Patient-derived xenografts as compatible models for precision oncology

PDX models for functional precision oncology and discovery science

Precision oncology relies on detailed molecular analysis of how diverse tumours respond to various therapies, with the aim to optimize treatment outcomes for individual patients. Patient-derived xenograft (PDX) models have been key to preclinical validation of precision oncology approaches, enabling the analysis of each tumour's unique genomic landscape and testing therapies that are predicted to be effective based on specific mutations, gene expression patterns or signalling abnormalities. To extend these standard precision oncology approaches, the field has strived to complement the otherwise static and often descriptive measurements with functional assays, termed functional precision oncology (FPO). By utilizing diverse PDX and PDX-derived models, FPO has gained traction as an effective preclinical and clinical tool to more precisely recapitulate patient biology using in vivo and ex vivo functional assays. Here, we explore advances and limitations of PDX and PDX-derived models for precision oncology and FPO. We also examine the future of PDX models for precision oncology in the age of artificial intelligence. Integrating these two disciplines could be the key to fast, accurate and cost-effective treatment prediction, revolutionizing oncology and providing patients with cancer with the most effective, personalized treatments.

Establishment and Its Utility of a Patient-Derived Cell Xenografts (PDCX) Model with Cryopreserved Cancer Cells from Human Tumor

Patient-derived xenograft (PDX) models are powerful tools in cancer research, offering an accurate platform for evaluating cancer treatment efficacy and predicting responsiveness. However, these models necessitate surgical techniques for tumor tissue transplantation and face challenges with non-uniform tumor growth among animals. To address these issues, we attempted to develop a new PDX modeling method using high-grade serous ovarian cancer (HGSC), a fatal disease with a 5-year survival rate of 29%, which requires personalized research due to its morphological, genetic, and molecular heterogeneities. In this study, we developed a new patient-derived cancer cell xenograft (PDCX) model with high engraftment efficiency (64%) that utilizes primary cancer cells instead of patient tissues. Primary cancer cells can be stably cryopreserved for extended periods (up to 485 days), and when transplanted into female NSGA mice, they maintain morphological and molecular characteristics without significant genetic differences compared to their original primary tumors. Furthermore, PDCX models can be easily produced using a syringe, allowing for uniform tumor sizes across multiple animals. Additionally, M2 PDCXs exhibited a significantly faster growth rate compared to M2 PDTXs. Consequently, our PDCX model offers a streamlined approach for evaluating personalized cancer treatments with minimal experimental variability.

A Comparative Study of B Cell Blast Isolation Methods from Bone Marrow Aspirates of Pediatric Leukemia Patients

Density gradient centrifugation is a conventional technique widely utilized to isolate bone marrow mononuclear cells (BM-MNC) from bone marrow (BM) aspirates obtained from pediatric B-cell acute lymphoblastic leukemia (B-ALL) patients. Nevertheless, this technique achieves incomplete recovery of mononuclear cells and is relatively time-consuming and expensive. Given that B-ALL is the most common childhood malignancy, alternative methods for processing B-ALL samples may be more cost-effective. In this pilot study, we use several readouts, including immune phenotype, cell viability, and leukemia-initiating capacity in immune-deficient mice, to directly compare the density gradient centrifugation and buffy coat processing methods. Our findings indicate that buffy coat isolation yields comparable BM-MNC product in terms of both immune and leukemia cell content and could provide a viable, lower cost alternative for biobanks processing pediatric leukemia samples.

Protocol for the development of a bioluminescent AML-PDX mouse model for the evaluation of CAR T cell therapy

Patient-derived xenograft (PDX) models of acute myeloid leukemia (AML-PDX) offer advantages over cell line models by capturing the complexity and heterogeneity of patient-derived samples. Here, we present a protocol for developing a bioluminescent AML-PDX model in mice to evaluate chimeric antigen receptor (CAR) T cell therapy. We describe steps for transducing, engrafting, expanding, and enriching AML-PDX cells. We then detail procedures for in vitro and in vivo validation of the AML-PDX model for the evaluation of CAR T cell immunotherapy. For complete details on the use and execution of this protocol, please refer to Caulier et al.1.

From bedside to bench: New insights in epilepsy-associated tumors based on recent classification updates and animal models on brain tumor networks

Low-grade neuroepithelial tumors (LGNTs), particularly those with glioneuronal histology, are highly associated with pharmacoresistant epilepsy. Increasing research focused on these neoplastic lesions did not translate into drug discovery; and anticonvulsant or antitumor therapies are not available yet. During the last years, animal modeling has improved, thereby leading to the possibility of generating brain tumors in mice mimicking crucial genetic, molecular and immunohistological features. Among them, intraventricular in utero electroporation (IUE) has been proven to be a valuable tool for the generation of animal models for LGNTs allowing endogenous tumor growth within the mouse brain parenchyma. Epileptogenicity is mostly determined by the slow-growing patterns of these tumors, thus mirroring intrinsic interactions between tumor cells and surrounding neurons is crucial to investigate the mechanisms underlying convulsive activity. In this review, we provide an updated classification of the human LGNT and summarize the most recent data from human and animal models, with a focus on the crosstalk between brain tumors and neuronal function.

