Phenotypic and transcriptional fidelity of patient-derived colon cancer xenografts in immune-deficient mice

Elucidating a Tumor-Selective Nanoparticle Delivery Mechanism at the Colorectal Lumen-Tumor Interface for Precise Local Cancer Therapy

Although various colorectal cancer (CRC)-targeted nanoparticles have been developed to selectively deliver anticancer agents to tumor tissues, severe off-target side effects still persist due to unwanted systemic nanoparticle distribution, limiting the therapeutic outcome. Here, by elucidating a tumor-selective nanoparticle delivery mechanism occurring at the colorectal lumen-tumor interface, an alternative CRC-targeted delivery route is proposed, which enables highly tumor-selective delivery without systemic distribution, through direct drug delivery from the outside of the body (colorectal lumen) to tumors in the colorectum. Owing to the presence of accessible tumor-specific receptors such as CD44 at the colorectal lumen-tumor interface, but not at the colorectal lumen-normal tissue interface, colorectal luminal surface (CLS)-targeting ligand-functionalized nanoparticles selectively accumulate in CRC tissues without systemic distribution, resulting in successful local CRC therapy. The findings suggest that CLS-targeted lumen-to-tumor delivery can be a suitable strategy for highly CRC-specific drug delivery for precise local CRC therapy.

Establishment and Clinical Significance of the Patient-Derived Xenograft Model of Colorectal Cancer

OBJECTIVES

Patient-derived xenograft (PDX) models are widely acknowledged for their ability to reflect the heterogeneity of human cancers and can be used to improve preclinical models. In this study, we evaluated the factors affecting the tumor formation rate of the PDX colorectal cancer (CRC) model and conducted preliminary drug sensitivity tests.

METHODS

CRC patients who underwent elective surgery at Shaoxing People's Hospital from November 2019 to October 2020 were included. The tumor tissue obtained from surgery was transplanted to the back of NSG mice, and the PDX model was established and subcultured to the F3 generation. Factors that affected tumorigenicity were analyzed and compared histologically. Drug interventions included 5-fluorouracil, oxaliplatin, and propofol.

RESULTS

Sixty CRC patients were included in this study, and tumorigenesis was observed in CRC tissue derived from 37 cases (62%). The primary tumor malignancy degree (tumor stage and degree of cell differentiation), preoperative carcinoembryonic antigen level, and tumor location in CRC patients could affect the tumorigenicity of the PDX model. Histopathological analysis of CRC-PDX transplanted tumor tissue was highly consistent with the patient's tumor tissue. All four chemotherapy regimens could inhibit tumor growth and cause tumor tissue damage. Propofol could inhibit diarrhea in mice and protect intestinal mucosa.

CONCLUSIONS

The CRC-PDX model established in this study can maintain the biological characteristics of primary tumors and can be used as a reference model for the individualized treatment of CRC patients. The degree of malignancy of the primary tumor is the primary factor affecting the tumorigenesis rate of the PDX model.

Patient-Derived Xenografts: A Valuable Preclinical Model for Drug Development and Biomarker Discovery

Patient-derived xenografts (PDXs), established by implanting patient tumor cells into immunodeficient mice, offer a platform for faithfully replicating human tumors. They closely mimic the histopathology, genomics, and drug sensitivity of patient tumors. This chapter highlights the versatile applications of PDXs, including studying tumor biology, metastasis, and chemoresistance, as well as their use in biomarker identification, drug screening, and personalized medicine. It also addresses challenges in using PDXs in cancer research, including variations in metastatic potential, lengthy establishment timelines, stromal changes, and limitations in immunocompromised models. Despite these challenges, PDXs remain invaluable tools guiding patient treatment and advancing preclinical drug development.

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

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

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.

