A systematic review of the validity of patient derived xenograft (PDX) models: the implications for translational research and personalised medicine

Establishing Patient-Derived Xenograft (PDX) Models of Lymphomas

Patient-derived xenograft (PDX) models of lymphoma typically involve the injection of human tumor cells into an immunocompromised murine host. PDXs have the advantage that the tumor cells grow in a 3D environment within the mouse, meaning the selection pressure of in vitro establishment is avoided and the tumor cells better maintain their genetic heterogeneity. Here, we outline a method for producing and maintaining a PDX model of lymphoma. We describe three different methods to isolate a single cell suspension of the primary patient tumor, followed by either subcutaneous or intraperitoneal injection into an immunocompromised mouse. We then detail how to monitor tumor growth and how to harvest, passage, and store the tumors once they have grown. We highlight and discuss important protocol considerations including technical hints as well as the advantages and disadvantages of the methods described.

Several methods for assessing research waste in reviews with a systematic search: a scoping review

Background

Research waste is present in all study designs and can have significant consequences for science, including reducing the reliability of research findings and contributing to the inefficient use of resources. Estimates suggest that as much as 85% of all biomedical research is wasted. However, it is uncertain how avoidable research waste is assessed in specific types of study designs and what methods could be used to examine different aspects of research waste. We aimed to investigate which methods, systematic reviews, scoping reviews, and overviews of reviews discussing research waste, have used to assess avoidable research waste.

Materials and Methods

We published a protocol in the Open Science Framework prospectively (https://osf.io/2fbp4). We searched PubMed and Embase with a 30-year limit (January 1993-August 2023). The concept examined was how research waste and related synonyms (e.g., unnecessary, redundant, duplicate, etc.) were assessed in reviews with a systematic search: systematic, scoping, or overviews of reviews. We extracted data on the method used in the review to examine for research waste and for which study design this method was applied.

Results

The search identified 4,285 records of which 93 reviews with systematic searches were included. The reviews examined a median of 90 (range 10-6,781) studies, where the study designs most commonly included were randomized controlled trials (48%) and systematic reviews (33%). In the last ten years, the number of reports assessing research waste has increased. More than 50% of examined reviews reported evaluating methodological research waste among included studies, typically using tools such as one of Cochrane Risk of Bias tools (n = 8) for randomized controlled trials or AMSTAR 1 or 2 (n = 12) for systematic reviews. One fourth of reviews assessed reporting guideline adherence to e.g., CONSORT (n = 4) for randomized controlled trials or PRISMA (n = 6) for systematic reviews.

Conclusion

Reviews with systematic searches focus on methodological quality and reporting guideline adherence when examining research waste. However, this scoping review revealed that a wide range of tools are used, which may pose difficulties in comparing examinations and performing meta-research. This review aids researchers in selecting methodologies and contributes to the ongoing discourse on optimizing research efficiency.

Personalizing non-small cell lung cancer treatment through patient-derived xenograft models: preclinical and clinical factors for consideration

PURPOSE

In the pursuit of creating personalized and more effective treatment strategies for lung cancer patients, Patient-Derived Xenografts (PDXs) have been introduced as preclinical platforms that can recapitulate the specific patient's tumor in an in vivo model. We investigated how well PDX models can preserve the tumor's clinical and molecular characteristics across different generations.

METHODS

A Non-Small Cell Lung Cancer (NSCLC) PDX model was established in NSG-SGM3 mice and clinical and preclinical factors were assessed throughout subsequent passages. Our cohort consisted of 40 NSCLC patients, which were used to create 20 patient-specific PDX models in NSG-SGM3 mice. Histopathological staining and Whole Exome Sequencing (WES) analysis were preformed to understand tumor heterogeneity throughout serial passages.

RESULTS

The main factors that contributed to the growth of the engrafted PDX in mice were a higher grade or stage of disease, in contrast to the long duration of chemotherapy treatment, which was negatively correlated with PDX propagation. Successful PDX growth was also linked to poorer prognosis and overall survival, while growth pattern variability was affected by the tumor aggressiveness, primarily affecting the first passage. Pathology analysis showed preservation of the histological type and grade; however, WES analysis revealed genomic instability in advanced passages, leading to the inconsistencies in clinically relevant alterations between the PDXs and biopsies.

CONCLUSIONS

Our study highlights the impact of multiple clinical and preclinical factors on the engraftment success, growth kinetics, and tumor stability of patient-specific NSCLC PDXs, and underscores the importance of considering these factors when guiding and evaluating prolonged personalized treatment studies for NSCLC patients in these models, as well as signaling the imperative for additional investigations to determine the full clinical potential of this technique.

Patient-Derived-Xenografts in Mice: A Preclinical Platform for Cancer Research

The use of patient-derived xenografts (PDXs) has dramatically improved drug development programs. PDXs (1) reproduce the pathological features and the genomic profile of the parental tumors more precisely than other preclinical models, and (2) more faithfully predict therapy response. However, PDXs have limitations. These include the inability to completely capture tumor heterogeneity and the role of the immune system, the low engraftment efficiency of certain tumor types, and the consequences of the human-host interactions. Recently, the use of novel mouse strains and specialized engraftment techniques has enabled the generation of "humanized" PDXs, partially overcoming such limitations. Importantly, establishing, characterizing, and maintaining PDXs is costly and requires a significant regulatory, administrative, clinical, and laboratory infrastructure. In this review, we will retrace the historical milestones that led to the implementation of PDXs for cancer research, review the most recent innovations in the field, and discuss future avenues to tackle deficiencies that still exist.

