Systematic Review of Patient-Derived Xenograft Models for Preclinical Studies of Anti-Cancer Drugs in Solid Tumors

A molecular glue RBM39-degrader induces synthetic lethality in cancer cells with homologous recombination repair deficiency

E7820 and Indisulam (E7070) are sulfonamide molecular glues that modulate RNA splicing by degrading the splicing factor RBM39 via ternary complex formation with the E3 ligase adaptor DCAF15. To identify biomarkers of the antitumor efficacy of E7820, we treated patient-derived xenograft (PDX) mouse models established from 42 patients with solid tumors. The overall response rate was 38.1% (16 PDXs), and tumor regression was observed across various tumor types. Exome sequencing of the PDX genome revealed that loss-of-function mutations in genes of the homologous recombination repair (HRR) system, such as ATM, were significantly enriched in tumors that responded to E7820 (p = 4.5 × 103). Interestingly, E7820-mediated double-strand breaks in DNA were increased in tumors with BRCA2 dysfunction, and knockdown of BRCA1/2 transcripts or knockout of ATM, ATR, or BAP1 sensitized cancer cells to E7820. Transcriptomic analyses revealed that E7820 treatment resulted in the intron retention of mRNAs and decreased transcription, especially for HRR genes. This induced HRR malfunction probably leads to the synthetic lethality of tumor cells with homologous recombination deficiency (HRD). Furthermore, E7820, in combination with olaparib, exerted a synergistic effect, and E7820 was even effective in an olaparib-resistant cell line. In conclusion, HRD is a promising predictive biomarker of E7820 efficacy and has a high potential to improve the prognosis of patients with HRD-positive cancers.

Nextflow pipeline for Visium and H&E data from patient-derived xenograft samples

We designed a Nextflow DSL2-based pipeline, Spatial Transcriptomics Quantification (STQ), for simultaneous processing of 10x Genomics Visium spatial transcriptomics data and a matched hematoxylin and eosin (H&E)-stained whole-slide image (WSI), optimized for patient-derived xenograft (PDX) cancer specimens. Our pipeline enables the classification of sequenced transcripts for deconvolving the mouse and human species and mapping the transcripts to reference transcriptomes. We align the H&E WSI with the spatial layout of the Visium slide and generate imaging and quantitative morphology features for each Visium spot. The pipeline design enables multiple analysis workflows, including single or dual reference genome input and stand-alone image analysis. We show the utility of our pipeline on a dataset from Visium profiling of four melanoma PDX samples. The clustering of Visium spots and clustering of H&E imaging features reveal similar patterns arising from the two data modalities.

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.

Contemporary preclinical mouse models for pediatric rhabdomyosarcoma: from bedside to bench to bedside

Background

Rhabdomyosarcoma (RMS) is the most common pediatric soft-tissue malignancy, characterized by high clinicalopathological and molecular heterogeneity. Preclinical in vivo models are essential for advancing our understanding of RMS oncobiology and developing novel treatment strategies. However, the diversity of scholarly data on preclinical RMS studies may challenge scientists and clinicians. Hence, we performed a systematic literature survey of contemporary RMS mouse models to characterize their phenotypes and assess their translational relevance.

Methods

We identified papers published between 01/07/2018 and 01/07/2023 by searching PubMed and Web of Science databases.

Results

Out of 713 records screened, 118 studies (26.9%) were included in the qualitative synthesis. Cell line-derived xenografts (CDX) were the most commonly utilized (n = 75, 63.6%), followed by patient-derived xenografts (PDX) and syngeneic models, each accounting for 11.9% (n = 14), and genetically engineered mouse models (GEMM) (n = 7, 5.9%). Combinations of different model categories were reported in 5.9% (n = 7) of studies. One study employed a virus-induced RMS model. Overall, 40.0% (n = 30) of the studies utilizing CDX models established alveolar RMS (aRMS), while 38.7% (n = 29) were embryonal phenotypes (eRMS). There were 20.0% (n = 15) of studies that involved a combination of both aRMS and eRMS subtypes. In one study (1.3%), the RMS phenotype was spindle cell/sclerosing. Subcutaneous xenografts (n = 66, 55.9%) were more frequently used compared to orthotopic models (n = 29, 24.6%). Notably, none of the employed cell lines were derived from primary untreated tumors. Only a minority of studies investigated disseminated RMS phenotypes (n = 16, 13.6%). The utilization areas of RMS models included testing drugs (n = 64, 54.2%), studying tumorigenesis (n = 56, 47.5%), tumor modeling (n = 19, 16.1%), imaging (n = 9, 7.6%), radiotherapy (n = 6, 5.1%), long-term effects related to radiotherapy (n = 3, 2.5%), and investigating biomarkers (n = 1, 0.8%). Notably, no preclinical studies focused on surgery.

Conclusions

This up-to-date review highlights the need for mouse models with dissemination phenotypes and cell lines from primary untreated tumors. Furthermore, efforts should be directed towards underexplored areas such as surgery, radiotherapy, and biomarkers.

Tumor Organoids: The Era of Personalized Medicine

The strategies of future medicine are aimed to modernize and integrate quality approaches including early molecular-genetic profiling, identification of new therapeutic targets and adapting design for clinical trials, personalized drug screening (PDS) to help predict and individualize patient treatment regimens. In the past decade, organoid models have emerged as an innovative in vitro platform with the potential to realize the concept of patient-centered medicine. Organoids are spatially restricted three-dimensional clusters of cells ex vivo that self-organize into complex functional structures through genetically programmed determination, which is crucial for reconstructing the architecture of the primary tissue and organs. Currently, there are several strategies to create three-dimensional (3D) tumor systems using (i) surgically resected patient tissue (PDTOs, patient-derived tumor organoids) or (ii) single tumor cells circulating in the patient's blood. Successful application of 3D tumor models obtained by co-culturing autologous tumor organoids (PDTOs) and peripheral blood lymphocytes have been demonstrated in a number of studies. Such models simulate a 3D tumor architecture in vivo and contain all cell types characteristic of this tissue, including immune system cells and stem cells. Components of the tumor microenvironment, such as fibroblasts and immune system cells, affect tumor growth and its drug resistance. In this review, we analyzed the evolution of tumor models from two-dimensional (2D) cell cultures and laboratory animals to 3D tissue-specific tumor organoids, their significance in identifying mechanisms of antitumor response and drug resistance, and use of these models in drug screening and development of precision methods in cancer treatment.

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

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

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.

Ex vivo to in vivo model of malignant peripheral nerve sheath tumors for precision oncology

BACKGROUND

Malignant peripheral nerve sheath tumors (MPNST) are aggressive soft tissue sarcomas that often develop in patients with neurofibromatosis type 1 (NF1). To address the critical need for novel therapeutics in MPNST, we aimed to establish an ex vivo 3D platform that accurately captured the genomic diversity of MPNST and could be utilized in a medium-throughput manner for drug screening studies to be validated in vivo using patient-derived xenografts (PDX).