Engineered colorectal cancer tissue recapitulates key attributes of a patient-derived xenograft tumor line

The development of physiologically relevantin vitrocolorectal cancer (CRC) models is vital for advancing understanding of tumor biology. Although CRC patient-derived xenografts (PDXs) recapitulate key patient tumor characteristics and demonstrate high concordance with clinical outcomes, the use of thisin vivomodel is costly and low-throughput. Here we report the establishment and in-depth characterization of anin vitrotissue-engineered CRC model using PDX cells. To form the 3D engineered CRC-PDX (3D-eCRC-PDX) tissues, CRC PDX tumors were expandedin vivo, dissociated, and the isolated cells encapsulated within PEG-fibrinogen hydrogels. Following PEG-fibrinogen encapsulation, cells remain viable and proliferate within 3D-eCRC-PDX tissues. Tumor cell subpopulations, including human cancer and mouse stromal cells, are maintained in long-term culture (29 days); cellular subpopulations increase ratiometrically over time. The 3D-eCRC-PDX tissues mimic the mechanical stiffness of originating tumors. Extracellular matrix protein production by cells in the 3D-eCRC-PDX tissues resulted in approximately 57% of proteins observed in the CRC-PDX tumors also being present in the 3D-eCRC-PDX tissues on day 22. Furthermore, we show congruence in enriched gene ontology molecular functions and Hallmark gene sets in 3D-eCRC-PDX tissues and CRC-PDX tumors compared to normal colon tissue, while prognostic Kaplan-Meier plots for overall and relapse free survival did not reveal significant differences between CRC-PDX tumors and 3D-eCRC-PDX tissues. Our results demonstrate high batch-to-batch consistency and strong correlation between ourin vitrotissue-engineered PDX-CRC model and the originatingin vivoPDX tumors, providing a foundation for future studies of disease progression and tumorigenic mechanisms.

Zebrafish Patient-Derived Xenograft Model to Predict Treatment Outcomes of Colorectal Cancer Patients

The use of zebrafish embryos for personalized medicine has become increasingly popular. We present a co-clinical trial aiming to evaluate the use of zPDX (zebrafish Patient-Derived Xenografts) in predicting the response to chemotherapy regimens used for colorectal cancer patients. zPDXs are generated by xenografting tumor tissues in two days post-fertilization zebrafish embryos. zPDXs were exposed to chemotherapy regimens (5-FU, FOLFIRI, FOLFOX, FOLFOXIRI) for 48 h. We used a linear mixed effect model to evaluate the zPDX-specific response to treatments showing for 4/36 zPDXs (11%), a statistically significant reduction of tumor size compared to controls. We used the RECIST criteria to compare the outcome of each patient after chemotherapy with the objective response of its own zPDX model. Of the 36 patients enrolled, 8 metastatic colorectal cancer (mCRC), response rate after first-line therapy, and the zPDX chemosensitivity profile were available. Of eight mCRC patients, five achieved a partial response and three had a stable disease. In 6/8 (75%) we registered a concordance between the response of the patient and the outcomes reported in the corresponding zPDX. Our results provide evidence that the zPDX model can reflect the outcome in mCRC patients, opening a new frontier to personalized medicine.

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

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

Zebrafish Patient-Derived Xenografts Identify Chemo-Response in Pancreatic Ductal Adenocarcinoma Patients

It is increasingly evident the necessity of new predictive tools for the treatment of pancreatic ductal adenocarcinoma in a personalized manner. We present a co-clinical trial testing the predictiveness of zPDX (zebrafish patient-derived xenograft) for assessing if patients could benefit from a therapeutic strategy (ClinicalTrials.gov: XenoZ, NCT03668418). zPDX are generated xenografting tumor tissues in zebrafish embryos. zPDX were exposed to chemotherapy regimens commonly used. We considered a zPDX a responder (R) when a decrease ≥50% in the relative tumor area was reported; otherwise, we considered them a non-responder (NR). Patients were classified as Responder if their own zPDX was classified as an R for the chemotherapy scheme she/he received an adjuvant treatment; otherwise, we considered them a Non-Responder. We compared the cancer recurrence rate at 1 year after surgery and the disease-free survival (DFS) of patients of both groups. We reported a statistically significant higher recurrence rate in the Non-Responder group: 66.7% vs. 14.3% (p = 0.036), anticipating relapse/no relapse within 1 year after surgery in 12/16 patients. The mean DFS was longer in the R-group than the NR-group, even if not statistically significant: 19.2 months vs. 12.7 months, (p = 0.123). The proposed strategy could potentially improve preclinical evaluation of treatment modalities and may enable prospective therapeutic selection in everyday clinical practice.

