atlas of Lobular Breast Cancer Models: Challenges and Strategic Directions

Invasive lobular carcinoma (ILC) accounts for up to 15% of all breast cancer (BC) cases and responds well to endocrine treatment when estrogen receptor α-positive (ER+) yet differs in many biological aspects from other ER+ BC subtypes. Up to 30% of patients with ILC will develop late-onset metastatic disease up to ten years after initial tumor diagnosis and may experience failure of systemic therapy. Unfortunately, preclinical models to study ILC progression and predict the efficacy of novel therapeutics are scarce. Here, we review the current advances in ILC modeling, including cell lines and organotypic models, genetically engineered mouse models, and patient-derived xenografts. We also underscore four critical challenges that can be addressed using ILC models: drug resistance, lobular tumor microenvironment, tumor dormancy, and metastasis. Finally, we highlight the advantages of shared experimental ILC resources and provide essential considerations from the perspective of the European Lobular Breast Cancer Consortium (ELBCC), which is devoted to better understanding and translating the molecular cues that underpin ILC to clinical diagnosis and intervention. This review will guide investigators who are considering the implementation of ILC models in their research programs.

Metagenomic analysis of gut microbiome reveals a dynamic change in Alistipes onderdonkii in the preclinical model of pancreatic cancer, suppressing its proliferation

Pancreatic cancer is a lethal cancer with aggressive and invasive characteristics. By the time it is diagnosed, patients already have tumors extended to other organs and show extremely low survival rates. The gut microbiome is known to be associated with many diseases and its imbalance affects the pathogenesis of pancreatic cancer. In this study, we established an orthotopic, patient-derived xenograft model to identify how the gut microbiome is linked to pancreatic ductal adenocarcinoma (PDAC). Using the 16S rDNA metagenomic sequencing, we revealed that the levels of Alistipes onderdonkii and Roseburia hominis decreased in the gut microbiome of the PDAC model. To explore the crosstalk between the two bacteria and PDAC cells, we collected the supernatant of the bacteria or cancer cell culture medium and treated it in a cross manner. While the cancer cell medium did not affect bacterial growth, we observed that the A. onderdonkii medium suppressed the growth of the pancreatic primary cancer cells. Using the bromodeoxyuridine/7-amino-actinomycin D (BrdU/7-AAD) staining assay, we confirmed that the A. onderdonkii medium inhibited the proliferation of the pancreatic primary cancer cells. Furthermore, RNA-seq analysis revealed that the A. onderdonkii medium induced unique transcriptomic alterations in the PDAC cells, compared to the normal pancreatic cells. Altogether, our data suggest that the reduction in the A. onderdonkii in the gut microbiome provides a proliferation advantage to the pancreatic cancer cells. KEY POINTS: • Metagenome analysis of pancreatic cancer model reveals A. onderdonkii downregulation. • A. onderdonkii culture supernatant suppressed the proliferation of pancreatic cancer cells. • RNA seq data reveals typical gene expression changes induced by A. onderdonkii.

Induced Pluripotent Stem Cells (iPSCs)-Roles in Regenerative Therapies, Disease Modelling and Drug Screening

The discovery of induced pluripotent stem cells (iPSCs) has made an invaluable contribution to the field of regenerative medicine, paving way for identifying the true potential of human embryonic stem cells (ESCs). Since the controversy around ethicality of ESCs continue to be debated, iPSCs have been used to circumvent the process around destruction of the human embryo. The use of iPSCs have transformed biological research, wherein increasing number of studies are documenting nuclear reprogramming strategies to make them beneficial models for drug screening as well as disease modelling. The flexibility around the use of iPSCs include compatibility to non-invasive harvesting, and ability to source from patients with rare diseases. iPSCs have been widely used in cardiac disease modelling, studying inherited arrhythmias, neural disorders including Alzheimer’s disease, liver disease, and spinal cord injury. Extensive research around identifying factors that are involved in maintaining the identity of ESCs during induction of pluripotency in somatic cells is undertaken. The focus of the current review is to detail all the clinical translation research around iPSCs and the strength of its ever-growing potential in the clinical space.

Drug discovery oncology in a mouse: concepts, models and limitations

The utilization of suitable mouse models is a critical step in the drug discovery oncology workflow as their generation and use are important for target identification and validation as well as toxicity and efficacy assessments. Current murine models have been instrumental in furthering insights into the mode of action of drugs before transitioning into the clinic. Recent advancements in genome editing with the development of the CRISPR/Cas9 system and the possibility of applying such technology directly in vivo have expanded the toolkit of preclinical models available. In this review, a brief presentation of the current models used in drug discovery will be provided with a particular emphasis on the novel CRISPR/Cas9 models.