Esophageal adenocarcinoma models: a closer look

Esophageal adenocarcinoma (EAC) is a subtype of esophageal cancer with significant morbidity and mortality rates worldwide. Despite advancements in tumor models, the underlying cellular and molecular mechanisms driving EAC pathogenesis are still poorly understood. Therefore, gaining insights into these mechanisms is crucial for improving patient outcomes. Researchers have developed various models to better understand EAC and evaluate clinical management strategies. However, no single model fully recapitulates the complexity of EAC. Emerging technologies, such as patient-derived organoids and immune-competent mouse models, hold promise for personalized EAC research and drug development. In this review, we shed light on the various models for studying EAC and discuss their advantages and limitations.

The role of ncRNAs and exosomes in the development and progression of endometrial cancer

Endometrial cancer (EC) is one of the most common gynecologic cancers. In recent years, research has focused on the genetic characteristics of the tumors to detail their prognosis and tailor therapy. In the case of EC, genetic mutations have been shown to underlie their formation. It is very important to know the mechanisms of EC formation related to mutations induced by estrogen, among other things. Noncoding RNAs (ncRNAs), composed of nucleotide transcripts with very low protein-coding capacity, are proving to be important. Their expression patterns in many malignancies can inhibit tumor formation and progression. They also regulate protein coding at the epigenetic, transcriptional, and posttranscriptional levels. MicroRNAs (miRNAs), several varieties of which are associated with normal endometrium as well as its tumor, also play a particularly important role in gene expression. MiRNAs and long noncoding RNAs (lncRNAs) affect many pathways in EC tissues and play important roles in cancer development, invasion, and metastasis, as well as resistance to anticancer drugs through mechanisms such as suppression of apoptosis and progression of cancer stem cells. It is also worth noting that miRNAs are highly precise, sensitive, and robust, making them potential markers for diagnosing gynecologic cancers and their progression. Unfortunately, as the incidence of EC increases, treatment becomes challenging and is limited to invasive tools. The prospect of using microRNAs as potential candidates for diagnostic and therapeutic use in EC seems promising. Exosomes are extracellular vesicles that are released from many types of cells, including cancer cells. They contain proteins, DNA, and various types of RNA, such as miRNAs. The noncoding RNA components of exosomes vary widely, depending on the physiology of the tumor tissue and the cells from which they originate. Exosomes contain both DNA and RNA and have communication functions between cells. Exosomal miRNAs mediate communication between EC cells, tumor-associated fibroblasts (CAFs), and tumor-associated macrophages (TAMs) and play a key role in tumor cell proliferation and tumor microenvironment formation. Oncogenes carried by tumor exosomes induce malignant transformation of target cells. During the synthesis of exosomes, various factors, such as genetic and proteomic data are upregulated. Thus, they are considered an interesting therapeutic target for the diagnosis and prognosis of endometrial cancer by analyzing biomarkers contained in exosomes. Expression of miRNAs, particularly miR-15a-5p, was elevated in exosomes derived from the plasma of EC patients. This may suggest the important utility of this biomarker in the diagnosis of EC. In recent years, researchers have become interested in the topic of prognostic markers for EC, as there are still too few identified markers to support the limited treatment of endometrial cancer. Further research into the effects of ncRNAs and exosomes on EC may allow for cancer treatment breakthroughs.

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.

Gastric cancer patient-derived organoids model for the therapeutic drug screening

BACKGROUND

Gastric cancer (GC) is a highly heterogeneous disease featuring many various histological and molecular subtypes. Therefore, it is imperative to have well-characterized in vitro models for personalized treatment development. Gastric cancer patient-derived organoids (PDOs), re-capitulating in vivo conditions, exhibit high clinical efficacy in predicting drug sensitivity to facilitate the development of cancer precision medicine.

METHODS

PDOs were established from surgically resected GC tumor tissues. Histological and molecular characterization of PDOs and primary tissues were performed via IHC and sequencing analysis. We also conducted drug sensitivity tests using PDO cultures with five chemotherapeutic drugs and twenty-two targeted drugs.

RESULTS

We have successfully constructed a PDOs biobank that included EBV+, intestinal/CIN, diffuse/GS, mixed and Her2+ GC subtypes, and these PDOs captured the pathological and genetic characteristics of corresponding tumors and exhibited different sensitivities to the tested agents. In a clinical case study, we performed an additional drug sensitivity test for a patient who reached an advanced progressive stage after surgery. We discovered that the combination of napabucasin and COTI-2 exhibited a stronger synergistic effect than either drug alone.

CONCLUSION

PDOs maintained the histological and genetic characteristics of original cancer tissues. PDOs biobank opens up new perspectives for studying cancer cell biology and personalized medicine as a preclinical study platform.

