Integrated next-generation sequencing and avatar mouse models for personalized cancer treatment

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

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

Personalized Treatment of Recurrent, Metastatic Head and Neck Cancer Guided by Patient-Derived Xenograft Models

Recurrent or metastatic head and neck squamous cell carcinoma (RMHNSCC) is associated with a poor prognosis and short survival duration. There is an urgent need to identify personalized predictors of drug response to guide the selection of the most effective therapy for each individual recurrence. We tested the feasibility of patient-derived xenografts (PDX) for guiding their RMHNSCC salvage treatment. Fresh tumor samples from eligible, consented patients were implanted into mice. Established tumors were expanded in mouse PDX cohorts to identify responses to candidate salvage drug treatments in parallel testing. Patients alive and suitable for chemotherapy were treated based on responses determined by PDX testing. Nine patient tumors were successfully engrafted in mice with an average time of 89.2±41.7 days. Four patients' PDX models underwent parallel drug testing. Two patients received PDX-guided therapy. In one of these patients, single agents of cetuximab and paclitaxel demonstrated the best responses in the PDX model, and this patient exhibited sequential partial responses to each drug, including a 17-month clinical response to cetuximab. The main limitation of PDX testing for RMHNSCC was the time delay in obtaining testing results. Despite this, parallel PDX testing may be feasible for a subset of patients and appears to correlate with clinical benefit.

Preclinical Models and Technologies in Glioblastoma Research: Evolution, Current State, and Future Avenues

Glioblastoma is the most common malignant primary central nervous system tumor and one of the most debilitating cancers. The prognosis of patients with glioblastoma remains poor, and the management of this tumor, both in its primary and recurrent forms, remains suboptimal. Despite the tremendous efforts that are being put forward by the research community to discover novel efficacious therapeutic agents and modalities, no major paradigm shifts have been established in the field in the last decade. However, this does not mirror the abundance of relevant findings and discoveries made in preclinical glioblastoma research. Hence, developing and utilizing appropriate preclinical models that faithfully recapitulate the characteristics and behavior of human glioblastoma is of utmost importance. Herein, we offer a holistic picture of the evolution of preclinical models of glioblastoma. We further elaborate on the commonly used in vitro and vivo models, delving into their development, favorable characteristics, shortcomings, and areas of potential improvement, which aids researchers in designing future experiments and utilizing the most suitable models. Additionally, this review explores progress in the fields of humanized and immunotolerant mouse models, genetically engineered animal models, 3D in vitro models, and microfluidics and highlights promising avenues for the future of preclinical glioblastoma research.

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

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

Pan-Cancer Pharmacogenomic Analysis of Patient-Derived Tumor Cells Using Clinically Relevant Drug Exposures

As a result of tumor heterogeneity and solid cancers harboring multiple molecular defects, precision medicine platforms in oncology are most effective when both genetic and pharmacologic determinants of a tumor are evaluated. Expandable patient-derived xenograft (PDX) mouse tumor and corresponding PDX culture (PDXC) models recapitulate many of the biological and genetic characteristics of the original patient tumor, allowing for a comprehensive pharmacogenomic analysis. Here, the somatic mutations of 23 matched patient tumor and PDX samples encompassing four cancers were first evaluated using next-generation sequencing (NGS). 19 antitumor agents were evaluated across 78 patient-derived tumor cultures using clinically relevant drug exposures. A binarization threshold sensitivity classification determined in culture (PDXC) was used to identify tumors that best respond to drug in vivo (PDX). Using this sensitivity classification, logic models of DNA mutations were developed for 19 antitumor agents to predict drug response. We determined that the concordance of somatic mutations across patient and corresponding PDX samples increased as variant allele frequency increased. Notable individual PDXC responses to specific drugs, as well as lineage-specific drug responses were identified. Robust responses identified in PDXC were recapitulated in vivo in PDX-bearing mice and logic modeling determined somatic gene mutation(s) defining response to specific antitumor agents. In conclusion, combining NGS of primary patient tumors, high-throughput drug screen using clinically relevant doses, and logic modeling, can provide a platform for understanding response to therapeutic drugs targeting cancer.

The development of a rapid patient-derived xenograft model to predict chemotherapeutic drug sensitivity/resistance in malignant glial tumors

BACKGROUND

High-grade gliomas (HGG) are aggressive brain tumors associated with short median patient survival and limited response to therapies, driving the need to develop tools to improve patient outcomes. Patient-derived xenograft (PDX) models, such as mouse PDX, have emerged as potential Avatar platforms for personalized oncology approaches, but the difficulty for some human grafts to grow successfully and the long time required for mice to develop tumors preclude their use for HGG.

METHODS

We used a rapid and efficient ex-ovo chicken embryo chorioallantoic membrane (CAM) culture system to evaluate the efficacy of oncologic drug options for HGG patients.

RESULTS

Implantation of fresh glioma tissue fragments from 59 of 60 patients, that include difficult-to-grow IDH-mutated samples, successfully established CAM tumor xenografts within 7 days, with a tumor take rate of 98.3%. These xenografts faithfully recapitulate the histological and molecular characteristics of the primary tumor, and the ability of individual fragments to form tumors was predictive of poor patient prognosis. Treatment of drug-sensitive or drug-resistant xenografts indicates that the CAM-glioma assay enables testing tumor sensitivity to temozolomide and carboplatin at doses consistent with those administered to patients. In a proof-of-concept study involving 14 HGG patients, we observed a correlation of 100% between the CAM xenograft response to temozolomide or carboplatin and the clinical response of patients.

CONCLUSION

The CAM-glioma model is a fast and reliable assay that has the potential to serve as a complementary model to drug discovery and a real-time Avatar platform to predict the best treatment for HGG patients.

Generation of Orthotopic Patient-Derived Xenografts in Humanized Mice for Evaluation of Emerging Targeted Therapies and Immunotherapy Combinations for Melanoma

Current methodologies for developing PDX in humanized mice in preclinical trials with immune-based therapies are limited by GVHD. Here, we compared two approaches for establishing PDX tumors in humanized mice: (1) PDX are first established in immune-deficient mice; or (2) PDX are initially established in humanized mice; then established PDX are transplanted to a larger cohort of humanized mice for preclinical trials. With the first approach, there was rapid wasting of PDX-bearing humanized mice with high levels of activated T cells in the circulation and organs, indicating immune-mediated toxicity. In contrast, with the second approach, toxicity was less of an issue and long-term human melanoma tumor growth and maintenance of human chimerism was achieved. Preclinical trials from the second approach revealed that rigosertib, but not anti-PD-1, increased CD8/CD4 T cell ratios in spleen and blood and inhibited PDX tumor growth. Resistance to anti-PD-1 was associated with PDX tumors established from tumors with limited CD8+ T cell content. Our findings suggest that it is essential to carefully manage immune editing by first establishing PDX tumors in humanized mice before expanding PDX tumors into a larger cohort of humanized mice to evaluate therapy response.

