PDX Finder: A portal for patient-derived tumor xenograft model discovery

Recent advances in breast cancer cell line research

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

Preclinical Modeling of Pathway-Targeted Therapy of Human Lung Cancer in the Mouse

Animal models, particularly genetically engineered mouse models (GEMMs), continue to have a transformative impact on our understanding of the initiation and progression of hematological malignancies and solid tumors. Furthermore, GEMMs have been employed in the design and optimization of potent anticancer therapies. Increasingly, drug responses are assessed in mouse models either prior, or in parallel, to the implementation of precision medical oncology, in which groups of patients with genetically stratified cancers are treated with drugs that target the relevant oncoprotein such that mechanisms of drug sensitivity or resistance may be identified. Subsequently, this has led to the design and preclinical testing of combination therapies designed to forestall the onset of drug resistance. Indeed, mouse models of human lung cancer represent a paradigm for how a wide variety of GEMMs, driven by a variety of oncogenic drivers, have been generated to study initiation, progression, and maintenance of this disease as well as response to drugs. These studies have now expanded beyond targeted therapy to include immunotherapy. We highlight key aspects of the relationship between mouse models and the evolution of therapeutic approaches, including oncogene-targeted therapies, immunotherapies, acquired drug resistance, and ways in which successful antitumor strategies improve on efficiently translating preclinical approaches into successful antitumor strategies in patients.

Engineering Heterogeneous Tumor Models for Biomedical Applications

Tumor tissue engineering holds great promise for replicating the physiological and behavioral characteristics of tumors in vitro. Advances in this field have led to new opportunities for studying the tumor microenvironment and exploring potential anti-cancer therapeutics. However, the main obstacle to the widespread adoption of tumor models is the poor understanding and insufficient reconstruction of tumor heterogeneity. In this review, the current progress of engineering heterogeneous tumor models is discussed. First, the major components of tumor heterogeneity are summarized, which encompasses various signaling pathways, cell proliferations, and spatial configurations. Then, contemporary approaches are elucidated in tumor engineering that are guided by fundamental principles of tumor biology, and the potential of a bottom-up approach in tumor engineering is highlighted. Additionally, the characterization approaches and biomedical applications of tumor models are discussed, emphasizing the significant role of engineered tumor models in scientific research and clinical trials. Lastly, the challenges of heterogeneous tumor models in promoting oncology research and tumor therapy are described and key directions for future research are provided.

Patient-Derived Xenograft Models for Translational Prostate Cancer Research and Drug Development

Patient-derived xenografts (PDXs) are a valuable preclinical research platform generated through transplantation of a patient's resected tumor into an immunodeficient or humanized mouse. PDXs serve as a high-fidelity avatar for both precision medicine and therapeutic testing against the cancer patient's disease state. While PDXs show mixed response to initial establishment, those that successfully engraft and can be sustained with serial passaging form a useful tool for basic and translational prostate cancer (PCa) research. While genetically engineered mouse (GEM) models and human cancer cell lines, and their xenografts, each play beneficial roles in discovery science and initial drug screening, PDX tumors are emerging as the gold standard approach for therapeutic proof-of-concept prior to entering clinical trial. PDXs are a powerful platform, with PCa PDXs shown to represent the original patient tumor cell population and architecture, histopathology, genomic and transcriptomic landscape, and heterogeneity. Furthermore, PDX response to anticancer drugs in mice has been closely correlated to the original patient's susceptibility to these treatments in the clinic. Several PDXs have been established and have undergone critical in-depth characterization at the cellular and molecular level across multiple PCa tumor subtypes representing both primary and metastatic patient tumors and their inherent levels of androgen responsiveness and/or treatment resistance, including androgen-sensitive, castration resistant, and neuroendocrine PCa. Multiple PDX networks and repositories have been generated for the collaborative and shared use of these vital translational cancer tools. Here we describe the creation of a PDX maintenance colony from an established well-characterized PDX, best practice for PDX maintenance in mice, and their subsequent application in preclinical drug testing. This chapter aims to serve as a go to resource for the preparation and adoption of PCa PDX models in the research laboratory and for their use as a valuable preclinical platform for translational research and therapeutic agent development.

A redox-responsive nanosystem to suppress chemoresistant lung cancer through targeting STAT3

Cancer stem cells (CSCs) have been demonstrated to be involved in tumor initiation and relapse, and the presence of CSCs in the tumor tissue often leads to therapeutic failure. BBI608 has been identified to eliminate CSCs by inhibiting signal transducer and activator of transcription 3 (STAT3). In this study, we confirm that BBI608 can efficiently suppress the proliferation and migration of non-small cell lung cancer (NSCLC) cells, and specifically kill the stemness-high population in chemoresistant NSCLC cells. To improve its bioavailability and tumor accumulation, BBI608 is successfully encapsulated into redox-responsive PEGylated branched N-(2-hydroxypropyl) methacrylamide (HPMA)-deoxy cholic acid (DA) polymeric nanoparticles (BBI608-SS-NPs). The BBI608-SS-NPs can release the drug in response to high concentrations of intracellular glutathione, and exhibit cytotoxicity against lung cancer cells and CSCs comparable to the free drug BBI608. Furthermore, the BBI608-SS-NPs preferentially accumulate in tumor sites, resulting in a superior anti-tumor efficacy in both cisplatin-resistant cell line-derived xenograft (CDX) and patient-derived xenograft (PDX) models of NSCLC. Mechanistic studies demonstrate that BBI608-SS-NPs not only directly inhibit the downstream genes of the STAT3 pathway, but also indirectly inhibit the Wnt pathway. Overall, this stimuli-responsive polymeric nanoformulation of BBI608 shows great potential in the treatment of chemoresistant NSCLC by targeting CSCs.

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

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

Effectively utilizing publicly available databases for cancer target evaluation

The majority of compounds designed against cancer drug targets do not progress to become approved drugs, mainly due to lack of efficacy and/or unmanageable toxicity. Robust target evaluation is therefore required before progressing through the drug discovery process to reduce the high attrition rate. There are a wealth of publicly available databases that can be mined to generate data as part of a target evaluation. It can, however, be challenging to learn what databases are available, how and when they should be used, and to understand the associated limitations. Here, we have compiled and present key, freely accessible and easy-to-use databases that house informative datasets from in vitro, in vivo and clinical studies. We also highlight comprehensive target review databases that aim to bring together information from multiple sources into one-stop portals. In the post-genomics era, a key objective is to exploit the extensive cell, animal and patient characterization datasets in order to deliver precision medicine on a patient-specific basis. Effective utilization of the highlighted databases will go some way towards supporting the cancer research community achieve these aims.