In-depth characterization of a new patient-derived xenograft model for metaplastic breast carcinoma to identify viable biologic targets and patterns of matrix evolution within rare tumor types

Metaplastic breast carcinoma (MBC) is a rare breast cancer subtype with rapid growth, high rates of metastasis, recurrence and drug resistance, and diverse molecular and histological heterogeneity. Patient-derived xenografts (PDXs) provide a translational tool and physiologically relevant system to evaluate tumor biology of rare subtypes. Here, we provide an in-depth comprehensive characterization of a new PDX model for MBC, TU-BcX-4IC. TU-BcX-4IC is a clinically aggressive tumor exhibiting rapid growth in vivo, spontaneous metastases, and elevated levels of cell-free DNA and circulating tumor cell DNA. Relative chemosensitivity of primary cells derived from TU-BcX-4IC was performed using the National Cancer Institute (NCI) oncology drug set, crystal violet staining, and cytotoxic live/dead immunofluorescence stains in adherent and organoid culture conditions. We employed novel spheroid/organoid incubation methods (Pu·MA system) to demonstrate that TU-BcX-4IC is resistant to paclitaxel. An innovative physiologically relevant system using human adipose tissue was used to evaluate presence of cancer stem cell-like populations ex vivo. Tissue decellularization, cryogenic-scanning electron microscopy imaging and rheometry revealed consistent matrix architecture and stiffness were consistent despite serial transplantation. Matrix-associated gene pathways were essentially unchanged with serial passages, as determined by qPCR and RNA sequencing, suggesting utility of decellularized PDXs for in vitro screens. We determined type V collagen to be present throughout all serial passage of TU-BcX-4IC tumor, suggesting it is required for tumor maintenance and is a potential viable target for MBC. In this study we introduce an innovative and translational model system to study cell–matrix interactions in rare cancer types using higher passage PDX tissue.

Co-Clinical Imaging Resource Program (CIRP): Bridging the Translational Divide to Advance Precision Medicine

The National Institutes of Health’s (National Cancer Institute) precision medicine initiative emphasizes the biological and molecular bases for cancer prevention and treatment. Importantly, it addresses the need for consistency in preclinical and clinical research. To overcome the translational gap in cancer treatment and prevention, the cancer research community has been transitioning toward using animal models that more fatefully recapitulate human tumor biology. There is a growing need to develop best practices in translational research, including imaging research, to better inform therapeutic choices and decision-making. Therefore, the National Cancer Institute has recently launched the Co-Clinical Imaging Research Resource Program (CIRP). Its overarching mission is to advance the practice of precision medicine by establishing consensus-based best practices for co-clinical imaging research by developing optimized state-of-the-art translational quantitative imaging methodologies to enable disease detection, risk stratification, and assessment/prediction of response to therapy. In this communication, we discuss our involvement in the CIRP, detailing key considerations including animal model selection, co-clinical study design, need for standardization of co-clinical instruments, and harmonization of preclinical and clinical quantitative imaging pipelines. An underlying emphasis in the program is to develop best practices toward reproducible, repeatable, and precise quantitative imaging biomarkers for use in translational cancer imaging and therapy. We will conclude with our thoughts on informatics needs to enable collaborative and open science research to advance precision medicine.

Co-clinical FDG-PET radiomic signature in predicting response to neoadjuvant chemotherapy in triple-negative breast cancer

Purpose

We sought to exploit the heterogeneity afforded by patient-derived tumor xenografts (PDX) to first, optimize and identify robust radiomic features to predict response to therapy in subtype-matched triple negative breast cancer (TNBC) PDX, and second, to implement PDX-optimized image features in a TNBC co-clinical study to predict response to therapy using machine learning (ML) algorithms.

Methods

TNBC patients and subtype-matched PDX were recruited into a co-clinical FDG-PET imaging trial to predict response to therapy. One hundred thirty-one imaging features were extracted from PDX and human-segmented tumors. Robust image features were identified based on reproducibility, cross-correlation, and volume independence. A rank importance of predictors using ReliefF was used to identify predictive radiomic features in the preclinical PDX trial in conjunction with ML algorithms: classification and regression tree (CART), Naïve Bayes (NB), and support vector machines (SVM). The top four PDX-optimized image features, defined as radiomic signatures (RadSig), from each task were then used to predict or assess response to therapy. Performance of RadSig in predicting/assessing response was compared to SUV_mean, SUV_max, and lean body mass-normalized SUL_peak measures.

Results

Sixty-four out of 131 preclinical imaging features were identified as robust. NB-RadSig performed highest in predicting and assessing response to therapy in the preclinical PDX trial. In the clinical study, the performance of SVM-RadSig and NB-RadSig to predict and assess response was practically identical and superior to SUV_mean, SUV_max, and SUL_peak measures.