Tumor heterogeneity: preclinical models, emerging technologies, and future applications

Heterogeneity describes the differences among cancer cells within and between tumors. It refers to cancer cells describing variations in morphology, transcriptional profiles, metabolism, and metastatic potential. More recently, the field has included the characterization of the tumor immune microenvironment and the depiction of the dynamics underlying the cellular interactions promoting the tumor ecosystem evolution. Heterogeneity has been found in most tumors representing one of the most challenging behaviors in cancer ecosystems. As one of the critical factors impairing the long-term efficacy of solid tumor therapy, heterogeneity leads to tumor resistance, more aggressive metastasizing, and recurrence. We review the role of the main models and the emerging single-cell and spatial genomic technologies in our understanding of tumor heterogeneity, its contribution to lethal cancer outcomes, and the physiological challenges to consider in designing cancer therapies. We highlight how tumor cells dynamically evolve because of the interactions within the tumor immune microenvironment and how to leverage this to unleash immune recognition through immunotherapy. A multidisciplinary approach grounded in novel bioinformatic and computational tools will allow reaching the integrated, multilayered knowledge of tumor heterogeneity required to implement personalized, more efficient therapies urgently required for cancer patients.

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.

Breaking the niche: multidimensional nanotherapeutics for tumor microenvironment modulation

Most of the current antitumor therapeutics were developed targeting the cancer cells only. Unfortunately, in the majority of tumors, this single-dimensional therapy is found to be ineffective. Advanced research has shown that cancer is a multicellular disorder. The tumor microenvironment (TME), which is made by a complex network of the bulk tumor cells and other supporting cells, plays a crucial role in tumor progression. Understanding the importance of the TME in tumor growth, different treatment modalities have been developed targeting these supporting cells. Recent clinical results suggest that simultaneously targeting multiple components of the tumor ecosystem with drug combinations can be highly effective. This type of "multidimensional" therapy has a high potential for cancer treatment. However, tumor-specific delivery of such multi-drug combinations remains a challenge. Nanomedicine could be utilized for the tumor-targeted delivery of such multidimensional therapeutics. In this review, we first give a brief overview of the major components of TME. We then highlight the latest developments in nanoparticle-based combination therapies, where one drug targets cancer cells and other drug targets tumor-supporting components in the TME for a synergistic effect. We include the latest preclinical and clinical studies and discuss innovative nanoparticle-mediated targeting strategies.

Application status and future prospects of the PDX model in lung cancer

Lung cancer is one of the most prevalent, fatal, and highly heterogeneous diseases that, seriously threaten human health. Lung cancer is primarily caused by the aberrant expression of multiple genes in the cells. Lung cancer treatment options include surgery, radiation, chemotherapy, targeted therapy, and immunotherapy. In recent decades, significant progress has been made in developing therapeutic agents for lung cancer as well as a biomarker for its early diagnosis. Nonetheless, the alternative applications of traditional pre-clinical models (cell line models) for diagnosis and prognosis prediction are constrained by several factors, including the lack of microenvironment components necessary to affect cancer biology and drug response, and the differences between laboratory and clinical results. The leading reason is that substantial shifts accrued to cell biological behaviors, such as cell proliferative, metastatic, invasive, and gene expression capabilities of different cancer cells after decades of growing indefinitely in vitro. Moreover, the introduction of individualized treatment has prompted the development of appropriate experimental models. In recent years, preclinical research on lung cancer has primarily relied on the patient-derived tumor xenograft (PDX) model. The PDX provides stable models with recapitulate characteristics of the parental tumor such as the histopathology and genetic blueprint. Additionally, PDXs offer valuable models for efficacy screening of new cancer drugs, thus, advancing the understanding of tumor biology. Concurrently, with the heightened interest in the PDX models, potential shortcomings have gradually emerged. This review summarizes the significant advantages of PDXs over the previous models, their benefits, potential future uses and interrogating open issues.

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

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

Precision oncology using ex vivo technology: a step towards individualised cancer care?

Despite advances in cancer genomics and the increased use of genomic medicine, metastatic cancer is still mostly an incurable and fatal disease. With diminishing returns from traditional drug discovery strategies, and high clinical failure rates, more emphasis is being placed on alternative drug discovery platforms, such as ex vivo approaches. Ex vivo approaches aim to embed biological relevance and inter-patient variability at an earlier stage of drug discovery, and to offer more precise treatment stratification for patients. However, these techniques also have a high potential to offer personalised therapies to patients, complementing and enhancing genomic medicine. Although an array of approaches are available to researchers, only a minority of techniques have made it through to direct patient treatment within robust clinical trials. Within this review, we discuss the current challenges to ex vivo approaches within clinical practice and summarise the contemporary literature which has directed patient treatment. Finally, we map out how ex vivo approaches could transition from a small-scale, predominantly research based technology to a robust and validated predictive tool. In future, these pre-clinical approaches may be integrated into clinical cancer pathways to assist in the personalisation of therapy choices and to hopefully improve patient experiences and outcomes.

Establishment and Identification of Patient-Derived Xenograft Model for Oral Squamous Cell Carcinoma

Oral squamous cell carcinoma is the most common head and neck malignancy with high morbidity and mortality. Currently, platinum-based chemotherapy is the conventional chemotherapy regimen for patients with oral squamous cell carcinoma. However, due to the heterogeneity of tumors and individual differences of patients, chemotherapy regimens lacking individualized evaluation of tumor patients are often less effective. Therefore, personalized tumor chemotherapy is one of the effective methods for the future treatment of malignant tumors. The patient-derived xenograft model is a relatively new tumor xenograft model that relies on immunodeficient mice. This model can better maintain various histological characteristics of primary tumor grafts, such as pathological structural features, molecular diversity, and gene expression profiles. Therefore, the patient-derived xenograft model combined with drug screening technology to explore new tumor chemotherapy is the critical research direction for future tumor treatment. This study successfully established the patient-derived xenograft model of oral squamous cell carcinoma. It was verified by hematoxylin-eosin staining and immunohistochemistry that the constructed patient-derived xenograft model retained the pathological and molecular biological characteristics of primary tumors. Our patient-derived xenograft model can be used further to study the oncological characteristics of oral squamous carcinoma and can also be applied to personalize the treatment of oral squamous carcinoma patients, providing a practical resource for screening chemotherapy drugs.

A clinically compatible drug-screening platform based on organotypic cultures identifies vulnerabilities to prevent and treat brain metastasis

We report a medium-throughput drug-screening platform (METPlatform) based on organotypic cultures that allows to evaluate inhibitors against metastases growing in situ. By applying this approach to the unmet clinical need of brain metastasis, we identified several vulnerabilities. Among them, a blood-brain barrier permeable HSP90 inhibitor showed high potency against mouse and human brain metastases at clinically relevant stages of the disease, including a novel model of local relapse after neurosurgery. Furthermore, in situ proteomic analysis applied to metastases treated with the chaperone inhibitor uncovered a novel molecular program in brain metastasis, which includes biomarkers of poor prognosis and actionable mechanisms of resistance. Our work validates METPlatform as a potent resource for metastasis research integrating drug-screening and unbiased omic approaches that is compatible with human samples. Thus, this clinically relevant strategy is aimed to personalize the management of metastatic disease in the brain and elsewhere.