High efficacy of huCD20-targeted AcTaferon in humanized patient derived xenograft models of aggressive B cell lymphoma

Type I interferon (IFN) is a potent antitumoral drug, with an important history in the treatment of hematologic malignancies. However, its pleiotropic nature leads to severe dose-limiting toxicities that blunt its therapeutic potential. To achieve selective targeting of specific immune or tumor cells, AcTakines (Activity-on-Target Cytokines), i.e., immunocytokines utilizing attenuated cytokines, and clinically optimized A-Kines™ were developed. In syngeneic murine models, the CD20-targeted murine IFNα2-based AcTaferons (AFNs) have demonstrated clear antitumoral effects, with excellent tolerability. The current study explores the antitumoral potential of the humanized huCD20-Fc-AFN in 5 different humanized patient derived xenograft (PDX) models of huCD20+ aggressive B non-Hodgkin lymphomas (B-NHLs). The huCD20-Fc-AFN consists of a huCD20-specific single-domain antibody (VHH) linked through a heterodimeric 'knob-in-hole' human IgG1 Fc molecule to an attenuated huIFNα2 sequence. An in vitro targeting efficacy of up to 1.000-fold could be obtained, without detectable in vivo toxicities, except for selective (on-target) and reversible B cell depletion. Treatment with huCD20-Fc-AFN significantly increased the median overall survival (mOS) in both non-humanized (mOS 31 to 45 days; HR = 0.26; p = 0.001), and humanized NSG/NOG mice (mOS 34 to 80 days; HR = 0.37; p < 0.0001). In humanized mice, there was a trend for increased survival when compared to equimolar rituximab (mOS 49 to 80 days; HR = 0.73; p = 0.09). The antitumoral effects of huCD20-Fc-AFN were partly due to direct effects of type I IFN on the tumor cells, but additional effects via the human immune system are essential to obtain long-term remissions. To conclude, huCD20-Fc-AFN could provide a novel therapeutic strategy for huCD20-expressing aggressive B-NHLs.

The Power and Promise of Patient-Derived Xenografts of Human Breast Cancer

In 2016, a group of researchers engaged in the development of patient-derived xenografts (PDXs) of human breast cancer provided a comprehensive review of the state of the field. In that review, they summarized the clinical problem that PDXs might address, the technical approaches to their generation (including a discussion of host animals and transplant conditions tested), and presented transplantation success (take) rates across groups and across transplantation conditions. At the time, there were just over 500 unique PDX models created by these investigators representing all three clinically defined subtypes (ER+, HER2+, and TNBC). Today, many of these PDX resources have at least doubled in size, and several more PDX development groups now exist, such that there may be well upward of 1000 PDX models of human breast cancer in existence worldwide. They also presented a series of open questions for the field. Many of these questions have been addressed. However, several remain open, or only partially addressed. Herein, we revisit these questions, and recount the progress that has been made in a number of areas with respect to generation, characterization, and use of PDXs in translational research, and re-present questions that remain open. These open questions, and others, are now being addressed not only by individual investigators, but also large, well-funded consortia including the PDXNet program of the National Cancer Institute in the United States, and the EuroPDX Consortium, an organization of PDX developers across Europe. Finally, we discuss the new opportunities in PDX-based research.

Consistency between Primary Uterine Corpus Malignancies and Their Corresponding Patient-Derived Xenograft Models

Patient-derived xenograft (PDX) models retain the characteristics of tumors and are useful tools for personalized therapy and translational research. In this study, we aimed to establish PDX models for uterine corpus malignancies (UC-PDX) and analyze their similarities. Tissue fragments obtained from 92 patients with uterine corpus malignancies were transplanted subcutaneously into immunodeficient mice. Histological and immunohistochemical analyses were performed to compare tumors of patients with PDX tumors. DNA and RNA sequencing were performed to validate the genetic profile. Furthermore, the RNA in extracellular vesicles (EVs) extracted from primary and PDX tumors was analyzed. Among the 92 cases, 52 UC-PDX models were established, with a success rate of 56.5%. The success rate depended on tumor histology and staging. The pathological and immunohistochemical features of primary and PDX tumors were similar. DNA sequencing revealed similarities in gene mutations between the primary and PDX tumors. RNA sequencing showed similarities in gene expressions between primary and PDX tumors. Furthermore, the RNA profiles of the EVs obtained from primary and PDX tumors were similar. As UC-PDX retained the pathological and immunohistochemical features and gene profiles of primary tumors, they may provide a platform for developing personalized medicine and translational research.

Patient-Derived Xenografts: Historical Evolution, Immunocompromised Host Models, and Translational Significance

Patient-derived xenografts (PDXs) represent a critical advancement in preclinical cancer research, wherein human tumor samples are implanted into animal models for evaluation of therapeutic responses. PDXs have emerged as indispensable tools in translational cancer research, facilitating investigation into tumor microenvironments and personalized medicine. This chapter elucidates the historical evolution of PDXs, from early attempts in the eighteenth century to contemporary immunocompromised host models that enhance engraftment success.

Patient-derived tumor models: a suitable tool for preclinical studies on esophageal cancer

Esophageal cancer (EC) is the tenth most common cancer worldwide and has high morbidity and mortality. Its main subtypes include esophageal squamous cell carcinoma and esophageal adenocarcinoma, which are usually diagnosed during their advanced stages. The biological defects and inability of preclinical models to summarize completely the etiology of multiple factors, the complexity of the tumor microenvironment, and the genetic heterogeneity of tumors severely limit the clinical treatment of EC. Patient-derived models of EC not only retain the tissue structure, cell morphology, and differentiation characteristics of the original tumor, they also retain tumor heterogeneity. Therefore, compared with other preclinical models, they can better predict the efficacy of candidate drugs, explore novel biomarkers, combine with clinical trials, and effectively improve patient prognosis. This review discusses the methods and animals used to establish patient-derived models and genetically engineered mouse models, especially patient-derived xenograft models. It also discusses their advantages, applications, and limitations as preclinical experimental research tools to provide an important reference for the precise personalized treatment of EC and improve the prognosis of patients.