The future of patient-derived xenografts in prostate cancer research

Patient-derived xenografts (PDXs) are generated by engrafting human tumours into mice. Serially transplantable PDXs are used to study tumour biology and test therapeutics, linking the laboratory to the clinic. Although few prostate cancer PDXs are available in large repositories, over 330 prostate cancer PDXs have been established, spanning broad clinical stages, genotypes and phenotypes. Nevertheless, more PDXs are needed to reflect patient diversity, and to study new treatments and emerging mechanisms of resistance. We can maximize the use of PDXs by exchanging models and datasets, and by depositing PDXs into biorepositories, but we must address the impediments to accessing PDXs, such as institutional, ethical and legal agreements. Through collaboration, researchers will gain greater access to PDXs representing diverse features of prostate cancer.

Patient-derived xenograft models in cancer therapy: technologies and applications

Patient-derived xenograft (PDX) models, in which tumor tissues from patients are implanted into immunocompromised or humanized mice, have shown superiority in recapitulating the characteristics of cancer, such as the spatial structure of cancer and the intratumor heterogeneity of cancer. Moreover, PDX models retain the genomic features of patients across different stages, subtypes, and diversified treatment backgrounds. Optimized PDX engraftment procedures and modern technologies such as multi-omics and deep learning have enabled a more comprehensive depiction of the PDX molecular landscape and boosted the utilization of PDX models. These irreplaceable advantages make PDX models an ideal choice in cancer treatment studies, such as preclinical trials of novel drugs, validating novel drug combinations, screening drug-sensitive patients, and exploring drug resistance mechanisms. In this review, we gave an overview of the history of PDX models and the process of PDX model establishment. Subsequently, the review presents the strengths and weaknesses of PDX models and highlights the integration of novel technologies in PDX model research. Finally, we delineated the broad application of PDX models in chemotherapy, targeted therapy, immunotherapy, and other novel therapies.

Development and characterization of patient-derived xenograft models of colorectal cancer for testing new pharmacological substances

The aim of the study was to create a patient-derived xenograft (PDX) model of human colorectal cancer and to determine its histologic and molecular characteristics, such as the status of KRAS, NRAS, and BRAF genes and the presence of microsatellite instability.

Materials and methods. First generation xenograft models in vivo were created using tumors from patients with colorectal cancer (n = 4) and immunodeficient Balb/c Nude mice (n = 20); second, third, and fourth generation models were created in the same mouse line (n = 3 for each generation). A caliper was used to measure subcutaneous xenografts; their size was calculated by the ellipsoid formula. Cryopreservation involved immersing the samples in a freezing medium (80% RPMI 1640, 10% fetal bovine serum, 10% dimethyl sulfoxide (DMSO)) and storing them at –80 °C. The histologic analysis was performed according to the standard technique (preparation of paraffin blocks and staining of microsections with hematoxylin and eosin). Mutations in the KRAS, NRAS, and BRAF genes were determined by direct Sanger sequencing; microsatellite instability was determined by the fragment analysis at five loci: Bat-25, Bat-26, NR21, NR24, and NR27.

Results. Stable, transplantable xenografts of colorectal cancer were obtained from two out of four patients. The average waiting time from the implantation to the growth of the first generation xenograft was 28 days. The latency phase after cryopreservation was comparable to that at the creation of the first generation PDX model. The model reproduced the histotype, grade and mutational status of the KRAS, NRAS, and BRAF genes, as well as microsatellite instability of the donor tumor.

Conclusion. The developed model of human colorectal cancer was characterized in terms of growth dynamics, cryopreservation tolerance, and histologic and molecular genetic parameters.

Synovial Sarcoma Preclinical Modeling: Integrating Transgenic Mouse Models and Patient-Derived Models for Translational Research

Synovial sarcomas (SyS) are rare malignant tumors predominantly affecting children, adolescents, and young adults. The genetic hallmark of SyS is the t(X;18) translocation encoding the SS18-SSX fusion gene. The fusion protein interacts with both the BAF enhancer and polycomb repressor complexes, and either activates or represses target gene transcription, resulting in genome-wide epigenetic perturbations and altered gene expression. Several experimental in in vivo models, including conditional transgenic mouse models expressing the SS18-SSX fusion protein and spontaneously developing SyS, are available. In addition, patient-derived xenografts have been estab-lished in immunodeficient mice, faithfully reproducing the complex clinical heterogeneity. This review focuses on the main molecular features of SyS and the related preclinical in vivo and in vitro models. We will analyze the different conditional SyS mouse models that, after combination with some of the few other recurrent alterations, such as gains in BCL2, Wnt-β-catenin signaling, FGFR family, or loss of PTEN and SMARCB1, have provided additional insight into the mechanisms of synovial sarcomagenesis. The recent advancements in the understanding of SyS biology and improvements in preclinical modeling pave the way to the development of new epigenetic drugs and immunotherapeutic approaches conducive to new treatment options.

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.

A Multicentre Clinical Study of Sarcoma Personalised Treatment Using Patient-Derived Tumour Xenografts

AIMS

Medication for advanced sarcomas has not improved for three decades. Patient-derived tumour xenografts (PDTX) are a promising solution for developing new therapies and real-time personalised medicine because of their highly effective prediction of drug efficacy. However, there is a dearth of PDTX models for sarcomas due to the scarcity and heterogeneity of the disease.

MATERIALS AND METHODS

A multicentre clinical collaborative study (ChiCTR-OOC-17013617) was carried out. Fresh patient tumour tissues via resection or biopsy were used for the PDTX set-up. The standard medical care chosen by the physician was given to the patient, in parallel with testing on multiple regimens. The outcomes of patients' responses and PDTX tests were compared. Comprehensive analyses were carried out to assess the clinical value of PDTX for the treatment of sarcomas. Living tissues from successfully engrafted cases were deposited into a repository.

RESULTS

Forty-two cases, including 36 bone sarcomas and six soft-tissue sarcomas, were enrolled; the overall engraftment rate was 73.8%. Histopathological examination showed a 100% consistency between primary tumours and tumour grafts. The engraftment rate was independent of age, gender and sampling methods, but was associated with subtypes of tumour. The outgrowth time of tumour grafts could be associated with prognosis. Major somatic mutations in tumour grafts occurred primarily in common tumour driver genes. Poor prognosis was associated with the KMT2C mutation. A drug efficacy test showed complete concordance between the PDTX model and patients' responses in 17 regimens.