Organoid Sensitivity Correlates with Therapeutic Response in Patients with Pancreatic Cancer

PURPOSE

Pancreatic ductal adenocarcinoma (PDAC) remains a significant health issue. For most patients there are no options for targeted therapy and existing treatments are limited by toxicity. The HOPE trial (Harnessing Organoids for PErsonalized Therapy) was a pilot feasibility trial aiming to prospectively generate patient derived organoids (PDOs) from patients with PDAC and test their drug sensitivity and correlation with clinical outcomes.

EXPERIMENTAL DESIGN

PDOs were established from a heterogeneous population of patients with PDAC including both basal and classical PDAC subtypes.

RESULTS

A method for classifying PDOs as sensitive or resistant to chemotherapy regimens was developed to predict the clinical outcome of study subjects. Drug sensitivity testing on PDOs correlated with clinical responses to treatment in individual patients.

CONCLUSION

These data support the investigation of PDOs to guide treatment in prospective interventional trials in PDAC.

Patient-Derived Xenografts Are a Reliable Preclinical Model for the Personalized Treatment of Epithelial Ovarian Cancer

To generate robust patient-derived xenograft (PDX) models for epithelial ovarian cancer (EOC), analyze the resemblance of PDX models to the original tumors, and explore factors affecting engraftment rates, fresh cancer tissues from a consecutive cohort of 158 patients with EOC were collected to construct subcutaneous PDX models. Paired samples of original tumors and PDX tumors were compared at the genome, transcriptome, protein levels, and the platinum-based chemotherapy response was evaluated to ensure the reliability of the PDXs. Univariate and multivariate analyses were used to determine the factors affecting the engraftment rates. The engraftment success rate was 58.23% (92/158) over 3–6 months. The Ki-67 index and receiving neoadjuvant chemotherapy can affect the engraftment rate in primary patients. The PDX models generated in this study were found to retain the histomorphology, protein expression, and genetic alteration patterns of the original tumors, despite the transcriptomic differences observed. Clinically, the PDX models demonstrated a high degree of similarity with patients in terms of the chemotherapy response and could predict prognosis. Thus, the PDX model can be considered a promising and reliable preclinical tool for personalized and precise treatment.

Future Perspective of Diabetic Animal Models

Objective

The need of today’s research is to develop successful and reliable diabetic animal models for understanding the disease susceptibility and pathogenesis. Enormous success of animal models had already been acclaimed for identifying key genetic and environmental factors like Idd loci and effects of microorganisms including the gut microbiota. Furthermore, animal models had also helped in identifying many therapeutic targets and strategies for immune-intervention. In spite of a quite success, we have acknowledged that many of the discovered immunotherapies are working on animals and did not have a significant impact on human. Number of animal models were developed in the past to accelerate drug discovery pipeline. However, due to poor initial screening and assessment on inequivalent animal models, the percentage of drug candidates who succeeded during clinical trials was very low. Therefore, it is essential to bridge this gap between pre-clinical research and clinical trial by validating the existing animal models for consistency.

Results and Conclusion

In this review, we have discussed and evaluated the significance of animal models on behalf of published data on PUBMED. Amongst the most popular diabetic animal models, we have selected six animal models (e.g. BioBreeding rat, “LEW IDDM rat”, “Nonobese Diabetic (NOD) mouse”, “STZ RAT”, “LEPR Mouse” and “Zucker Diabetic Fatty (ZDF) rat” and ranked them as per their published literature on PUBMED. Moreover, the vision and brief imagination for developing an advanced and robust diabetic model of 21^st century was discussed with the theme of one mice-one human concept including organs-on-chips.

Rare Primary Malignant Bone Sarcomas

Simple Summary

Primary malignant bone tumors are infrequent cancers. More than 90% of these neoplasms are classified as osteosarcomas, Ewing sarcomas or chondrosarcomas, and their clinical presentation, diagnosis, and treatment principles are well-established. The entities described in this article, are ultra-rare varieties of bone sarcomas, and there clinical and histological characteristics are not well known. Therefore, they are very difficult to be diagnosed and there is a lot of uncertainty on their treatment. Because of their rarity, it is also extremely difficult to perform clinical research on these cancers. This article creates more awareness of these very rare bone tumors. It explains how to recognize and diagnose each entity and it summarizes the medical scientific literature that is available on these cancers. Increasing awareness and clinical research for these cancers are key elements to improve the prognosis for patients with these diseases in the long term.

Abstract

Rare primary malignant bone sarcomas (RPMBS), other than osteosarcoma, chondrosarcoma, chordoma, and Ewing sarcoma, account for about 5–10% of primary bone tumors and represent a major diagnostic challenge. These tumors include spindle cell and round cell sarcoma entities, hemangiopericytoma-like and vascular tumors. Additionally, several histotypes, traditionally described in the soft tissues, such as myxofibrosarcoma, synovial sarcoma, and malignant peripheral nerve sheath tumor of bone, have been reported in patients with primary bone tumors. While wide surgical resection is the mainstay of local treatment, systemic therapy of these rare entities is controversial. Patients with undifferentiated spindle cell or pleomorphic high-grade tumors of bone, are usually treated with osteosarcoma-like chemotherapy, while patients with round cell and undifferentiated round cell tumors (URCTs), may respond to sarcoma treatment regimens for Ewing sarcoma patients. Studies on analogies and differences among these ultra-rare tumors have seldom been reported. This review describes relevance, clinical aspects, diagnostic procedures, staging, treatment recommendations, and current research in this composite tumor group.

Spontaneous and Induced Animal Models for Cancer Research

Considering the complexity of the current framework in oncology, the relevance of animal models in biomedical research is critical in light of the capacity to produce valuable data with clinical translation. The laboratory mouse is the most common animal model used in cancer research due to its high adaptation to different environments, genetic variability, and physiological similarities with humans. Beginning with spontaneous mutations arising in mice colonies that allow for pursuing studies of specific pathological conditions, this area of in vivo research has significantly evolved, now capable of generating humanized mice models encompassing the human immune system in biological correlation with human tumor xenografts. Moreover, the era of genetic engineering, especially of the hijacking CRISPR/Cas9 technique, offers powerful tools in designing and developing various mouse strains. Within this article, we will cover the principal mouse models used in oncology research, beginning with behavioral science of animals vs. humans, and continuing on with genetically engineered mice, microsurgical-induced cancer models, and avatar mouse models for personalized cancer therapy. Moreover, the area of spontaneous large animal models for cancer research will be briefly presented.

