The OVAREX study: Establishment of ex vivo ovarian cancer models to validate innovative therapies and to identify predictive biomarkers.

BACKGROUND: Ovarian cancer is the first cause of death from gynecological malignancies mainly due to development of chemoresistance. Despite the emergence of PARP inhibitors, which have revolutionized the therapeutic management of some of these ovarian cancers, the 5-year overall survival rate remains around 45%. Therefore, it is crucial to develop new therapeutic strategies, to identify predictive biomarkers and to predict the response to treatments. In this context, functional assays based on patient-derived tumor models could constitute helpful and relevant tools for identifying efficient therapies or to guide clinical decision making. METHOD: The OVAREX study is a single-center non-interventional study which aims at investigating the feasibility of establishing in vivo and ex vivo models and testing ex vivo models to predict clinical response of ovarian cancer patients. Patient-Derived Xenografts (PDX) will be established from tumor fragments engrafted subcutaneously into immunocompromised mice. Explants will be generated by slicing tumor tissues and Ascites-Derived Spheroids (ADS) will be isolated following filtration of ascites. Patient-derived tumor organoids (PDTO) will be established after dissociation of tumor tissues or ADS, cell embedding into extracellular matrix and culture in specific medium. Molecular and histological characterizations will be performed to compare tumor of origin and paired models. Response of ex vivo tumor-derived models to conventional chemotherapy and PARP inhibitors will be assessed and compared to results of companion diagnostic test and/or to the patient's response to evaluate their predictive value. DISCUSSION: This clinical study aims at generating PDX and ex vivo models (PDTO, ADS, and explants) from tumors or ascites of ovarian cancer patients who will undergo surgical procedure or paracentesis. We aim at demonstrating the predictive value of ex vivo models for their potential use in routine clinical practice as part of precision medicine, as well as establishing a collection of relevant ovarian cancer models that will be useful for the evaluation of future innovative therapies. TRIAL REGISTRATION: The clinical trial has been validated by local research ethic committee on January 25th 2019 and registered at ClinicalTrials.gov with the identifier NCT03831230 on January 28th 2019, last amendment v4 accepted on July 18, 2023.

Nanoparticle-Encapsulated Epirubicin Efficacy in the Inhibition of Growth of Orthotopic Ovarian Patient-Derived Xenograft in Immunocompromised Mice.

Epirubicin hydrochloride (EPI) is an anticancer drug widely used in the treatment of many solid tumors, including ovarian cancer. Because of its anatomical location, ovarian cancer shows symptoms when it is already in an advanced stage and is thus more difficult to treat. Epirubicin hydrochloride kills cancer cells effectively, but its dose escalation is limited by its severe toxicity. By encapsulating epirubicin in dextran-based nanoparticles (POLEPI), we expected to deliver higher and thus clinically more effective doses directly to tumors, where epirubicin would be released and retained longer in the tumor. The antitumor activity of POLEPI compared to EPI was first tested ex vivo in a series of ovarian cancer patient-derived tumor xenografts (PDX). The most promising PDX was then implanted orthotopically into immunocompromised mice, and tumor growth was monitored via magnetic resonance imaging (MRI). Although we succeeded in suppressing the growth of ovarian cancer derived from a patient, in a mouse model by 70% compared to 40% via EPI in 5 days after only one injection, we could not eliminate serious side effects, and the study was terminated prematurely for humane reasons.

Transcriptome profiling of patient-derived tumor xenografts suggests novel extracellular matrix-related signatures for gastric cancer prognosis prediction.