CONCLUSION

PDTX is an ideal preclinical model for sarcomas because of its faithful preservation of the heterogeneity of the disease, a satisfactory engraftment rate and high accuracy in its prediction of drug efficacy.

PDCM Finder: an open global research platform for patient-derived cancer models

PDCM Finder (www.cancermodels.org) is a cancer research platform that aggregates clinical, genomic and functional data from patient-derived xenografts, organoids and cell lines. It was launched in April 2022 as a successor of the PDX Finder portal, which focused solely on patient-derived xenograft models. Currently the portal has over 6200 models across 13 cancer types, including rare paediatric models (17%) and models from minority ethnic backgrounds (33%), making it the largest free to consumer and open access resource of this kind. The PDCM Finder standardises, harmonises and integrates the complex and diverse data associated with PDCMs for the cancer community and displays over 90 million data points across a variety of data types (clinical metadata, molecular and treatment-based). PDCM data is FAIR and underpins the generation and testing of new hypotheses in cancer mechanisms and personalised medicine development.

A Genomically and Clinically Annotated Patient-Derived Xenograft Resource for Preclinical Research in Non-Small Cell Lung Cancer

Patient-derived xenograft (PDX) models are an effective preclinical in vivo platform for testing the efficacy of novel drugs and drug combinations for cancer therapeutics. Here we describe a repository of 79 genomically and clinically annotated lung cancer PDXs available from The Jackson Laboratory that have been extensively characterized for histopathologic features, mutational profiles, gene expression, and copy-number aberrations. Most of the PDXs are models of non-small cell lung cancer (NSCLC), including 37 lung adenocarcinoma (LUAD) and 33 lung squamous cell carcinoma (LUSC) models. Other lung cancer models in the repository include four small cell carcinomas, two large cell neuroendocrine carcinomas, two adenosquamous carcinomas, and one pleomorphic carcinoma. Models with both de novo and acquired resistance to targeted therapies with tyrosine kinase inhibitors are available in the collection. The genomic profiles of the LUAD and LUSC PDX models are consistent with those observed in patient tumors from The Cancer Genome Atlas and previously characterized gene expression-based molecular subtypes. Clinically relevant mutations identified in the original patient tumors were confirmed in engrafted PDX tumors. Treatment studies performed in a subset of the models recapitulated the responses expected on the basis of the observed genomic profiles. These models therefore serve as a valuable preclinical platform for translational cancer research.

SIGNIFICANCE

Patient-derived xenografts of lung cancer retain key features observed in the originating patient tumors and show expected responses to treatment with standard-of-care agents, providing experimentally tractable and reproducible models for preclinical investigations.

How to build a tumor: An industry perspective

During the past 15 years, a plethora of innovative 3D in vitro systems has been developed. They offer the possibility of identifying crucial cellular and molecular contributors to the disease by permitting manipulation of each in isolation. However, improvements are needed particularly with respect to the predictivity and validity of those models. The major challenge now is to identify which assay and readout combination(s) best suits the current scientific question(s). A deep understanding of the different platforms along with their pros and cons is a prerequisite to make this decision. This review aims to give an overview of the most prominent systems with a focus on applications, translational relevance and adoption drivers from an industry perspective.

Towards precision oncology with patient-derived xenografts

Under the selective pressure of therapy, tumours dynamically evolve multiple adaptive mechanisms that make static interrogation of genomic alterations insufficient to guide treatment decisions. Clinical research does not enable the assessment of how various regulatory circuits in tumours are affected by therapeutic insults over time and space. Likewise, testing different precision oncology approaches informed by composite and ever-changing molecular information is hard to achieve in patients. Therefore, preclinical models that incorporate the biology and genetics of human cancers, facilitate analyses of complex variables and enable adequate population throughput are needed to pinpoint randomly distributed response predictors. Patient-derived xenograft (PDX) models are dynamic entities in which cancer evolution can be monitored through serial propagation in mice. PDX models can also recapitulate interpatient diversity, thus enabling the identification of response biomarkers and therapeutic targets for molecularly defined tumour subgroups. In this Review, we discuss examples from the past decade of the use of PDX models for precision oncology, from translational research to drug discovery. We elaborate on how and to what extent preclinical observations in PDX models have confirmed and/or anticipated findings in patients. Finally, we illustrate emerging methodological efforts that could broaden the application of PDX models by honing their predictive accuracy or improving their versatility.

Harnessing preclinical models for the interrogation of ovarian cancer

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

Establishment of 6 pediatric rhabdomyosarcoma patient’s derived xenograft models closely recapitulating patients’ tumor characteristics

Introduction:

The prognosis for patients with metastatic and recurrent pediatric rhabdomyosarcoma (RMS) remains poor. The availability of preclinical models is essential to identify promising treatments We established a series of pediatric RMS patient derived xenografts (PDXs), all faithfully mirroring primary tumor characteristics and representing a unique tool for clarifying the biological processes underlying RMS progression and relapse.

Methods:

Fresh tumor samples from 12 RMS patients were implanted subcutaneously in both flanks of immunocompromised mice. PDXs were considered as grafted after accomplishing three passages in mice. Characterization of tumor tissues and models was performed by comparing both morphology and immunoistochemical and fluorescence in situ hybridization (FISH) characteristics.

Results:

Six PDXs were established, with a successful take rate of 50%. All models closely mirrored parental tumor characteristics. An increased grafting rate for tumors derived from patients with worse outcome (p = 0.006) was detected. For 50% PDXs grafting occurred when the corresponding patient was still alive.

Conclusion:

Our findings increase the number of available RMS PDX models and strengthen the role of PDXs as useful preclinical tools for patients with unmet medical needs and to develop personalized therapies.

Opportunities for pharmacoproteomics in biomarker discovery

Proteomic data are a uniquely valuable resource for drug response prediction and biomarker discovery because most drugs interact directly with proteins in target cells rather than with DNA or RNA. Recent advances in mass spectrometry and associated processing methods have enabled the generation of large-scale proteomic datasets. Here we review the significant opportunities that currently exist to combine large-scale proteomic data with drug-related research, a field termed pharmacoproteomics. We describe successful applications of drug response prediction using molecular data, with an emphasis on oncology. We focus on technical advances in data-independent acquisition mass spectrometry (DIA-MS) that can facilitate the discovery of protein biomarkers for drug responses, alongside the increased availability of big biomedical data. We spotlight new opportunities for machine learning in pharmacoproteomics, driven by the combination of these large datasets and improved high-performance computing. Finally, we explore the value of pre-clinical models for pharmacoproteomic studies and the accompanying challenges of clinical validation. We propose that pharmacoproteomics offers the potential for novel discovery and innovation within the cancer landscape. This article is protected by copyright. All rights reserved.