Integrated approaches for precision oncology in colorectal cancer: The more you know, the better

Colorectal cancer (CRC) is one of the most common human malignancies accounting for approximately 10 % of worldwide cancer incidence and mortality. While early-stage CRC is mainly a preventable and curable disease, metastatic colorectal cancer (mCRC) remains an unmet clinical need. Moreover, about 25 % of CRC cases are diagnosed only at the metastatic stage. Despite the extensive molecular and functional knowledge on this disease, systemic therapy for mCRC still relies on traditional 5-fluorouracil (5-FU)-based chemotherapy regimens. On the other hand, targeted therapies and immunotherapy have shown effectiveness only in a limited subset of patients. For these reasons, there is a growing need to define the molecular and biological landscape of individual patients to implement novel, rationally driven, tailored therapies. In this review, we explore current and emerging approaches for CRC management such as genomic, transcriptomic and metabolomic analysis, the use of liquid biopsies and the implementation of patients' preclinical avatars. In particular, we discuss the contribution of each of these tools in elucidating patient specific features, with the aim of improving our ability in advancing the diagnosis and treatment of colorectal tumors.

A bladder cancer patient-derived xenograft displays aggressive growth dynamics in vivo and in organoid culture

Bladder cancer is among the most prevalent cancers worldwide. Currently, few bladder cancer models have undergone thorough characterization to assess their fidelity to patient tumors, especially upon propagation in the laboratory. Here, we establish and molecularly characterize CoCaB 1, an aggressive cisplatin-resistant muscle-invasive bladder cancer patient-derived xenograft (PDX) and companion organoid system. CoCaB 1 was a subcutaneous PDX model reliably transplanted in vivo and demonstrated an acceleration in growth upon serial transplantation, which was reflected in organoid and 2D cell culture systems. Transcriptome analysis revealed progression towards an increasingly proliferative and stem-like expression profile. Gene expression differences between organoid and PDX models reflected expected differences in cellular composition, with organoids enriched in lipid biosynthesis and metabolism genes and deprived of extracellular components observed in PDXs. Both PDX and organoid models maintained the histological fidelity and mutational heterogeneity of their parental tumor. This study establishes the CoCaB 1 PDX and organoid system as companion representative tumor models for the development of novel bladder cancer therapies.

Desmoglein-2 as a prognostic and biomarker in ovarian cancer

Greater than 80% of all cancer cases are carcinomas, formed by the malignant transformation of epithelial cells. One of the key features of epithelial tumors is the presence of intercellular junctions, which link cells to one another and act as barriers to the penetration of molecules. This study assessed the expression of desmoglein-2, an epithelial junction protein, as a prognostic and diagnostic biomarker for ovarian cancer. Ovarian cancer sections were stained for DSG2 and signal intensity was correlated to cancer type and grade. DSG2 immunohistochemistry signals and mRNA levels were analyzed in chemo-resistant and chemo-sensitive cases. Ovarian cancer patient serum levels of shed DSG2 were correlated to disease-free and overall survival. Primary ovarian cancer cells were used to study DSG2 levels as they changed in response to cisplatin treatment. DSG2 expression was found to be positively correlated with cancer grade. Ovarian cancer patients with high serum levels of shed DSG2 fared significantly worse in both progression-free survival (median survival of 16 months vs. 26 months, p = .0023) and general survival (median survival of 37 months vs. undefined, p < .0001). A subgroup of primary chemotherapy-resistant cases had stronger DSG2 IHC/Western signals and higher DSG2 mRNA levels. Furthermore, our in vitro studies indicate that non-cytotoxic doses of cisplatin can enhance DSG2 expression, which, in turn, can contribute to chemo-resistance. We suggest that DSG2 can be used in stratifying patients, deciding on where to use aggressive treatment strategies, predicting chemoresistance, and as a companion diagnostic for treatments targeting DSG2.