Systematic Establishment of Robustness and Standards in Patient-Derived Xenograft Experiments and Analysis

Patient-derived xenografts (PDX) are tumor-in-mouse models for cancer. PDX collections, such as the NCI PDXNet, are powerful resources for preclinical therapeutic testing. However, variations in experimental and analysis procedures have limited interpretability. To determine the robustness of PDX studies, the PDXNet tested temozolomide drug response for three prevalidated PDX models (sensitive, resistant, and intermediate) across four blinded PDX Development and Trial Centers using independently selected standard operating procedures. Each PDTC was able to correctly identify the sensitive, resistant, and intermediate models, and statistical evaluations were concordant across all groups. We also developed and benchmarked optimized PDX informatics pipelines, and these yielded robust assessments across xenograft biological replicates. These studies show that PDX drug responses and sequence results are reproducible across diverse experimental protocols. In addition, we share the range of experimental procedures that maintained robustness, as well as standardized cloud-based workflows for PDX exome-sequencing and RNA-sequencing analyses and for evaluating growth. SIGNIFICANCE: The PDXNet Consortium shows that PDX drug responses and sequencing results are reproducible across diverse experimental protocols, establishing the potential for multisite preclinical studies to translate into clinical trials.

Update on in-vivo preclinical research models in adrenocortical carcinoma

PURPOSE OF REVIEW

The aim of this review is to summarize recent advances on development of in vivo preclinical models of adrenocortical carcinoma (ACC).

RECENT FINDINGS

Significant progress has been achieved in the underlying molecular mechanisms of adrenocortical tumorigenesis over the last decade, and recent comprehensive profiling analysis of ACC tumors identified several genetic and molecular drivers of this disease. Therapeutic breakthroughs, however, have been limited because of the lack of preclinical models recapitulating the molecular features and heterogeneity of the tumors. Recent publications on genetically engineered mouse models and development of patient-derived ACC xenografts in both nude mice and humanized mice now provide researchers with novel tools to explore therapeutic targets in the context of heterogeneity and tumor microenvironment in human ACC.

SUMMARY

We review current in-vivo models of ACC and discuss potential therapeutic opportunities that have emerged from these studies.

Precision medicine needs pioneering clinical bioinformaticians

Success in precision medicine depends on accessing high-quality genetic and molecular data from large, well-annotated patient cohorts that couple biological samples to comprehensive clinical data, which in conjunction can lead to effective therapies. From such a scenario emerges the need for a new professional profile, an expert bioinformatician with training in clinical areas who can make sense of multi-omics data to improve therapeutic interventions in patients, and the design of optimized basket trials. In this review, we first describe the main policies and international initiatives that focus on precision medicine. Secondly, we review the currently ongoing clinical trials in precision medicine, introducing the concept of 'precision bioinformatics', and we describe current pioneering bioinformatics efforts aimed at implementing tools and computational infrastructures for precision medicine in health institutions around the world. Thirdly, we discuss the challenges related to the clinical training of bioinformaticians, and the urgent need for computational specialists capable of assimilating medical terminologies and protocols to address real clinical questions. We also propose some skills required to carry out common tasks in clinical bioinformatics and some tips for emergent groups. Finally, we explore the future perspectives and the challenges faced by precision medicine bioinformatics.

Zebrafish Avatars towards Personalized Medicine-A Comparative Review between Avatar Models

Cancer frequency and prevalence have been increasing in the past decades, with devastating impacts on patients and their families. Despite the great advances in targeted approaches, there is still a lack of methods to predict individual patient responses, and therefore treatments are tailored according to average response rates. “Omics” approaches are used for patient stratification and choice of therapeutic options towards a more precise medicine. These methods, however, do not consider all genetic and non-genetic dynamic interactions that occur upon drug treatment. Therefore, the need to directly challenge patient cells in a personalized manner remains. The present review addresses the state of the art of patient-derived in vitro and in vivo models, from organoids to mouse and zebrafish Avatars. The predictive power of each model based on the retrospective correlation with the patient clinical outcome will be considered. Finally, the review is focused on the emerging zebrafish Avatars and their unique characteristics allowing a fast analysis of local and systemic effects of drug treatments at the single-cell level. We also address the technical challenges that the field has yet to overcome.

An interventional image-guided microdevice implantation and retrieval method for in-vivo drug response assessment

PURPOSE

Recently developed implantable microdevices can perform multi-drug response assessment of cancer drugs in-vivo, with potential to develop highly optimized personalized cancer treatment strategies. However, minimally invasive/interventional image-guided methods of in-vivo microdevice implantation, securement, and retrieval are needed for broad clinical translation. Here we demonstrate proof-of-concept of an interventional microdevice implantation and retrieval method for personalized drug response assessment, using ex-vivo phantom, ex-vivo tissue, and in-vivo murine models.

METHODS

A method for minimally-invasive microdevice implantation and retrieval was developed, by which a custom-prototyped 6 mm retrievable microdevice can be implanted into a live tumor, deliver drugs into 10 discrete regions of adjacent tissue, and retrieved along with the adjacent drug-exposed tissue with a custom-prototyped retrieval needle device to allow in-vivo multi-drug response assessment. Computed tomography (CT) and ultrasound (US)-guided minimally invasive microdevice implantation and retrieval were tested in ex-vivo phantom and tissue models. Successful retrieval was defined as retrieval of the microdevice and adjacent core phantom/tissue sample containing at least 4/10 drug delivery sites. Subsequently, 10 implantation and retrieval trials in phantom models were performed using bi-axial and tri-axial retrieval needles; success rates were calculated and compared using a two-proportion z-test and the number of successfully retrieved drug release sites per microdevice was calculated and compared using a one-tailed independent t-test. Finally, five microdevices, each containing ten reservoirs preloaded with chemotherapy agent Doxorubicin, were implanted into mouse tumors in-vivo, secured for 24-h during drug release, and microdevice/tissue retrieval was performed under ultrasound guidance. Fluorescence microscopy of the retrieved tissue was used to confirm drug delivery and apoptosis staining assessed in-vivo tissue response; correlation of drug release and apoptosis staining were used to assess in-vivo drug efficacy.

RESULTS

Image-guided microdevice implantation and retrieval were successful in ex-vivo phantom and tissue models with both US and CT guidance. Bi-axial retrieval success rate was significantly higher than triaxial retrieval in ex-vivo phantom trials (90% vs 50%, z = 1.95, P = 0.026), and had nonsignificantly higher number of retrieved drug-release sites per microdevice (8.3 vs 7.0, t = 1.37, P = 0.097). Bi-axial retrieval was successful in all five in-vivo mouse tumor models, and allowed in-vivo drug response assessment at up to ten discrete drug delivery sites per microdevice. An average of 6.8/10 discrete tumor sites containing micro-doses of delivered drug were retrieved per in-vivo attempt (min 5, max 10, std 1.93). Tissue regions of drug delivery, as assessed with fluorescent Doxorubicin drug signal, correlated with regions of apoptosis staining in all in-vivo models, indicating drug efficacy. No bleeding, microdevice migration, or other complications were noted during implantation, 24-h observation, or retrieval.