Unlocking Translational Potential: Conditionally Reprogrammed Cells in Advancing Breast Cancer Research

Preclinical in vitro models play an important role in studying cancer cell biology and facilitating translational research, especially in the identification of drug targets and drug discovery studies. This is particularly relevant in breast cancer, where the global burden of disease is quite high based on prevalence and a relatively high rate of lethality. Predictive tools to select patients who will be responsive to invasive or morbid therapies (radiotherapy, chemotherapy, immunotherapy, and/or surgery) are relatively lacking. To be clinically relevant, a model must accurately replicate the biology and cellular heterogeneity of the primary tumor. Addressing these requirements and overcoming the limitations of most existing cancer cell lines, which are typically derived from a single clone, we have recently developed conditional reprogramming (CR) technology. The CR technology refers to a co-culture system of primary human normal or tumor cells with irradiated murine fibroblasts in the presence of a Rho-associated kinase inhibitor to allow the primary cells to acquire stem cell properties and the ability to proliferate indefinitely in vitro without any exogenous gene or viral transfection. This innovative approach fulfills many of these needs and offers an alternative that surpasses the deficiencies associated with traditional cancer cell lines. These CR cells (CRCs) can be reprogrammed to maintain a highly proliferative state and reproduce the genomic and histological characteristics of the parental tissue. Therefore, CR technology may be a clinically relevant model to test and predict drug sensitivity, conduct gene profile analysis and xenograft research, and undertake personalized medicine. This review discusses studies that have applied CR technology to conduct breast cancer research.

Rabies virus glycoprotein 29 (RVG29) promotes CAR-T immunotherapy for glioma

Chimeric antigen receptor T cell (CAR-T) therapy has limited efficacy for treating glioma because of the infiltrative nature of the blood-brain barrier (BBB) and T cell exhaustion. Conjugation with rabies virus glycoprotein (RVG) 29 enhances the brain-related efficacy of various agents. Here we assess whether RVG enhances the ability of CAR-T cells to cross the BBB and improves their immunotherapy. We generated 70R CAR-T cells (anti-CD70 CAR-T modified with RVG29) and validated their tumor-killing efficacy in vitro and in vivo. We validated their effects on tumor regression in a human glioma mouse orthotopic xenograft model as well as in patient-derived orthotopic xenograft (PDOX) models. The signaling pathways activated in 70R CAR-T cells were revealed by RNA sequencing. The 70R CAR-T cells we generated showed effective antitumor function against CD70+ glioma cells both in vitro and in vivo. 70R CAR-T cells were better able to cross the BBB into the brain than CD70 CAR-T cells under the same treatment conditions. Moreover, 70R CAR-T cells significantly promote the regression of glioma xenografts and improve the physical characteristics of mice without causing overt adverse effects. RVG modification enables CAR-T cells to cross the BBB, and stimulation with glioma cells induces 70R CAR-T cells to expand in a resting state. The modification of RVG29 has a positive impact on CAR-T therapy for brain tumors and may have potential in CAR-T therapy for glioma.

Proteomics in Childhood Acute Lymphoblastic Leukemia: Challenges and Opportunities

Acute lymphoblastic leukemia (ALL) is the most common cancer in children and one of the success stories in cancer therapeutics. Risk-directed therapy based on clinical, biologic and genetic features has played a significant role in this accomplishment. Despite the observed improvement in survival rates, leukemia remains one of the leading causes of cancer-related deaths. Implementation of next-generation genomic and transcriptomic sequencing tools has illustrated the genomic landscape of ALL. However, the underlying dynamic changes at protein level still remain a challenge. Proteomics is a cutting-edge technology aimed at deciphering the mechanisms, pathways, and the degree to which the proteome impacts leukemia subtypes. Advances in mass spectrometry enable high-throughput collection of global proteomic profiles, representing an opportunity to unveil new biological markers and druggable targets. The purpose of this narrative review article is to provide a comprehensive overview of studies that have utilized applications of proteomics in an attempt to gain insight into the pathogenesis and identification of biomarkers in childhood ALL.

A new tumorgraft panel to accelerate precision medicine in prostate cancer

Background

Despite the significant advances in the management of advanced prostate cancer (PCa), metastatic PCa is currently considered incurable. For further investigations in precision treatment, the development of preclinical models representing the complex prostate tumor heterogeneity are mandatory. Accordingly, we aimed to establish a resource of patient-derived xenograft (PDX) models that exemplify each phase of this multistage disease for accurate and rapid evaluation of candidate therapies.

Methods

Fresh tumor samples along with normal corresponding tissues were obtained directly from patients at surgery. To ensure that the established models reproduce the main features of patient's tumor, both PDX tumors at multiple passages and patient's primary tumors, were processed for histological characteristics. STR profile analyses were also performed to confirm patient identity. Finally, the responses of the PDX models to androgen deprivation, PARP inhibitors and chemotherapy were also evaluated.

Results

In this study, we described the development and characterization of 5 new PDX models of PCa. Within this collection, hormone-naïve, androgen-sensitive and castration-resistant (CRPC) primary tumors as well as prostate carcinoma with neuroendocrine differentiation (CRPC-NE) were represented. Interestingly, the comprehensive genomic characterization of the models identified recurrent cancer driver alterations in androgen signaling, DNA repair and PI3K, among others. Results were supported by expression patterns highlighting new potential targets among gene drivers and the metabolic pathway. In addition, in vivo results showed heterogeneity of response to androgen deprivation and chemotherapy, like the responses of patients to these treatments. Importantly, the neuroendocrine model has been shown to be responsive to PARP inhibitor.