Chromatin Remodeling in Patient-Derived Colorectal Cancer Models

Patient-Derived Organoids (PDO) and Xenografts (PDX) are the current gold standards for patient-derived models of cancer (PDMC). Nevertheless, how patient tumor cells evolve in these models and the impact on drug response remains unclear. Herein, the transcriptomic and chromatin accessibility landscapes of matched colorectal cancer (CRC) PDO, PDX, PDO-derived PDX (PDOX), and original patient tumors (PT) are compared. Two major remodeling axes are discovered. The first axis delineates PDMC from PT, and the second axis distinguishes PDX and PDO. PDOX are more similar to PDX than PDO, indicating the growth environment is a driving force for chromatin adaptation. Transcription factors (TF) that differentially bind to open chromatins between matched PDO and PDOX are identified. Among them, KLF14 and EGR2 footprints are enriched in PDOX relative to matched PDO, and silencing of KLF14 or EGR2 promoted tumor growth. Furthermore, EPHA4, a shared downstream target gene of KLF14 and EGR2, altered tumor sensitivity to MEK inhibitor treatment. Altogether, patient-derived CRC cells undergo both common and distinct chromatin remodeling in PDO and PDX/PDOX, driven largely by their respective microenvironments, which results in differences in growth and drug sensitivity and needs to be taken into consideration when interpreting their ability to predict clinical outcome.

Patient-Derived Microphysiological Systems for Precision Medicine

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

Establishment and Thorough Characterization of Xenograft (PDX) Models Derived from Patients with Pancreatic Cancer for Molecular Analyses and Chemosensitivity Testing

Patient-derived xenograft (PDX) tumor models are essential for identifying new biomarkers, signaling pathways and novel targets, to better define key factors of therapy response and resistance mechanisms. Therefore, this study aimed at establishing pancreas carcinoma (PC) PDX models with thorough molecular characterization, and the identification of signatures defining responsiveness toward drug treatment. In total, 45 PC-PDXs were generated from 120 patient tumor specimens and the identity of PDX and corresponding patient tumors was validated. The majority of engrafted PDX models represent ductal adenocarcinomas (PDAC). The PDX growth characteristics were assessed, with great variations in doubling times (4 to 32 days). The mutational analyses revealed an individual mutational profile of the PDXs, predominantly showing alterations in the genes encoding KRAS, TP53, FAT1, KMT2D, MUC4, RNF213, ATR, MUC16, GNAS, RANBP2 and CDKN2A. Sensitivity of PDX toward standard of care (SoC) drugs gemcitabine, 5-fluorouracil, oxaliplatin and abraxane, and combinations thereof, revealed PDX models with sensitivity and resistance toward these treatments. We performed correlation analyses of drug sensitivity of these PDX models and their molecular profile to identify signatures for response and resistance. This study strongly supports the importance and value of PDX models for improvement in therapies of PC.

Domestic Animal Models of Central Nervous System Tumors: Focus on Meningiomas

Intracranial primary tumors (IPTs) are aggressive forms of malignancies that cause high mortality in both humans and domestic animals. Meningiomas are frequent adult IPTs in humans, dogs, and cats, and both benign and malignant forms cause a decrease in life quality and survival. Surgery is the primary therapeutic approach to treat meningiomas, but, in many cases, it is not resolutive. The chemotherapy and targeted therapy used to treat meningiomas also display low efficacy and many side effects. Therefore, it is essential to find novel pharmacological approaches to increase the spectrum of therapeutic options for meningiomas. This review analyzes the similarities between human and domestic animal (dogs and cats) meningiomas by evaluating the molecular and histological characteristics, diagnosis criteria, and treatment options and highlighting possible research areas to identify novel targets and pharmacological approaches, which are useful for the diagnosis and therapy of this neoplasia to be used in human and veterinary medicine.

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.

Experimental Murine Models for Colorectal Cancer Research

Colorectal cancer (CRC) is the third most prevalent malignancy worldwide and in both sexes. Numerous animal models for CRC have been established to study its biology, namely carcinogen-induced models (CIMs) and genetically engineered mouse models (GEMMs). CIMs are valuable for assessing colitis-related carcinogenesis and studying chemoprevention. On the other hand, CRC GEMMs have proven to be useful for evaluating the tumor microenvironment and systemic immune responses, which have contributed to the discovery of novel therapeutic approaches. Although metastatic disease can be induced by orthotopic injection of CRC cell lines, the resulting models are not representative of the full genetic diversity of the disease due to the limited number of cell lines suitable for this purpose. On the other hand, patient-derived xenografts (PDX) are the most reliable for preclinical drug development due to their ability to retain pathological and molecular characteristics. In this review, the authors discuss the various murine CRC models with a focus on their clinical relevance, benefits, and drawbacks. From all models discussed, murine CRC models will continue to be an important tool in advancing our understanding and treatment of this disease, but additional research is required to find a model that can correctly reflect the pathophysiology of CRC.

OncoVee™-MiniPDX-guided anticancer treatment for HER2-negative intermediate-advanced gastric cancer patients: a single-arm, open-label phase I clinical study

BACKGROUND

Chemotherapy is the main treatment strategy for patients with advanced HER2-negative gastric cancer (GC); yet, many patients do not respond well to treatment. This study evaluated the sensitivity of a mini patient-derived xenograft (MiniPDX) animal model in patients with HER2-negative intermediate-advanced GC.

METHODS

In this single-arm, open-label clinical study, we consecutively recruited patients with HER2-negative advanced or recurrent GC from September 2018 to July 2021. Tumor tissues were subjected to MiniPDX drug sensitivity tests for screening individualized anti-tumor drugs; appropriate drug types or combinations were selected based on drug screening results. The primary endpoints were progression-free survival (PFS) and safety, and the secondary endpoints were overall survival (OS) and objective response rate (ORR).