Conclusions

We optimized robust FDG-PET radiomic signatures (RadSig) to predict and assess response to therapy in the context of a co-clinical imaging trial.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00259-021-05489-8.

Establishment and preclinical application of a patient-derived xenograft model for uterine cancer

BACKGROUND

The patient-derived xenograft (PDX) model is a promising translational platform for duplicating the characteristics of primary tumors. Here, we established and characterized PDX models of uterine cancer to demonstrate their utility for preclinical drug testing.

MATERIALS AND METHODS

We generated PDX tumors surgically derived from 58 cases of uterine cancer. Subrenal capsule xenografts and primary tumors were compared using microscopic examination, short tandem repeat analyses, and targeted sequencing analyses. A phosphatidylinositol 3-kinase (PI3K) inhibitor was administered to mice whose PDX tumors harbored a PTEN deletion or PIK3CA mutation. We also generated an orthotopic PDX model using uterine horn implantation.

RESULTS

Thirty-three (56.9%) PDXs were successfully generated and passaged to maintain tumors. The histological features of the PDX tumors were stable over subsequent passages. By contrast, the proportions of epithelial and mesenchymal components of carcinosarcoma PDX models varied by generation. Targeted sequencing analyses revealed that all mutated cancer-related genes were stable during establishment and subgrafting. Treatment with a PI3K inhibitor cased a significant decrease in tumor weight in the clear cell carcinoma PDX harboring a frameshift PTEN deletion (p = 0.049) and in the serous carcinoma PDX harboring a missense PI3KCA mutation (p = 0.003) compared with matched controls. We also successfully established orthotopic PDX models (3/3; 100.0%).

CONCLUSIONS

The histological and genetic features of PDXs were similar to those of primary tumors. This model is a promising translational platform for preclinical testing of new anticancer drugs and will enable the personalized development of therapeutic options for uterine cancer.

Breast Cancer Reconstruction: Design Criteria for a Humanized Microphysiological System

International regulatory agencies such as the Food and Drug Administration have mandated that the scientific community develop humanized microphysiological systems (MPS) as an in vitro alternative to animal models in the near future. While the breast cancer research community has long appreciated the importance of three-dimensional growth dynamics in their experimental models, there are remaining obstacles preventing a full conversion to humanized MPS for drug discovery and pathophysiological studies. This perspective evaluates the current status of human tissue-derived cells and scaffolds as building blocks for an "idealized" breast cancer MPS based on bioengineering design principles. It considers the utility of adipose tissue as a potential source of endothelial, lymphohematopoietic, and stromal cells for the support of breast cancer epithelial cells. The relative merits of potential MPS scaffolds derived from adipose tissue, blood components, and synthetic biomaterials is evaluated relative to the current "gold standard" material, Matrigel, a murine chondrosarcoma-derived basement membrane-enriched hydrogel. The advantages and limitations of a humanized breast cancer MPS are discussed in the context of in-process and destructive read-out assays. Impact statement Regulatory authorities have highlighted microphysiological systems as an emerging tool in breast cancer research. This has been led by calls for more predictive human models and reduced animal experimentation. This perspective describes how human-derived cells, extracellular matrices, and hydrogels will provide the building blocks to create breast cancer models that accurately reflect diversity at multiple levels, that is, patient ethnicity, pathophysiology, and metabolic status.

A Method for the Establishment of Human Lung Adenocarcinoma Patient-Derived Xenografts in Mice

Patient-derived xenografts (PDXs) are created by implanting human tumor tissue or cells into immunodeficent mice, and enable the study of tumor biology, biomarkers and response to therapy in vivo. This chapter describes a method for lung adenocarcinoma (LAC) PDX generation using subcutaneous implantation of tumor tissue and cell suspensions and incorporating the humanization of PDX models by reconstitution with human immune cells.

Advances in Rodent Models for Breast Cancer Formation, Progression, and Therapeutic Testing

Breast cancer is a highly complicated disease. Advancement in the treatment and prevention of breast cancer lies in elucidation of the mechanism of carcinogenesis and progression. Rodent models of breast cancer have developed into premier tools for investigating the mechanisms and genetic pathways in breast cancer progression and metastasis and for developing and evaluating clinical therapeutics. Every rodent model has advantages and disadvantages, and the selection of appropriate rodent models with which to investigate breast cancer is a key decision in research. Design of a suitable rodent model for a specific research purpose is based on the integration of the advantages and disadvantages of different models. Our purpose in writing this review is to elaborate on various rodent models for breast cancer formation, progression, and therapeutic testing.