Patient-derived cancer models: Valuable platforms for anticancer drug testing

Molecularly targeted treatments and immunotherapy are cornerstones in oncology, with demonstrated efficacy across different tumor types. Nevertheless, the overwhelming majority metastatic disease is incurable due to the onset of drug resistance. Preclinical models including genetically engineered mouse models, patient-derived xenografts and two- and three-dimensional cell cultures have emerged as a useful resource to study mechanisms of cancer progression and predict efficacy of anticancer drugs. However, variables including tumor heterogeneity and the complexities of the microenvironment can impair the faithfulness of these platforms. Here, we will discuss advantages and limitations of these preclinical models, their applicability for drug testing and in co-clinical trials and potential strategies to increase their reliability in predicting responsiveness to anticancer medications.

CombPDX: a unified statistical framework for evaluating drug synergism in patient-derived xenografts

Anticancer combination therapy has been developed to increase efficacy by enhancing synergy. Patient-derived xenografts (PDXs) have emerged as reliable preclinical models to develop effective treatments in translational cancer research. However, most PDX combination study designs focus on single dose levels, and dose-response surface models are not appropriate for testing synergism. We propose a comprehensive statistical framework to assess joint action of drug combinations from PDX tumor growth curve data. We provide various metrics and robust statistical inference procedures that locally (at a fixed time) and globally (across time) access combination effects under classical drug interaction models. Integrating genomic and pharmacological profiles in non-small-cell lung cancer (NSCLC), we have shown the utilities of combPDX in discovering effective therapeutic combinations and relevant biological mechanisms. We provide an interactive web server, combPDX ( https://licaih.shinyapps.io/CombPDX/ ), to analyze PDX tumor growth curve data and perform power analyses.

The first Japanese biobank of patient-derived pediatric acute lymphoblastic leukemia xenograft models

A lack of practical resources in Japan has limited preclinical discovery and testing of therapies for pediatric relapsed and refractory acute lymphoblastic leukemia (ALL), which has poor outcomes. Here, we established 57 patient-derived xenografts (PDXs) in NOD.Cg-Prkdc^scid ll2rg^tm1Sug /ShiJic (NOG) mice and created a biobank by preserving PDX cells including 3 extramedullary relapsed ALL PDXs. We demonstrated that our PDX mice and PDX cells mimicked the biological features of relapsed ALL and that PDX models reproduced treatment-mediated clonal selection. Our PDX biobank is a useful scientific resource for capturing drug sensitivity features of pediatric patients with ALL, providing an essential tool for the development of targeted therapies.

Theranostic Potentials of Gold Nanomaterials in Hematological Malignancies

Simple Summary

Hematological malignancies (HMs) cover 50% of all malignancies, and people of all ages can be affected by these deadly diseases. In many cases, conventional diagnostic tools fail to diagnose HMs at an early stage, due to heterogeneity and the long-term indolent phase of HMs. Therefore, many patients start their treatment at the late stage of HMs and have poor survival. Gold nanomaterials (GNMs) have shown promise as a cancer theranostic agent. GNMs are 1 nm to 100 nm materials having magnetic resonance and surface-plasmon-resonance properties. GNMs conjugated with antibodies, nucleic acids, peptides, photosensitizers, chemotherapeutic drugs, synthetic-drug candidates, bioactive compounds, and other theranostic biomolecules may enhance the efficacy and efficiency of both traditional and advanced theranostic approaches to combat HMs.

Abstract

Hematological malignancies (HMs) are a heterogeneous group of blood neoplasia generally characterized by abnormal blood-cell production. Detection of HMs-specific molecular biomarkers (e.g., surface antigens, nucleic acid, and proteomic biomarkers) is crucial in determining clinical states and monitoring disease progression. Early diagnosis of HMs, followed by an effective treatment, can remarkably extend overall survival of patients. However, traditional and advanced HMs’ diagnostic strategies still lack selectivity and sensitivity. More importantly, commercially available chemotherapeutic drugs are losing their efficacy due to adverse effects, and many patients develop resistance against these drugs. To overcome these limitations, the development of novel potent and reliable theranostic agents is urgently needed to diagnose and combat HMs at an early stage. Recently, gold nanomaterials (GNMs) have shown promise in the diagnosis and treatment of HMs. Magnetic resonance and the surface-plasmon-resonance properties of GNMs have made them a suitable candidate in the diagnosis of HMs via magnetic-resonance imaging and colorimetric or electrochemical sensing of cancer-specific biomarkers. Furthermore, GNMs-based photodynamic therapy, photothermal therapy, radiation therapy, and targeted drug delivery enhanced the selectivity and efficacy of anticancer drugs or drug candidates. Therefore, surface-tuned GNMs could be used as sensitive, reliable, and accurate early HMs, metastatic HMs, and MRD-detection tools, as well as selective, potent anticancer agents. However, GNMs may induce endothelial leakage to exacerbate cancer metastasis. Studies using clinical patient samples, patient-derived HMs models, or healthy-animal models could give a precise idea about their theranostic potential as well as biocompatibility. The present review will investigate the theranostic potential of vectorized GNMs in HMs and future challenges before clinical theranostic applications in HMs.

Mass spectrometry-based draft of the mouse proteome

The laboratory mouse ranks among the most important experimental systems for biomedical research and molecular reference maps of such models are essential informational tools. Here, we present a quantitative draft of the mouse proteome and phosphoproteome constructed from 41 healthy tissues and several lines of analyses exemplify which insights can be gleaned from the data. For instance, tissue- and cell-type resolved profiles provide protein evidence for the expression of 17,000 genes, thousands of isoforms and 50,000 phosphorylation sites in vivo. Proteogenomic comparison of mouse, human and Arabidopsis reveal common and distinct mechanisms of gene expression regulation and, despite many similarities, numerous differentially abundant orthologs that likely serve species-specific functions. We leverage the mouse proteome by integrating phenotypic drug (n > 400) and radiation response data with the proteomes of 66 pancreatic ductal adenocarcinoma (PDAC) cell lines to reveal molecular markers for sensitivity and resistance. This unique atlas complements other molecular resources for the mouse and can be explored online via ProteomicsDB and PACiFIC.