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.

Exploring and modelling colon cancer inter-tumour heterogeneity: opportunities and challenges

Colon cancer inter-tumour heterogeneity is installed on multiple levels, ranging from (epi)genetic driver events to signalling pathway rewiring reflected by differential gene expression patterns. Although the existence of heterogeneity in colon cancer has been recognised for a longer period of time, it is sparingly incorporated as a determining factor in current clinical practice. Here we describe how unsupervised gene expression-based classification efforts, amongst which the consensus molecular subtypes (CMS), can stratify patients in biological subgroups associated with distinct disease outcome and responses to therapy. We will discuss what is needed to extend these subtyping efforts to the clinic and we will argue that preclinical models recapitulate CMS subtypes and can be of vital use to increase our understanding of treatment response and resistance and to discover novel targets for therapy.

A multi-modal data resource for investigating topographic heterogeneity in patient-derived xenograft tumors

Patient-derived xenografts (PDXs) are an essential pre-clinical resource for investigating tumor biology. However, cellular heterogeneity within and across PDX tumors can strongly impact the interpretation of PDX studies. Here, we generated a multi-modal, large-scale dataset to investigate PDX heterogeneity in metastatic colorectal cancer (CRC) across tumor models, spatial scales and genomic, transcriptomic, proteomic and imaging assay modalities. To showcase this dataset, we present analysis to assess sources of PDX variation, including anatomical orientation within the implanted tumor, mouse contribution, and differences between replicate PDX tumors. A unique aspect of our dataset is deep characterization of intra-tumor heterogeneity via immunofluorescence imaging, which enables investigation of variation across multiple spatial scales, from subcellular to whole tumor levels. Our study provides a benchmark data resource to investigate PDX models of metastatic CRC and serves as a template for future, quantitative investigations of spatial heterogeneity within and across PDX tumor models.

Current and Future Horizons of Patient-Derived Xenograft Models in Colorectal Cancer Translational Research

Our poor understanding of the intricate biology of cancer and the limited availability of preclinical models that faithfully recapitulate the complexity of tumors are primary contributors to the high failure rate of novel therapeutics in oncology clinical studies. To address this need, patient-derived xenograft (PDX) platforms have been widely deployed and have reached a point of development where we can critically review their utility to model and interrogate relevant clinical scenarios, including tumor heterogeneity and clonal evolution, contributions of the tumor microenvironment, identification of novel drugs and biomarkers, and mechanisms of drug resistance. Colorectal cancer (CRC) constitutes a unique case to illustrate clinical perspectives revealed by PDX studies, as they overcome limitations intrinsic to conventional ex vivo models. Furthermore, the success of molecularly annotated "Avatar" models for co-clinical trials in other diseases suggests that this approach may provide an additional opportunity to improve clinical decisions, including opportunities for precision targeted therapeutics, for patients with CRC in real time. Although critical weaknesses have been identified with regard to the ability of PDX models to predict clinical outcomes, for now, they are certainly the model of choice for preclinical studies in CRC. Ongoing multi-institutional efforts to develop and share large-scale, well-annotated PDX resources aim to maximize their translational potential. This review comprehensively surveys the current status of PDX models in translational CRC research and discusses the opportunities and considerations for future PDX development.

Application of Highly Immunocompromised Mice for the Establishment of Patient-Derived Xenograft (PDX) Models