Patient-derived xenograft models for the study of benign human neoplasms

Preclinical models are a core feature of translational research, and patient-derived xenograft (PDX) models have increasingly been used with such purpose. PDX involves the transplantation of fresh human tumor samples into immunodeficient mice to overcome immunologic rejection. It is a valuable tool for basic as well as preclinical research, contributing to the establishment of models to characterize the neoplasms to drug screening and to allow the identification of therapeutic targets. The use of these models is justified because they retain the histological and genomic features of the primary tumor. PDX models are well described for malignant neoplasms, for which the advantages are clear and include the development of drug treatments. The establishment of malignant tumors PDX is undeniably important from a medical perspective. However, few studies have used such models for benign neoplasms. The use of PDX for benign neoplasm studies can help to clarify the pathobiology of these diseases, as well as invasion and malignant transformation mechanisms, which from a biological perspective is equally important to the study of malignant tumors. Therefore, the aim of this study is to review the current methodology for PDX model generation and to cover its main applications, focusing on benign neoplasms.

Cancer Immune Evasion Through Loss of MHC Class I Antigen Presentation

Major histocompatibility class I (MHC I) molecules bind peptides derived from a cell's expressed genes and then transport and display this antigenic information on the cell surface. This allows CD8 T cells to identify pathological cells that are synthesizing abnormal proteins, such as cancers that are expressing mutated proteins. In order for many cancers to arise and progress, they need to evolve mechanisms to avoid elimination by CD8 T cells. MHC I molecules are not essential for cell survival and therefore one mechanism by which cancers can evade immune control is by losing MHC I antigen presentation machinery (APM). Not only will this impair the ability of natural immune responses to control cancers, but also frustrate immunotherapies that work by re-invigorating anti-tumor CD8 T cells, such as checkpoint blockade. Here we review the evidence that loss of MHC I antigen presentation is a frequent occurrence in many cancers. We discuss new insights into some common underlying mechanisms through which some cancers inactivate the MHC I pathway and consider some possible strategies to overcome this limitation in ways that could restore immune control of tumors and improve immunotherapy.

Conservation of copy number profiles during engraftment and passaging of patient-derived cancer xenografts

Patient-derived xenografts (PDXs) are resected human tumors engrafted into mice for preclinical studies and therapeutic testing. It has been proposed that the mouse host affects tumor evolution during PDX engraftment and propagation, affecting the accuracy of PDX modeling of human cancer. Here, we exhaustively analyze copy number alterations (CNAs) in 1,451 PDX and matched patient tumor (PT) samples from 509 PDX models. CNA inferences based on DNA sequencing and microarray data displayed substantially higher resolution and dynamic range than gene expression-based inferences, and they also showed strong CNA conservation from PTs through late-passage PDXs. CNA recurrence analysis of 130 colorectal and breast PT/PDX-early/PDX-late trios confirmed high-resolution CNA retention. We observed no significant enrichment of cancer-related genes in PDX-specific CNAs across models. Moreover, CNA differences between patient and PDX tumors were comparable to variations in multiregion samples within patients. Our study demonstrates the lack of systematic copy number evolution driven by the PDX mouse host.

PLK1 inhibition exhibits strong anti-tumoral activity in CCND1-driven breast cancer metastases with acquired palbociclib resistance

A significant proportion of patients with oestrogen receptor (ER) positive breast cancers (BC) develop resistance to endocrine treatments (ET) and relapse with metastatic disease. Here we perform whole exome sequencing and gene expression analysis of matched primary breast tumours and bone metastasis-derived patient-derived xenografts (PDX). Transcriptomic analyses reveal enrichment of the G2/M checkpoint and up-regulation of Polo-like kinase 1 (PLK1) in PDX. PLK1 inhibition results in tumour shrinkage in highly proliferating CCND1-driven PDX, including different RB-positive PDX with acquired palbociclib resistance. Mechanistic studies in endocrine resistant cell lines, suggest an ER-independent function of PLK1 in regulating cell proliferation. Finally, in two independent clinical cohorts of ER positive BC, we find a strong association between high expression of PLK1 and a shorter metastases-free survival and poor response to anastrozole. In conclusion, our findings support clinical development of PLK1 inhibitors in patients with advanced CCND1-driven BC, including patients progressing on palbociclib treatment.