BACKGROUND: A major obstacle to the development of personalized therapies for gastric cancer (GC) is the prevalent heterogeneity at the intra-tumor, intra-patient, and inter-patient levels. Although the pathological stage and histological subtype diagnosis can approximately predict prognosis, GC heterogeneity is rarely considered. The extracellular matrix (ECM), a major component of the tumor microenvironment (TME), extensively interacts with tumor and immune cells, providing a possible proxy to investigate GC heterogeneity. However, ECM consists of numerous protein components, and there are no suitable models to screen ECM-related genes contributing to tumor growth and prognosis. We constructed patient-derived tumor xenograft (PDTX) models to obtain robust ECM-related transcriptomic signatures to improve GC prognosis prediction and therapy design. METHODS: One hundred twenty two primary GC tumor tissues were collected to construct PDTX models. The tumorigenesis rate and its relationship with GC prognosis were investigated. Transcriptome profiling was performed for PDTX-originating tumors, and least absolute shrinkage and selection operator (LASSO) Cox regression analysis was applied to extract prognostic ECM signatures and establish PDTX tumorigenicity-related gene (PTG) scores. The predictive ability of the PTG score was validated using two independent cohorts. Finally, we combined PTG score, age, and pathological stage information to establish a robust nomogram for GC prognosis prediction. RESULTS: We found that PDTX tumorigenicity indicated a poor prognosis in patients with GC, even at the same pathological stage. Transcriptome profiling of PDTX-originating GC tissues and corresponding normal controls identified 383 differentially expressed genes, with enrichment of ECM-related genes. A robust prognosis prediction model using the PTG score showed robust performance in two validation cohorts. A high PTG score was associated with elevated M2 polarized macrophage and cancer-associated fibroblast infiltration. Finally, combining the PTG score with age and TNM stage resulted in a more effective prognostic model than age or TNM stage alone. CONCLUSIONS: We found that ECM-related signatures may contribute to PDTX tumorigenesis and indicate a poor prognosis in GC. A feasible survival prediction model was built based on the PTG score, which was associated with immune cell infiltration. Together with patient ages and pathological TNM stages, PTG score could be a new approach for GC prognosis prediction.

Metastatic seeding of human colon cancer cell clusters expressing the hybrid epithelial/mesenchymal state.

Emerging evidence supports the theory that tumor cell clusters efficiently metastasize to distant organs. However, the roles of epithelial-to-mesenchymal transition (EMT) in metastasizing tumor cell clusters have not yet been fully elucidated. To investigate this issue, tumor fragments were dissected from 40 colorectal cancer (CRC) patients and implanted subcutaneously into immunodeficient mice. We observed that tumors developed from the tumor fragments obtained from 28 of the 40 CRC patients. The tumors were then dissociated into cell suspensions to be orthotopically injected into secondary mice. The tumors from 13 of the 28 patients progressed. Furthermore, metastases formed spontaneously in the liver and lungs from the tumor fragments obtained from 8 of these 13 patients. Moreover, employing a mathematical analysis, we showed that tumor cell clusters seeded these metastases significantly more often than did single tumor cells. Membrane E-cadherin- and nuclear ZEB1-positive tumor cells indicating the hybrid epithelial/mesenchymal state were also detected in primary tumors of various CRC patients, and in the corresponding patient-derived xenografts (PDXs) and circulating tumor cell clusters in the bloodstreams of mice. In contrast, ZEB1 staining was barely detectable in the patient-matched liver metastases presumably developing through mesenchymal-to-epithelial transition. Inhibition of E-cadherin or ZEB1 expression by shRNA notably prevented the PDX-derived tumor organoids from colonizing the liver, when injected intrasplenically into mice, indicating E-cadherin and ZEB1 expressions to be required for their metastatic colonization. Taken together, these findings suggest that the epithelial/mesenchymal state mediates metastatic seeding of human CRC cell clusters into distant organs.

Characterization of patient-derived tumor xenograft models of endometrial cancer for preclinical evaluation of targeted therapies.

OBJECTIVE: Endometrial carcinoma (EC) is the sixth most common cancer in women and therapies are limited for advanced and recurrent disease. Patient-derived tumor xenograft (PDTX) models are becoming popular tools in translational research because of their histological and genetic similarity to the original tumors and the ability to predict therapeutic response to treatments. Here, we established and characterized a panel of 24 EC PDTX models which includes the major histological and genetic subtypes observed in patients. METHODS: Fresh tumor tissues collected from primary, metastatic and recurrent type I and type II EC patients were engrafted in immunocompromised mice. Histology, vimentin, and cytokeratin expression were evaluated, together with Microsatellite instability (MSI), mutation profiling by Whole Exome Sequencing and copy number profiling by Whole Genome Low Coverage Sequencing. The efficacy of both PI3K and MEK inhibitors was evaluated in a model of endometrioid carcinoma harboring PTEN, PIK3CA and KRAS mutations. RESULTS: We observed good similarity between primary tumors and the corresponding xenografts, at histological and genetic level. Among the engrafted endometrioid models, we found a significant enrichment of MSI and POLE mutated tumors, compared to non-engrafted samples. Combination treatment with NVP-BEZ235 and AZD6244 showed the possibility to stabilize the tumor growth in one model originated from a patient who already received several lines of chemotherapy. CONCLUSION: The established EC PDTX models, resembling the original human tumors, promise to be useful for preclinical evaluation of novel combination and targeted therapies in specific EC subgroups.