METHODS

Genomic analysis was performed on all PDX-tumor pairs. Selected PDX were harvested for assembly into 3D microtissues. Based on prior work in our labs, we evaluated drugs (trabectedin, olaparib, and mirdametinib) ex vivo and in vivo. For 3D microtissue studies, cell viability was the endpoint as assessed by Zeiss Axio Observer. For PDX drug studies, tumor volume was measured twice weekly. Bulk RNA sequencing was performed to identify pathways enriched in cells.

RESULTS

We developed 13 NF1-associated MPNST-PDX and identified mutations or structural abnormalities in NF1 (100%), SUZ12 (85%), EED (15%), TP53 (15%), CDKN2A (85%), and chromosome 8 gain (77%). We successfully assembled PDX into 3D microtissues, categorized as robust (>90% viability at 48 h), good (>50%), or unusable (<50%). We evaluated drug response to "robust" or "good" microtissues, namely MN-2, JH-2-002, JH-2-079-c, and WU-225. Drug response ex vivo predicted drug response in vivo, and enhanced drug effects were observed in select models.

CONCLUSIONS

These data support the successful establishment of a novel 3D platform for drug discovery and MPNST biology exploration in a system representative of the human condition.

ATM-Inhibitor AZD1390 Is a Radiosensitizer for Breast Cancer CNS Metastasis

PURPOSE

Limited effective treatments are currently available for central nervous system (CNS) metastasis (CM). This is largely driven by the inability of current therapeutics to penetrate the blood brain barrier (BBB) and the lack of preclinical models for testing new therapies. Here we study the efficacy of AZD1390, a BBB penetrating ataxia-telangiectasia mutated inhibitor, as a radiosensitizer for breast cancer CM treatment.

EXPERIMENTAL DESIGN

Three patient-derived xenograft (PDX) tumors including 2 HER2+ and 1 triple-negative breast cancer harboring DNA damage response (DDR) gene mutations, were implanted subcutaneously in the flank of mice to assess tumor growth inhibition by AZD1390 combined with radiation. Animal survival was further assessed by implanting the best responding PDX model orthotopically in the brain.

RESULTS

Pretreatment with AZD1390 followed by radiation therapy inhibited growth of PDX tumors implanted in the flank, and improved survival in orthotopic models with average survival of 222 days compared with 123 days in controls. Administration of AZD1390 posttreatment for 21 days had no further benefits. While the combination therapy resulted in sustained tumor inhibition, sporadic regrowth was observed in some mice 50 to 100 days posttreatment in all models. Gene expression comparing these tumors with complete responders demonstrated changes in upregulation of oncogenic proteins, which are potential drivers of tumor growth after treatment.

CONCLUSIONS

Our results demonstrate that AZD1390 effectively sensitizes breast cancer CM to radiation therapy in DDR mutant tumors. This study demonstrates the potential of using AZD1390 as a novel therapeutic agent for patients with breast cancer CM.

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.

PROBABILISTIC LEARNING OF TREATMENT TREES IN CANCER

Accurate identification of synergistic treatment combinations and their underlying biological mechanisms is critical across many disease domains, especially cancer. In translational oncology research, preclinical systems such as patient-derived xenografts (PDX) have emerged as a unique study design evaluating multiple treatments administered to samples from the same human tumor implanted into genetically identical mice. In this paper, we propose a novel Bayesian probabilistic tree-based framework for PDX data to investigate the hierarchical relationships between treatments by inferring treatment cluster trees, referred to as treatment trees (Rx-tree). The framework motivates a new metric of mechanistic similarity between two or more treatments accounting for inherent uncertainty in tree estimation; treatments with a high estimated similarity have potentially high mechanistic synergy. Building upon Dirichlet Diffusion Trees, we derive a closed-form marginal likelihood encoding the tree structure, which facilitates computationally efficient posterior inference via a new two-stage algorithm. Simulation studies demonstrate superior performance of the proposed method in recovering the tree structure and treatment similarities. Our analyses of a recently collated PDX dataset produce treatment similarity estimates that show a high degree of concordance with known biological mechanisms across treatments in five different cancers. More importantly, we uncover new and potentially effective combination therapies that confer synergistic regulation of specific downstream biological pathways for future clinical investigations. Our accompanying code, data, and shiny application for visualization of results are available at: https://github.com/bayesrx/RxTree.

Nextflow Pipeline for Visium and H&E Data from Patient-Derived Xenograft Samples

Highlights

We have developed an automated data processing pipeline to quantify mouse and human data from patient-derived xenograft samples assayed by Visium spatial transcriptomics with matched hematoxylin and eosin (H&E) stained image. We enable deconvolution of reads with Xenome, quantification of spatial gene expression from host and graft species with Space Ranger, extraction of B-allele frequencies, and splicing quantification with Velocyto. In the H&E image processing sub-workflow, we generate morphometric and deep learning-derived feature quantifications complementary to the Visium spots, enabling multi-modal H&E/expression comparisons. We have wrapped the pipeline into Nextflow DSL2 in a scalable, portable, and easy-to-use framework.

Summary

We designed a Nextflow DSL2-based pipeline, Spatial Transcriptomics Quantification (STQ), for simultaneous processing of 10x Genomics Visium spatial transcriptomics data and a matched hematoxylin and eosin (H&E)-stained whole slide image (WSI), optimized for Patient-Derived Xenograft (PDX) cancer specimens. Our pipeline enables the classification of sequenced transcripts for deconvolving the mouse and human species and mapping the transcripts to reference transcriptomes. We align the H&E WSI with the spatial layout of the Visium slide and generate imaging and quantitative morphology features for each Visium spot. The pipeline design enables multiple analysis workflows, including single or dual reference genomes input and stand-alone image analysis. We showed the utility of our pipeline on a dataset from Visium profiling of four melanoma PDX samples. The clustering of Visium spots and clustering of imaging features of H&E data reveal similar patterns arising from the two data modalities.

Optimizing Precision Medicine for Breast Cancer Brain Metastases with Functional Drug Response Assessment

The development of novel therapies for brain metastases is an unmet need. Brain metastases may have unique molecular features that could be explored as therapeutic targets. A better understanding of the drug sensitivity of live cells coupled to molecular analyses will lead to a rational prioritization of therapeutic candidates. We evaluated the molecular profiles of 12 breast cancer brain metastases (BCBM) and matched primary breast tumors to identify potential therapeutic targets. We established six novel patient-derived xenograft (PDX) from BCBM from patients undergoing clinically indicated surgical resection of BCBM and used the PDXs as a drug screening platform to interrogate potential molecular targets. Many of the alterations were conserved in brain metastases compared with the matched primary. We observed differential expressions in the immune-related and metabolism pathways. The PDXs from BCBM captured the potentially targetable molecular alterations in the source brain metastases tumor. The alterations in the PI3K pathway were the most predictive for drug efficacy in the PDXs. The PDXs were also treated with a panel of over 350 drugs and demonstrated high sensitivity to histone deacetylase and proteasome inhibitors. Our study revealed significant differences between the paired BCBM and primary breast tumors with the pathways involved in metabolisms and immune functions. While molecular targeted drug therapy based on genomic profiling of tumors is currently evaluated in clinical trials for patients with brain metastases, a functional precision medicine strategy may complement such an approach by expanding potential therapeutic options, even for BCBM without known targetable molecular alterations.