RESULTS

A total of 17 patients were screened, and 14 eligible patients were included.The median follow-up time was 9 (2-34) months. The median PFS time was 14.1 (2-34) months, the median OS time was 16.9 (2-34) months, ORR was 42.9% (6/14), and DCR was 92.9% (13/14). The most common treatment-related adverse events (TRAE) were fatigue (14 (100%)), anorexia (13 (93%)) and insomnia (12 (86%)), and the most common grade 3 or worse TRAE was fatigue (6 (43%)), and anorexia (6 (43%)). The occurrence rate of myelosuppression, nausea and vomiting, abnormal liver enzymes, and other grade 3-4 chemotherapy adverse reactions were relatively low, and no grade 5 treatment-related adverse events occurred.

CONCLUSION

Screening HER2-negative medium-advanced GC/GJC chemotherapy regimens and targeted drugs based on MiniPDX animal models showed good tumor activity and safety.

Pioneering models of pediatric brain tumors

Among children and adolescents in the United States (0 to 19 years old), brain and other central nervous system tumors are the second most common types of cancers, surpassed in incidence only by leukemias. Despite significant progress in the diagnosis and treatment modalities, brain cancer remains the leading cause of death in the pediatric population. There is an obvious unfulfilled need to streamline the therapeutic strategies and improve survival for these patients. For that purpose, preclinical models play a pivotal role. Numerous models are currently used in pediatric brain tumor research, including genetically engineered mouse models, patient-derived xenografts and cell lines, and newer models that utilize novel technologies such as genome engineering and organoids. Furthermore, extensive studies by the Children's Brain Tumor Network (CBTN) researchers and others have revealed multiomic landscapes of variable pediatric brain tumors. Combined with such integrative data, these novel technologies have enabled numerous applicable models. Genome engineering, including CRISPR/Cas9, expanded the flexibility of modeling. Models generated through genome engineering enabled studying particular genetic alterations in clean isogenic backgrounds, facilitating the dissection of functional mechanisms of those mutations in tumor biology. Organoids have been applied to study tumor-to-tumor-microenvironment interactions and to address developmental aspects of tumorigenesis, which is essential in some pediatric brain tumors. Other modalities, such as humanized mouse models, could potentially be applied to pediatric brain tumors. In addition to current valuable models, such novel models are anticipated to expedite functional tumor biology study and establish effective therapeutics for pediatric brain tumors.

Zebrafish Cancer Avatars: A Translational Platform for Analyzing Tumor Heterogeneity and Predicting Patient Outcomes

The increasing number of available anti-cancer drugs presents a challenge for oncologists, who must choose the most effective treatment for the patient. Precision cancer medicine relies on matching a drug with a tumor's molecular profile to optimize the therapeutic benefit. However, current precision medicine approaches do not fully account for intra-tumoral heterogeneity. Different mutation profiles and cell behaviors within a single heterogeneous tumor can significantly impact therapy response and patient outcomes. Patient-derived avatar models recapitulate a patient's tumor in an animal or dish and provide the means to functionally assess heterogeneity's impact on drug response. Mouse xenograft and organoid avatars are well-established, but the time required to generate these models is not practical for clinical decision-making. Zebrafish are emerging as a time-efficient and cost-effective cancer avatar model. In this review, we highlight recent developments in zebrafish cancer avatar models and discuss the unique features of zebrafish that make them ideal for the interrogation of cancer heterogeneity and as part of precision cancer medicine pipelines.

Human Metastatic Cholangiocarcinoma Patient-Derived Xenografts and Tumoroids for Preclinical Drug Evaluation

PURPOSE

Cholangiocarcinoma (CCA) is usually diagnosed at advanced stages, with limited therapeutic options. Preclinical models focused on unresectable metastatic CCA are necessary to develop rational treatments. Pathogenic mutations in IDH1/2, ARID1A/B, BAP1, and BRCA1/2 have been identified in 30%-50% of patients with CCA. Several types of tumor cells harboring these mutations exhibit homologous recombination deficiency (HRD) phenotype with enhanced sensitivity to PARP inhibitors (PARPi). However, PARPi treatment has not yet been tested for effectiveness in patient-derived models of advanced CCA.

EXPERIMENTAL DESIGN

We have established a collection of patient-derived xenografts from patients with unresectable metastatic CCA (CCA_PDX). The CCA_PDXs were characterized at both histopathologic and genomic levels. We optimized a protocol to generate CCA tumoroids from CCA_PDXs. We tested the effects of PARPis in both CCA tumoroids and CCA_PDXs. Finally, we used the RAD51 assay to evaluate the HRD status of CCA tissues.

RESULTS

This collection of CCA_PDXs recapitulates the histopathologic and molecular features of their original tumors. PARPi treatments inhibited the growth of CCA tumoroids and CCA_PDXs with pathogenic mutations of BRCA2, but not those with mutations of IDH1, ARID1A, or BAP1. In line with these findings, only CCA_PDX and CCA patient biopsy samples with mutations of BRCA2 showed RAD51 scores compatible with HRD.