Breast Cancer: A Molecularly Heterogenous Disease Needing Subtype-Specific Treatments

Breast cancer is the most commonly occurring cancer in women. There were over two-million new cases in world in 2018. It is the second leading cause of death from cancer in western countries. At the molecular level, breast cancer is a heterogeneous disease, which is characterized by high genomic instability evidenced by somatic gene mutations, copy number alterations, and chromosome structural rearrangements. The genomic instability is caused by defects in DNA damage repair, transcription, DNA replication, telomere maintenance and mitotic chromosome segregation. According to molecular features, breast cancers are subdivided in subtypes, according to activation of hormone receptors (estrogen receptor and progesterone receptor), of human epidermal growth factors receptor 2 (HER2), and or BRCA mutations. In-depth analyses of the molecular features of primary and metastatic breast cancer have shown the great heterogeneity of genetic alterations and their clonal evolution during disease development. These studies have contributed to identify a repertoire of numerous disease-causing genes that are altered through different mutational processes. While early-stage breast cancer is a curable disease in about 70% of patients, advanced breast cancer is largely incurable. However, molecular studies have contributed to develop new therapeutic approaches targeting HER2, CDK4/6, PI3K, or involving poly(ADP-ribose) polymerase inhibitors for BRCA mutation carriers and immunotherapy.

Applications of patient-derived tumor xenograft models and tumor organoids

Patient-derived tumor xenografts (PDXs), in which tumor fragments surgically dissected from cancer patients are directly transplanted into immunodeficient mice, have emerged as a useful model for translational research aimed at facilitating precision medicine. PDX susceptibility to anti-cancer drugs is closely correlated with clinical data in patients, from whom PDX models have been derived. Accumulating evidence suggests that PDX models are highly effective in predicting the efficacy of both conventional and novel anti-cancer therapeutics. This also allows “co-clinical trials,” in which pre-clinical investigations in vivo and clinical trials could be performed in parallel or sequentially to assess drug efficacy in patients and PDXs. However, tumor heterogeneity present in PDX models and in the original tumor samples constitutes an obstacle for application of PDX models. Moreover, human stromal cells originally present in tumors dissected from patients are gradually replaced by host stromal cells as the xenograft grows. This replacement by murine stroma could preclude analysis of human tumor-stroma interactions, as some mouse stromal cytokines might not affect human carcinoma cells in PDX models. The present review highlights the biological and clinical significance of PDX models and three-dimensional patient-derived tumor organoid cultures of several kinds of solid tumors, such as those of the colon, pancreas, brain, breast, lung, skin, and ovary.

The promises and challenges of patient-derived tumor organoids in drug development and precision oncology

In the era of precision medicine, cancer researchers and oncologists are eagerly searching for more realistic, cost effective, and timely tumor models to aid drug development and precision oncology. Tumor models that can faithfully recapitulate the histological and molecular characteristics of various human tumors will be extremely valuable in increasing the successful rate of oncology drug development and discovering the most efficacious treatment regimen for cancer patients. Two-dimensional (2D) cultured cancer cell lines, genetically engineered mouse tumor (GEMT) models, and patient-derived tumor xenograft (PDTX) models have been widely used to investigate the biology of various types of cancers and test the efficacy of oncology drug candidates. However, due to either the failure to faithfully recapitulate the complexity of patient tumors in the case of 2D cultured cancer cells, or high cost and untimely for drug screening and testing in the case of GEMT and PDTX, new tumor models are urgently needed. The recently developed patient-derived tumor organoids (PDTO) offer great potentials in uncovering novel biology of cancer development, accelerating the discovery of oncology drugs, and individualizing the treatment of cancers. In this review, we will summarize the recent progress in utilizing PDTO for oncology drug discovery. In addition, we will discuss the potentials and limitations of the current PDTO tumor models.

Genomic data analysis workflows for tumors from patient-derived xenografts (PDXs): challenges and guidelines

BACKGROUND

Patient-derived xenograft (PDX) models are in vivo models of human cancer that have been used for translational cancer research and therapy selection for individual patients. The Jackson Laboratory (JAX) PDX resource comprises 455 models originating from 34 different primary sites (as of 05/08/2019). The models undergo rigorous quality control and are genomically characterized to identify somatic mutations, copy number alterations, and transcriptional profiles. Bioinformatics workflows for analyzing genomic data obtained from human tumors engrafted in a mouse host (i.e., Patient-Derived Xenografts; PDXs) must address challenges such as discriminating between mouse and human sequence reads and accurately identifying somatic mutations and copy number alterations when paired non-tumor DNA from the patient is not available for comparison.

RESULTS

We report here data analysis workflows and guidelines that address these challenges and achieve reliable identification of somatic mutations, copy number alterations, and transcriptomic profiles of tumors from PDX models that lack genomic data from paired non-tumor tissue for comparison. Our workflows incorporate commonly used software and public databases but are tailored to address the specific challenges of PDX genomics data analysis through parameter tuning and customized data filters and result in improved accuracy for the detection of somatic alterations in PDX models. We also report a gene expression-based classifier that can identify EBV-transformed tumors. We validated our analytical approaches using data simulations and demonstrated the overall concordance of the genomic properties of xenograft tumors with data from primary human tumors in The Cancer Genome Atlas (TCGA).