Significance

Examining genomic alterations and differentially expressed pathways in brain metastases may inform future therapeutic strategies. This study supports genomically-guided therapy for BCBM and further investigation into incorporating real-time functional evaluation will increase confidence in efficacy estimations during drug development and predictive biomarker assessment for BCBM.

A 3D Perfusable Platform for In Vitro Culture of Patient Derived Xenografts

Many advanced cancer models, such as patient-derived xenografts (PDXs), offer significant benefits in their preservation of the native tumor's heterogeneity and susceptibility to treatments, but face significant barriers to use in their reliance on a rodent host for propagation and screening. PDXs remain difficult to implement in vitro, particularly in configurations that enable both detailed cellular analysis and high-throughput screening (HTS). Complex multilineage co-cultures with stromal fibroblasts, endothelium, and other cellular and structural components of the tumor microenvironment (TME) further complicate ex vivo implementation. Herein, the culture of multiple prostate cancer (PCa)-derived PDX models as 3D clusters within engineered biomimetic hydrogel matrices, in a HTS-compatible multiwell microfluidic format, alongside bone marrow-derived stromal cells and a perfused endothelial channel. Polymeric hydrogel matrices are customized for each cell type, enabling cell survival in vitro and facile imaging across all conditions. PCa PDXs demonstrate unique morphologies and reliance on TME partners, retention of known phenotype, and expected sensitivity or resistance to standard PCa therapeutics. This novel integration of technologies provides a fully human model, and expands the information to be gathered from each specimen, while avoiding the time and labor involved with animal-based testing.

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

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

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

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

Eribulin inhibits growth of cutaneous squamous cell carcinoma cell lines and a novel patient-derived xenograft

Advanced cutaneous squamous cell carcinoma (cSCC) is treated with chemotherapy and/or radiotherapy, but these typically fail to achieve satisfactory clinical outcomes. There have been no preclinical studies to evaluate the effectiveness of eribulin against cSCC. Here, we examine the effects of eribulin using cSCC cell lines and a novel cSCC patient-derived xenograft (PDX) model. In the cSCC cell lines (A431 and DJM-1 cells), eribulin was found to inhibit tumor cell proliferation in vitro as assessed by cell ATP levels. DNA content analysis by fluorescence-activated cell sorting (FACS) showed that eribulin induced G2/M cell cycle arrest and apoptosis. In xenograft models of cSCC cell lines, the administration of eribulin suppressed tumor growth in vivo. We also developed a cSCC patient-derived xenograft (PDX) which reproduces the histological and genetic characteristics of a primary tumor. Pathogenic mutations in TP53 and ARID2 were detected in the patient's metastatic tumor and in the PDX tumor. The cSCC-PDX responded well to the administration of eribulin and cisplatin. In conclusion, the present study shows the promising antineoplastic effects of eribulin in cSCC. Also, we established a novel cSCC-PDX model that preserves the patient's tumor. This PDX could assist researchers who are exploring innovative therapies for cSCC.

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

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

Application of a 3D hydrogel-based model to replace use of animals for passaging patient-derived xenografts

Purpose

This 3D in vitro cancer model for propagation of patient-derived cells, using a synthetic self-assembling peptide gel, allows the formation of a fully characterised, tailorable tumour microenvironment. Unlike many existing 3D cancer models, the peptide gel is inert, apart from molecules and motifs deliberately added or produced by cells within the model.

Methods

Breast cancer patient-derived xenografts (PDXs) were disaggregated and embedded in a peptide hydrogel. Growth was monitored by microscopic examination and at intervals, cells were extracted from the gels and passaged on into fresh gels. Passaged cells were assessed by qPCR and immunostaining techniques for the retention of characteristic markers.

Results

Breast cancer PDXs were shown to be capable of expansion over four or more passages in the peptide gel. Contaminating mouse cells were found to be rapidly removed by successive passages. The resulting human cells were shown to be compatible with a range of common assays useful for assessing survival, growth and maintenance of heterogeneity.

Conclusions

Based on these findings, the hydrogel has the potential to provide an effective and practical breast cancer model for the passage of PDXs which will have the added benefits of being relatively cheap, fully-defined and free from the use of animals or animal products. Encapsulated cells will require further validation to confirm the maintenance of cell heterogeneity, genotypes and phenotypes across passage, but with further development, including the addition of bespoke cell and matrix components of the tumour microenvironment, there is clear potential to model other cancer types.

Supplementary Information

The online version contains supplementary material available at 10.1007/s44164-023-00048-x.

Model-based assessment of combination therapies - ranking of radiosensitizing agents in oncology

BACKGROUND

To increase the chances of finding efficacious anticancer drugs, improve development times and reduce costs, it is of interest to rank test compounds based on their potential for human use as early as possible in the drug development process. In this paper, we present a method for ranking radiosensitizers using preclinical data.

METHODS

We used data from three xenograft mice studies to calibrate a model that accounts for radiation treatment combined with radiosensitizers. A nonlinear mixed effects approach was utilized where between-subject variability and inter-study variability were considered. Using the calibrated model, we ranked three different Ataxia telangiectasia-mutated inhibitors in terms of anticancer activity. The ranking was based on the Tumor Static Exposure (TSE) concept and primarily illustrated through TSE-curves.

RESULTS

The model described data well and the predicted number of eradicated tumors was in good agreement with experimental data. The efficacy of the radiosensitizers was evaluated for the median individual and the 95% population percentile. Simulations predicted that a total dose of 220 Gy (5 radiation sessions a week for 6 weeks) was required for 95% of tumors to be eradicated when radiation was given alone. When radiation was combined with doses that achieved at least 8 [Formula: see text] of each radiosensitizer in mouse blood, it was predicted that the radiation dose could be decreased to 50, 65, and 100 Gy, respectively, while maintaining 95% eradication.

CONCLUSIONS

A simulation-based method for calculating TSE-curves was developed, which provides more accurate predictions of tumor eradication than earlier, analytically derived, TSE-curves. The tool we present can potentially be used for radiosensitizer selection before proceeding to subsequent phases of the drug discovery and development process.

A high-density 3-dimensional culture model of human glioblastoma for rapid screening of therapeutic resistance

Glioblastoma is among the most lethal cancers, with no known cure. A multitude of therapeutics are being developed or in clinical trials, but currently there are no ways to predict which patient may benefit the most from which drug. Assays that allow prediction of the tumor's response to anti-cancer drugs may improve clinical decision-making. Here, we present a high-density 3D primary cell culture model for short-term testing from resected glioblastoma tissue that is set up on the day of surgery, established within 7 days and viable for at least 3 weeks. High-density 3D cultures contain tumor and host cells, including microglia, and retain key histopathological characteristics of their parent tumors, including proliferative activity, expression of the marker GFAP, and presence of giant cells. This provides a proof-of-concept that 3D primary cultures may be useful to model tumor heterogeneity. Importantly, we show that high-density 3D cultures can be used to test chemotherapy response within a 2-3-week timeframe and are predictive of patient response to Temozolomide therapy. Thus, primary high-density 3D cultures could be a useful tool for brain cancer research and prediction of therapeutic resistance.