CONCLUSIONS

Our results suggest that patients with advanced CCA with pathogenic mutations of BRCA2, but not those with mutations of IDH1, ARID1A, or BAP1, are likely to benefit from PARPi therapy. This collection of CCA_PDXs provides new opportunities for evaluating drug response and prioritizing clinical trials.

Pre-clinical models for evaluating glioma targeted immunotherapies

Gliomas have an extremely poor prognosis in both adult and pediatric patient populations as these tumors are known to grow aggressively and respond poorly to standard of care treatment. Currently, treatment for gliomas involves surgical resection followed by chemoradiation therapy. However, some gliomas, such as diffuse midline glioma, have more limited treatment options such as radiotherapy alone. Even with these interventions, the prognosis for those diagnosed with a glioma remains poor. Immunotherapy is highly effective for some cancers and there is great interest in the development of effective immunotherapies for the treatment of gliomas. Clinical trials evaluating the efficacy of immunotherapies targeted to gliomas have largely failed to date, and we believe this is partially due to the poor choice in pre-clinical mouse models that are used to evaluate these immunotherapies. A key consideration in evaluating new immunotherapies is the selection of pre-clinical models that mimic the glioma-immune response in humans. Multiple pre-clinical options are currently available, each one with their own benefits and limitations. Informed selection of pre-clinical models for testing can facilitate translation of more promising immunotherapies in the clinical setting. In this review we plan to present glioma cell lines and mouse models, as well as alternatives to mouse models, that are available for pre-clinical glioma immunotherapy studies. We plan to discuss considerations of model selection that should be made for future studies as we hope this review can serve as a guide for investigators as they choose which model is best suited for their study.

DRPreter: Interpretable Anticancer Drug Response Prediction Using Knowledge-Guided Graph Neural Networks and Transformer

Some of the recent studies on drug sensitivity prediction have applied graph neural networks to leverage prior knowledge on the drug structure or gene network, and other studies have focused on the interpretability of the model to delineate the mechanism governing the drug response. However, it is crucial to make a prediction model that is both knowledge-guided and interpretable, so that the prediction accuracy is improved and practical use of the model can be enhanced. We propose an interpretable model called DRPreter (drug response predictor and interpreter) that predicts the anticancer drug response. DRPreter learns cell line and drug information with graph neural networks; the cell-line graph is further divided into multiple subgraphs with domain knowledge on biological pathways. A type-aware transformer in DRPreter helps detect relationships between pathways and a drug, highlighting important pathways that are involved in the drug response. Extensive experiments on the GDSC (Genomics of Drug Sensitivity and Cancer) dataset demonstrate that the proposed method outperforms state-of-the-art graph-based models for drug response prediction. In addition, DRPreter detected putative key genes and pathways for specific drug-cell-line pairs with supporting evidence in the literature, implying that our model can help interpret the mechanism of action of the drug.

Bridging the Scientific Gaps to Identify Effective Treatments in Adrenocortical Cancer

Adrenocortical cancer (ACC) typically presents in advanced stages of disease and has a dismal prognosis. One of the foremost reasons for this is the lack of available systemic therapies, with mitotane remaining the backbone of treatment since its discovery in the 1960s, despite underwhelming efficacy. Surgery remains the only potentially curative option, but about half of patients will recur post-operatively, often with metastatic disease. Other local treatment options have been attempted but are only used practically on a case-by-case basis. Over the past few decades there have been significant advances in understanding the molecular background of ACC, but this has not yet translated to better treatment options. Attempts at novel treatment strategies have not provided significant clinical benefit. This paper reviews our current treatment options and molecular understanding of ACC and the reasons why a successful treatment has remained elusive. Additionally, we discuss the knowledge gaps that need to be overcome to bring us closer to successful treatment and ways to bridge them.

Variants of creating heterotopic and orthotopic PDX models of human colorectal cancer

Aim. To create heterotopic and orthotopic patient-derived xenograft (PDX) models of colorectal cancer (CRC) by transplantation of patient’s tumor samples into immunodeficient BALB / c Nude mice.

Materials and methods. The study was performed on 15 female BALB / c Nude mice aged 6–8 weeks weighing 21–25 g. All animals underwent transplantation of the tumor material obtained from CRC patients into the following sites: heterotopic transplantation (under the skin of the thigh and into the omentum), orthotopic transplantation (into the descending and ascending colon and into the cecum). Weight and general condition of the animals and the size of the tumor nodule had been monitored for 80 days. The success of each model was assessed by the degree of engraftment, the dynamics of tumor growth, and the reproducibility of histopathologic characteristics. At the end of the experiment, the animals were euthanized by cervical dislocation.

Results. 100% survival of the animals and similar tumor growth dynamics in the xenograft models were observed throughout the experiment. The analysis of histologic specimens obtained from the xenografts and patient’s tumor showed their correspondence to moderately differentiated intestinal adenocarcinoma. The main advantages and disadvantages of different variants of PDX models were described.

Conclusion. Heterotopic and orthotopic PDX models reproduce the morpho-histologic characteristics of human tumors and demonstrate stable growth dynamics. Therefore, they are a suitable tool for the development, testing, and validation of potential anticancer drugs.