Patient-derived xenograft (PDX) models are created by engraftment of patient tumor tissues into immunocompetent mice. Since a PDX model retains the characteristics of the primary patient tumor including gene expression profiles and drug responses, it has become the most reliable in vivo human cancer model. The engraftment rate increases with the introduction of Non-obese diabetic Severe combined immunodeficiency (NOD/SCID)-based immunocompromised mice, especially the NK-deficient NOD strains NOD/SCID/interleukin-2 receptor gamma chain(IL2Rγ)null (NOG/NSG) and NOD/SCID/Jak3(Janus kinase 3)null (NOJ). Success rates differ with tumor origin: gastrointestinal tumors acquire a higher engraftment rate, while the rate is lower for breast cancers. Subcutaneous transplantation is the most popular method to establish PDX, but some tumors require specific environments, e.g., orthotropic or renal capsule transplantation. Human hormone treatment is necessary to establish hormone-dependent cancers such as prostate and breast cancers. PDX mice with human hematopoietic and immune systems (humanized PDX) are powerful tools for the analysis of tumor-immune system interaction and evaluation of immunotherapy response. A PDX biobank equipped with patients' clinical data, gene-expression patterns, mutational statuses, tumor tissue architects, and drug responsiveness will be an authoritative resource for developing specific tumor biomarkers for chemotherapeutic predictions, creating individualized therapy, and establishing precise cancer medicine.

Molecular profiling stratifies diverse phenotypes of treatment-refractory metastatic castration-resistant prostate cancer

Metastatic castration-resistant prostate cancer (mCRPC) is a heterogeneous disease with diverse drivers of disease progression and mechanisms of therapeutic resistance. We conducted deep phenotypic characterization of CRPC metastases and patient-derived xenograft (PDX) lines using whole genome RNA sequencing, gene set enrichment analysis and immunohistochemistry. Our analyses revealed five mCRPC phenotypes based on the expression of well-characterized androgen receptor (AR) or neuroendocrine (NE) genes: (i) AR-high tumors (ARPC), (ii) AR-low tumors (ARLPC), (iii) amphicrine tumors composed of cells co-expressing AR and NE genes (AMPC), (iv) double-negative tumors (i.e. AR-/NE-; DNPC) and (v) tumors with small cell or NE gene expression without AR activity (SCNPC). RE1-silencing transcription factor (REST) activity, which suppresses NE gene expression, was lost in AMPC and SCNPC PDX models. However, knockdown of REST in cell lines revealed that attenuated REST activity drives the AMPC phenotype but is not sufficient for SCNPC conversion. We also identified a subtype of DNPC tumors with squamous differentiation and generated an encompassing 26-gene transcriptional signature that distinguished the five mCRPC phenotypes. Together, our data highlight the central role of AR and REST in classifying treatment-resistant mCRPC phenotypes. These molecular classifications could potentially guide future therapeutic studies and clinical trial design.

Processing of Primary Patient Tumors and Subsequent Generation of Primary Cell Lines

Patient tumor tissue processing is an important step in the generation of clinically relevant specimens for in vitro and in vivo studies. Proper disassociation and tissue sample cleanup is a multistep, time-consuming process that ultimately effects the generation of patient derived xenografts and neurosphere cultures. Here we describe a detailed protocol on how to process and disassociate patient glioma tissue and subsequent steps on orthotopic implantation and in vitro generation of neurospheres.

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.

Consensus molecular subtypes of colorectal cancer are recapitulated in in vitro and in vivo models

Colorectal cancer (CRC) is a highly heterogeneous disease both from a molecular and clinical perspective. Several distinct molecular entities, such as microsatellite instability (MSI), have been defined that make up biologically distinct subgroups with their own clinical course. Recent data indicated that CRC can be best segregated into four groups called consensus molecular subtypes (CMS1-4), each of which has a unique biology and gene expression pattern. In order to develop improved, subtype-specific therapies and to gain insight into the molecular wiring and origin of these subtypes, reliable models are needed. This study was designed to determine the heterogeneity and identify the presence of CMSs in a large panel of CRC cell lines, primary cultures and patient-derived xenografts (PDX). We provide a repository encompassing this heterogeneity and moreover describe that a large part of the models can be robustly assigned to one of the four CMSs, independent of the stromal contribution. We subsequently validate our CMS stratification by functional analysis which for instance shows mesenchymal enrichment in CMS4 and metabolic dysregulation in CMS3. Finally, we observe a clear difference in sensitivity to chemotherapy-induced apoptosis, specifically between CMS2 and CMS4. This relates to the in vivo efficacy of chemotherapy, which delays outgrowth of CMS2, but not CMS4 xenografts. Combined our data indicate that molecular subtypes are faithfully modelled in CRC cell cultures and PDXs, representing tumour cell intrinsic and stable features. This repository provides researchers with a platform to study CRC using the existing heterogeneity.