Identifying biomarkers of differential chemotherapy response in TNBC patient-derived xenografts with a CTD/WGCNA approach

Although systemic chemotherapy remains the standard of care for TNBC, even combination chemotherapy is often ineffective. The identification of biomarkers for differential chemotherapy response would allow for the selection of responsive patients, thus maximizing efficacy and minimizing toxicities. Here, we leverage TNBC PDXs to identify biomarkers of response. To demonstrate their ability to function as a preclinical cohort, PDXs were characterized using DNA sequencing, transcriptomics, and proteomics to show consistency with clinical samples. We then developed a network-based approach (CTD/WGCNA) to identify biomarkers of response to carboplatin (MSI1, TMSB15A, ARHGDIB, GGT1, SV2A, SEC14L2, SERPINI1, ADAMTS20, DGKQ) and docetaxel (c, MAGED4, CERS1, ST8SIA2, KIF24, PARPBP). CTD/WGCNA multigene biomarkers are predictive in PDX datasets (RNAseq and Affymetrix) for both taxane- (docetaxel or paclitaxel) and platinum-based (carboplatin or cisplatin) response, thereby demonstrating cross-expression platform and cross-drug class robustness. These biomarkers were also predictive in clinical datasets, thus demonstrating translational potential.

De Novo Design of Reversibly pH-Switchable NIR-II Aggregation-Induced Emission Luminogens for Efficient Phototheranostics of Patient-Derived Tumor Xenografts

Phototheranostics has received sustained attention due to its great potential in revolutionizing conventional strategies of cancer treatment. However, trapped by the complexity, poor reproducibility, insufficient phototheranostic outputs, and inevitable damage to normal tissue of most multicomponent phototheranostic systems, its clinical translation has been severely hindered. Therefore, the exploration of "one for all" smart phototheranostic agents with versatile functionalities remains an appealing yet enormously challenging task. Herein, a reversibly pH-switchable and near-infrared second photosensitizer featuring aggregation-induced emission was tactfully designed by molecular engineering for precise tumor-targeting fluorescence imaging-guided phototherapy. Thanks to the strong intramolecular charge transfer, enhanced highly efficient intersystem crossing, and sufficient intramolecular motion, the developed agent DTTVBI was endowed with boosted type-I superoxide anion radical generation and excellent photothermal performance under 808 nm laser irradiation. More importantly, DTTVBI nanoparticles with high biocompatibility exhibit remarkably enhanced type-I photodynamic/photothermal therapy in the tumor region, thus offering significant antitumor effects both in vitro and in the patient-derived tumor xenograft model of colon cancer. This work sheds new light on the development of superior versatile phototheranostics for cancer therapy.

Optimizing Drug Response Study Design in Patient-Derived Tumor Xenografts

Patient-derived tumor xenograft (PDX) models were used to evaluate the effectiveness of preclinical anticancer agents. A design using 1 mouse per patient per drug (1 × 1 × 1) was considered practical for large-scale drug efficacy studies. We evaluated modifiable parameters that could increase the statistical power of this design based on our consolidated PDX experiments. Real studies were used as a reference to investigate the relationship between statistical power with treatment effect size, inter-mouse variation, and tumor measurement frequencies. Our results showed that large effect sizes could be detected at a significance level of .2 or .05 under a 1 × 1 × 1 design. We found that the minimum number of mice required to achieve 80% power at an alpha level of .05 under all situations explored was 21 mice per group for a small effect size and 5 mice per group for a medium effect size.

The Barretos Cancer Hospital Animal Facility: Implementation and Results of a Dedicated Platform for Preclinical Oncology Models

The Barretos Cancer Hospital Animal Facility (BCHAF) is a unique facility in Brazil exclusively dedicated to working with animal models for cancer research. In this article, we briefly present our modern facility and the main experiments performed, focusing on mutant strains of mice (PTCH-knockout and ApcMin mice), xenograft models, and patient-derived xenografts (PDXs). Our results show the progress and challenges in establishing these models and the need for having an appropriate representation of our cancer population to better understand tumor biology and to identify cancer biomarkers, which could be putatively targeted, allowing for personalized therapy.

Generation and Integrated Analysis of Advanced Patient-Derived Orthoxenograft Models (PDOX) for the Rational Assessment of Targeted Therapies in Endometrial Cancer

Clinical management of endometrial cancer (EC) is handicapped by the limited availability of second line treatments and bona fide molecular biomarkers to predict recurrence. These limitations have hampered the treatment of these patients, whose survival rates have not improved over the last four decades. The advent of coordinated studies such as The Cancer Genome Atlas Uterine Corpus Endometrial Carcinoma (TCGA_UCEC) has partially solved this issue, but the lack of proper experimental systems still represents a bottleneck that precludes translational studies from successful clinical testing in EC patients. Within this context, the first study reporting the generation of a collection of endometrioid-EC-patient-derived orthoxenograft (PDOX) mouse models is presented that is believed to overcome these experimental constraints and pave the way toward state-of-the-art precision medicine in EC. The collection of primary tumors and derived PDOXs is characterized through an integrative approach based on transcriptomics, mutational profiles, and morphological analysis; and it is demonstrated that EC tumors engrafted in the mouse uterus retain the main molecular and morphological features from analogous tumor donors. Finally, the molecular properties of these tumors are harnessed to assess the therapeutic potential of trastuzumab, a human epidermal growth factor receptor 2 (HER2) inhibitor with growing interest in EC, using patient-derived organotypic multicellular tumor spheroids and in vivo experiments.

Gold-Nanoparticle Hybrid Nanostructures for Multimodal Cancer Therapy

With the urgent need for bio-nanomaterials to improve the currently available cancer treatments, gold nanoparticle (GNP) hybrid nanostructures are rapidly rising as promising multimodal candidates for cancer therapy. Gold nanoparticles (GNPs) have been hybridized with several nanocarriers, including liposomes and polymers, to achieve chemotherapy, photothermal therapy, radiotherapy, and imaging using a single composite. The GNP nanohybrids used for targeted chemotherapy can be designed to respond to external stimuli such as heat or internal stimuli such as intratumoral pH. Despite their promise for multimodal cancer therapy, there are currently no reviews summarizing the current status of GNP nanohybrid use for cancer theragnostics. Therefore, this review fulfills this gap in the literature by providing a critical analysis of the data available on the use of GNP nanohybrids for cancer treatment with a specific focus on synergistic approaches (i.e., triggered drug release, photothermal therapy, and radiotherapy). It also highlights some of the challenges that hinder the clinical translation of GNP hybrid nanostructures from bench to bedside. Future studies that could expedite the clinical progress of GNPs, as well as the future possibility of improving GNP nanohybrids for cancer theragnostics, are also summarized.