Xenograft and organoid models in developing precision medicine for gastric cancer (Review).

Gastric cancer (GC), a highly heterogeneous disease, has diverse histological and molecular subtypes. For precision medicine, well­characterized models encompassing the full spectrum of subtypes are necessary. Patient­derived tumor xenografts and organoids serve as important preclinical models in GC research. The main advantage of these models is the retention of phenotypic and genotypic heterogeneity present in parental tumor tissues. Utilizing diverse sequencing techniques and preclinical models for GC research facilitates accuracy in predicting personalized clinical responses to anti­cancer treatments. The present review summarizes the latest advances of these two preclinical models in GC treatment and drug response assessment.

HDS screening with patient-derived primary cells guided individualized therapy for esophageal squamous cell carcinoma-in vivo and vitro.

Objective: To analyze and evaluate the role of the High-throughput Drug Sensitivity (HDS) screening strategy in identifying highly sensitive drugs against esophageal squamous cell carcinoma (ESCC). Methods: A total of 80 patients with progressive ESCC were randomly divided into the observation (40 cases) and the control groups (40 cases). In the observation group, primary ESCC cells were isolated from the tumor tissues with a gastroscope, and drug sensitivity screening was performed on cells derived from the 40 ESCC cases using the HDS method, followed by verification in a patient-derived tumor xenograft (PDX) mouse model. Finally, the differences in the therapeutic efficacy (levels of CEA, CYFRA21-1, SCCA after chemotherapy and the rates of overall survival, local progression, and distant metastasis at 12 months and 18 months time points after chemotherapy) were compared between the observation group (Screened drug-treated) and the control group (Paclitaxel combined with cisplatin regimen-treated). Results: Forty ESCC patients were screened for nine different high-sensitive chemotherapeutics, with the majority showing sensitivity to Bortezomib. Experiments on animal models revealed that the tumor tissue mass of PDX mice treated with the HDS-screened drug was significantly lower than that of the Paclitaxel-treated mice (p < 0.05), and the therapeutic efficacy of the observation group was better than the control group (p < 0.05). Conclusion: HDS screening technology can be beneficial in screening high-efficacy anticancer drugs for advanced-stage ESCC patients, thereby minimizing adverse drug toxicity in critically ill patients. Moreover, this study provides a new avenue for treating advanced ESCC patients with improved outcomes.

Evolutionary predictability of genetic versus nongenetic resistance to anticancer drugs in melanoma.

Therapy resistance arises from heterogeneous drug-tolerant persister cells or minimal residual disease (MRD) through genetic and nongenetic mechanisms. A key question is whether specific molecular features of the MRD ecosystem determine which of these two distinct trajectories will eventually prevail. We show that, in melanoma exposed to mitogen-activated protein kinase therapeutics, emergence of a transient neural crest stem cell (NCSC) population in MRD concurs with the development of nongenetic resistance. This increase relies on a glial cell line-derived neurotrophic factor-dependent signaling cascade, which activates the AKT survival pathway in a focal adhesion kinase (FAK)-dependent manner. Ablation of the NCSC population through FAK inhibition delays relapse in patient-derived tumor xenografts. Strikingly, all tumors that ultimately escape this treatment exhibit resistance-conferring genetic alterations and increased sensitivity to extracellular signal-regulated kinase inhibition. These findings identify an approach that abrogates the nongenetic resistance trajectory in melanoma and demonstrate that the cellular composition of MRD deterministically imposes distinct drug resistance evolutionary paths.

Gemcitabine induces Epithelial-to-Mesenchymal Transition in patient-derived pancreatic ductal adenocarcinoma xenografts.

