Primary human non-small cell lung and pancreatic tumorgraft models--utility and applications in drug discovery and tumor biology

Patient Derived Xenografts (PDX) Models as an Avatar to Assess Personalized Therapy Options in Uveal Melanoma: A Feasibility Study

Uveal melanoma is the most common primary intraocular malignancy in adults. Up to 50% of UM patients develop metastatic disease, usually in the liver. When metastatic, the prognosis is poor, and few treatment options exist. Here, we investigated the feasibility of establishing patient-derived xenografts (PDXs) from a patient's tumor in order to screen for therapies that the patient could benefit from. Samples obtained from 29 primary tumors and liver metastases of uveal melanoma were grafted into SCID mice. PDX models were successfully established for 35% of primary patient tumors and 67% of liver metastases. The tumor take rate was proportional to the risk of metastases. PDXs showed the same morphology, the same GNAQ/11, BAP1, and SF3B1 mutations, and the same chromosome 3 and 8q status as the corresponding patient samples. Six PDX models were challenged with two compounds for 4 weeks. We show that, for 31% of patients with high or intermediate risk of metastasis, the timing to obtain efficacy results on PDX models derived from their primary tumors was compatible with the selection of the therapy to treat the patient after relapse. PDXs could thus be a valid tool ("avatar") to select the best personalized therapy for one third of patients that are most at risk of relapse.

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.

Porous Paclitaxel Mesh Reduces Local Recurrence in Patient-Derived Xenograft Resection Model

BACKGROUND

Drug-loaded meshes offer a promising delivery strategy for the prevention of local recurrence. Patient-derived xenograft (PDX) models are representative of individual patient tumors and predictive of clinical outcomes.

METHODS

A PDX model was established in NSG (NOD-scid IL2Rgamma^null) mice using tumor tissue from a patient with aggressive lung adenocarcinoma. Polyglycolic acid (PGA) meshes loaded with paclitaxel (PGA+PTX) were electrospun. Tumor-bearing mice were randomized into 4 groups after macroscopic complete resection: (1) no treatment (n = 10); (2) intraperitoneal PTX at 20 mg/kg (n = 10); (3) PGA mesh without drug (n = 14); and (4) PGA+PTX mesh at 12 mg/kg (n = 14). A 1-cm² mesh was placed onto the tumor resection beds. Groups were observed for local recurrence for 120 postoperative days.

RESULTS

PDX mice treated with PGA+PTX meshes after resection exhibited a >5-fold increase in recurrence-free survival (P < .0001) compared with systemically treated and untreated control groups. Median recurrence-free survival was 24 days for untreated and intraperitoneal PTX groups, 28 days for unloaded PGA mesh group, and undefined for the PGA+PTX mesh group.

CONCLUSIONS

Development of a PDX surgical resection model of non-small cell lung cancer permits robust assessment of postresection local recurrence for preclinical studies of patient-derived tumors. Intraoperative placement of drug-loaded meshes demonstrates superior local disease treatment, suggesting that this approach may improve recurrence-free survival in early-stage non-small cell lung cancer patients undergoing limited resection.

Molecular and Phenotypic Profiling for Precision Medicine in Pancreatic Cancer: Current Advances and Future Perspectives

Pancreatic cancer is the most common lethal malignancy, with little improvement in patient outcomes over the decades. The development of early detection methods and effective therapeutic strategies are needed to improve the prognosis of patients with this disease. Recent advances in cancer genomics have revealed the genetic landscape of pancreatic cancer, and clinical trials are currently being conducted to match the treatment to underlying mutations. Liquid biopsy-based diagnosis is a promising method to start personalized treatment. In addition to genome-based medicine, personalized models have been studied as a tool to test candidate drugs to select the most efficacious treatment. The innovative three-dimensional organoid culture platform, as well as patient-derived xenografts can be used to conduct genomic and functional studies to enable personalized treatment approaches. Combining genome-based medicine with drug screening based on personalized models may fulfill the promise of precision medicine for pancreatic cancer.

Current Status and Perspectives of Patient-Derived Models for Ewing's Sarcoma

Simple Summary

A paucity of patient-derived cancer models hinders the development of novel therapeutic strategies in Ewing’s sarcoma. This review aimed to demonstrate the utility and possibility of popular patient-derived cancer models by overviewing the reported studies and to encourage the researchers to develop more models for Ewing’s sarcoma.