CONCLUSIONS

The demonstrated image-guided minimally invasive microdevice implantation and retrieval method is similar to routine outpatient biopsy procedures, obviates the need for surgery, and can be performed at varying depths under CT and/or US guidance. There is potential for this method to enable clinical translation of in-vivo personalized drug response assessment/prediction in a much larger number of patients than currently possible.

Patient-derived orthotopic xenograft models of sarcoma

Sarcoma is a rare and recalcitrant malignancy. Although immune and novel targeted therapies have been tested on many cancer types, few sarcoma patients have had durable responses with such therapy. Doxorubicin and cisplatinum are still first-line chemotherapy after four decades. Our laboratory has established the patient-derived orthotopic xenograft (PDOX) model using surgical orthotopic implantation (SOI). Many promising results have been obtained using the sarcoma PDOX model for identifying effective approved drugs and experimental therapeutics, as well as combinations of them for individual patients. In this review, we present our laboratory's experience with PDOX models of sarcoma, and the ability of the PDOX models to identify effective approved agents, as well as experimental therapeutics.

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

BACKGROUND

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

RESULTS

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

CONCLUSIONS

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

Pharmacogenetic and pharmacogenomic discovery strategies

Genetic/genomic profiling at a single-patient level is expected to provide critical information for determining inter-individual drug toxicity and potential efficacy in cancer therapy. A better definition of cancer subtypes at a molecular level, may correspondingly complement such pharmacogenetic and pharmacogenomic approaches, for more effective personalized treatments. Current pharmacogenetic/pharmacogenomic strategies are largely based on the identification of known polymorphisms, thus limiting the discovery of novel or rarer genetic variants. Recent improvements in cost and throughput of next generation sequencing (NGS) are now making whole-genome profiling a plausible alternative for clinical procedures. Beyond classical pharmacogenetic/pharmacogenomic traits for drug metabolism, NGS screening programs of cancer genomes may lead to the identification of novel cancer-driving mutations. These may not only constitute novel therapeutic targets, but also effector determinants for metabolic pathways linked to drug metabolism. An additional advantage is that cancer NGS profiling is now leading to discovering targetable mutations, e.g., in glioblastomas and pancreatic cancers, which were originally discovered in other tumor types, thus allowing for effective repurposing of active drugs already on the market.

Molecularly annotation of mouse avatar models derived from patients with colorectal cancer liver metastasis

Background: Liver is the most common metastatic site in advanced colorectal cancer. Most patients with colorectal cancer liver metastasis (CRLM) do not benefit from current treatment. Patient-derived xenografts (PDXs) with defined molecular signatures are attractive models for preclinical studies. Methods: Successfully established PDXs were evaluated to elucidate their fidelity of patients' biologic characteristics (pathologic, genetic and protein properties, together with chemosensitivity). The genomic variations of PDXs were analyzed by next-generation sequencing to explore the underlying molecular mechanism of metastasis and potential therapeutic targets. Results: CRLM (N=73) showed a significantly higher successful PDX establishment rate than primary specimens (N=26; 76.7% vs. 57.7%). CRLM PDXs recapitulated the pathologic, genetic and protein properties of parental tumors, as well as chemosensitivity. Frequent altered genes in PDXs showed high consistency compared to patients' genomic alterations and were enriched in MAPK, ErbB, cell cycle, focal adhesion pathways for CRLM PDXs, whereas primary tumor-derived PDXs only exhibited genomic variations involving ErbB and cell cycle. The genetic alterations showed high concordance between paired PDXs from primary and metastatic tissues, except for recurrent gene mutations (ARID1A, CDK8, ETV1, STAT5B and WNK3) and common copy number gains in chromosomes 20q (e.g., SRC/AURKA). Several potential drug targets such as KRAS, HER2, and FGFR2 were validated using corresponding inhibitors. Additionally, PDX models could also be used in screening efficient regimens for patients with no druggable alterations. Conclusion: This study has successfully established and validated a large panel of molecularly annotated platforms from patients with CRLM for preclinical studies.

Establishment and characterization of melanoma patient-derived xenograft models for preclinical evaluation of novel therapeutics

Patient-derived xenograft (PDX) models mostly retain the histological and genetic features of their donor tumors, which have been used for investigating various types of cancer. However, PDX models for melanoma, especially acral melanoma, are reported occasionally. We aimed to establish a large panel of melanoma PDX models representing the predominant Asian melanomas. Ninety-three fresh melanoma samples were implanted subcutaneously into nonobese diabetic/severe combined immunodeficiency mice. The histological and genetic characteristics were analyzed in both patient tumors and PDX models using immunohistochemistry, PCR amplification, and Sanger sequencing. Furthermore, the sensitivities of PDX models harboring distinct mutation profiles to binimetinib (a MEK inhibitor), vemubrafenib (a BRAF inhibitor), and imatinib (a KIT inhibitor) were also evaluated. Twenty-five PDX models were established successfully [25/93 (26.9%)] and passaged to maintain tumors in vivo. Clinical stage and origin of tumor sample were correlated with successful establishment rates (P=0.008 and <0.001, respectively). The histological (expression of NRAS, P16, and RB) and genetic (mutation status of NRAS, BRAF, and KIT) characteristics were stably maintained from patient tumors to PDX models. Targeted drugs could inhibit the tumor growth of PDX models harboring the corresponding target gene mutations. These PDX models constitute a pharmacological platform, enabling personalized development of therapeutic strategies for Asian melanomas.

Patient-derived xenograft models to optimize kidney cancer therapies

Renal cell carcinoma (RCC) is the most common solid neoplasm of the adult kidney and has a high potential for developing metastatic spread. Approximately 25-30% of RCC patients have metastatic disease at presentation, and 30-40% of patients develop metastases after the initial diagnosis. Advanced renal cancer is a deadly and difficult-to-treat cancer. The 5-year survival rate of patients with metastatic disease is less than 10%, partly because RCC metastases become resistant to current therapies. Pre-clinical models may help to identify the optimum therapeutic options for individual patients. Here we reviewed various mouse xenograft methods for RCC treatment screening especially patient-derived orthotopic xenograft models. Advantages and disadvantaged of some of the models are also discussed.

Microfluidic device for expedited tumor growth towards drug evaluation

Patient derived organoids have emerged as robust preclinical models for screening anti-cancer therapeutics. Current 2D culturing methods do not provide physiological responses to therapeutics, therefore 3D models are being developed to better reproduce physiological responses. 3D culturing however often requires large initial cell populations and one week to one month to grow tumors ready for therapeutic testing. As a solution a 3D culturing system has been developed capable of producing physiologically relevant tumors in an expedited fashion while only requiring a small number of initial cancer cells. A bi-layer microfluidic system capable of facilitating active convective nutrient supply to populations of cancer cells facilitates expedited growth of cancer cells when starting with populations as small as 8 cells. The system has been shown to function well with adherent and non-adherent cell types by expediting cell growth by a factor ranging from 1.27 to 4.76 greater than growth under static conditions. Utilizing such an approach has enable to formation of tumors ready for therapeutic screening within 3 days and the ability to perform therapeutic screening within the microfluidic system is demonstrated. A mathematical model has been developed which allows for adjustments to be made to the dynamic delivery of nutrients in order to efficiently use culture media without excessive waste. We believe this work to be the first attempt to grow cancers in an expedited fashion utilizing only a convective nutrient supply within a microfluidic system which also facilitates on-device therapeutic screening. The developed microfluidic system and cancer growth method have the potential to offer improved drug screening for patients in clinical settings.