Establishment and genetically characterization of patient-derived xenograft models of cervical cancer

Purpose

Patient-derived xenograft (PDX) models were established to reproduce the clinical situation of original cancers and have increasingly been applied to preclinical cancer research. Our study was designed to establish and genetically characterize cervical cancer PDX models.

Methods

A total of 91 fresh fragments obtained from 22 surgically resected cervical cancer tissues were subcutaneously engrafted into female NOD-SCID mice. Hematoxylin and eosin (H&E) staining was performed to assess whether the established PDX models conserved the histological features of original patient cervical cancer tissues. Moreover, a Venn diagram was applied to display the overlap of all mutations detected in whole-genome sequencing (WGS) data from patient original cervical cancer (F0) and F2-, F3-PDX models. The whole exome sequencing (WES) and the “maftools” package were applied to determine the somatic mutations among primary cervical cancers and the established PDX models.

Results

Our study successfully developed a panel of cervical cancer PDX models and the latency time of cervical cancer PDX model establishment was variable with a progressive decrease as the passage number increased, with a mean time to initial growth of 94.71 days in F1 engraftment to 40.65 days in F3 engraftment. Moreover, the cervical cancer PDX models preserved the histological features of their original cervical cancer. WGS revealed that the genome of original cervical cancer was preserved with high fidelity in cervical cancer PDX models throughout the xenografting and passaging process. Furthermore, WES demonstrated that the cervical cancer PDX models maintained the majority somatic mutations of original cervical cancer, of which the KMT2D, LRP1B, NAV3, TP53, FAT1, MKI67 and PKHD1L1 genes were identified as the most frequently mutated genes.

Conclusions

The cervical cancer PDX models preserved the histologic and genetic characteristics of their original cervical cancer, which helped to gain a deeper insight into the genetic alterations and lay a foundation for further investigation of the molecular targeted therapy of cervical cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-022-01342-5.

Evaluating Translational Methods for Personalized Medicine—A Scoping Review

The introduction of personalized medicine, through the increasing multi-omics characterization of disease, brings new challenges to disease modeling. The scope of this review was a broad evaluation of the relevance, validity, and predictive value of the current preclinical methodologies applied in stratified medicine approaches. Two case models were chosen: oncology and brain disorders. We conducted a scoping review, following the Joanna Briggs Institute guidelines, and searched PubMed, EMBASE, and relevant databases for reports describing preclinical models applied in personalized medicine approaches. A total of 1292 and 1516 records were identified from the oncology and brain disorders search, respectively. Quantitative and qualitative synthesis was performed on a final total of 63 oncology and 94 brain disorder studies. The complexity of personalized approaches highlights the need for more sophisticated biological systems to assess the integrated mechanisms of response. Despite the progress in developing innovative and complex preclinical model systems, the currently available methods need to be further developed and validated before their potential in personalized medicine endeavors can be realized. More importantly, we identified underlying gaps in preclinical research relating to the relevance of experimental models, quality assessment practices, reporting, regulation, and a gap between preclinical and clinical research. To achieve a broad implementation of predictive translational models in personalized medicine, these fundamental deficits must be addressed.

In vitro grafting of hepatic spheroids and organoids on a microfluidic vascular bed

With recent progress in modeling liver organogenesis and regeneration, the lack of vasculature is becoming the bottleneck in progressing our ability to model human hepatic tissues in vitro. Here, we introduce a platform for routine grafting of liver and other tissues on an in vitro grown microvascular bed. The platform consists of 64 microfluidic chips patterned underneath a 384-well microtiter plate. Each chip allows the formation of a microvascular bed between two main lateral vessels by inducing angiogenesis. Chips consist of an open-top microfluidic chamber, which enables addition of a target tissue by manual or robotic pipetting. Upon grafting a liver microtissue, the microvascular bed undergoes anastomosis, resulting in a stable, perfusable vascular network. Interactions with vasculature were found in spheroids and organoids upon 7 days of co-culture with space of Disse-like architecture in between hepatocytes and endothelium. Veno-occlusive disease was induced by azathioprine exposure, leading to impeded perfusion of the vascularized spheroid. The platform holds the potential to replace animals with an in vitro alternative for routine grafting of spheroids, organoids, or (patient-derived) explants.

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

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

PDXNet portal: patient-derived Xenograft model, data, workflow and tool discovery

We created the PDX Network (PDXNet) portal (https://portal.pdxnetwork.org/) to centralize access to the National Cancer Institute-funded PDXNet consortium resources, to facilitate collaboration among researchers and to make these data easily available for research. The portal includes sections for resources, analysis results, metrics for PDXNet activities, data processing protocols and training materials for processing PDX data. Currently, the portal contains PDXNet model information and data resources from 334 new models across 33 cancer types. Tissue samples of these models were deposited in the NCI's Patient-Derived Model Repository (PDMR) for public access. These models have 2134 associated sequencing files from 873 samples across 308 patients, which are hosted on the Cancer Genomics Cloud powered by Seven Bridges and the NCI Cancer Data Service for long-term storage and access with dbGaP permissions. The portal includes results from freely available, robust, validated and standardized analysis workflows on PDXNet sequencing files and PDMR data (3857 samples from 629 patients across 85 disease types). The PDXNet portal is continuously updated with new data and is of significant utility to the cancer research community as it provides a centralized location for PDXNet resources, which support multi-agent treatment studies, determination of sensitivity and resistance mechanisms, and preclinical trials.

Correlation between Patient-Derived Xenograft Modeling and Prognosis in Osteosarcoma

Objective

To retrospectively analyze and compare the relationship between the success rate of patient‐derived xenograft (PDX) modeling of osteosarcoma and prognosis (3‐year overall survival rate and disease‐free survival rate) and incidence of lung metastasis.

Methods

The sample group consisted of 57 osteosarcoma patients with definite pathological diagnoses from Shanghai General Hospital from 2015–2017. PDX models in 57 patients were analyzed by retrospective analyses. Among the patients currently inoculated, 20 were tumorigenic in the PDX model, and 37 were nontumorigenic. According to the tumorigenicity of PDXs, the corresponding osteosarcoma patients were divided into two groups. The effects of clinically related indicators on the model were retrospectively compared. The patients were followed, and the 3‐year survival, 3‐year disease‐free survival (DFS), and lung metastasis rates were collected. The relationship between the modeling success and patient prognosis was investigated.

Results

In the chemotherapy‐treated group, the PDX modeling success rate was 17.4%, and in the nonchemotherapy group, the success rate was 47.1%. The success of PDX modeling was related to whether patients received chemotherapy. The success rate of PDX modeling is significantly reduced after receiving chemotherapy. The 3‐year overall survival rate of the PDX‐grafted group was 49.23%, and that of the PDX‐nongrafted group was 65.71%. There was a significant difference between the two groups, showing a strong negative correlation between the 3‐year survival rate and the success rate of the PDX model. The 3‐year disease‐free survival rate of the PDX‐grafted group was 29.54%. The 3‐year DFS of the PDX‐nongrafted group was 50.34%. There was a significant difference between the two groups. Lower grafted rates indicate a higher DFS rate. The incidence of lung metastasis in the PDX‐grafted group was 32.4%, and that in the nongrafted group was 13.1%. There was a significant difference between the two groups. The successful establishment of the PDX model indicates that patients are more likely to have lung metastases.