Conclusion

We have developed a biobank of 5 PDX models from hormone-naïve, androgen-sensitive to CRPC primary tumors and CRPC-NE. Increased copy-number alterations and accumulation of mutations within cancer driver genes as well as the metabolism shift are consistent with the increased resistance mechanisms to treatment. The pharmacological characterization suggested that the CRPC-NE could benefit from the PARP inhibitor treatment. Given the difficulties in developing such models, this relevant panel of PDX models of PCa will provide the scientific community with an additional resource for the further development of PDAC research.

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.

Experimental mouse models for translational human cancer research

The development and growth of tumors remains an important and ongoing threat to human life around the world. While advanced therapeutic strategies such as immune checkpoint therapy and CAR-T have achieved astonishing progress in the treatment of both solid and hematological malignancies, the malignant initiation and progression of cancer remains a controversial issue, and further research is urgently required. The experimental animal model not only has great advantages in simulating the occurrence, development, and malignant transformation mechanisms of tumors, but also can be used to evaluate the therapeutic effects of a diverse array of clinical interventions, gradually becoming an indispensable method for cancer research. In this paper, we have reviewed recent research progress in relation to mouse and rat models, focusing on spontaneous, induced, transgenic, and transplantable tumor models, to help guide the future study of malignant mechanisms and tumor prevention.

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.

NetCellMatch: Multiscale Network-Based Matching of Cancer Cell Lines to Patients Using Graphical Wavelets

Cancer cell lines serve as model in vitro systems for investigating therapeutic interventions. Recent advances in high-throughput genomic profiling have enabled the systematic comparison between cell lines and patient tumor samples. The highly interconnected nature of biological data, however, presents a challenge when mapping patient tumors to cell lines. Standard clustering methods can be particularly susceptible to the high level of noise present in these datasets and only output clusters at one unknown scale of the data. In light of these challenges, we present NetCellMatch, a robust framework for network-based matching of cell lines to patient tumors. NetCellMatch first constructs a global network across all cell line-patient samples using their genomic similarity. Then, a multi-scale community detection algorithm integrates information across topologically meaningful (clustering) scales to obtain Network-Based Matching Scores (NBMS). NBMS are measures of cluster robustness which map patient tumors to cell lines. We use NBMS to determine representative "avatar" cell lines for subgroups of patients. We apply NetCellMatch to reverse-phase protein array data obtained from The Cancer Genome Atlas for patients and the MD Anderson Cell Line Project for cell lines. Along with avatar cell line identification, we evaluate connectivity patterns for breast, lung, and colon cancer and explore the proteomic profiles of avatars and their corresponding top matching patients. Our results demonstrate our framework's ability to identify both patient-cell line matches and potential proteomic drivers of similarity. Our methods are general and can be easily adapted to other'omic datasets.

Multiparametric Magnetic Resonance Imaging and Metabolic Characterization of Patient-Derived Xenograft Models of Clear Cell Renal Cell Carcinoma

Patient-derived xenografts (PDX) are high-fidelity cancer models typically credentialled by genomics, transcriptomics and proteomics. Characterization of metabolic reprogramming, a hallmark of cancer, is less frequent. Dysregulated metabolism is a key feature of clear cell renal cell carcinoma (ccRCC) and authentic preclinical models are needed to evaluate novel imaging and therapeutic approaches targeting metabolism. We characterized 5 PDX from high-grade or metastatic ccRCC by multiparametric magnetic resonance imaging (MRI) and steady state metabolic profiling and flux analysis. Similar to MRI of clinical ccRCC, T2-weighted images of orthotopic tumors of most PDX were homogeneous. The increased hyperintense (cystic) areas observed in one PDX mimicked the cystic phenotype typical of some RCC. The negligible hypointense (necrotic) areas of PDX grown under the highly vascularized renal capsule are beneficial for preclinical studies. Mean apparent diffusion coefficient (ADC) values were equivalent to those of ccRCC in human patients. Hyperpolarized (HP) [1-13C]pyruvate MRI of PDX showed high glycolytic activity typical of high-grade primary and metastatic ccRCC with considerable intra- and inter-tumoral variability, as has been observed in clinical HP MRI of ccRCC. Comparison of steady state metabolite concentrations and metabolic flux in [U-13C]glucose-labeled tumors highlighted the distinctive phenotypes of two PDX with elevated levels of numerous metabolites and increased fractional enrichment of lactate and/or glutamate, capturing the metabolic heterogeneity of glycolysis and the TCA cycle in clinical ccRCC. Culturing PDX cells and reimplanting to generate xenografts (XEN), or passaging PDX in vivo, altered some imaging and metabolic characteristics while transcription remained like that of the original PDX. These findings show that PDX are realistic models of ccRCC for imaging and metabolic studies but that the plasticity of metabolism must be considered when manipulating PDX for preclinical studies.

Functional precision cancer medicine: drug sensitivity screening enabled by cell culture models

Functional precision medicine is a new, emerging area that can guide cancer treatment by capturing information from direct perturbations of tumor-derived, living cells, such as by drug sensitivity screening. Precision cancer medicine as currently implemented in clinical practice has been driven by genomics, and current molecular tumor boards rely extensively on genomic characterization to advise on therapeutic interventions. However, genomic biomarkers can only guide treatment decisions for a fraction of the patients. In this review we provide an overview of the current state of functional precision medicine, highlight advances for drug-sensitivity screening enabled by cell culture models, and discuss how artificial intelligence (AI) can be coupled to functional precision medicine to guide patient stratification.