Validation of a preclinical model for assessment of drug efficacy in melanoma

The aim of personalized medicine is to improve our understanding of the disease at molecular level and to optimize therapeutic management. In this context, we have developed in vivo and ex vivo preclinical strategies evaluating the efficacy of innovative drugs in melanomas. Human melanomas (n = 17) of different genotypes (mutated BRAF, NRAS, amplified cKIT and wild type) were successfully engrafted in mice then amplified by successive transplantations. The exhaustive characterization of patient-derived xenografts (PDX) at genomic level (transcriptomic and CGH arrays) revealed a similar distribution pattern of genetic abnormalities throughout the successive transplantations compared to the initial patient tumor, enabling their use for mutation-specific therapy strategies. The reproducibility of their spontaneous metastatic potential in mice was assessed in 8 models. These PDXs were used for the development of histoculture drug response assays (ex vivo) for the evaluation of innovative drug efficacy (BRAF and MEK inhibitors). The pharmacological effects of BRAF and MEK inhibitors were similar between PDX-derived histocultures and their corresponding PDX, on 2 models of BRAF and NRAS-mutated melanomas. These models constitute a validated, effective tool for preclinical investigation of new therapeutic agents, and improve therapeutic strategies in the treatment of metastatic melanoma.

Patient-derived xenografts undergo mouse-specific tumor evolution

Patient-derived xenografts (PDXs) have become a prominent cancer model system, as they are presumed to faithfully represent the genomic features of primary tumors. Here we monitored the dynamics of copy number alterations (CNAs) in 1,110 PDX samples across 24 cancer types. We observed rapid accumulation of CNAs during PDX passaging, often due to selection of pre-existing minor clones. CNA acquisition in PDXs was correlated with the tissue-specific levels of aneuploidy and genetic heterogeneity observed in primary tumors. However, the particular CNAs acquired during PDX passaging differed from those acquired during tumor evolution in patients. Several CNAs recurrently observed in primary tumors gradually disappeared in PDXs, indicating that events undergoing positive selection in humans can become dispensable during propagation in mice. Importantly, the genomic stability of PDXs was associated with their response to chemotherapy and targeted drugs. These findings have important implications for PDX-based modeling of human cancer.

Patient-derived Xenografts from Colorectal Carcinoma: A Temporal and Hierarchical Study of Murine Stromal Cell Replacement

BACKGROUND/AIM

Patient-derived xenografting (PDX) of human colorectal cancer (CRC) is the preferred experimental model to study tumor response to therapeutic agents. Gradually, human stromal cells are replaced by mouse stromal cells; however, the exact timing of the replacement of human with murine stromal cells in human CRC xenograft has not been fully elucidated. We hypothesize that orthologous murine transcripts functionally substitutes for the loss due to replacement of human stromal genes.

MATERIALS AND METHODS

Human CRC were implanted in athymic nude mice in replicates and followed-up over time. Using next-generation sequencing, we determined the temporal kinetics of human stromal cell replacement with the orthologous murine transcripts.

RESULTS

CRC cell-induced re-organization of the normal, quiescent murine stromal cells into a protumorigenic phenotype supporting human CRC growth occurs at initial implantation.

CONCLUSION

Murine cell replacement occurs in a time- and size-dependent manner.