Conclusions

The success of PDX modeling often indicates poor prognosis (low 3‐year overall survival rate and disease‐free survival rate) and a greater possibility of lung metastasis. Therefore, PDX modeling in osteosarcoma patients can accurately predict the prognosis of patients and the risk of lung metastasis in advance to help us develop better therapeutic strategies.

Use of conditional reprogramming cell, patient derived xenograft and organoid for drug screening for individualized prostate cancer therapy: Current and future perspectives (Review)

Prostate cancer mortality is ranked second among all cancer mortalities in men worldwide. There is a great need for a method of efficient drug screening for precision therapy, especially for patients with existing drug‑resistant prostate cancer. Based on the concept of bacterial cell culture and drug sensitivity testing, the traditional approach of cancer drug screening is inadequate. The current and more innovative use of cancer cell culture and in vivo tumor models in drug screening for potential individualization of anti‑cancer therapy is reviewed and discussed in the present review. An ideal screening model would have the ability to identify drug activity for the targeted cells resembling what would have occurred in the in vivo environment. Based on this principle, three available cell culture/tumor screening models for prostate cancer are reviewed and considered. The culture conditions, advantages and disadvantages for each model together with ideas to best utilize these models are discussed. The first screening model uses conditional reprogramed cells derived from patient cancer cells. Although these cells are convenient to grow and use, they are likely to have different markers and characteristics from original tumor cells and thus not likely to be informative. The second model employs patient derived xenograft (PDX) which resembles an in vivo approach, but its main disadvantages are that it cannot be easily genetically modified and it is not suitable for high‑throughput drug screening. Finally, high‑throughput screening is more feasible with tumor organoids grown from patient cancer cells. The last system still needs a large number of tumor cells. It lacks in situ blood vessels, immune cells and the extracellular matrix. Based on these current models, future establishment of an organoid data bank would allow the selection of a specific organoid resembling that of an individual's prostate cancer and used for screening of suitable anticancer drugs. This can be further confirmed using the PDX model. Thus, this combined organoid‑PDX approach is expected to be able to provide the drug sensitivity testing approach for individualization of prostate cancer therapy in the near future.

Establishing Imaging Biomarkers of Host Immune System Efficacy during Glioblastoma Therapy Response: Challenges, Obstacles and Future Perspectives

This hypothesis proposal addresses three major questions: (1) Why do we need imaging biomarkers for assessing the efficacy of immune system participation in glioblastoma therapy response? (2) Why are they not available yet? and (3) How can we produce them? We summarize the literature data supporting the claim that the immune system is behind the efficacy of most successful glioblastoma therapies but, unfortunately, there are no current short-term imaging biomarkers of its activity. We also discuss how using an immunocompetent murine model of glioblastoma, allowing the cure of mice and the generation of immune memory, provides a suitable framework for glioblastoma therapy response biomarker studies. Both magnetic resonance imaging and magnetic resonance-based metabolomic data (i.e., magnetic resonance spectroscopic imaging) can provide non-invasive assessments of such a system. A predictor based in nosological images, generated from magnetic resonance spectroscopic imaging analyses and their oscillatory patterns, should be translational to clinics. We also review hurdles that may explain why such an oscillatory biomarker was not reported in previous imaging glioblastoma work. Single shot explorations that neglect short-term oscillatory behavior derived from immune system attack on tumors may mislead actual response extent detection. Finally, we consider improvements required to properly predict immune system-mediated early response (1–2 weeks) to therapy. The sensible use of improved biomarkers may enable translatable evidence-based therapeutic protocols, with the possibility of extending preclinical results to human patients.

Personalized Treatment of Advanced Gastric Cancer Guided by the MiniPDX Model

Background

The morbidity and mortality of gastric cancer are high in China. There are challenges to develop precise and individualized drug regimens for patients with gastric cancer after a standard treatment. Choosing the most appropriate anticancer drug after a patient developing drug resistance is very important to improve the patient's prognosis. MiniPDX has been widely used as a new and reliable preclinical research model to predict the sensitivity of anticancer drugs.

Methods

The OncoVee® MiniPDX system developed by Shanghai LIDE Biotech Co., Ltd. was used to establish the MiniPDX models using specimens of patients with gastric cancer. The cancer tissues were biopsied under endoscopy, and then, the tumor cell suspension was prepared for a drug sensitivity test by subcutaneously implanting into Balb/c-nude mice. The selected optimal regimen obtained from the MiniPDX assay was used to treat patients with drug-resistant gastric cancer.

Results

We successfully established an individualized and sensitive drug screening system for four patients from January 2021 to July 2021. MiniPDX models identified potentially effective drugs for these four patients, with partial remission in two of the patients after treatment and disease progression in the remaining of two patients. Severe side effects from chemotherapy or targeted therapy were not observed in all patients.

Conclusion

Establishing a personalized drug screening system for patients with drug-resistant gastric cancer can guide the selection of clinical drugs, improve the clinical benefit of patients, and avoid ineffective treatments. It can be an effective supplement for treatment options.

Synthesis of Novel Suramin Analogs With Anti-Proliferative Activity via FGF1 and FGFRD2 Blockade

A promising approach in cancer therapy is the inhibition of cell proliferation using small molecules. In this study, we report the synthesis of suramin derivatives and their applications. We used NMR spectroscopy and docking simulations to confirm binding sites and three-dimensional models of the ligand-protein complex. The WST-1 assay was used to assess cell viability and cell proliferation in vitro to evaluate the inhibition of protein-protein interactions and to investigate the anti-proliferative activities in a breast cancer cell line. All the suramin derivatives showed anti-proliferative activity by blocking FGF1 binding to its receptor FGFRD2. The dissociation constant was measured by fluorescence spectroscopy. The suramin compound derivatives synthesized herein show potential as novel therapeutic agents for their anti-proliferative activity via the inhibition of protein-protein interactions. The cytotoxicity of these suramin derivatives was lower than that of the parent suramin compound, which may be considered a significant advancement in this field. Thus, these novel suramin derivatives may be considered superior anti-metastasis molecules than those of suramin.

Microsatellite instability-high colorectal cancer patient-derived xenograft models for cancer immunity research

Context

There is an increasing demand for appropriate preclinical mice models for evaluating the efficacy of cancer immunotherapies.

Aims

Therefore, we established a humanized patient-derived xenograft (PDX) model using microsatellite instability-high (MSI-H) colorectal cancer (CRC) tissues and patient-derived peripheral blood mononuclear cells (PBMCs).

Subjects and Methods

The CRC tissues of patients scheduled for surgery were tested for MSI status, and CRC tumors were transplanted into NOD/LtSz-scid/IL-2Rg-/-(NSG) mice to establish MSI-H PDX models. PDX tumors were compared to the original patient tumors in terms of histological and genetic characteristics. To humanize the immune system of MSI-H PDX models, patient PBMCs were injected through the tail vein.