IC50: an unsuitable measure for large-sized prostate cancer spheroids in drug sensitivity evaluation

Preclinical models of tumors have the potential to become valuable tools for commercial drug research and development, and 3D culture systems are gaining traction in this area, particularly in prostate cancer (PCa) research. However, nearly all 3D drug design and screening assessments are based on 2D experiments, suggesting limitations of 3D drug testing. To simulate the natural response of human cells to the drug, we detected the half-maximal inhibitory concentration (IC50) changes of 2D/3D LNCaP cells in the drug docetaxel, as well as the sensitivity of different morphologies of 2D/3D LNCaP to docetaxel treatment. In contrast to 2D LNCaP cells, the evaluation of LNCaP spheroids' susceptibility to treatment was more complicated; the fitness of IC50 curves of 2D and 3D tumor cell preclinical models differs significantly. IC50 curves were unsuitable for large-sized LNCaP spheroids. More evaluation indexes (such as max inhibition) and experiments (such as spheroids formation) should be explored and performed to evaluate the susceptibility systematically.

Immuno-PET imaging of PD-L1 expression in patient-derived lung cancer xenografts with [68Ga]Ga-NOTA-Nb109

Background

Accurate evaluation of programmed death-ligand 1 (PD-L1) expression levels in cancer patients may be useful in the identification of potential candidates for anti-programmed death-1/PD-L1 (anti-PD-1/PD-L1) immune checkpoint therapy to improve the response rate of immune checkpoint blockade therapy. This study evaluated the feasibility of the nanobody-based positron emission tomography (PET) tracer [68Ga]Ga-NOTA-Nb109 for immuno-PET imaging of PD-L1 in lung cancer patient-derived xenograft (PDX).

Methods

We constructed 2 PDXs of lung adenocarcinoma (ADC) and lung squamous cell carcinoma (SCC) and used them for immuno-PET imaging. A 2-hour dynamic PET scanning was performed on the samples and the in vivo biodistribution and metabolism of [68Ga]Ga-NOTA-Nb109 were investigated using region of interest (ROI) analysis. The ex vivo biodistribution of [68Ga]Ga-NOTA-Nb109 in the 2 PDXs was investigated by static PET scanning. In addition, tumor PD-L1 expression in the 2 PDXs was evaluated by autoradiography, western blot, and immunohistochemical (IHC) analysis.

Results

Noninvasive PET imaging showed that [68Ga]Ga-NOTA-Nb109 can accurately and sensitively assess the PD-L1 expression in non-small cell lung cancer (NSCLC) PDX models. The maximum [68Ga]Ga-NOTA-Nb109 uptake by the ADC PDX LU6424 and the SCC PDX LU6437 were 3.13%±0.35% and 2.60%±0.32% injected dose per milliliter of tissue volume (ID/mL), respectively, at 20 min post injection. In vivo and ex vivo biodistribution analysis showed that [68Ga]Ga-NOTA-Nb109 was rapidly cleared through renal excretion and an enhanced signal-to-noise ratio (SNR) was achieved. Ex vivo PD-L1 expression analysis showed good agreement with in vivo PET imaging results.

Conclusions

This study demonstrated that [68Ga]Ga-NOTA-Nb109 could be applied with PET imaging to noninvasively and accurately monitor PD-L1 expression in vivo for screening patients who may be responsive to immunotherapy and to guide the development of appropriate treatment strategies for such patients.

Patient-derived xenograft (PDX) models, applications and challenges in cancer research

The establishing of the first cancer models created a new perspective on the identification and evaluation of new anti-cancer therapies in preclinical studies. Patient-derived xenograft models are created by tumor tissue engraftment. These models accurately represent the biology and heterogeneity of different cancers and recapitulate tumor microenvironment. These features have made it a reliable model along with the development of humanized models. Therefore, they are used in many studies, such as the development of anti-cancer drugs, co-clinical trials, personalized medicine, immunotherapy, and PDX biobanks. This review summarizes patient-derived xenograft models development procedures, drug development applications in various cancers, challenges and limitations.

Immune cell infiltration pattern in non-small cell lung cancer PDX models is a model immanent feature and correlates with a distinct molecular and phenotypic make-up

BACKGROUND

The field of cancer immunology is rapidly moving towards innovative therapeutic strategies, resulting in the need for robust and predictive preclinical platforms reflecting the immunological response to cancer. Well characterized preclinical models are essential for the development of predictive biomarkers in the oncology as well as the immune-oncology space. In the current study, gold standard preclinical models are being refined and combined with novel image analysis tools to meet those requirements.

METHODS

A panel of 14 non-small cell lung cancer patient-derived xenograft models (NSCLC PDX) was propagated in humanized NOD/Shi-scid/IL-2Rnull mice. The models were comprehensively characterized for relevant phenotypic and molecular features, including flow cytometry, immunohistochemistry, histology, whole exome sequencing and cytokine secretion.

RESULTS

Models reflecting hot (>5% tumor-infiltrating lymphocytes/TILs) as opposed to cold tumors (<5% TILs) significantly differed regarding their cytokine profiles, molecular genetic aberrations, stroma content, and programmed cell death ligand-1 status. Treatment experiments including anti cytotoxic T-lymphocyte-associated protein 4, anti-programmed cell death 1 or the combination thereof across all 14 models in the single mouse trial format showed distinctive tumor growth response and spatial immune cell patterns as monitored by computerized analysis of digitized whole-slide images. Image analysis provided for the first time qualitative evaluation of the extent to which PDX models retain the histological features from their original human donors.