CONCLUSIONS

The analysis workflows that we have developed to accurately predict somatic profiles of tumors from PDX models that lack normal tissue for comparison enable the identification of the key oncogenic genomic and expression signatures to support model selection and/or biomarker development in therapeutic studies. A reference implementation of our analysis recommendations is available at https://github.com/TheJacksonLaboratory/PDX-Analysis-Workflows .

Spontaneous murine tumors in the development of patient-derived xenografts: a potential pitfall

Patient-derived xenografts (PDX) are generated in immune deficient mice and demonstrate histologic and molecular features similar to their corresponding human tumors. However, murine tumors (non-human) spontaneously occur in these models. 120 consecutive patients with high-risk primary breast cancer enrolled in the prospective neoadjuvant BEAUTY study had tumor tissue obtained at the time of diagnosis. These tumor cells, including initial tissue and subsequent generations, were injected into either NSG (n = 365) or NOD-SCID (n = 396) female mice. Mice with initial tumor growth sufficient for transfer to the 2nd generation underwent histologic review by pathologists, including Ki67 staining. After passaging the tumors for up to 4 generations, at least one primary mouse tumor was detected from 24 of the 54 PDX-lines, for a frequency of 3.2% (24 mice out of 761 mice), including murine lymphomas (n = 13), mammary tumors (n = 7), osteosarcomas (n = 2), and hemangiosarcomas (n = 2). While true PDX showed scattered strong staining with Ki67, murine tumors were Ki67 negative. No significant differences (p = 0.062) were observed comparing development of murine tumors in NOD-SCID (n = 8) vs NSG mice (n = 16). While PDX are a useful tool in cancer research, there is a potential for spontaneous murine tumors to arise, which could alter results of studies utilizing PDX. Morphologic review by a pathologist, potentially along with Ki67 staining, is necessary to ensure that tumor growth represents the desired PDX prior to use in downstream studies. This study is the first prospective study evaluating the frequency, type, and time frame for development of non-human tumors.

Integrative In Vivo Drug Testing Using Gene Expression Signature and Patient-Derived Xenografts from Treatment-Refractory HER2 Positive and Triple-Negative Subtypes of Breast Cancer

Patient-derived xenografts (PDXs) are powerful tools for translational cancer research. Here, we established PDX models from different molecular subtypes of breast cancer for in vivo drug tests and compared the histopathologic features of PDX model tumors with those of patient tumors. Predictive biomarkers were identified by gene expression analysis of PDX samples using Nanostring nCount cancer panels. Validation of predictive biomarkers for treatment response was conducted in established PDX models by in vivo drug testing. Twenty breast cancer PDX models were generated from different molecular subtypes (overall success rate, 17.5%; 3.6% for HR⁺/HER2^-, 21.4% for HR⁺/HER2⁺, 21.9% for HR^-/HER2⁺ and 22.5% for triple-negative breast cancer (TNBC)). The histopathologic features of original tumors were retained in the PDX models. We detected upregulated HIF1A, RAF1, AKT2 and VEGFA in TNBC cases and demonstrated the efficacy of combined treatment with sorafenib and everolimus or docetaxel and bevacizumab in each TNBC model. Additionally, we identified upregulated HIF1A in two cases of trastuzumab-exposed HR^-/HER2⁺ PDX models and validated the efficacy of the HIF1A inhibitor, PX-478, alone or in combination with neratinib. Our results demonstrate that PDX models can be used as effective tools for predicting therapeutic markers and evaluating personalized treatment strategies in breast cancer patients with resistance to standard chemotherapy regimens.

Gene expression differences between matched pairs of ovarian cancer patient tumors and patient-derived xenografts

As patient derived xenograft (PDX) models are increasingly used for preclinical drug development, strategies to account for the nonhuman component of PDX RNA expression data are critical to its interpretation. A bioinformatics pipeline to separate donor tumor and mouse stroma transcriptome profiles was devised and tested. To examine the molecular fidelity of PDX versus donor tumors, we compared mRNA differences between paired PDX-donor tumors from nine ovarian cancer patients. 1,935 differentially expressed genes were identified between PDX and donor tumors. Over 90% (n = 1767) of these genes were down-regulated in PDX models and enriched in stroma-specific functions. Several protein kinases were also differentially expressed in PDX tumors, e.g. PDGFRA, PDGFRB and CSF1R. Upon in silico removal of these PDX-donor tumor differentially expressed genes, a stronger transcriptional resemblance between PDX-donor tumor pairs was seen (average correlation coefficient increases from 0.91 to 0.95). We devised and validated an effective bioinformatics strategy to separate mouse stroma expression from human tumor expression for PDX RNAseq. In addition, we showed most of the PDX-donor differentially expressed genes were implicated in stromal components. The molecular similarities and differences between PDX and donor tumors have implications in future therapeutic trial designs and treatment response evaluations using PDX models.