Modulation of Hedgehog Signaling for the Treatment of Basal Cell Carcinoma and the Development of Preclinical Models

Basal Cell Carcinoma (BCC) is the most commonly diagnosed cancer worldwide. While the survivability of BCC is high, many patients are excluded from clinically available treatments due to health risks or personal choice. Further, patients with advanced or metastatic disease have severely limited treatment options. The dysregulation of the Hedgehog (Hh) signaling cascade drives onset and progression of BCC. As such, the modulation of this pathway has driven advancements in BCC research. In this review, we focus firstly on inhibitors that target the Hh pathway as chemotherapeutics against BCC. Two therapies targeting Hh signaling have been made clinically available for BCC patients, but these treatments suffer from limited initial efficacy and a high rate of chemoresistant tumor recurrence. Herein, we describe more recent developments of chemical scaffolds that have been designed to hopefully improve upon the available therapeutics. We secondly discuss the history and recent efforts involving modulation of the Hh genome as a method of producing in vivo models of BCC for preclinical research. While there are many advancements left to be made towards improving patient outcomes with BCC, it is clear that targeting the Hh pathway will remain at the forefront of research efforts in designing more effective chemotherapeutics as well as relevant preclinical models.

Evaluating Translational Methods for Personalized Medicine-A Scoping Review

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

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.

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

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

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

INTRODUCTION

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

OBJECTIVE

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

METHOD

We obtained information from previously published articles and BBMRI directory.

RESULTS AND CONCLUSION

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

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.

Tumor-Targeted Fluorescent Proteinoid Nanocapsules Encapsulating Synergistic Drugs for Personalized Cancer Therapy

Personalized cancer treatment based on specific mutations offers targeted therapy and is preferred over "standard" chemotherapy. Proteinoid polymers produced by thermal step-growth polymerization of amino acids may form nanocapsules (NCs) that encapsulate drugs overcoming miscibility problems and allowing passive targeted delivery with reduced side effects. The arginine-glycine-glutamic acid (RGD) sequence is known for its preferential attraction to αvβ3 integrin, which is highly expressed on neovascular endothelial cells that support tumor growth. Here, tumor-targeted RGD-based proteinoid NCs entrapping a synergistic combination of Palbociclib (Pal) and Alpelisib (Alp) were synthesized by self-assembly to induce the reduction of tumor cell growth in different types of cancers. The diameters of the hollow and drug encapsulating poly(RGD) NCs were 34 ± 5 and 22 ± 3 nm, respectively; thereby, their drug targeted efficiency is due to both passive and active targeting. The encapsulation yield of Pal and Alp was 70 and 90%, respectively. In vitro experiments with A549, MCF7 and HCT116 human cancer cells demonstrate a synergistic effect of Pal and Alp, controlled release and dose dependence. Preliminary results in a 3D tumor spheroid model with cells derived from patient-derived xenografts of colon cancer illustrate disassembly of spheroids, indicating that the NCs have therapeutic potential.

Characterization of the large-scale Japanese patient-derived xenograft (J-PDX) library

The use of patient‐derived xenografts (PDXs) has recently attracted attention as a drug discovery platform with a high predictive clinical efficacy and a preserved tumor heterogeneity. Given the racial differences in genetic variations, it would be desirable to establish a PDX library from Japanese cancer patients on a large scale. We thus tried to construct the Japanese PDX (J‐PDX) library with a detailed clinical information for further clinical utilization. Between August 2018 and May 2020, a total of 1126 cancer specimens from 1079 patients were obtained at the National Cancer Center Hospital and National Cancer Center Hospital East, Japan, and were immediately transplanted to immunodeficient mice at the National Cancer Center Research Institute. A total of 298 cross‐cancer PDXs were successfully established. The time to engraftment varied greatly by cancer subtypes, especially in the first passage. The engraftment rate was strongly affected by the clinical stage and survival time of the original patients. Approximately 1 year was needed from tumor collection to the time when coclinical trials were conducted to test the clinical utility. The 1‐year survival rates of the patients who were involved in establishing the PDX differed significantly, from 95.6% for colorectal cancer to 56.3% for lung cancer. The J‐PDX library consisting of a wide range of cancer subtypes has been successfully established as a platform for drug discovery and development in Japan. When conducting coclinical trials, it is necessary to consider the target cancer type, stage, and engraftment rate in light of this report.

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

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

Novel Patient Metastatic Pleural Effusion-Derived Xenograft Model of Renal Medullary Carcinoma Demonstrates Therapeutic Efficacy of Sunitinib

Background

Renal medullary carcinoma (RMC) is a rare but aggressive tumor often complicated by early lung metastasis with few treatment options and very poor outcomes. There are currently no verified RMC patient-derived xenograft (PDX) mouse models established from metastatic pleural effusion (PE) available to study RMC and evaluate new therapeutic options.

Methods

Renal tumor tissue and malignant PE cells from an RMC patient were successfully engrafted into 20 NOD.Cg-Prkdc^scid Il2rg^tm1Wjl/SzJ (NSG) mice. We evaluated the histopathological similarity of the renal tumor and PE PDXs with the original patient renal tumor and PE, respectively. We then evaluated the molecular integrity of the renal tumor PDXs between passages, as well as the PE PDX compared to two generations of renal tumor PDXs, by microarray analysis. The therapeutic efficacy of sunitinib and temsirolimus was tested in a serially-transplanted generation of 27 PE PDX mice.