Factors that influence response classifications in chemotherapy treated patient-derived xenografts (PDX)

In this study, we investigated the impact of initial tumor volume, rate of tumor growth, cohort size, study duration, and data analysis method on chemotherapy treatment response classifications in patient-derived xenografts (PDXs). The analyses were conducted on cisplatin treatment response data for 70 PDX models representing ten cancer types with up to 28-day study duration and cohort sizes of 3-10 tumor-bearing mice. The results demonstrated that a 21-day dosing study using a cohort size of eight was necessary to reliably detect responsive models (i.e., tumor volume ratio of treated animals to control between 0.1 and 0.42)-independent of analysis method. A cohort of three tumor-bearing animals led to a reliable classification of models that were both highly responsive and highly nonresponsive to cisplatin (i.e., tumor volume ratio of treated animals to control animals less than 0.10). In our set of PDXs, we found that tumor growth rate in the control group impacted treatment response classification more than initial tumor volume. We repeated the study design factors using docetaxel treated PDXs with consistent results. Our results highlight the importance of defining endpoints for PDX dosing studies when deciding the size of cohorts to use in dosing studies and illustrate that response classifications for a study do not differ significantly across the commonly used analysis methods that are based on tumor volume changes in treatment versus control groups.

Electrospun Polylactic Acid (PLLA) Microtube Array Membrane (MTAM)-An Advanced Substrate for Anticancer Drug Screening

The advent of personalized cancer treatment resulted in the shift from the administration of cytotoxic drugs with broad activity spectrum to a targeted tumor-specific therapy. Aligned to this development, the focus of this study revolved around the application of our novel and patented microtube array membrane (MTAM) in the US National Cancer Institute (NCI) developed an HFA (hollow fiber assay) assay; hereinafter known as MTAM/HFA. Electrospun poly-L-lactic acid (PLLA) MTAM was sterilized and loaded with cell lines/patient derived tumor cells (PDTC) and subcutaneously implanted into the backs of BALB/C mice. Anticancer drugs were administered at the respective time points and the respective MTAMs were retrieved and the viability tumor cells within were quantified with the MTT assay. Results revealed that the MTAMs were excellent culture substrate for various cancer cell lines and PDTCs (patient derived tumor cells). Compared to traditional HFA systems that utilize traditional hollow fibers, MTAM/HFA revealed superior drug sensitivity for a wide range of anticancer drug classes. Additionally, the duration for each test was <14 days; all this while capable of producing similar trend outcome to the current gold-standard xenograft models. These benefits were observed in both the in vitro and in vivo stages, making it a highly practical phenotypic-based solution that could potentially be applied in personalized medicine.

A potential novel therapy for FGFR1-amplified pancreatic cancer with bone metastasis, screened by next-generation sequencing and a patient-derived xenograft model

Effective therapies are limited for pancreatic cancer, particularly for those with distant tumour metastases. Therefore, more individualised drug screening is urgently required. Next-generation sequencing (NGS) is a powerful tool to investigate the genomic landscape of patients and the mechanism of drug response, which may provide a broader vision for potential clinical drug screening. Patient-derived xenograft (PDX) models may have a significant advantage in predicting clinical treatment response. In our previous study, a PDX of pancreatic cancer bone metastasis was established, and NGS was conducted to investigate the molecular information. In the present study, these data were further analysed and fibroblast growth factor receptor 1 (FGFR1) amplification was identified in a panel of 416 cancer-associated genes. Thus, AZD4547, an inhibitor against FGFR, was selected as a potential therapy, and was evaluated using the PDX model. AZD4547 was shown to exhibit antitumor activity by reducing the expression of FGFR1 and its targets. The present study also demonstrated the high potential of the novel NGS/PDX-based drug screening platform to improve individualised cancer treatment.

Tumor heterogeneity and nanoparticle-mediated tumor targeting: the importance of delivery system personalization

After the discovery of the enhanced permeability and retention effect in 1986, it was envisioned that nanoparticle-mediated tumor-targeted delivery of chemotherapeutics would make a radical change in cancer therapy. However, after three decades of extensive research, only a few nanotherapeutics have been approved for clinical use. Although significant advantages of nanomedicines have been demonstrated in pre-clinical studies, clinical outcome was found to be variable. Advanced research has revealed that significant biochemical and structural variations exist between (and among) different tumors. These variations can considerably affect the tumor delivery and efficacy of nanomedicines. Tumor penetration is an important determining factor for positive therapeutic outcome and same nanomedicine can show diverse efficacy against different tumors depending on the extent of tumor accumulation and penetration. Recent research has started shading light on how the tumor variations can influence nanoparticle tumor delivery. These findings indicate that there is no "ideal" design of nanoparticles for exhibiting equally high efficacy against a broad spectrum of tumors. For achieving maximum benefit of the nanotherapeutics, it is necessary to analyze the tumor microenvironment for understanding the biological and structural characteristics of the tumor. Designing of the nanomedicine should be done according to the tumor characteristics. In this comprehensive review, we have first given a brief overview of the design characteristics of nanomedicine which impact their tumor delivery. Then we discussed about the variability in the tumor architecture and how it influences nanomedicine delivery. Finally, we have discussed the possibility of delivery system personalization based on the tumor characteristics.

Assessment of a mouse xenograft model of primary colorectal cancer with special reference to perfluorooctane sulfonate

Colorectal cancer ranks third among the most commonly diagnosed cancers in the United States. Current therapies have a range of side effects, and the development of a reliable animal model to speed the discovery of safe effective preventative therapies would be of great value. A cross-sectional study in a large Appalachian population recently showed an association between low circulating levels of perfluorooctane sulfonate (PFOS) and a reduced prevalence of colorectal cancer. A study using APC_min (C57BL/6J-Apc^Min/J) mice prone to familial adenomatous polyposis found PFOS was protective when exposure occurred during tumor development. To test the possible benefit of PFOS on spontaneous colorectal cancer, we developed a mouse model utilizing primary patient colorectal cancer implants into NSG (NOD.Cg-Prkdc^scidIl2rg^tm1Wjl /Sz) mice. Study goals included: (1) to assess potential factors supporting the successful use of colorectal cancer from heterogeneous tumors for PDX studies; and, (2) evaluate PFOS as a therapy in tumor matched pairs of mice randomized to receive PFOS or vehicle. The time in days for mice to grow primary tumors to 5 mm took almost 2 months (mean = 53.3, se = 5.7, range = 17-136). Age of mice at implantation, patient age, gender and race appeared to have no discernable effect on engraftment rates. Engraftment rates for low and high-grade patient tumors were similar. PFOS appeared to reduce tumor size dramatically in one group of tumors, those from the right ascending colon. That is, by 5 weeks of treatment in two mice, PFOS had eliminated their 52.4 mm³ and 124.6 mm³ masses completely, an effect that was sustained for 10 weeks of treatment; in contrast, their corresponding matched vehicle control mice had tumors that grew to 472.7 mm³ and 340.1 mm³ in size respectively during the same period. In a third xenograft mouse, the tumor growth was dramatically blunted although not eliminated, and compared favorably to their matched vehicle controls over the same period. These preliminary findings suggested that this mouse model may be advantageous for testing compounds of potential value in the treatment of colorectal cancer, and PFOS may have utility in selected cases.