Selective colony area method for heterogeneous patient-derived tumor cell lines in anti-cancer drug screening system

We aimed to establish a fluorescence intensity-based colony area sweeping method by selecting the area of highest viability among patient-derived cancer cells (PDC) which has high tumor heterogeneity. Five gastric cancer cell lines and PDCs were screened with 24 small molecule compounds using a 3D micropillar/microwell chip. 100 tumor cells per well were immobilized in alginate, treated with the compounds, and then stained and scanned for viable cells. Dose response curves and IC50 values were obtained based on total or selected area intensity based on fluorescence. Unlike homogeneous cell lines, PDC comprised of debris and low-viability cells, which resulted in an inaccurate estimation of cell viability using total fluorescence intensity as determined by high IC50 values. However, the IC50 of these cells was lower and accurate when calculated based on the selected-colony-area method that eliminated the intensity offset associated with the heterogeneous nature of PDC. The selected-colony-area method was optimized to accurately predict drug response in micropillar environment using heterogeneous nature of PDCs.

Tumor organoids: From inception to future in cancer research

Tumor models have created new avenues for personalized medicine and drug development. A new culture model derived from a three-dimensional system, the tumor organoid, is gradually being used in many fields. An organoid can simulate the physiological structure and function of tissue in situ and maintain the characteristics of tumor cells in vivo, overcoming the disadvantages of traditional experimental tumor models. Organoids can mimic pathological features of tumors and maintain genetic stability, making them suitable for both molecular mechanism studies and pharmacological experiments of clinical transformation. In addition, the application of tumor organoids combined with other technologies, such as liquid biopsy technology, microraft array (MRA), and high-content screening (HCS), for the development of personalized diagnosis and cancer treatment has a promising future. In this review, we introduce the evolution of organoids and discuss their specific application and advantages. We also summarize the characteristics of several tumor organoids culture systems.

A large collection of integrated genomically characterized patient-derived xenografts highlighting the heterogeneity of triple-negative breast cancer

Triple-negative breast cancer (TNBC) represents 10% of all breast cancers and is a very heterogeneous disease. Globally, women with TNBC have a poor prognosis, and the development of effective targeted therapies remains a real challenge. Patient-derived xenografts (PDX) are clinically relevant models that have emerged as important tools for the analysis of drug activity and predictive biomarker discovery. The purpose of this work was to analyze the molecular heterogeneity of a large panel of TNBC PDX (n = 61) in order to test targeted therapies and identify biomarkers of response. At the gene expression level, TNBC PDX represent all of the various TNBC subtypes identified by the Lehmann classification except for immunomodulatory subtype, which is underrepresented in PDX. NGS and copy number data showed a similar diversity of significantly mutated gene and somatic copy number alteration in PDX and the Cancer Genome Atlas TNBC patients. The genes most commonly altered were TP53 and oncogenes and tumor suppressors of the PI3K/AKT/mTOR and MAPK pathways. PDX showed similar morphology and immunohistochemistry markers to those of the original tumors. Efficacy experiments with PI3K and MAPK inhibitor monotherapy or combination therapy showed an antitumor activity in PDX carrying genomic mutations of PIK3CA and NRAS genes. TNBC PDX reproduce the molecular heterogeneity of TNBC patients. This large collection of PDX is a clinically relevant platform for drug testing, biomarker discovery and translational research.

Preclinical Organotypic Models for the Assessment of Novel Cancer Therapeutics and Treatment

The immense costs in both financial terms and preclinical research effort that occur in the development of anticancer drugs are unfortunately not matched by a substantial increase in improved clinical therapies due to the high rate of failure during clinical trials. This may be due to issues with toxicity or lack of clinical effectiveness when the drug is evaluated in patients. Currently, much cancer research is driven by the need to develop therapies that can exploit cancer cell adaptations to conditions in the tumor microenvironment such as acidosis and hypoxia, the requirement for more-specific, targeted treatments, or the exploitation of 'precision medicine' that can target known genomic changes in patient DNA. The high attrition rate for novel anticancer therapies suggests that the preclinical methods used in screening anticancer drugs need improvement. This chapter considers the advantages and disadvantages of 3D organotypic models in both cancer research and cancer drug screening, particularly in the areas of targeted drugs and the exploitation of genomic changes that can be used for therapeutic advantage in precision medicine.