Results

The pathologic characteristics of the patient renal tumor and patient PE were retained in the PDXs. Gene expression profiling revealed high concordance between the two generations of renal tumor PDXs (RMC-P0 vs. RMC-P1, r=0.865), as well as between the first generation PE PDX and each generation of the renal tumor PDX (PE-P0 vs. RMC-P0, r=0.919 and PE-P0 vs. RMC-P1, r=0.843). A low number (626) of differentially-expressed genes (DEGs) was seen between the first generation PE PDX and the first generation renal tumor PDX. In the PE-P1 xenograft, sunitinib significantly reduced tumor growth (p<0.001) and prolonged survival (p=0.004) compared to the vehicle control.

Conclusions

A metastatic PE-derived RMC PDX model was established and shown to maintain histologic features of the patient cancer. Molecular integrity of the PDX models was well maintained between renal tumor and PE PDX as well as between two successive renal tumor PDX generations. Using the PE PDX model, sunitinib demonstrated therapeutic efficacy for RMC. This model can serve as a foundation for future mechanistic and therapeutic studies for primary and metastatic RMC.

Establishment of a Patient-Derived Xenograft Model of Colorectal Cancer in CIEA NOG Mice and Exploring Smartfish Liquid Diet as a Source of Omega-3 Fatty Acids

Cancer patient-derived xenografts (PDXs) better preserve tumor characteristics and microenvironment than traditional cancer cell line derived xenografts and are becoming a valuable model in translational cancer research and personalized medicine. We have established a PDX model for colorectal cancer (CRC) in CIEA NOG mice with a 50% engraftment rate. Tumor fragments from patients with CRC (n = 5) were engrafted in four mice per tumor (n = 20). Mice with established PDXs received a liquid diet enriched with fish oil or placebo, and fatty acid profiling was performed to measure fatty acid content in whole blood. Moreover, a biobank consisting of tissue and blood samples from patients was established. Histology, immunohistochemistry and in situ hybridization procedures were used for staining of tumor and xenograft tissue slides. Results demonstrate that key histological characteristics of the patients’ tumors were retained in the established PDXs, and the liquid diets were consumed as intended by the mice. Some of the older mice developed lymphomas that originated from human Ki67⁺, CD45⁺, and EBV⁺ lymphoid cells. We present a detailed description of the process and methodology, as well as possible issues that may arise, to refine the method and improve PDX engraftment rate for future studies. The established PDX model for CRC can be used for exploring different cancer treatment regimes, and liquid diets enriched with fish oil may be successfully delivered to the mice through the drinking flasks.

Current methods in translational cancer research

Recent developments in pre-clinical screening tools, that more reliably predict the clinical effects and adverse events of candidate therapeutic agents, has ushered in a new era of drug development and screening. However, given the rapid pace with which these models have emerged, the individual merits of these translational research tools warrant careful evaluation in order to furnish clinical researchers with appropriate information to conduct pre-clinical screening in an accelerated and rational manner. This review assesses the predictive utility of both well-established and emerging pre-clinical methods in terms of their suitability as a screening platform for treatment response, ability to represent pharmacodynamic and pharmacokinetic drug properties, and lastly debates the translational limitations and benefits of these models. To this end, we will describe the current literature on cell culture, organoids, in vivo mouse models, and in silico computational approaches. Particular focus will be devoted to discussing gaps and unmet needs in the literature as well as current advancements and innovations achieved in the field, such as co-clinical trials and future avenues for refinement.

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.

The Essential Factors of Establishing Patient-derived Tumor Model

Establishing an applicable preclinical model is vital for translational cancer research. Patient-derived xenograft has been important preclinical model systems and widely used for cancer research. Patient-derived xenograft models that represent the tumors of the patients are necessary to better translate research discoveries and to test potential therapeutic approaches. However, research in this field is hampered by the limited engraftment rate. In this review, we go over a large number of researches on patient-derived xenograft transplantation and firstly systematically summarize the main factors in methodology to successfully establish models. These results will be applied to the development of patient-derived xenograft leading to better preclinical research.

ZenoFishDb v1.1: A Database for Xenotransplantation Studies in Zebrafish

Rapidly accumulating literature has proven feasibility of the zebrafish xenograft models in cancer research. Nevertheless, online databases for searching the current zebrafish xenograft literature are in great demand. Herein, we have developed a manually curated database, called ZenoFishDb v1.1 (https://konulab.shinyapps.io/zenofishdb), based on R Shiny platform aiming to provide searchable information on ever increasing collection of zebrafish studies for cancer cell line transplantation and patient-derived xenografts (PDXs). ZenoFishDb v1.1 user interface contains four modules: DataTable, Visualization, PDX Details, and PDX Charts. The DataTable and Visualization pages represent xenograft study details, including injected cell lines, PDX injections, molecular modifications of cell lines, zebrafish strains, as well as technical aspects of the xenotransplantation procedures in table, bar, and/or pie chart formats. The PDX Details module provides comprehensive information on the patient details in table format and can be searched and visualized. Overall, ZenoFishDb v1.1 enables researchers to effectively search, list, and visualize different technical and biological attributes of zebrafish xenotransplantation studies particularly focusing on the new trends that make use of reporters, RNA interference, overexpression, or mutant gene constructs of transplanted cancer cells, stem cells, and PDXs, as well as distinguished host modifications.

PDX: Moving Beyond Drug Screening to Versatile Models for Research Discovery

Patient-derived xenografts (PDXs) are tools of the trade for many researchers from all disciplines and medical specialties. Most endocrinologists, and especially those working in oncology, commonly use PDXs for preclinical drug testing and development, and over the last decade large collections of PDXs have emerged across all tumor streams. In this review, we examine how the field has evolved to include PDXs as versatile resources for research discoveries, providing evidence for guidelines and changes in clinical practice.

Head and neck cancer patient-derived xenograft models - A systematic review

BACKGROUND

Patient-derived xenograft (PDX) involve the direct surgical transfer of fresh human tumor samples to immunodeficient mice. This systematic review aimed to identify publications of head and neck cancer PDX (HNC-PDX) models, describing the main methodological characteristics and outcomes.