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

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

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.

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.

Biomimetic hydrogel supports initiation and growth of patient-derived breast tumor organoids

Patient-derived tumor organoids (PDOs) are a highly promising preclinical model that recapitulates the histology, gene expression, and drug response of the donor patient tumor. Currently, PDO culture relies on basement-membrane extract (BME), which suffers from batch-to-batch variability, the presence of xenogeneic compounds and residual growth factors, and poor control of mechanical properties. Additionally, for the development of new organoid lines from patient-derived xenografts, contamination of murine host cells poses a problem. We propose a nanofibrillar hydrogel (EKGel) for the initiation and growth of breast cancer PDOs. PDOs grown in EKGel have histopathologic features, gene expression, and drug response that are similar to those of their parental tumors and PDOs in BME. In addition, EKGel offers reduced batch-to-batch variability, a range of mechanical properties, and suppressed contamination from murine cells. These results show that EKGel is an improved alternative to BME matrices for the initiation, growth, and maintenance of breast cancer PDOs.

Patient-derived tumour organoids are important preclinical models but suffer from variability from the use of basement-membrane extract and cell contamination. Here, the authors report on the development of mimetic nanofibrilar hydrogel which supports tumour organoid growth with reduced batch variability and cell contamination.

The EurOPDX Data Portal: an open platform for patient-derived cancer xenograft data sharing and visualization

Background

Patient-derived xenografts (PDX) mice models play an important role in preclinical trials and personalized medicine. Sharing data on the models is highly valuable for numerous reasons – ethical, economical, research cross validation etc. The EurOPDX Consortium was established 8 years ago to share such information and avoid duplicating efforts in developing new PDX mice models and unify approaches to support preclinical research. EurOPDX Data Portal is the unified data sharing platform adopted by the Consortium.

Main body

In this paper we describe the main features of the EurOPDX Data Portal (https://dataportal.europdx.eu/), its architecture and possible utilization by researchers who look for PDX mice models for their research. The Portal offers a catalogue of European models accessible on a cooperative basis. The models are searchable by metadata, and a detailed view provides molecular profiles (gene expression, mutation, copy number alteration) and treatment studies. The Portal displays the data in multiple tools (PDX Finder, cBioPortal, and GenomeCruzer in future), which are populated from a common database displaying strictly mutually consistent views.

(Short) Conclusion

EurOPDX Data Portal is an entry point to the EurOPDX Research Infrastructure offering PDX mice models for collaborative research, (meta)data describing their features and deep molecular data analysis according to users’ interests.

Zebrafish patient-derived xenograft models predict lymph node involvement and treatment outcome in non-small cell lung cancer

Background

Accurate predictions of tumor dissemination risks and medical treatment outcomes are critical to personalize therapy. Patient-derived xenograft (PDX) models in mice have demonstrated high accuracy in predicting therapeutic outcomes, but methods for predicting tumor invasiveness and early stages of vascular/lymphatic dissemination are still lacking. Here we show that a zebrafish tumor xenograft (ZTX) platform based on implantation of PDX tissue fragments recapitulate both treatment outcome and tumor invasiveness/dissemination in patients, within an assay time of only 3 days.

Methods

Using a panel of 39 non-small cell lung cancer PDX models, we developed a combined mouse-zebrafish PDX platform based on direct implantation of cryopreserved PDX tissue fragments into zebrafish embryos, without the need for pre-culturing or expansion. Clinical proof-of-principle was established by direct implantation of tumor samples from four patients.

Results

The resulting ZTX models responded to Erlotinib and Paclitaxel, with similar potency as in mouse-PDX models and the patients themselves, and resistant tumors similarly failed to respond to these drugs in the ZTX system. Drug response was coupled to elevated expression of EGFR, Mdm2, Ptch1 and Tsc1 (Erlotinib), or Nras and Ptch1 (Paclitaxel) and reduced expression of Egfr, Erbb2 and Foxa (Paclitaxel). Importantly, ZTX models retained the invasive phenotypes of the tumors and predicted lymph node involvement of the patients with 91% sensitivity and 62% specificity, which was superior to clinically used tests. The biopsies from all four patient tested implanted successfully, and treatment outcome and dissemination were quantified for all patients in only 3 days.

Conclusions

We conclude that the ZTX platform provide a fast, accurate, and clinically relevant system for evaluation of treatment outcome and invasion/dissemination of PDX models, providing an attractive platform for combined mouse-zebrafish PDX trials and personalized medicine.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13046-022-02280-x.

Establishment and characterization of NCC‑DMM1‑C1, a novel patient‑derived cell line of desmoplastic malignant pleural mesothelioma

Desmoplastic malignant pleural mesothelioma (DMM) is a rare histological variant of malignant pleural mesothelioma, which is a highly aggressive neoplasm of the mesothelium. DMM is associated with distant metastases and short survival. Effective treatments for DMM are not established and the development of histotype-tailored treatments is difficult due to the rarity of the disease. Although patient-derived cancer models are crucial tools for the development of novel therapeutics, they are difficult to obtain for DMM; no DMM cell lines or xenografts are available from public biobanks and only two cell lines have been reported. Thus, the present study aimed to establish a novel cell line of DMM as a resource for drug screening. A cell line of DMM was established, designated as NCC-DMM1-C1, using surgically resected tumor tissues from a 73-year-old male patient with DMM. Characteristics of NCC-DMM1-C1 cells were examined, such as growth, spheroid formation and invasion capability. Drug targets and anti-cancer drugs with anti-proliferative efficacy were examined using a comprehensive kinase activity assay and drug screening of 213 anti-cancer agents, respectively. NCC-DMM1-C1 exhibited fast growth, spheroid formation and invasion capability, suggesting that the NCC-DMM1-C1 cells retained the aggressive features of DMM. NCC-DMM1-C1 cells and the tumor tissue shared common activity profiles of kinases, which included FES, Wee1, platelet-derived growth factor receptor-β and Src. The drug screening revealed that bortezomib, fostamatinib, gemcitabine, homoharringtonine and vinorelbine had anti-proliferative effects, which have not been previously reported for DMM. It was concluded that NCC-DMM1-C1 cells may be a useful tool for the study of DMM.