Three-Dimensional Organoids in Cancer Research: The Search for the Holy Grail of Preclinical Cancer Modeling

Most solid tumors become therapy resistant and will relapse, with no durable treatment option available. One major impediment to our understanding of cancer biology and finding innovative approaches to cancer treatment stems from the lack of better preclinical tumor models that address and explain tumor heterogeneity and person-to-person differences in therapeutic and toxic responses. Past cancer research has been driven by inadequate in vitro assays utilizing two-dimensional monolayers of cancer cells and animal models. Additionally, animal models do not truly mimic the original human tumor, are time consuming, and usually costly. New preclinical models are needed for innovation in cancer translational research. Hence, it is time to welcome the three-dimensional (3D) organoids: self-organizing cells grown in 3D culture systems mimicking the parent tissues from which the primary cells originate. The 3D organoids offer deeper insights into the crucial cellular processes in tissue and organ formation and pathological processes. Generation of near-perfect physiological microenvironments allow 3D organoids to couple with gene editing tools, such as the clustered regularly interspersed short palindromic repeat (CRISPR)/CRISPR-associated 9 and the transcription activator-like effector nucleases to model human diseases, offering distinct advantages over current models. We explain in this expert review that through recapitulating patients' normal and tumor tissues, organoid technology can markedly advance personalized medicine and help reveal once hidden aspects of cancers. The use of defined tissue- or organ-specific matrices, among other factors, will likely allow organoid technology to realize its potential in innovating many fields of life sciences.

The next generation personalized models to screen hidden layers of breast cancer tumorigenicity

BACKGROUND

Breast cancer (BC) is a challenging disease and major cause of death amongst women worldwide who die due to tumor relapse or sidelong diseases. BC main complexity comes from the heterogeneous nature of breast tumors that demands customized treatments in the form of personalized medicine.

REVIEW OF THE LITERATURE AND DISCUSSION

Spatiotemporally dynamic and heterogeneous nature of BC tumors is shaped by their clonal evolution and sub-clonal selections and shapes resistance to collective or group therapies that drives cancer recurrence and tumor metastasis. Personalized intervention promises to administer medications that selectively target each individual patient tumor and even further each colonized secondary tumor. Such personalized regimens will require creation of in vitro and in vivo models genuinely recapitulating characteristics of each tumor type as initiating platforms for two main purposes: to closely monitor the tumorigenic processes that shape tumor heterogeneity and evolution as the main driving forces behind tumor chemo-resistance and relapse, and subsequently to establish patient-specific preventive and therapeutic measures. While application of tumor modeling for personalized drug screening and design requires a separate review, here we discuss the personalized utilities of xenograft modeling in investigating BC tumor formation and progression toward metastasis. We will further elaborate on the impact of innovative technologies on personalized modeling of BC tumorigenicity at improved resolution.

CONCLUSION

Heterogeneous nature of each BC tumor requires personalized intervention implying that modeling breast tumors is inevitable for better disease understanding, detection and cure. Patient-derived xenografts are just the initiating piece of the puzzle for ideal management of breast cancer. Emerging technologies promise to model BC more personalized than before.

Mouse-Derived Isograft (MDI) In Vivo Tumor Models I. Spontaneous sMDI Models: Characterization and Cancer Therapeutic Approaches

Syngeneic in vivo tumor models are valuable for the development and investigation of immune-modulating anti-cancer drugs. In the present study, we established a novel syngeneic in vivo model type named mouse-derived isografts (MDIs). Spontaneous MDIs (sMDIs) were obtained during a long-term observation period (more than one to two years) of naïve and untreated animals of various mouse strains (C3H/HeJ, CBA/J, DBA/2N, BALB/c, and C57BL/6N). Primary tumors or suspicious tissues were assessed macroscopically and re-transplanted in a PDX-like manner as small tumor pieces into sex-matched syngeneic animals. Nine outgrowing primary tumors were histologically characterized either as adenocarcinomas, histiocytic carcinomas, or lymphomas. Growth of the tumor pieces after re-transplantation displayed model heterogeneity. The adenocarcinoma sMDI model JA-0009 was further characterized by flow cytometry, RNA-sequencing, and efficacy studies. M2 macrophages were found to be the main tumor infiltrating leukocyte population, whereas only a few T cells were observed. JA-0009 showed limited sensitivity when treated with antibodies against inhibitory checkpoint molecules (anti-mPD-1 and anti-mCTLA-4), but high sensitivity to gemcitabine treatment. The generated sMDI are spontaneously occurring tumors of low passage number, propagated as tissue pieces in mice without any tissue culturing, and thus conserving the original tumor characteristics and intratumoral immune cell populations.