CONCLUSIONS

Deep phenotyping of PDX models in a humanized setting by combinations of computational pathology, immunohistochemistry, flow cytometry and proteomics enables the exhaustive analysis of innovative preclinical models and paves the way towards the development of translational biomarkers for immuno-oncology drugs.

Multicellular Modelling of Difficult-to-Treat Gastrointestinal Cancers: Current Possibilities and Challenges

Cancers affecting the gastrointestinal system are highly prevalent and their incidence is still increasing. Among them, gastric and pancreatic cancers have a dismal prognosis (survival of 5–20%) and are defined as difficult-to-treat cancers. This reflects the urge for novel therapeutic targets and aims for personalised therapies. As a prerequisite for identifying targets and test therapeutic interventions, the development of well-established, translational and reliable preclinical research models is instrumental. This review discusses the development, advantages and limitations of both patient-derived organoids (PDO) and patient-derived xenografts (PDX) for gastric and pancreatic ductal adenocarcinoma (PDAC). First and next generation multicellular PDO/PDX models are believed to faithfully generate a patient-specific avatar in a preclinical setting, opening novel therapeutic directions for these difficult-to-treat cancers. Excitingly, future opportunities such as PDO co-cultures with immune or stromal cells, organoid-on-a-chip models and humanised PDXs are the basis of a completely new area, offering close-to-human models. These tools can be exploited to understand cancer heterogeneity, which is indispensable to pave the way towards more tumour-specific therapies and, with that, better survival for patients.

Identification of Prognostic Markers of Gynecologic Cancers Utilizing Patient-Derived Xenograft Mouse Models

Patient-derived xenografts (PDXs) are important in vivo models for the development of precision medicine. However, challenges exist regarding genetic alterations and relapse after primary treatment. Thus, PDX models are required as a new approach for preclinical and clinical studies. We established PDX models of gynecologic cancers and analyzed their clinical information. We subcutaneously transplanted 207 tumor tissues from patients with gynecologic cancer into nude mice from 2014 to 2019. The successful engraftment rate of ovarian, cervical, and uterine cancer was 47%, 64%, and 56%, respectively. The subsequent passages (P2 and P3) showed higher success and faster growth rates than the first passage (P1). Using gynecologic cancer PDX models, the tumor grade is a common clinical factor affecting PDX establishment. We found that the PDX success rate correlated with the patient’s prognosis, and also that ovarian cancer patients with a poor prognosis had a faster PDX growth rate (p < 0.0001). Next, the gene sets associated with inflammation and immune responses were shown in high-ranking successful PDX engraftment through gene set enrichment analysis and RNA sequencing. Up-regulated genes in successful engraftment were found to correlate with ovarian clear cell cancer patient outcomes via Gene Expression Omnibus dataset analysis.

Murine Models of Chronic Viral Infections and Associated Cancers

Viruses are now recognized as bona fide etiologic factors of human cancer. Carcinogenic viruses include Epstein– Barr virus (EBV), high-risk human papillomaviruses (HPVs), hepatitis B virus (HBV), hepatitis C virus (HCV), human T-cell leukemia virus type 1 (HTLV-1), human immunodeficiency virus type 1 (HIV-1, indirectly), and several candidate human cancer viruses. It is estimated that 15% of all human tumors worldwide are caused by viruses. Tumor viruses establish long-term persistent infections in humans, and cancer is an accidental side effect of viral replication strategies. Viruses are usually not complete carcinogens, supporting the concept that cancer results from the accumulation of multiple cooperating events, in which human cancer viruses display different, often opposing roles. The laboratory mouse Mus musculus is one of the best in vivo experimental systems for modeling human pathology, including viral infections and cancer. However, mice are unsusceptible to infection with the known carcinogenic viruses. Many murine models were developed to overcome this limitation and to address various aspects of virus-associated carcinogenesis, from tumors resulting from xenografts of human tissues and cells, including cancerous and virus infected, to genetically engineered mice susceptible to viral infections and associated cancer. The review considers the main existing models, analyzes their advantages and drawbacks, describes their applications, outlines the prospects of their further development.

Antitumor effect of radiation therapy on orthotopic PDX models of human esophageal adenocarcinoma

As a rule, esophageal adenocarcinoma develops in the lower esophagus. Life expectancy and survival rates depend on the cancer stage and the general health of the patient. Chemoradiotherapy is the most successful treatment approach to this type of cancer. The choice of optimal radiation doses for achieving the best possible therapeutic effect is still a challenge. The aim of this paper was to study effective radiation doses and assess response of human esophageal adenocarcinoma to radiation using a PDX model. The study was conducted in female Balb/c nude mice (n = 25). Fragments of the donor tumor were implanted into the cervical esophagus of immunodeficient mice. Effects of radiation on the obtained orthotopic xenografts were studied after each of 3 irradiation sessions (4, 6, 8, and 10 Gy in each of the experimental groups, respectively). First-passage xenografts reproduced the morphology of the donor tumor. The mean tumor volume differed significantly between the control group and the experimental groups exposed to 6, 8 or 10 Gy (р ≤ 0.01) after each irradiation session. Tumor growth delay was significant after exposure to the total dose of 18 Gy. The further radiation dose increase was ineffective. The reduction of tumor volume in the xenografts was correlated to the increase in the one-time radiation dose. The total dose over 18 Gy produced a detrimental effect on the hematopoietic system and blood biochemistry of the experimental mice.