Multi-tumor analysis of cancer-stroma interactomes of patient-derived xenografts unveils the unique homeostatic process in renal cell carcinomas

The patient-derived xenograft (PDX) model is a versatile tool used to study the tumor microenvironment (TME). However, limited studies have described multi-tumor PDX screening strategies to detect hub regulators during cancer-stroma interaction. Transcriptomes of cancer (human) and stroma (mouse) components of 70 PDX samples comprising 9 distinctive tumor types were analyzed in this study. PDX models recapitulated the original tumors' features, including tumor composition and putative signaling. Particularly, kidney renal clear cell carcinoma (KIRC) stood out, with altered hypoxia-related pathways and a high proportion of endothelial cells in the TME. Furthermore, an integrated analysis conducted to predict paracrine effectors in the KIRC cancer-to-stroma communication detected well-established soluble factors responsible for the hypoxia-related reaction and the so-far unestablished soluble factor, apelin (APLN). Subsequent experiments also supported the potential role of APLN in KIRC tumor progression. Therefore, this paper hereby provides an analytical workflow to find hub regulators in cancer-stroma interactions.

BAG3 induces α-SMA expression in human fibroblasts and its over-expression correlates with poorer survival in fibrotic cancer patients

Hypoxia and angiogenesis in solid tumors are often strictly linked to the development of fibrotic tissues, a detrimental event that compromises the antitumor immunity. As a consequence, tumor aggressiveness and poor patient prognosis relate to higher incidence of tissue fibrosis and stromal stiffness. The molecular pathways through which normal fibroblasts are converted in cancer-associated fibroblasts (CAFs) have a central role in the onset of fibrosis in tumor stroma, thus emerging as a strategic target of novel therapeutic approaches for cancer disease. Several studies addressed the role of BAG3 in sustaining growth and survival of cancer cell and also shed light on the different mechanisms in which the intracellular protein is involved. More recently, new pieces of evidence revealed a pivotal role of extracellular BAG3 in pro-tumor cell signaling in the tumor microenvironment, as well as its involvement in the development of fibrosis in tumor tissues. Here we report further data showing the presence of the BAG3 receptor (Interferon-induced transmembrane protein [IFITM]-2) on the plasma membrane of normal dermal fibroblasts and the activity of BAG3 as a factor able to induce the expression of α-smooth muscle actin and the phosphorylation of AKT and focal adhesion kinase, that sustain CAF functions in tumor microenvironment. Furthermore, in agreement with these findings, bag3 gene expression has been analyzed by high throughput RNA sequencing databases from patients-derived xenografts. A strong correlation between bag3 gene expression and patients' survival was found in several types of fibrotic tumors. The results obtained provide encouraging data that identify BAG3 as a promising therapeutic target to counteract fibrosis in tumors.

TISMO: syngeneic mouse tumor database to model tumor immunity and immunotherapy response

Syngeneic mouse models are tumors derived from murine cancer cells engrafted on genetically identical mouse strains. They are widely used tools for studying tumor immunity and immunotherapy response in the context of a fully functional murine immune system. Large volumes of syngeneic mouse tumor expression profiles under different immunotherapy treatments have been generated, although a lack of systematic collection and analysis makes data reuse challenging. We present Tumor Immune Syngeneic MOuse (TISMO), a database with an extensive collection of syngeneic mouse model profiles with interactive visualization features. TISMO contains 605 in vitro RNA-seq samples from 49 syngeneic cancer cell lines across 23 cancer types, of which 195 underwent cytokine treatment. TISMO also includes 1518 in vivo RNA-seq samples from 68 syngeneic mouse tumor models across 19 cancer types, of which 832 were from immune checkpoint blockade (ICB) studies. We manually annotated the sample metadata, such as cell line, mouse strain, transplantation site, treatment, and response status, and uniformly processed and quality-controlled the RNA-seq data. Besides data download, TISMO provides interactive web interfaces to investigate whether specific gene expression, pathway enrichment, or immune infiltration level is associated with differential immunotherapy response. TISMO is available at http://tismo.cistrome.org.

Moving pan-cancer studies from basic research toward the clinic

Although all cancers share common hallmarks, we have long realized that there is no silver-bullet treatment for the disease. Many clinical oncologists specialize in a single cancer type, based predominantly on the tissue of origin. With advances brought by genetics and cancer genomic research, we now know that cancers are profoundly different, both in origins and in genetic alterations. At the same time, commonalities such as key driver mutations, altered pathways, mutational, immune and microbial signatures and other areas (many revealed by pan-cancer studies) point to the intriguing possibility of targeting common traits across diverse cancer types with the same therapeutic strategies. Studies designed to delineate differences and similarities across cancer types are thus critical in discerning the basic dynamics of oncogenesis, as well as informing diagnoses, prognoses and therapies. We anticipate growing emphases on the development and application of therapies targeting underlying commonalities of different cancer types, while tailoring to the unique tissue environment and intrinsic molecular fingerprints of each cancer type and subtype. Here we summarize the facets of pan-cancer research and how they are pushing progress toward personalized medicine.

Comprehensive characterization of 536 patient-derived xenograft models prioritizes candidatesfor targeted treatment

Development of candidate cancer treatments is a resource-intensive process, with the research community continuing to investigate options beyond static genomic characterization. Toward this goal, we have established the genomic landscapes of 536 patient-derived xenograft (PDX) models across 25 cancer types, together with mutation, copy number, fusion, transcriptomic profiles, and NCI-MATCH arms. Compared with human tumors, PDXs typically have higher purity and fit to investigate dynamic driver events and molecular properties via multiple time points from same case PDXs. Here, we report on dynamic genomic landscapes and pharmacogenomic associations, including associations between activating oncogenic events and drugs, correlations between whole-genome duplications and subclone events, and the potential PDX models for NCI-MATCH trials. Lastly, we provide a web portal having comprehensive pan-cancer PDX genomic profiles and source code to facilitate identification of more druggable events and further insights into PDXs' recapitulation of human tumors.

A gentle introduction to understanding preclinical data for cancer pharmaco-omic modeling

A central goal of precision oncology is to administer an optimal drug treatment to each cancer patient. A common preclinical approach to tackle this problem has been to characterize the tumors of patients at the molecular and drug response levels, and employ the resulting datasets for predictive in silico modeling (mostly using machine learning). Understanding how and why the different variants of these datasets are generated is an important component of this process. This review focuses on providing such introduction aimed at scientists with little previous exposure to this research area.