Abstract

Patient-derived cancer models, including cell lines, organoids, and xenografts, are indispensable tools in cancer research. These models, which recapitulate molecular features of original tumors, allow studies on the biological significance of cancer-associated genes, antitumor effects of novel agents, and molecular mechanisms underlying clinical behaviors of tumors. Moreover, the predictive utility of patient-derived cancer models is expected to facilitate drug development and precision medicine. Ewing’s sarcoma is a highly aggressive mesenchymal tumor with a high metastasis rate. Previous studies demonstrated the utility of cell lines and xenografts in Ewing’s sarcoma research and clinical studies. However, the number of Ewing’s sarcoma models available from public biobanks is limited; this creates an obstacle for research on Ewing’s sarcoma. Novel Ewing’s sarcoma models are needed to establish their utility, further our understanding of the molecular mechanisms, and help develop effective therapeutic strategies. In this review, the current status of patient-derived cancer models is overviewed, and future prospects of model development are discussed from the perspective of Ewing’s sarcoma research. It should be of interest to researchers and clinicians who work on patient-derived cancer models.

[Comparison of B-NDG? and BALB/c mouse models bearing patient-derived xenografts of esophageal squamous cell carcinoma]

OBJECTIVE

To investigate the difference of tumor formation in different mouse strains bearing patient-derived xenograft of esophageal squamous cell carcinoma(ESCC) and establish a better animal model for preclinical study of individualized treatment of ESCC.

METHODS

The tumor tissues collected from 22 ESCC patients were used to establish tumor-bearing mouse models in B-NDG^? (NSG) mice and BALB/c nude mice. The tumor formation rate and tumor formation time were compared between the two mouse models, and HE staining, immunohistochemistry and genome sequencing were carried out to assess the consistency between transplanted tumor tissues in the models and patient-derived tumor tissues.

RESULTS

The tumor-bearing models were established successfully in both NSG mice (50%, 11/22) and BALB/c nude mice (18.18%, 4/22). The average tumor formation time was significantly shorter in NSG mice than in BALB/c nude mice (75.95 vs 91.67 days, P &lt; 0.001). In both of the mouse models, the transplanted tumors maintained morphological characteristics identical to those of patient-derived ESCC tumors. Genetic analysis showed that the xenografts in NSG mice had a greater genetic similarity to the patients' tumors than those in BALB/c nude mice (P &lt; 0.0001).

CONCLUSIONS

Mouse models bearing xenografts of patient-derived ESCC can be successfully established in both NSG mice and BALB/c nude mice, but the models in the former mouse strain can be more reliable.

Animal Models to Study Cancer and Its Microenvironment

Cancers are complex tissues composed by genetically altered cancer cells and stromal elements such as inflammatory/immune cells, fibroblasts, endothelial cells and pericytes, neuronal cells, and a non-cellular component, the extracellular matrix. The complex network of interactions and crosstalk established between cancer cells and the supportig cellular and non-cellular components of the microenvironment are of extreme importance for tumor initiation and progression, strongly impacting the course and the outcome of the disease. Therefore, a better understanding of the tumorigenic processes implies the combined study of the cancer cell and the biologic, chemical and mechanic constituents of the tumor microenvironment, as their concerted action plays a major role in the carcinogenic pathway and is a key determinant of the efficacy of anti-cancer treatments. The use of animal models (e.g. Mouse, Zebrafish and Drosophila) to study cancer has greatly impacted our understanding of the processes governing initiation, progression and metastasis and allowed the discovery and pre-clinical validation of novel cancer treatments as it allows to recreate tumor development in a more pathophysiologic environment.

Characterization of drug responses of mini patient-derived xenografts in mice for predicting cancer patient clinical therapeutic response

Background

Patient-derived organoids and xenografts (PDXs) have emerged as powerful models in functional diagnostics with high predictive power for anticancer drug response. However, limitations such as engraftment failure and time-consuming for establishing and expanding PDX models followed by testing drug efficacy, and inability to subject to systemic drug administration for ex vivo organoid culture hinder realistic and fast decision-making in selecting the right therapeutics in the clinic. The present study aimed to develop an advanced PDX model, namely MiniPDX, for rapidly testing drug efficacy to strengthen its value in personalized cancer treatment.