Results

PDX models were established from two patients with MSI-H CRC; one patient had a germline mutation in MLH1 (c.1990-2A > G), and the other patient had MLH1 promoter hypermethylation. PDX with the germline mutation was histologically similar to the patient tumor, and retained the genetic characteristics, including MSI-H, deficient mismatch repair (dMMR), and MLH1 mutation. In contrast, the histological features of the other PDX from a tumor with MLH1 promoter hypermethylation were clearly different from those of the original tumor, and MLH1 promoter hypermethylation and MSI-H/dMMR were lost in the PDX. When T cells from the same patient with MLH1 mutation were injected into the PDX through the tail vein, they were detected in the PDX tumor.

Conclusions

The MSI-H tumor with an MMR mutation is suitable for MSI-H PDX model generation. The PBMC humanized MSI-H PDX has the potential to be used as an efficient model for cancer immunotherapy research.

Role of Biobanks for Cancer Research and Precision Medicine in Hepatocellular Carcinoma

INTRODUCTION

Hepatocellular carcinoma (HCC) is a highly complex and deadly cancer. There is an urgent need for new and effective treatment modalities. Since the primary goal in the management of cancer is to cure and improve survival, personalized therapy can increase survival, reduce mortality rates, and improve quality of life. Biobanks hold potential in leading to breakthroughs in biomedical research and precision medicine (PM). They serve as a biorepository, collecting, processing, storing, and supplying specimens and relevant data for basic, translational, and clinical research.

OBJECTIVE

We aimed to highlight the fundamental role of biobanks, harboring high quality, sustainable collections of patient samples in adequate size and variability, for developing diagnostic, prognostic, and predictive biomarkers to develop and PM approaches in the management of HCC.

METHOD

We obtained information from previously published articles and BBMRI directory.

RESULTS AND CONCLUSION

Biobanking of high-quality biospecimens along with patient clinical information provides a fundamental scientific infrastructure for basic, translational, and clinical research. Biobanks that control and eliminate pre-analytical variability of biospecimens, provide a platform to identify reliable biomarkers for the application of PM. We believe, establishing HCC biobanks will empower to underpin molecular mechanisms of HCC and generate strategies for PM. Thus, first, we will review current therapy approaches in HCC care. Then, we will summarize challenges in HCC management. Lastly, we will focus on the best practices for establishing HCC biobanking to support research, translational medicine in the light of new experimental research conducted with the aim of delivering PM for HCC patients.

Deciphering intratumoral heterogeneity using integrated clonal tracking and single-cell transcriptome analyses

Cellular heterogeneity is a major cause of treatment resistance in cancer. Despite recent advances in single-cell genomic and transcriptomic sequencing, it remains difficult to relate measured molecular profiles to the cellular activities underlying cancer. Here, we present an integrated experimental system that connects single cell gene expression to heterogeneous cancer cell growth, metastasis, and treatment response. Our system integrates single cell transcriptome profiling with DNA barcode based clonal tracking in patient-derived xenograft models. We show that leukemia cells exhibiting unique gene expression respond to different chemotherapies in distinct but consistent manners across multiple mice. In addition, we uncover a form of leukemia expansion that is spatially confined to the bone marrow of single anatomical sites and driven by cells with distinct gene expression. Our integrated experimental system can interrogate the molecular and cellular basis of the intratumoral heterogeneity underlying disease progression and treatment resistance.

DNA barcoding is a promising technology for the simultaneous analysis of genetic and phenotypic heterogeneity. Here, the authors combine DNA barcoding and single-cell RNA-seq to study heterogeneity, progression and response to therapy in B-cell acute lymphoblastic leukaemia patient-derived xenografts.

Human Organoid and Supporting Technologies for Cancer and Toxicological Research

Recent progress in the field of organoid-based cell culture systems has enabled the use of patient-derived cells in conditions that resemble those in cancer tissue, which are better than two-dimensional (2D) cultured cell lines. In particular, organoids allow human cancer cells to be handled in conditions that resemble those in cancer tissue, resulting in more efficient establishment of cells compared with 2D cultured cell lines, thus enabling the use of multiple patient-derived cells with cells from different genetic background, in keeping with the heterogeneity of the cells. One of the most valuable points of using organoids is that human cells from either healthy or cancerous tissue can be used. Using genome editing technology such as clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein, organoid genomes can be modified to, for example, cancer-prone genomes. The normal, cancer, or genome-modified organoids can be used to evaluate whether chemicals have genotoxic or non-genotoxic carcinogenic activity by evaluating the cancer incidence, cancer progression, and cancer metastasis. In this review, the organoid technology and the accompanying technologies were summarized and the advantages of organoid-based toxicology and its application to pancreatic cancer study were discussed.

Engrafting Horse Immune Cells into Mouse Hosts for the Study of the Acute Equine Immune Responses

Simple Summary

For decades, studies using research mice as models for disease have been critical to our current understanding of disease processes and associated immune responses, highlighting the ways in which mouse physiology is different from human and other species. Recent work has been directed at creating mice that can host human immune cells, allowing the study and manipulation of the human immune response without harm to patients. The purpose of this study was to explore to use of mouse hosts for horse immune cells. Horses are difficult to study immunologically as they are expensive to keep, and keeping their environment free of immune triggers is very difficult. Using mice allows us to increase our study numbers and control the environment which improves study reproducibility. In this study, we transferred both horse blood lymphocytes as well as horse bone marrow into specially modified mouse hosts. We found that mice are able to host horse immune cells and that these transferred cells are active. Future work can now build on this study to understand the horse immune response to infectious agents using mice, helping to identify new therapeutic tools to help equine patients.

Abstract

Immunological studies in the horse are frequently hampered by lack of environmental control, complicated study design, and ethical concerns when performing high risk studies. The purpose of the current study was to investigate the utility of a xenograft model for studying acute equine immune responses. Immunocompromised non obese diabetic (NOD). sudden combined immunodeficiency (scid).gamma-/- (NSG) mice were engrafted with either equine peripheral blood lymphocytes (PBLs) or equine bone marrow to determine an optimal protocol for equine lymphocyte engraftment. We found that both PBL and bone marrow grafts populated the host mice successfully. Bone marrow transplants were technically more challenging and required further processing to retard graft versus host disease. Graft vs host disease was apparent at 28 days post-PBL transfer and 56 days post-bone marrow transfer. The results of these studies support the use of mouse hosts to study acute equine immune responses and that different engraftment techniques can be used depending on the experimental design.

In vitro 3D liver tumor microenvironment models for immune cell therapy optimization

Despite diagnostic and therapeutic advances, liver cancer kills more than 18 million people every year worldwide, urging new strategies to model the disease and to improve the current therapeutic options. In vitro tumor models of human cancer continue to evolve, and they represent an important screening tool. However, there is a tremendous need to improve the physiological relevance and reliability of these in vitro models to fulfill today's research requirements for better understanding of cancer progression and treatment options at different stages of the disease. This review describes the hepatocellular carcinoma microenvironmental characteristics and illustrates the current immunotherapy strategy to fight the disease. Moreover, we present a recent collection of 2D and 3D in vitro liver cancer models and address the next generation of in vitro systems recapitulating the tumor microenvironment complexity in more detail.