Establishment and characterization of patient-derived head and neck cancer models from surgical specimens and endoscopic biopsies

Background

Head and neck squamous cell carcinoma (HNSCC) is heterogeneous in etiology, phenotype and biology. Patient-derived xenografts (PDX) maintain morphology and molecular profiling of the original tumors and have become a standard “Avatar” model for human cancer research. However, restricted availability of tumor samples hindered the widespread use of PDX. Most PDX-projects include only surgical specimens because reliable engraftment from biopsies is missing. Therefore, sample collection is limited and excludes recurrent and metastatic, non-resectable cancer from preclinical models as well as future personalized medicine.

Methods

This study compares the PDX-take rate, -growth, histopathology, and molecular characteristics of endoscopic specimens with surgical specimens. HNSCC samples (n = 55) were collected ad hoc, fresh frozen and implanted into NOD.Cg-Prkdc^scidIl2rg^tm1Wjl/SzJ mice.

Results

Engraftment was successful in both sample types. However, engraftment rate was lower (21 vs. 52%) and growth delayed (11.2 vs. 6.7 weeks) for endoscopic biopsies. Following engraftment, growth kinetic was similar. Comparisons of primary tumors and corresponding PDX models confirmed preservation of histomorphology (HE histology) and molecular profile (Illumina Cancer Hotspot Panel) of the patients’ tumors. Accompanying flow cytometry on primary tumor specimens revealed a heterogeneous tumor microenvironment among individual cases and identified M2-like macrophages as positive predictors for engraftment. Vice versa, a high PD-L1 expression (combined positive score on tumor/immune cells) predicted PDX rejection.

Conclusion

Including biopsy samples from locally advanced or metastatic lesions from patients with non-surgical treatment strategies, increases the availability of PDX for basic and translational research. This facilitates (pre-) clinical studies for individual response prediction based on immunological biomarkers.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13046-021-02047-w.

Oncoimmunology Meets Organs-on-Chip

Oncoimmunology represents a biomedical research discipline coined to study the roles of immune system in cancer progression with the aim of discovering novel strategies to arm it against the malignancy. Infiltration of immune cells within the tumor microenvironment is an early event that results in the establishment of a dynamic cross-talk. Here, immune cells sense antigenic cues to mount a specific anti-tumor response while cancer cells emanate inhibitory signals to dampen it. Animals models have led to giant steps in this research context, and several tools to investigate the effect of immune infiltration in the tumor microenvironment are currently available. However, the use of animals represents a challenge due to ethical issues and long duration of experiments. Organs-on-chip are innovative tools not only to study how cells derived from different organs interact with each other, but also to investigate on the crosstalk between immune cells and different types of cancer cells. In this review, we describe the state-of-the-art of microfluidics and the impact of OOC in the field of oncoimmunology underlining the importance of this system in the advancements on the complexity of tumor microenvironment.

Druggable genome and precision medicine in cancer: current challenges

The past decades have seen tremendous developments with respect to "specific" therapeutics that target key signaling molecules to conquer cancer. The key advancements with multiomics technologies, especially genomics, have allowed physicians and molecular oncologists to design "tailor-made" solutions to the specific oncogenes that are deregulated in individual patients, a strategy which has turned out to be successful though the patients quickly develop resistance. The swift integration of multidisciplinary approaches has led to the development of "next generation" therapeutics and, with synergistic therapeutic regimes combined with immune checkpoint inhibitors to reactivate the dampened immune response, has provided the much-needed promise for cancer patients. Despite these advances, a large portion of the druggable genome remains understudied, and the role of druggable genome in the immune system needs further attention. Establishment of patient-derived organoid models has fastened the preclinical validation of novel therapeutics for swift clinical translation. We summarized the current advances and challenges and also stress the importance of biobanking and collection of longitudinal data sets with structured clinical information, as well as the critical role these "high content data sets" will play in designing new therapeutic regimes in a tailor-made fashion.

Pre-clinical tumor models of primary brain tumors: Challenges and opportunities

Primary brain tumors are a heterogeneous group of malignancies that originate in cells of the central nervous system. A variety of models tractable for preclinical studies have been developed to recapitulate human brain tumors, allowing us to understand the underlying pathobiology and explore potential treatments. However, many promising therapeutic strategies identified using preclinical models have shown limited efficacy or failed at the clinical trial stage. The inability to develop therapeutic strategies that significantly improve survival rates in patients highlight the compelling need to revisit the design of currently available animal models and explore the use of new models that allow us to bridge the gap between promising preclinical findings and clinical translation. In this review, we discuss current strategies used to model glioblastoma, the most malignant brain tumor in adults and highlight the shortcomings of specific models that must be circumvented for the development of innovative therapeutic strategies.