The Avatar Acceptability Study: Survivor, Parent and Community Willingness to Use Patient-Derived Xenografts to Personalize Cancer Care

BACKGROUND

Using patient-derived xenografts (PDXs) to assess chemosensitivity to anti-cancer agents in real-time may improve cancer care by enabling individualized clinical decision-making. However, it is unknown whether this new approach will be met with acceptance by patients, family and community.

METHODS

We used a cross-sectional structured survey to investigate PDX acceptability with 1550 individuals across Australia and New Zealand (648 survivors of adult and childhood cancer, versus 650 community comparisons; and 48 parents of childhood cancer survivors versus 204 community parents). We identified factors influencing willingness-to-use PDXs, willingness-to-pay, maximum acceptable wait-time, and maximum acceptable number of mice used per patient.

FINDINGS

PDXs were highly acceptable: >80% of those affected by cancer felt the potential advantages of PDXs outweighed the disadvantages (community participants: 68%). Survivors' and survivors' parents' most highly endorsed advantage was 'increased chance of survival'. 'Harm to animals' was the least endorsed disadvantage for all groups. Cancer survivors were more willing to use PDXs than community comparisons [p < ·001]. Survivors and survivors' parents were willing to pay more [p < ·001; p = ∙004 respectively], wait longer for results [p = ·03; p = ∙01], and use more mice [p = ·01; p < ∙001] than community comparisons. Male survivors found PDXs more acceptable [p = ·01] and were willing to pay more [p < ·001] than female survivors. Survivors with higher incomes found PDXs more acceptable [p = ·002] and were willing to pay more [p < ·001] than survivors with lower incomes. Mothers found PDXs more acceptable [p = ·04] but were less willing to wait [p = ·02] than fathers.

INTERPRETATION

We found significant attitudinal support for PDX-guided cancer care. Willingness-to-pay and maximum acceptable number of mice align well with likely future usage. Maximum acceptable wait-times were lower than is currently achievable, highlighting an important area for future patient education until technology has caught up.

Patient-derived xenograft mouse models: A high fidelity tool for individualized medicine

Patient-derived xenograft (PDX) mouse models involve the direct transfer of fresh human tumor samples into immunodeficient mice following surgical resection or other medical operations. Gene expression in tumors may be maintained by serial passages of tumors from mouse to mouse. These models aid research into tumor biology and pharmacology without manual manipulation of cell cultures in vitro. and are widely used in individualized cancer therapy/translational medicine, drug development and coclinical trials. PDX models exhibit higher predictive values for clinical outcomes than cell line-derived xenograft models and genetically engineered mouse models. However, PDX models are associated with certain challenges in clinical application. The present study reviewed current collections of PDX models and assessed the challenges and future directions of this field.

Next-Generation in vivo Modeling of Human Cancers

Animal models of human cancers played a major role in our current understanding of tumor biology. In pre-clinical oncology, animal models empowered drug target and biomarker discovery and validation. In turn, this resulted in improved care for cancer patients. In the quest for understanding and treating a diverse spectrum of cancer types, technological breakthroughs in genetic engineering and single cell "omics" offer tremendous potential to enhance the informative value of pre-clinical models. Here, I review the state-of-the-art in modeling human cancers with focus on animal models for human malignant gliomas. The review highlights the use of glioma models in dissecting mechanisms of tumor initiation, in the retrospective identification of tumor cell-of-origin, in understanding tumor heterogeneity and in testing the potential of immuno-oncology. I build on the deep review of glioma models as a basis for a more general discussion of the potential ways in which transformative technologies may shape the next-generation of pre-clinical models. I argue that refining animal models along the proposed lines will benefit the success rate of translation for pre-clinical research in oncology.

Model organism data evolving in support of translational medicine

Model organism databases (MODs) have been collecting and integrating biomedical research data for 30 years and were designed to meet specific needs of each model organism research community. The contributions of model organism research to understanding biological systems would be hard to overstate. Modern molecular biology methods and cost reductions in nucleotide sequencing have opened avenues for direct application of model organism research to elucidating mechanisms of human diseases. Thus, the mandate for model organism research and databases has now grown to include facilitating use of these data in translational applications. Challenges in meeting this opportunity include the distribution of research data across many databases and websites, a lack of data format standards for some data types, and sustainability of scale and cost for genomic database resources like MODs. The issues of widely distributed data and application of data standards are some of the challenges addressed by FAIR (Findable, Accessible, Interoperable, and Re-usable) data principles. The Alliance of Genome Resources is now moving to address these challenges by bringing together expertly curated research data from fly, mouse, rat, worm, yeast, zebrafish, and the Gene Ontology consortium. Centralized multi-species data access, integration, and format standardization will lower the data utilization barrier in comparative genomics and translational applications and will provide a framework in which sustainable scale and cost can be addressed. This article presents a brief historical perspective on how the Alliance model organisms are complementary and how they have already contributed to understanding the etiology of human diseases. In addition, we discuss four challenges for using data from MODs in translational applications and how the Alliance is working to address them, in part by applying FAIR data principles. Ultimately, combined data from these animal models are more powerful than the sum of the parts.

Protocol for the avatar acceptability study: a multiperspective cross-sectional study evaluating the acceptability of using patient-derived xenografts to guide personalised cancer care in Australia and New Zealand

INTRODUCTION

Patient-derived xenografts (PDXs) have the potential to transform personalised cancer care, however, little is known about the acceptability of using PDXs to guide treatment decision-making. Given that patient and community preferences can influence satisfaction with care as well as the success of new technologies, we will evaluate the acceptability of PDXs in individuals affected by cancer and community comparisons.

METHODS AND ANALYSIS

This comparative cross-sectional study will recruit 323 individuals affected by cancer (cancer survivors (of childhood or adult cancer) and parents of childhood cancer survivors) and 323 community comparisons (adults and parents). We will collect data via structured interviews and questionnaires. To determine the acceptability of PDXs, we will assess five domains: willingness to use PDXs when/if diagnosed with cancer, perceived advantages and disadvantages of PDXs, maximum acceptable out-of-pocket costs per patient, maximum acceptable turnaround time to receive results and maximum acceptable number of mice sacrificed per patient. The primary endpoint will be participants' decisional balance ratio (calculated as participants' advantages ratings divided by perceived disadvantages ratings).