In vivo inducible reverse genetics in patients' tumors to identify individual therapeutic targets

High-throughput sequencing describes multiple alterations in individual tumors, but their functional relevance is often unclear. Clinic-close, individualized molecular model systems are required for functional validation and to identify therapeutic targets of high significance for each patient. Here, we establish a Cre-ER^T2-loxP (causes recombination, estrogen receptor mutant T2, locus of X-over P1) based inducible RNAi- (ribonucleic acid interference) mediated gene silencing system in patient-derived xenograft (PDX) models of acute leukemias in vivo. Mimicking anti-cancer therapy in patients, gene inhibition is initiated in mice harboring orthotopic tumors. In fluorochrome guided, competitive in vivo trials, silencing of the apoptosis regulator MCL1 (myeloid cell leukemia sequence 1) correlates to pharmacological MCL1 inhibition in patients´ tumors, demonstrating the ability of the method to detect therapeutic vulnerabilities. The technique identifies a major tumor-maintaining potency of the MLL-AF4 (mixed lineage leukemia, ALL1-fused gene from chromosome 4) fusion, restricted to samples carrying the translocation. DUX4 (double homeobox 4) plays an essential role in patients’ leukemias carrying the recently described DUX4-IGH (immunoglobulin heavy chain) translocation, while the downstream mediator DDIT4L (DNA-damage-inducible transcript 4 like) is identified as therapeutic vulnerability. By individualizing functional genomics in established tumors in vivo, our technique decisively complements the value chain of precision oncology. Being broadly applicable to tumors of all kinds, it will considerably reinforce personalizing anti-cancer treatment in the future.

Preclinical molecular models are useful that mimic a patient´s response to targeted therapy. Here, the authors establish an in vivo inducible RNAi-mediated gene silencing system in patient-derived xenograft models of acute leukemia to identify individual vulnerabilities and therapeutic targets.

In Vivo and Ex Vivo Patient-Derived Tumor Xenograft Models of Lymphoma for Drug Discovery

In the hemato-oncology field, remarkable scientific progress has been achieved, primarily propelled by the discovery of new technologies, improvement in genomics, and novel in vitro and in vivo models. The establishment of multiple cell line collections and the development of instrumental mouse models enhanced our ability to discover effective therapeutics. However, cancer models that faithfully mimic individual cancers are still imperfect. Patient-derived tumor xenografts (PDTXs) have emerged as a powerful tool for identifying the mechanisms which drive tumorigenesis and for testing potential therapeutic interventions. The recognition that PDTXs can maintain many of the donor samples' properties enabled the development of new strategies for discovering and implementing therapies. Described in this article are protocols for the generation and characterization of lymphoma PDTXs that may be used as the basis of shared procedures. Universal protocols will foster the model utilization, enable the integration of public and private repositories, and aid in the development of shared platforms. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Tissue handling and cryopreservation of primary and PDTX samples Basic Protocol 2: Performing tumor implant in immunocompromised mice PDTX models Alternate Protocol 1: Intra-medullary femoral injection Alternate Protocol 2: Intravenous injection Alternate Protocol 3: Intraperitoneal injection Support Protocol 1: Phenotypical characterization of PDTXs by flow cytometry Support Protocol 2: Biological and molecular characterization of PDTX tumors by PCR detection of IGK, IGH, and TCR rearrangements Basic Protocol 3: Harvesting PDTX-derived tumor cells for ex vivo experiments Basic Protocol 4: In vivo testing of multiple compounds in a PDTX mouse model.

Combination Strategies to Augment Immune Check Point Inhibitors Efficacy - Implications for Translational Research

Immune checkpoint inhibitor therapy has revolutionized the field of cancer immunotherapy. Even though it has shown a durable response in some solid tumors, several patients do not respond to these agents, irrespective of predictive biomarker (PD-L1, MSI, TMB) status. Multiple preclinical, as well as early-phase clinical studies are ongoing for combining immune checkpoint inhibitors with anti-cancer and/or non-anti-cancer drugs for beneficial therapeutic interactions. In this review, we discuss the mechanistic basis behind the combination of immune checkpoint inhibitors with other drugs currently being studied in early phase clinical studies including conventional chemotherapy drugs, metronomic chemotherapy, thalidomide and its derivatives, epigenetic therapy, targeted therapy, inhibitors of DNA damage repair, other small molecule inhibitors, anti-tumor antibodies hormonal therapy, multiple checkpoint Inhibitors, microbiome therapeutics, oncolytic viruses, radiotherapy, drugs targeting myeloid-derived suppressor cells, drugs targeting Tregs, drugs targeting renin-angiotensin system, drugs targeting the autonomic nervous system, metformin, etc. We also highlight how translational research strategies can help better understand the true therapeutic potential of such combinations.

Human cancer xenografts in immunocompromised mice provide an advanced genuine tumor model for research and drug development-A revisit of murine models for human cancers

Molecular and cell biology studies have proven that human cancers are an enormously heterogenous disease, even if they originate from the same organ and tissue with identical morphological characteristics. Cancer cells in tumors from different individuals exhibit somewhat different characteristics on multiple levels, such as with respect to 1) their genetic polymorphism; 2) epigenetic mechanisms; 3) group gene activation/inactivation; 4) cell metabolism behavior; 5) aberrant incomplete terminal differentiation; 6) proliferative potential; and 7) hierarchical structure. These multiple parameters and their different combinations determine the biological characteristics of the cancer cells and their malignant/metastatic manifestations. With progress in medical research, numerous unique vulnerable targets of cancer cells have been identified from different tumors. Modern anti-cancer drug development focuses on target-based cancer cell inhibition and elimination have greatly improved the outcome of patients with some specific cancers. The murine model of human cancer has proven to be an essential procedure for the evaluation of drug efficacy in mammalian and a key link in transferring anti-cancer drug from laboratory to clinics. As classical murine cancer xenograft models with different human cancer cell lines display limited value for personalized precision medicine, creating a complete human xenograft cancer bank with all levels of abnormalities in mice has become desperately needed. This article is a review of the pros and cons of different human x murine cancer models and an attempt to find a more suitable model for the study and discovery of new anti-cancer drugs and different combination therapies in this small animal model.

Preclinical Models for Bladder Cancer Research

At diagnosis, more than 70% of bladder cancers (BCs) are at the non-muscle-invasive bladder cancer (NMIBC) stages, which are usually treated with transurethral resection followed by intravesical instillation. For the remaining advanced cancers, systemic therapy is the standard of care, with addition of radical cystectomy in cases of locally advanced cancer. Because of the difference in treatment modalities, different models are needed to advance the care of NMIBC and advanced BC. This article gives a comprehensive review of both in vitro and in vivo BC models and compares the advantages and drawbacks of these preclinical systems in BC research.