Methods

We developed a rapid in vivo drug sensitivity assay, OncoVee^® MiniPDX, for screening clinically relevant regimens for cancer. In this model, patient-derived tumor cells were arrayed within hollow fiber capsules, implanted subcutaneously into mice and cultured for 7 days. The cellular activity morphology and pharmacokinetics were systematically evaluated. MiniPDX performance (sensitivity, specificity, positive and negative predictive values) was examined using PDX as the reference. Drug responses were examined by tumor cell growth inhibition rate and tumor growth inhibition rate in PDX models and MiniPDX assays respectively. The results from MiniPDX were also used to evaluate its predictive power for clinical outcomes.

Results

Morphological and histopathological features of tumor cells within the MiniPDX capsules matched those both in PDX models and in original tumors. Drug responses in the PDX tumor graft assays correlated well with those in the corresponding MiniPDX assays using 26 PDX models generated from patients, including 14 gastric cancer, 10 lung cancer and 2 pancreatic cancer. The positive predictive value of MiniPDX was 92%, and the negative predictive value was 81% with a sensitivity of 80% and a specificity of 93%. Through expanding to clinical tumor samples, MiniPDX assay showed potential of wide clinical application.

Conclusions

Fast in vivo MiniPDX assay based on capsule implantation was developed-to assess drug responses of both PDX tumor grafts and clinical cancer specimens. The high correlation between drug responses of paired MiniPDX and PDX tumor graft assay, as well as translational data suggest that MiniPDX assay is an advanced tool for personalized cancer treatment.

A comparison of next-generation sequencing analysis methods for cancer xenograft samples

The application of next-generation sequencing (NGS) technology in cancer is influenced by the quality and purity of tissue samples. This issue is especially critical for patient-derived xenograft (PDX) models, which have proven to be by far the best preclinical tool for investigating human tumor biology, because the sensitivity and specificity of NGS analysis in xenograft samples would be compromised by the contamination of mouse DNA and RNA. This definitely affects downstream analyses by causing inaccurate mutation calling and gene expression estimates. The reliability of NGS data analysis for cancer xenograft samples is therefore highly dependent on whether the sequencing reads derived from the xenograft could be distinguished from those originated from the host. That is, each sequence read needs to be accurately assigned to its original species. Here, we review currently available methodologies in this field, including Xenome, Disambiguate, bamcmp and pdxBlacklist, and provide guidelines for users.

Establishing and Maintaining an Extensive Library of Patient-Derived Xenograft Models

Patient-derived xenograft (PDX) models have recently emerged as a highly desirable platform in oncology and are expected to substantially broaden the way in vivo studies are designed and executed and to reshape drug discovery programs. However, acquisition of patient-derived samples, and propagation, annotation and distribution of PDXs are complex processes that require a high degree of coordination among clinic, surgery and laboratory personnel, and are fraught with challenges that are administrative, procedural and technical. Here, we examine in detail the major aspects of this complex process and relate our experience in establishing a PDX Core Laboratory within a large academic institution.

Patient-Derived Bladder Cancer Xenografts

Patient-derived xenograft (PDX ) tumors are models developed by direct transplant of human tumors into immune-compromised hosts such as nude mice. These models retain the histological and genetic characteristics of the primary tumor and are considered a valuable platform for translational cancer research. This chapter describes the methodology to establish and propagate bladder cancer PDX model.

The Generation and Application of Patient-Derived Xenograft Model for Cancer Research

Establishing an appropriate preclinical model is crucial for translational cancer research. The most common way that has been adopted by far is grafting cancer cell lines, derived from patients. Although this xenograft model is easy to generate, but has several limitations because this cancer model could not represent the unique features of each cancer patient sufficiently. Moreover, accumulating evidences demonstrate cancer is a highly heterogeneous disease so that a tumor is comprised of cancer cells with diverse characteristics. In attempt to avoid these discrepancies between xenograft model and patients' tumor, a patient-derived xenograft (PDX) model has been actively generated and applied. The PDX model can be developed by the implantation of cancerous tissue from a patient's tumor into an immune-deficient mouse directly, thereby it preserves both cell-cell interactions and tumor microenvironment. In addition, the PDX model has shown advantages as a preclinical model in drug screening, biomarker development and co-clinical trial. In this review, we will summarize the methodology and applications of PDX in detail, and cover critical issues for the development of this model for preclinical research.