There is a lack of well-characterized models for pancreatic ductal adenocarcinoma (PDAC). PDAC itself is unique because of its pronounced tumor microenvironment that influences tumor progression, behavior and therapeutic resistance. Here we investigated, in patient-derived tumor xenograft (PDTX) models developed from fine needle biopsies, the cancer cells behavior, Epithelial-to-Mesenchymal Transition (EMT) and drug response. For this, we studied two behaviorally distinct PDTX models. Tumor volume measurement, histology, immuno-histochemical staining, RT-qPCR, RNA sequencing and Western blotting were used to further characterize these models and investigate the effect of two classes of drugs (gemcitabine and acriflavine (HIF-inhibitor)). The models recapitulated the corresponding primary tumors. The growth-rate of the poorly differentiated tumor (PAC010) was faster than that of the moderately differentiated tumor (PAC006) (P<0.05). The PAC010 model showed increased cell proliferation (Ki-67 staining) and markers indicating survival (increased p-AKT, p-ERK and p-NF-kB65 and suppression of cleaved PARP). Gene and protein analysis showed higher expression of mesenchymal markers in PAC010 model (e.g. VIM, SNAI2). Pathway analysis demonstrated activation of processes related to EMT, tumor progression and aggressiveness in PAC010. Gemcitabine treatment resulted in shrinking of the tumor volume and reduced proliferation in both models. Importantly, gemcitabine treatment significantly enhanced the expression of mesenchymal marker supportive of metastatic behavior and of survival pathways, particularly in the non-aggressive PAC006 model. Acriflavine had little effect on tumor growth in both models. In conclusion, we observed in this unique model of PDAC, a clear link between EMT and poor tumor differentiation and found that gemcitabine can increase EMT.

Optimizing the design of population-based patient-derived tumor xenograft studies to better predict clinical response.

The high attrition rate of preclinical agents entering oncology clinical trials has been associated with poor understanding of the heterogeneous patient response, arising from limitations in the preclinical pipeline with cancer models. Patient-derived tumor xenograft (PDX) models have been shown to better recapitulate the patient drug response. However, the platform of evidence generated to support clinical development in a drug discovery project typically employs a limited number of models, which may not accurately predict the response at a population level. Population PDX studies, large-scale screens of PDX models, have been proposed as a strategy to model the patient inter-tumor heterogeneity. Here, we present a freely available interactive tool that explores the design of a population PDX study and how it impacts the sensitivity and false-positive rate experienced. We discuss the reflection process needed to optimize the design for the therapeutic landscape being studied and manage the risk of false-negative and false-positive outcomes that the sponsor is willing to take. The tool has been made freely available to allow the optimal design to be determined for each drug-disease area. This will allow researchers to improve their understanding of treatment efficacy in the presence of genetic variability before taking a drug to clinic. In addition, the tool serves to refine the number of animals to be used for population-based PDX studies, ensuring researchers meet their ethical obligation when performing animal research.

PLK1 as a potential prognostic marker of gastric cancer through MEK-ERK pathway on PDTX models.

BACKGROUND: PLK1 has been identified as having a great effect on cell division and maintaining genomic stability in mitosis, spindle assembly, and DNA damage response by current studies. MATERIALS AND METHODS: We assessed PLK1 expression in cervical cancer tissues and cells. We have also evaluated the effects of PLK1 on gastric cancer cell proliferation, migration, and apoptosis both in vitro and in vivo. RESULTS: Our results show that PLK1 is overexpressed in gastric cancer tissues and cells. Inhibition of PLK1 contributes cell cycle G2-phase arrest and inhibits the proliferation, migration, and apoptosis of gastric cancer (GC) cells, whereas its overexpression promotes proliferation, migration, and apoptosis in these cells. Moreover, PLK1 inhibition reduces expression of pMEK and pERK. More importantly, in vivo by analyzing tumorigenesis in patient-derived tumor xenograft (PDTX) models, the inhibition of PLK1 activity by BI6727 significantly decreased the volume and weight of the tumors compared with control group (P<0.01). CONCLUSION: Our results found that PLK1 has a significant impact on the survival of GC cells; it may become a prognostic judge, a potential therapeutic target, and a preventative biomarker of GC.