Selective analysis of cancer-cell intrinsic transcriptional traits defines novel clinically relevant subtypes of colorectal cancer

Stromal content heavily impacts the transcriptional classification of colorectal cancer (CRC), with clinical and biological implications. Lineage-dependent stromal transcriptional components could therefore dominate over more subtle expression traits inherent to cancer cells. Since in patient-derived xenografts (PDXs) stromal cells of the human tumour are substituted by murine counterparts, here we deploy human-specific expression profiling of CRC PDXs to assess cancer-cell intrinsic transcriptional features. Through this approach, we identify five CRC intrinsic subtypes (CRIS) endowed with distinctive molecular, functional and phenotypic peculiarities: (i) CRIS-A: mucinous, glycolytic, enriched for microsatellite instability or KRAS mutations; (ii) CRIS-B: TGF-β pathway activity, epithelial–mesenchymal transition, poor prognosis; (iii) CRIS-C: elevated EGFR signalling, sensitivity to EGFR inhibitors; (iv) CRIS-D: WNT activation, IGF2 gene overexpression and amplification; and (v) CRIS-E: Paneth cell-like phenotype, TP53 mutations. CRIS subtypes successfully categorize independent sets of primary and metastatic CRCs, with limited overlap on existing transcriptional classes and unprecedented predictive and prognostic performances.

Stromal cells contribute to the gene expression profiles based on which colorectal cancer (CRC) molecular subtypes are classified. Here, patient-derived xenografts enable the authors to obtain cancer cell-specific transcriptomes by excluding transcripts from murine stromal cells, based on which they define CRC intrinsic subtypes (CRIS) and evaluate their prognostic and predictive potential.

Modeling of Patient-Derived Xenografts in Colorectal Cancer

Developing realistic preclinical models using clinical samples that mirror complex tumor biology and behavior are vital to advancing cancer research. While cell line cultures have been helpful in generating preclinical data, the genetic divergence between these and corresponding primary tumors has limited clinical translation. Conversely, patient-derived xenografts (PDX) in colorectal cancer are highly representative of the genetic and phenotypic heterogeneity in the original tumor. Coupled with high-throughput analyses and bioinformatics, these PDXs represent robust preclinical tools for biomarkers, therapeutic target, and drug discovery. Successful PDX engraftment is hypothesized to be related to a series of anecdotal variables namely, tissue source, cancer stage, tumor grade, acquisition strategy, time to implantation, exposure to prior systemic therapy, and genomic heterogeneity of tumors. Although these factors at large can influence practices and patterns related to xenotransplantation, their relative significance in determining the success of establishing PDXs is uncertain. Accordingly, we systematically examined the predictive ability of these factors in establishing PDXs using 90 colorectal cancer patient specimens that were subcutaneously implanted into immunodeficient mice. Fifty (56%) PDXs were successfully established. Multivariate analyses showed tissue acquisition strategy [surgery 72.0% (95% confidence interval (CI): 58.2-82.6) vs. biopsy 35% (95% CI: 22.1%-50.6%)] to be the key determinant for successful PDX engraftment. These findings contrast with current empiricism in generating PDXs and can serve to simplify or liberalize PDX modeling protocols. Better understanding the relative impact of these factors on efficiency of PDX formation will allow for pervasive integration of these models in care of colorectal cancer patients. Mol Cancer Ther; 16(7); 1435-42. ©2017 AACR.

Interrogating open issues in cancer precision medicine with patient-derived xenografts

Patient-derived xenografts (PDXs) have emerged as an important platform to elucidate new treatments and biomarkers in oncology. PDX models are used to address clinically relevant questions, including the contribution of tumour heterogeneity to therapeutic responsiveness, the patterns of cancer evolutionary dynamics during tumour progression and under drug pressure, and the mechanisms of resistance to treatment. The ability of PDX models to predict clinical outcomes is being improved through mouse humanization strategies and the implementation of co-clinical trials, within which patients and PDXs reciprocally inform therapeutic decisions. This Opinion article discusses aspects of PDX modelling that are relevant to these questions and highlights the merits of shared PDX resources to advance cancer medicine from the perspective of EurOPDX, an international initiative devoted to PDX-based research.

Patient-derived xenograft models of colorectal cancer in pre-clinical research: a systematic review

AIMS

We sought to objectively assess the internal and external validity of patient-derived xenograft (PDX) models as a platform in pre-clinical research into colorectal cancer (CRC). Metastatic disease is the most common cause of death from CRC, and despite significant research, the results of current combination chemotherapy and targeted therapies have been underwhelming for most of this patient group. One of the key factors limiting the success of translational CRC research is the biologically inaccurate models in which new therapies are developed.