Circulating tumor cells and CDX models as a tool for preclinical drug development

Lung cancers are the main cause of cancer-related deaths worldwide. Efforts placed to improve the survival of lung cancer patients and untangle the complexity of this disease, have resulted in the generation of hundreds of lung cancer cell lines and several genetically engineered mouse models (GEMMs). Although these research tools have extended our knowledge of lung cancer, improvement in the clinical care of lung cancer patients have been limited overall, with measured optimism regarding initial responses to targeted therapies in stratified subgroups of patients. Patient-derived xenograft (PDX) models are beginning to assist 'personalized therapy' approaches particularly in non-small cell lung cancer (NSCLC) however biopsies of lung cancers to generate PDXs are not without challenges and risks to the patient. Liquid biopsies, on the other hand, are a rapid and non-invasive procedure allowing the collection of circulating tumor cells (CTCs) with a single 10 mL blood draw. These CTCs recapitulate the molecular heterogeneity of the corresponding tumors and, therefore, can be used as surrogates to study tumor biology and generate new patient-derived models. Here, we discuss the CTC-derived models that have been generated, most notably in small cell lung cancer (SCLC), highlighting challenges and opportunities related to these novel preclinical tools.

Patient-derived xenograft models: an emerging platform for translational cancer research

Recently, there has been an increasing interest in the development and characterization of patient-derived tumor xenograft (PDX) models for cancer research. PDX models mostly retain the principal histologic and genetic characteristics of their donor tumor and remain stable across passages. These models have been shown to be predictive of clinical outcomes and are being used for preclinical drug evaluation, biomarker identification, biologic studies, and personalized medicine strategies. This article summarizes the current state of the art in this field, including methodologic issues, available collections, practical applications, challenges and shortcomings, and future directions, and introduces a European consortium of PDX models.

SIGNIFICANCE

PDX models are increasingly used in translational cancer research. These models are useful for drug screening, biomarker development, and the preclinical evaluation of personalized medicine strategies. This review provides a timely overview of the key characteristics of PDX models and a detailed discussion of future directions in the field.

Xenopatients 2.0: reprogramming the epigenetic landscapes of patient-derived cancer genomes

In the science-fiction thriller film Minority Report, a specialized police department called "PreCrime" apprehends criminals identified in advance based on foreknowledge provided by 3 genetically altered humans called "PreCogs". We propose that Yamanaka stem cell technology can be similarly used to (epi)genetically reprogram tumor cells obtained directly from cancer patients and create self-evolving personalized translational platforms to foresee the evolutionary trajectory of individual tumors. This strategy yields a large stem cell population and captures the cancer genome of an affected individual, i.e., the PreCog-induced pluripotent stem (iPS) cancer cells, which are immediately available for experimental manipulation, including pharmacological screening for personalized "stemotoxic" cancer drugs. The PreCog-iPS cancer cells will re-differentiate upon orthotopic injection into the corresponding target tissues of immunodeficient mice (i.e., the PreCrime-iPS mouse avatars), and this in vivo model will run through specific cancer stages to directly explore their biological properties for drug screening, diagnosis, and personalized treatment in individual patients. The PreCog/PreCrime-iPS approach can perform sets of comparisons to directly observe changes in the cancer-iPS cell line vs. a normal iPS cell line derived from the same human genetic background. Genome editing of PreCog-iPS cells could create translational platforms to directly investigate the link between genomic expression changes and cellular malignization that is largely free from genetic and epigenetic noise and provide proof-of-principle evidence for cutting-edge "chromosome therapies" aimed against cancer aneuploidy. We might infer the epigenetic marks that correct the tumorigenic nature of the reprogrammed cancer cell population and normalize the malignant phenotype in vivo. Genetically engineered models of conditionally reprogrammable mice to transiently express the Yamanaka stemness factors following the activation of phenotypic copies of specific cancer diseases might crucially evaluate a "reprogramming cure" for cancer. A new era of xenopatients 2.0 generated via nuclear reprogramming of the epigenetic landscapes of patient-derived cancer genomes might revolutionize the current personalized translational platforms in cancer research.