ETHICS AND DISSEMINATION

The study protocol has been approved by the South Eastern Sydney Local Health District Human Research Ethics Committee (HREC:12/173) and UNSW Sydney (HC15773). The results will be disseminated in peer-reviewed journals and at scientific conferences. A lay summary will be published on the Behavioural Sciences Unit website.

Preclinical Models of Prostate Cancer: Patient-Derived Xenografts, Organoids, and Other Explant Models

Prostate cancer remains a lethal disease. Preclinical cancer models that accurately represent the tumors of the patients they are intended to help are necessary to test potential therapeutic approaches and to better translate research discoveries. However, research in the prostate cancer field is hampered by the limited number of human cell lines and xenograft models, most of which do not recapitulate the human disease seen in the clinic today. This work reviews the recent advances in human patient-derived xenograft, organoid, and other explant models to address this need. In contrast to other tumor streams, the prostate cancer field is challenged by this approach, yet despite the limitations, patient-derived models remain an integral component of the preclinical testing pathway leading to better treatments for men with prostate cancer.

The chick chorioallantoic membrane (CAM) as a versatile patient-derived xenograft (PDX) platform for precision medicine and preclinical research

Patient-derived xenografts (PDX) are an increasingly valuable tool in oncology, providing biologically faithful models of many different cancer types, and potential platforms for the development of precision oncology approaches. However, PDX have primarily been established in immunodeficient rodent models, with accompanying cost and efficiency constraints that pose barriers to more widespread adoption. The chicken egg chorioallantoic membrane (CAM) is an alternative in vivo PDX model. We provide here a comprehensive review of studies that grafted primary human tissue, as opposed to cell lines, onto the CAM. Twenty publications met our criteria of having inoculated patient-derived tumor tissue onto the CAM. Successful engraftment has been reported for over a dozen tumor subtypes, supporting the appropriateness of the CAM as a PDX platform. Resemblance of xenografts to the original patient tumor, increased vascularity of the CAM following engraftment, and micrometastasis into the chick mesenchyme were frequently reported. Application of standard or experimental cancer therapies to xenografts has also been undertaken, with the discovery of both synergistic drug effects and positive associations between the assay and clinical outcome. The CAM provides opportunities for RNA and DNA based sequencing of patient tumors, and the ability to efficiently (in 5-10 days) test multiple targeted therapies on fragments derived from the same tumor. While routine use of the CAM-based PDX model would benefit from a more-complete understanding of the stromal environment of CAM xenografts and interaction with the developing avian immune system, current literature supports the model's potential as an efficient, scalable precision medicine platform.

PanDrugs: a novel method to prioritize anticancer drug treatments according to individual genomic data

BACKGROUND

Large-sequencing cancer genome projects have shown that tumors have thousands of molecular alterations and their frequency is highly heterogeneous. In such scenarios, physicians and oncologists routinely face lists of cancer genomic alterations where only a minority of them are relevant biomarkers to drive clinical decision-making. For this reason, the medical community agrees on the urgent need of methodologies to establish the relevance of tumor alterations, assisting in genomic profile interpretation, and, more importantly, to prioritize those that could be clinically actionable for cancer therapy.

RESULTS

We present PanDrugs, a new computational methodology to guide the selection of personalized treatments in cancer patients using the variant lists provided by genome-wide sequencing analyses. PanDrugs offers the largest database of drug-target associations available from well-known targeted therapies to preclinical drugs. Scoring data-driven gene cancer relevance and drug feasibility PanDrugs interprets genomic alterations and provides a prioritized evidence-based list of anticancer therapies. Our tool represents the first drug prescription strategy applying a rational based on pathway context, multi-gene markers impact and information provided by functional experiments. Our approach has been systematically applied to TCGA patients and successfully validated in a cancer case study with a xenograft mouse model demonstrating its utility.

CONCLUSIONS

PanDrugs is a feasible method to identify potentially druggable molecular alterations and prioritize drugs to facilitate the interpretation of genomic landscape and clinical decision-making in cancer patients. Our approach expands the search of druggable genomic alterations from the concept of cancer driver genes to the druggable pathway context extending anticancer therapeutic options beyond already known cancer genes. The methodology is public and easily integratable with custom pipelines through its programmatic API or its docker image. The PanDrugs webtool is freely accessible at http://www.pandrugs.org .

Patient-derived xenografts effectively capture responses to oncology therapy in a heterogeneous cohort of patients with solid tumors

Background

While patient-derived xenografts (PDXs) offer a powerful modality for translational cancer research, a precise evaluation of how accurately patient responses correlate with matching PDXs in a large, heterogeneous population is needed for assessing the utility of this platform for preclinical drug-testing and personalized patient cancer treatment.

Patients and methods

Tumors obtained from surgical or biopsy procedures from 237 cancer patients with a variety of solid tumors were implanted into immunodeficient mice and whole-exome sequencing was carried out. For 92 patients, responses to anticancer therapies were compared with that of their corresponding PDX models.

Results

We compared whole-exome sequencing of 237 PDX models with equivalent information in The Cancer Genome Atlas database, demonstrating that tumorgrafts faithfully conserve genetic patterns of the primary tumors. We next screened PDXs established for 92 patients with various solid cancers against the same 129 treatments that were administered clinically and correlated patient outcomes with the responses in corresponding models. Our analysis demonstrates that PDXs accurately replicate patients' clinical outcomes, even as patients undergo several additional cycles of therapy over time, indicating the capacity of these models to correctly guide an oncologist to treatments that are most likely to be of clinical benefit.

Conclusions

Integration of PDX models as a preclinical platform for assessment of drug efficacy may allow a higher success-rate in critical end points of clinical benefit.

Chemo brain or tumor brain - that is the question: the presence of extracranial tumors profoundly affects molecular processes in the prefrontal cortex of TumorGraft mice

Cancer chemotherapy causes numerous persistent central nervous system complications. This condition is known as chemo brain. Cognitive impairments occur even before treatment, and hence are referred to as cancer associated cognitive changes, or tumor brain. There is much yet to be learned about the mechanisms of both chemo brain and tumor brain. The frequency and timing of chemo brain and tumor brain occurrence and persistence strongly suggest they may be epigenetic in nature and associated with altered gene expression. Here we used TumorGraft^TM models wherein part of a patient's tumor is removed and grafted into immune-deficient mice and conducted global gene expression and DNA methylation analysis. We show that malignant non-central nervous system tumor growth causes profound molecular alterations in the brain. Mice harbouring triple negative or progesterone positive breast cancer TumorGrafts exhibited altered gene expression, decreased levels of DNA methylation, increased levels of DNA hydroxymethylation, and oxidative stress in the prefrontal cortex. Interestingly, chemotherapy did not have any additional synergistic effects on the analyzed processes. The molecular changes observed in this study are known signs of neurodegeneration and brain aging. This study provides an important roadmap for future large-scale analysis of the molecular and cellular mechanisms of tumor brain.