METHODS

We used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist and SYRCLE (Systematic Review Centre for Laboratory animal Experimentation) guidelines to search Ovid MEDLINE and Embase databases up to July 2015 to identify studies involving PDX models of CRC where the model had been validated across multiple parameters. Data was extracted including host mouse strain, engraftment rate, site of engraftment, donor tumour source and development of metastases in the model.

RESULTS

Thirteen articles satisfied the inclusion criteria. There was significant heterogeneity amongst the included studies, but overall the median engraftment rate was high (70%) and PDX models faithfully recapitulated the characteristics of their patient tumours on the microscopic, genetic and functional levels.

CONCLUSIONS

PDX models of CRC have a reasonable internal validity and a high external validity. Developments in xenografting technology are broadening the applications of the PDX platform. However, the included studies could be improved by standardising reporting standards and closed following the ARRIVE (Animals in Research: Reporting In Vivo Experiments) guidelines.

3-Dimensional Patient-Derived Lung Cancer Assays Reveal Resistance to Standards-of-Care Promoted by Stromal Cells but Sensitivity to Histone Deacetylase Inhibitors

There is a growing recognition that current preclinical models do not reflect the tumor microenvironment in cellular, biological, and biophysical content and this may have a profound effect on drug efficacy testing, especially in the era of molecular-targeted agents. Here, we describe a method to directly embed low-passage patient tumor-derived tissue into basement membrane extract, ensuring a low proportion of cell death to anoikis and growth complementation by coculture with patient-derived cancer-associated fibroblasts (CAF). A range of solid tumors proved amenable to growth and pharmacologic testing in this 3D assay. A study of 30 early-stage non-small cell lung cancer (NSCLC) specimens revealed high levels of de novo resistance to a large range of standard-of-care agents, while histone deacetylase (HDAC) inhibitors and their combination with antineoplastic drugs displayed high levels of efficacy. Increased resistance was seen in the presence of patient-derived CAFs for many agents, highlighting the utility of the assay for tumor microenvironment-educated drug testing. Standard-of-care agents showed similar responses in the 3D ex vivo and patient-matched in vivo models validating the 3D-Tumor Growth Assay (3D-TGA) as a high-throughput screen for close-to-patient tumors using significantly reduced animal numbers. Mol Cancer Ther; 15(4); 753-63. ©2016 AACR.

Stromal contribution to the colorectal cancer transcriptome

Recent studies identified a poor-prognosis stem/serrated/mesenchymal (SSM) transcriptional subtype of colorectal cancer (CRC). We noted that genes upregulated in this subtype are also prominently expressed by stromal cells, suggesting that SSM transcripts could derive from stromal rather than epithelial cancer cells. To test this hypothesis, we analyzed CRC expression data from patient-derived xenografts, where mouse stroma supports human cancer cells. Species-specific expression analysis showed that the mRNA levels of SSM genes were mostly due to stromal expression. Transcriptional signatures built to specifically report the abundance of cancer-associated fibroblasts (CAFs), leukocytes or endothelial cells all had significantly higher expression in human CRC samples of the SSM subtype. High expression of the CAF signature was associated with poor prognosis in untreated CRC, and joint high expression of the stromal signatures predicted resistance to radiotherapy in rectal cancer. These data show that the distinctive transcriptional and clinical features of the SSM subtype can be ascribed to its particularly abundant stromal component.

Modeling human carcinomas: physiologically relevant 3D models to improve anti-cancer drug development

Anti-cancer drug development is inefficient, mostly due to lack of efficacy in human patients. The high fail rate is partly due to the lack of predictive models or the inadequate use of existing preclinical test systems. However, progress has been made and preclinical models were improved or newly developed, which all account for basic features of solid cancers, three-dimensionality and heterotypic cell interaction. Here we give an overview of available in vivo and in vitro models of cancer, which meet the criteria of being 3D and mirroring human tumor-stroma interactions. We only focus on drug response models without touching models for pharmacokinetic and dynamic, toxicity or delivery aspects.