Pancreas 3D Organoids: Current and Future Aspects as a Research Platform for Personalized Medicine in Pancreatic Cancer.

Pancreatic ductal adenocarcinoma is one of the most aggressive forms of cancer, and the third leading cause of cancer-related mortality in the United States. Although important advances have been made in the last decade, the mortality rate of pancreatic ductal adenocarcinoma has not changed appreciably. This review summarizes a rapidly emerging model of pancreatic cancer research, focusing on 3-dimensional organoids as a powerful tool for several applications, but above all, representing a step toward personalized medicine.

Characterization of patient-derived tumor xenografts (PDXs) as models for estrogen receptor positive (ER+HER2- and ER+HER2+) breast cancers.

The research was to appraise the utility of the patient-derived tumor xenografts (PDXs) as models of estrogen receptor positive (ER+HER2- and ER+HER2+) breast cancers. We compared protein expression profiles by Reverse Phase Protein Array (RPPA) in tumors that resulted in PDXs compared to those that did not. Our overall PDX intake rate for ER+ breast cancer was 9% (9/97). The intake rate for ER+HER2+ tumors (3/16, 19%) was higher than for ER+HER2- tumors (6/81, 7%). Heat map analyses of RPPA data showed that ER+HER2- tumors were divided into 2 groups by luminal A/B signature [protein expression of ER, AR, Bcl-2, Bim (BCL2L11), GATA3 and INPP4b], and this expression signature was also associated with the rate of PDX intake. Cell survival pathways such as the PI3K/AKT signaling and RAS/ERK pathways were more activated in the specimens that could be established as PDX in both classes. Expression of the ER protein itself may have a bearing on the potential success of an ER+ PDX model. In addition, HER2 and its downstream protein expressions were up-regulated in the ER+HER2+ patient tumors that were successfully established as PDX models. Moreover, the comparison of RPPA data between original and PDX tumors suggested that the selection/adaptation process required to grow the tumors in mice is unavoidable for generation of ER+ PDX models, and we identified differences between patient tumor samples and paired PDX tumors. A better understanding of the biological characteristics of ER+PDX would be the key to using PDX models in assessing treatment strategies in a preclinical setting.

Gastric Cancer in the Era of Precision Medicine.

Gastric cancer (GC) remains the third most common cause of cancer death worldwide, with limited therapeutic strategies available. With the advent of next-generation sequencing and new preclinical model technologies, our understanding of its pathogenesis and molecular alterations continues to be revolutionized. Recently, the genomic landscape of GC has been delineated. Molecular characterization and novel therapeutic targets of each molecular subtype have been identified. At the same time, patient-derived tumor xenografts and organoids now comprise effective tools for genetic evolution studies, biomarker identification, drug screening, and preclinical evaluation of personalized medicine strategies for GC patients. These advances are making it feasible to integrate clinical, genome-based and phenotype-based diagnostic and therapeutic methods and apply them to individual GC patients in the era of precision medicine.

Successful establishment of patient-derived tumor xenografts from gastrointestinal stromal tumor-a single center experience.

Patient-derived tumor xenografts (PDTX) generally represent a kind of more reliable model of human disease, by which a potential drugs' preclinical efficacy could be evaluated. To date, no stable gastrointestinal stromal tumor (GIST) PDTX models have been reported. In this study, we aimed to establish stable GIST PDTX models and to evaluate whether these models accurately reflected the histological feature of the corresponding patient tumors and create a reliable GIST PDTX models for our future experiment. By engrafting fresh patient GIST tissues into immune-compromised mice (BALB/c athymic mice), 4 PDTX models were established. Histological features were assessed by a qualified pathologist based on H&E staining, CD117 and DOG-1. We also conduct whole exome sequencing(WES) for the 4 established GIST PDTX models to test if the model still harbored the same mutation detected in corresponding patient tumors and get a more intensive vision for the genetic profile of the models we have established, which will help a lot for our future experiment. To explore the tumorigenesis mechanism for GIST, we also have a statistical analysis for the genes detected as nonsynchronous-mutated simultaneously in 4 samples. All 4 GIST PDTX models retained the histological features of the corresponding human tumors, with original morphology type and positive stains for CD117 and DOG-1. Between the GIST PDTX models and their parental tumors, a same mutation site was detected, which confirmed the genetic consistency. The stability of molecular profiles observed within the GIST PDTX models provides confidence in the utility and translational significance of these models for in vivo testing of personalized therapies. To date, we conducted the first study to successfully establish a GIST PDTX model whose genetic profiles were revealed by whole exome sequencing. Our experience could be of great use.