The Application Progress of Patient-Derived Tumor Xenograft Models After Cholangiocarcinoma Surgeries

Surgical treatment is the only possible cure for cholangiocarcinoma (CCA) at present. However, the high recurrence rate of postoperative CCA leads to a very poor prognosis for patients, effective postoperative chemotherapy is hence the key to preventing the recurrence of CCA. The sensitivity of CCA to cytotoxic chemotherapy drugs and targeted drugs varies from person to person, and therefore, the screening of sensitive drugs has become an important topic after CCA surgeries. Patient-Derived tumor Xenograft models (PDX) can stably retain the genetic and pathological characteristics of primary tumors, and better simulate the tumor microenvironment of CCA. The model is also of great significance in screening therapeutic targeted drugs after CCA, analyzing predictive biomarkers, and improving signal pathways in prognosis and basic research. This paper will review the current established methods and applications of the patient-derived tumor xenograft model of cholangiocarcinoma, aiming to provide new ideas for basic research and individualized treatment of cholangiocarcinoma after surgery.

Basic principles of biobanking: from biological samples to precision medicine for patients

The term "biobanking" is often misapplied to any collection of human biological materials (biospecimens) regardless of requirements related to ethical and legal issues or the standardization of different processes involved in tissue collection. A proper definition of biobanks is large collections of biospecimens linked to relevant personal and health information (health records, family history, lifestyle, genetic information) that are held predominantly for use in health and medical research. In addition, the International Organization for Standardization, in illustrating the requirements for biobanking (ISO 20387:2018), stresses the concept of biobanks being legal entities driving the process of acquisition and storage together with some or all of the activities related to collection, preparation, preservation, testing, analysing and distributing defined biological material as well as related information and data. In this review article, we aim to discuss the basic principles of biobanking, spanning from definitions to classification systems, standardization processes and documents, sustainability and ethical and legal requirements. We also deal with emerging specimens that are currently being generated and shaping the so-called next-generation biobanking, and we provide pragmatic examples of cancer-associated biobanking by discussing the process behind the construction of a biobank and the infrastructures supporting the implementation of biobanking in scientific research.

Machine learning in the prediction of cancer therapy

Resistance to therapy remains a major cause of cancer treatment failures, resulting in many cancer-related deaths. Resistance can occur at any time during the treatment, even at the beginning. The current treatment plan is dependent mainly on cancer subtypes and the presence of genetic mutations. Evidently, the presence of a genetic mutation does not always predict the therapeutic response and can vary for different cancer subtypes. Therefore, there is an unmet need for predictive models to match a cancer patient with a specific drug or drug combination. Recent advancements in predictive models using artificial intelligence have shown great promise in preclinical settings. However, despite massive improvements in computational power, building clinically useable models remains challenging due to a lack of clinically meaningful pharmacogenomic data. In this review, we provide an overview of recent advancements in therapeutic response prediction using machine learning, which is the most widely used branch of artificial intelligence. We describe the basics of machine learning algorithms, illustrate their use, and highlight the current challenges in therapy response prediction for clinical practice.

Patient-derived tumor xenograft and organoid models established from resected pancreatic, duodenal and biliary cancers

Patient-derived xenograft (PDX) and their xenograft-derived organoid (XDO) models that recapitulate the genotypic and phenotypic landscape of patient cancers could help to advance research and lead to improved clinical management. PDX models were established from 276 pancreato-duodenal and biliary cancer resections. Initial, passage 0 (P0) engraftment rates were 59% (118/199) for pancreatic, 86% (25/29) for duodenal, and 35% (17/48) for biliary ductal tumors. Pancreatic ductal adenocarcinoma (PDAC), had a P0 engraftment rate of 62% (105/169). KRAS mutant and wild-type PDAC models were molecularly profiled, and XDO models were generated to perform initial drug response evaluations. Subsets of PDAC PDX models showed global copy number variants and gene expression profiles that were retained with serial passaging, and they showed a spectrum of somatic mutations represented in patient tumors. PDAC XDO models were established, with a success rate of 71% (10/14). Pathway activation of KRAS-MAPK in PDXs was independent of KRAS mutational status. Four wild-type KRAS models were characterized by one with EGFR (L747-P753 del), two with BRAF alterations (N486_P490del or V600E), and one with triple negative KRAS/EGFR/BRAF. Model OCIP256, characterized by BRAF (N486-P490 del), had activated phospho-ERK. A combination treatment of a pan-RAF inhibitor (LY3009120) and a MEK inhibitor (trametinib) effectively suppressed phospho-ERK and inhibited growth of OCIP256 XDO and PDX models. PDAC/duodenal adenocarcinoma have high success rates forming PDX/organoid and retaining their phenotypic and genotypic features. These models may be effective tools to evaluate novel drug combination therapies.

Large-scale pharmacogenomic studies and drug response prediction for personalized cancer medicine

The response rate of most anti-cancer drugs is limited because of the high heterogeneity of cancer and the complex mechanism of drug action. Personalized treatment that stratifies patients into subgroups using molecular biomarkers is promising to improve clinical benefit. With the accumulation of preclinical models and advances in computational approaches of drug response prediction, pharmacogenomics has made great success over the last 20 years and is increasingly used in the clinical practice of personalized cancer medicine. In this article, we first summarize FDA-approved pharmacogenomic biomarkers and large-scale pharmacogenomic studies of preclinical cancer models such as patient-derived cell lines, organoids, and xenografts. Furthermore, we comprehensively review the recent developments of computational methods in drug response prediction, covering network, machine learning, and deep learning technologies and strategies to evaluate immunotherapy response. In the end, we discuss challenges and propose possible solutions for further improvement.

Preclinical In Vivo Modeling of Pediatric Sarcoma-Promises and Limitations

Pediatric sarcomas are an extremely heterogeneous group of genetically distinct diseases. Despite the increasing knowledge on their molecular makeup in recent years, true therapeutic advancements are largely lacking and prognosis often remains dim, particularly for relapsed and metastasized patients. Since this is largely due to the lack of suitable model systems as a prerequisite to develop and assess novel therapeutics, we here review the available approaches to model sarcoma in vivo. We focused on genetically engineered and patient-derived mouse models, compared strengths and weaknesses, and finally explored possibilities and limitations to utilize these models to advance both biological understanding as well as clinical diagnosis and therapy.