METHODS

An electronic search was undertaken in four databases, including publications having used HNC-PDX. Data were analyzed descriptively.

RESULTS

63 articles were yielded. The nude mouse was one most commonly animal model used (38.8 %), and squamous cell carcinoma accounted for the majority of HNC-PDX (80.6 %). Tumors were mostly implanted in the flank (86.3 %), and the latency period ranged from 30 to 401 days. The successful rate ranged from 17 % to 100 %. Different drugs and pathways were identified.

CONCLUSION

HNC-PDX appears to significantly recapitulate the morphology of the original HNC and represents a valuable method in translational research for the assessment of the in vivo effect of novel therapies for HNC.

A Biobank of Colorectal Cancer Patient-Derived Xenografts

Colorectal cancer (CRC) is a challenging disease, with a high mortality rate and limited effective treatment options, particularly for late-stage disease. Patient-derived xenografts (PDXs) have emerged as an informative, renewable experimental resource to model CRC architecture and biology. Here, we describe the generation of a biobank of CRC PDXs from stage I to stage IV patients. We demonstrate that PDXs within our biobank recapitulate the histopathological and mutation features of the original patient tumor. In addition, we demonstrate the utility of this resource in pre-clinical chemotherapy and targeted treatment studies, highlighting the translational potential of PDX models in the identification of new therapies that will improve the overall survival of CRC patients.

Patient-Derived Tumor Xenograft Models: Toward the Establishment of Precision Cancer Medicine

Patient-derived xenografts (PDXs) describe models involving the implantation of patient-derived tumor tissue into immunodeficient mice. Compared with conventional preclinical models involving the implantation of cancer cell lines into mice, PDXs can be characterized by the preservation of tumor heterogeneity, and the tumor microenvironment (including stroma/vasculature) more closely resembles that in patients. Consequently, the use of PDX models has improved the predictability of clinical therapeutic responses to 80% or greater, compared with approximately 5% for existing models. In the future, molecular biological analyses, omics analyses, and other experiments will be conducted using recently prepared PDX models under the strong expectation that the analysis of cancer pathophysiology, stem cells, and novel treatment targets and biomarkers will be improved, thereby promoting drug development. This review outlines the methods for preparing PDX models, advances in cancer research using PDX mice, and perspectives for the establishment of precision cancer medicine within the framework of personalized cancer medicine.

The Application of Patient-Derived Xenograft Models in Gynecologic Cancers

Recently, due to the limitations of cell line models and animal models in the preclinical research with insufficient reflecting the physiological situation of humans, patient-derived xenograft (PDX) models of many cancers have been widely developed because of their better representation of the tumor heterogeneity and tumor microenvironment with retention of the cellular complexity, cytogenetics, and stromal architecture. PDX models now have been identified as a powerful tool for determining cancer characteristics, developing new treatment, and predicting drug efficacy. An increase in attempts to generate PDX models in gynecologic cancers has emerged in recent years to understand tumorigenesis. Hence, this review summarized the generation of PDX models and engraftment success of PDX models in gynecologic cancers. Furthermore, we illustrated the similarity between PDX model and original tumor, and described preclinical utilization of PDX models in gynecologic cancers. It would help supply better personalized therapy for gynecologic cancer patients.

Conditional reprogramming: Modeling urological cancer and translation to clinics

Patient-derived models, including cell models (organoids and conditionally reprogrammed cells [CRCs]) and patient-derived xenografts, are urgently needed for both basic and translational cancer research. Conditional reprogramming (CR) technique refers to a co-culture system of primary human normal or tumor cells with irradiated murine fibroblasts in the presence of a Rho-associated kinase inhibitor to allow the primary cells to acquire stem cell properties and the ability to proliferate indefinitely in vitro without any exogenous gene or viral transfection. Considering its robust features, the CR technique may facilitate cancer research in many aspects. Under in vitro culturing, malignant CRCs can share certain genetic aberrations and tumor phenotypes with their parental specimens. Thus, tumor CRCs can promisingly be utilized for the study of cancer biology, the discovery of novel therapies, and the promotion of precision medicine. For normal CRCs, the characteristics of normal karyotype maintenance and lineage commitment suggest their potential in toxicity testing and regenerative medicine. In this review, we discuss the applications, limitations, and future potential of CRCs in modeling urological cancer and translation to clinics.

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.

Recent advances in mass spectrometry based clinical proteomics: applications to cancer research

Cancer biomarkers have transformed current practices in the oncology clinic. Continued discovery and validation are crucial for improving early diagnosis, risk stratification, and monitoring patient response to treatment. Profiling of the tumour genome and transcriptome are now established tools for the discovery of novel biomarkers, but alterations in proteome expression are more likely to reflect changes in tumour pathophysiology. In the past, clinical diagnostics have strongly relied on antibody-based detection strategies, but these methods carry certain limitations. Mass spectrometry (MS) is a powerful method that enables increasingly comprehensive insights into changes of the proteome to advance personalized medicine. In this review, recent improvements in MS-based clinical proteomics are highlighted with a focus on oncology. We will provide a detailed overview of clinically relevant samples types, as well as, consideration for sample preparation methods, protein quantitation strategies, MS configurations, and data analysis pipelines currently available to researchers. Critical consideration of each step is necessary to address the pressing clinical questions that advance cancer patient diagnosis and prognosis. While the majority of studies focus on the discovery of clinically-relevant biomarkers, there is a growing demand for rigorous biomarker validation. These studies focus on high-throughput targeted MS assays and multi-centre studies with standardized protocols. Additionally, improvements in MS sensitivity are opening the door to new classes of tumour-specific proteoforms including post-translational modifications and variants originating from genomic aberrations. Overlaying proteomic data to complement genomic and transcriptomic datasets forges the growing field of proteogenomics, which shows great potential to improve our understanding of cancer biology. Overall, these advancements not only solidify MS-based clinical proteomics' integral position in cancer research, but also accelerate the shift towards becoming a regular component of routine analysis and clinical practice.