Patient-derived tumor xenograft models for melanoma drug discovery.

INTRODUCTION: Cutaneous metastatic melanoma (MM) is an aggressive form of skin cancer, with treatment providing cures to a minority of patients. The multiple risk factors that contribute to MM development suggest that cutaneous melanomas embody a repertoire of altered genetic events requiring studies to better understand its biology in order to develop novel therapies. AREAS COVERED: Patient-derived tumor xenograft (PDTX) mouse models are noted to be superior for novel drug discovery and tumor biology studies due to their ability to maintain tumor heterogeneity and their use as real-time individualized patient models. In this review, the authors highlight the utility of PDTX models in advancing treatment options for patients with MM by creating invaluable preclinical models that exhibit patient-relevant treatment outcomes. EXPERT OPINION: There is a strong necessity to reassess current approaches in which preclinical experiments are designed and executed in order to minimize unwarranted clinical trials. With rigorously performed preclinical studies, PDTX models have the capability to effectively confirm or deny drug effective outcomes. The ability to do this, however, will demand better aids to guide experimental design, the redefining of preclinical efficacy, and the understanding that these models should be viewed as complementary to other drug prediction and efficacy tools.

Current State of Animal (Mouse) Modeling in Melanoma Research.

Despite the considerable progress in understanding the biology of human cancer and technological advancement in drug discovery, treatment failure remains an inevitable outcome for most cancer patients with advanced diseases, including melanoma. Despite FDA-approved BRAF-targeted therapies for advanced stage melanoma showed a great deal of promise, development of rapid resistance limits the success. Hence, the overall success rate of melanoma therapy still remains to be one of the worst compared to other malignancies. Advancement of next-generation sequencing technology allowed better identification of alterations that trigger melanoma development. As development of successful therapies strongly depends on clinically relevant preclinical models, together with the new findings, more advanced melanoma models have been generated. In this article, besides traditional mouse models of melanoma, we will discuss recent ones, such as patient-derived tumor xenografts, topically inducible BRAF mouse model and RCAS/TVA-based model, and their advantages as well as limitations. Although mouse models of melanoma are often criticized as poor predictors of whether an experimental drug would be an effective treatment, development of new and more relevant models could circumvent this problem in the near future.

Establishment of patient-derived tumor xenograft (PDTX) models using samples from CT-guided percutaneous biopsy

This study aimed to investigate the feasibility of the establishment of a human cancer xenograft model using samples from computed tomography (CT)-guided percutaneous biopsy. Fresh tumor tissues obtained from 10 cancer patients by CT-guided percutaneous biopsy were subcutaneously inoculated into NOD-Prkdcem26Il2rgem26Nju (NCG) mice to establish human patient-derived tumor xenograft (PDTX) models. The formation of first and second generation xenografts was observed, and tumor volume was recorded over time. Tumor tissue consistency between the PDTX model and primary tumors in patients was compared using H&E staining and immunohistochemistry. Pharmacodynamic tests of clinically used chemotherapeutic drugs were conducted on second generation xenografts, and their effects on tumor growth and body weight were observed. CT-guided percutaneous biopsy samples were successfully collected from 10 patients with advanced cancers. The PDTX model was established in mice using tumor samples obtained from 4 cancer patients, including one small cell carcinoma sample, two adenocarcinoma samples, and one squamous cell carcinoma sample. The success rate was 40%. The obtained PDTX model maintained a degree of differentiation, and morphological and structural characteristics were similar to primary tumors. The pharmacodynamic test of chemotherapeutic drugs in the PDTX model revealed a therapeutic effect on tumor growth, as expected. CT-guided percutaneous biopsy samples can be effectively used to establish a PDTX model, and test these chemotherapy regimens.