Mouse avatars of human cancers: the temporality of translation in precision oncology

Patient-derived xenografts (PDXs) are currently promoted as new translational models in precision oncology. PDXs are immunodeficient mice with human tumors that are used as surrogate models to represent specific types of cancer. By accounting for the genetic heterogeneity of cancer tumors, PDXs are hoped to provide more clinically relevant results in preclinical research. Further, in the function of so-called "mouse avatars", PDXs are hoped to allow for patient-specific drug testing in real-time (in parallel to treatment of the corresponding cancer patient). This paper examines the circulation of knowledge and bodily material across the species boundary of human and personalized mouse model, historically as well as in contemporary practices. PDXs raise interesting questions about the relation between animal model and human patient, and about the capacity of hybrid or interspecies models to close existing translational gaps. We highlight that the translational potential of PDXs not only depends on representational matching of model and target, but also on temporal alignment between model development and practical uses. Aside from the importance of ensuring temporal stability of human tumors in a murine body, the mouse avatar concept rests on the possibility of aligning the temporal horizons of the clinic and the lab. We examine strategies to address temporal challenges, including cryopreservation and biobanking, as well as attempts to speed up translation through modification and use of faster developing organisms. We discuss how featured model virtues change with precision oncology, and contend that temporality is a model feature that deserves more philosophical attention.

Patient Derived Xenografts for Genome-Driven Therapy of Osteosarcoma

Osteosarcoma (OS) is a rare malignant primary tumor of mesenchymal origin affecting bone. It is characterized by a complex genotype, mainly due to the high frequency of chromothripsis, which leads to multiple somatic copy number alterations and structural rearrangements. Any effort to design genome-driven therapies must therefore consider such high inter- and intra-tumor heterogeneity. Therefore, many laboratories and international networks are developing and sharing OS patient-derived xenografts (OS PDX) to broaden the availability of models that reproduce OS complex clinical heterogeneity. OS PDXs, and new cell lines derived from PDXs, faithfully preserve tumor heterogeneity, genetic, and epigenetic features and are thus valuable tools for predicting drug responses. Here, we review recent achievements concerning OS PDXs, summarizing the methods used to obtain ectopic and orthotopic xenografts and to fully characterize these models. The availability of OS PDXs across the many international PDX platforms and their possible use in PDX clinical trials are also described. We recommend the coupling of next-generation sequencing (NGS) data analysis with functional studies in OS PDXs, as well as the setup of OS PDX clinical trials and co-clinical trials, to enhance the predictive power of experimental evidence and to accelerate the clinical translation of effective genome-guided therapies for this aggressive disease.

Current status and future outlook for patient-derived cancer models from a rare cancer research perspective

Rare cancers are a group of approximately 200 malignancies with extremely low incidences and with a wide variety of genotypes and phenotypes. Collectively, they are more common than any single malignancy. However, given the small numbers of individuals diagnosed with rare cancers, it is difficult to design clinical trials with sufficient patient numbers. Therefore, few effective anticancer drugs have been developed, and evidence‐based medicine is not always feasible for rare cancers. Consequently, their clinical outcomes are generally poorer. Cancer research requires adequate models that faithfully recapitulate molecular features and reproduce treatment responses of the original tumors. Such models allow us to focus on more efficacious drugs in the clinical studies. For rare cancers, patient‐derived cancer models are particularly important because the enrollment of sufficient patients is rarely attainable within a reasonable period of time. However, extremely few models are available for rare cancers. For example, cell lines and xenografts are available for only a limited number of histological subtypes of sarcomas; therefore, most sarcoma research is performed without such models, and a lack of adequate cancer models causes a lag in therapeutic development. The establishment of novel rare cancer models will dramatically facilitate rare cancer research and treatment development in the near future. This review focuses on the status of patient‐derived rare cancer models and discusses their pivotal problems and possibilities, using sarcomas as a representative rare cancer type. Multi‐institutional collaboration will help address the scarcity of patient‐derived rare cancer models.

PCAT: an integrated portal for genomic and preclinical testing data of pediatric cancer patient-derived xenograft models

Although cancer is the leading cause of disease-related mortality in children, the relative rarity of pediatric cancers poses a significant challenge for developing novel therapeutics to further improve prognosis. Patient-derived xenograft (PDX) models, which are usually developed from high-risk tumors, are a useful platform to study molecular driver events, identify biomarkers and prioritize therapeutic agents. Here, we develop PDX for Childhood Cancer Therapeutics (PCAT), a new integrated portal for pediatric cancer PDX models. Distinct from previously reported PDX portals, PCAT is focused on pediatric cancer models and provides intuitive interfaces for querying and data mining. The current release comprises 324 models and their associated clinical and genomic data, including gene expression, mutation and copy number alteration. Importantly, PCAT curates preclinical testing results for 68 models and 79 therapeutic agents manually collected from individual agent testing studies published since 2008. To facilitate comparisons of patterns between patient tumors and PDX models, PCAT curates clinical and molecular data of patient tumors from the TARGET project. In addition, PCAT provides access to gene fusions identified in nearly 1000 TARGET samples. PCAT was built using R-shiny and MySQL. The portal can be accessed at http://pcat.zhenglab.info or http://www.pedtranscriptome.org.

Patient-derived organoids and orthotopic xenografts of primary and recurrent gliomas represent relevant patient avatars for precision oncology

Patient-based cancer models are essential tools for studying tumor biology and for the assessment of drug responses in a translational context. We report the establishment a large cohort of unique organoids and patient-derived orthotopic xenografts (PDOX) of various glioma subtypes, including gliomas with mutations in IDH1, and paired longitudinal PDOX from primary and recurrent tumors of the same patient. We show that glioma PDOXs enable long-term propagation of patient tumors and represent clinically relevant patient avatars that retain histopathological, genetic, epigenetic, and transcriptomic features of parental tumors. We find no evidence of mouse-specific clonal evolution in glioma PDOXs. Our cohort captures individual molecular genotypes for precision medicine including mutations in IDH1, ATRX, TP53, MDM2/4, amplification of EGFR, PDGFRA, MET, CDK4/6, MDM2/4, and deletion of CDKN2A/B, PTCH, and PTEN. Matched longitudinal PDOX recapitulate the limited genetic evolution of gliomas observed in patients following treatment. At the histological level, we observe increased vascularization in the rat host as compared to mice. PDOX-derived standardized glioma organoids are amenable to high-throughput drug screens that can be validated in mice. We show clinically relevant responses to temozolomide (TMZ) and to targeted treatments, such as EGFR and CDK4/6 inhibitors in (epi)genetically defined subgroups, according to MGMT promoter and EGFR/CDK status, respectively. Dianhydrogalactitol (VAL-083), a promising bifunctional alkylating agent in the current clinical trial, displayed high therapeutic efficacy, and was able to overcome TMZ resistance in glioblastoma. Our work underscores the clinical relevance of glioma organoids and PDOX models for translational research and personalized treatment studies and represents a unique publicly available resource for precision oncology.