Mouse models of colorectal cancer: Past, present and future perspectives

Colorectal cancer (CRC) is the third most common diagnosed malignancy among both sexes in the United States as well as in the European Union. While the incidence and mortality rates in western, high developed countries are declining, reflecting the success of screening programs and improved treatment regimen, a rise of the overall global CRC burden can be observed due to lifestyle changes paralleling an increasing human development index. Despite a growing insight into the biology of CRC and many therapeutic improvements in the recent decades, preclinical in vivo models are still indispensable for the development of new treatment approaches. Since the development of carcinogen-induced rodent models for CRC more than 80 years ago, a plethora of animal models has been established to study colon cancer biology. Despite tenuous invasiveness and metastatic behavior, these models are useful for chemoprevention studies and to evaluate colitis-related carcinogenesis. Genetically engineered mouse models (GEMM) mirror the pathogenesis of sporadic as well as inherited CRC depending on the specific molecular pathways activated or inhibited. Although the vast majority of CRC GEMM lack invasiveness, metastasis and tumor heterogeneity, they still have proven useful for examination of the tumor microenvironment as well as systemic immune responses; thus, supporting development of new therapeutic avenues. Induction of metastatic disease by orthotopic injection of CRC cell lines is possible, but the so generated models lack genetic diversity and the number of suited cell lines is very limited. Patient-derived xenografts, in contrast, maintain the pathological and molecular characteristics of the individual patient's CRC after subcutaneous implantation into immunodeficient mice and are therefore most reliable for preclinical drug development - even in comparison to GEMM or cell line-based analyses. However, subcutaneous patient-derived xenograft models are less suitable for studying most aspects of the tumor microenvironment and anti-tumoral immune responses. The authors review the distinct mouse models of CRC with an emphasis on their clinical relevance and shed light on the latest developments in the field of preclinical CRC models.

Patient-Derived Orthotopic Xenograft models in gastric cancer: a systematic review

Patient-Derived Xenografts (PDXs) are, so far, the best preclinical model to validate targets and predictors of response to therapy. While subcutaneous implantation very rarely allows metastatic dissemination, orthotopic implantation (Patient-Derived Orthotopic Xenograft-PDOX) increases metastatic capability. Using a modified tool to analyze model validity, we performed a systematic review of Embase, PubMed, and Web of Science up to December 2018 to identify all original publications describing gastric cancer (GC) PDOXs. We identified ten studies of PDOX model validation from January 1981 to December 2018 that fulfilled the inclusion and exclusion criteria. Most models (70%) were derived from human GC cell lines rather than tissue fragments. In 90% of studies, the implantation was performed in the subserosal layer. Tumour engraftment rate ranged from 0 to 100%, despite the technique. Metastases were observed in 40% of PDOX models implanted into the subserosal layer, employing either cell suspension or cell line-derived tumour fragments. According to our modified model validity tool, half of the studies were defined as unclear because one or more validation criteria were not reported. Available GC PDOX models are not adequate according to our model validity tool. There is no demonstration that the submucosal site is more effective than the subserosal layer, and that tissue fragments are better than cell suspensions for successful engraftment and metastatic spread. Further studies should strictly employ model validity tools and large samples with orthotopic implant sites mirroring as much as possible the donor tumour characteristics.

Entering the era of computationally driven drug development

Historically, failure rates in drug development are high; increased sophistication and investment throughout the process has shifted the reasons for attrition, but the overall success rates have remained stubbornly and consistently low. Only 8% of new entities entering clinical testing gain regulatory approval, indicating that significant obstacles still exist for efficient therapeutic development. The continued high failure rate can be partially attributed to the inability to link drug exposure with the magnitude of observed safety and efficacy-related pharmacodynamic (PD) responses; frequently, this is a result of nonclinical models exhibiting poor prediction of human outcomes across a wide range of disease conditions, resulting in faulty evaluation of drug toxicology and efficacy. However, the increasing quality and standardization of experimental methods in preclinical stages of testing has created valuable data sets within companies that can be leveraged to further improve the efficiency and accuracy of preclinical prediction for both pharmacokinetics (PK) and PD. Models of Quantitative structure-activity relationships (QSAR), physiologically based pharmacokinetics (PBPK), and PK/PD relationships have also improved efficiency. Founded on a core understanding of biochemistry and physiological interactions of xenobiotics, these in silico methods have the potential to increase the probability of compound success in clinical trials. Integration of traditional computational methods with machine-learning approaches and existing internal pharma databases stands to make a fundamental impact on the speed and accuracy of predictions during the process of drug development and approval.

Scaffold-Free 3-D Cell Sheet Technique Bridges the Gap between 2-D Cell Culture and Animal Models

Various tissue engineering techniques have been created in research spanning two centuries, resulting in new opportunities for growing cells in culture and the creation of 3-D tissue-like constructs. These techniques are classified as scaffold-based and scaffold-free techniques. Cell sheet, as a scaffold-free technique, has attracted research interest in the context of drug discovery and tissue repair, because it provides more predictive data for in vivo testing. It is one of the most promising techniques and has the potential to treat degenerative tissues such as heart, kidneys, and liver. In this paper, we argue the advantages of cell sheets as a scaffold-free approach, compared to other techniques, including scaffold-based and scaffold-free techniques such as the classic systemic injection of cell suspension.