Revisiting ovarian cancer preclinical models: Implications for a better management of the disease

tRNA-derived small RNA 3'U-tRFValCAC promotes tumour migration and early progression in ovarian cancer

INTRODUCTION

Despite recent advances in epithelial ovarian cancer (EOC) management, the highly heterogenous histological/molecular tumour background and patients' treatment response obstructs personalised prognosis and therapeutics. Herein, we have studied the role and clinical utility of the novel subclass of tRNA-derived small RNA fragments emerging via 3'-trailer processing of pre-tRNAs (3'U-tRFs) in EOC.

METHODS

SK-OV-3 and OVCAR-3 cells were used for in vitro study. Following transfection, cell growth and migration were assessed by CCK8 and wound healing assays, respectively. 3'U-tRFs levels were assessed by reverse transcription quantitative PCR (RT-qPCR), following 3'-end RNA polyadenylation. A screening (OVCAD, n = 100) and institutionally independent validation (TU Munich, n = 103) cohorts were employed for survival analysis using disease progression and patients' death as clinical end-points. Bootstrap analysis was performed for internal validation, and decision curve analysis was used to evaluate clinical benefit on disease prognosis.

RESULTS

Following primary clinical assessment, target prediction and gene ontology analyses, the 3'U-tRF^ValCAC (derived from pre-tRNA^ValCAC) was highlighted to regulate cell proliferation and adhesion, and to correlate with inferior patients' outcome. 3'U-tRF^ValCAC transfection of SK-OV-3 and OVCAR-3 cells resulted in significantly increased cell growth and migration, in a dose-dependent manner. Elevated tumour 3'U-tRF^ValCAC levels were associated with significantly higher risk for early progression and worse survival following first-line platinum-based chemotherapy, independently of patients' clinicopathological data, chemotherapy response, and residual tumour. Interestingly, 3'U-tRF^ValCAC-fitted multivariate models improved risk stratification and provided superior clinical net benefit in prediction of treatment outcome compared to disease established markers.

CONCLUSIONS

3'U-tRF^ValCAC promotes tumour cell growth and migration and supports modern risk stratification and prognosis in EOC.

Identification of Novel Hypoxia Subtypes for Prognosis Based on Machine Learning Algorithms

Objective

A reduced level or tension or the deprivation of oxygen is termed hypoxia. It is common for tumours to outgrow their natural source of nutrients, which causes hypoxia in some tumour regions. Hypoxia affects ovarian cancer (OC) in several ways.

Methods

In this study, the expression patterns of prognostic hypoxia-related genes were curated, and consensus clustering analyses were performed to determine hypoxia subtypes in OC included in The Cancer Genome Atlas cohort. Two hypoxia-related subtypes were observed and considered for further investigation. The analyses of differentially expressed genes (DEGs), gene ontology, mutation, and immune cell infraction were performed to explore the underlying molecular mechanisms.

Results

In total, 377 patients with OC were classified into two subgroups based on the subtype of hypoxia. The clinical outcome was considerably poor for patients with hypoxia subtype 2. DEG and protein-protein interaction analyses revealed that the expression levels of CLIP2 and SH3PXD2A were low in OC tissues. Immune cell infarction analysis revealed that the subtypes were associated with the tumour microenvironment (TME).

Conclusion

Our findings established the existence of two distinctive, complex, and varied hypoxia subtypes in OC. Findings from the quantitative analysis of hypoxia subtypes in patients improved our understanding of the characteristics of the TME and may facilitate the development of more efficient treatment regimens.

Disabling partners in crime: Gold nanoparticles disrupt multicellular communications within the tumor microenvironment to inhibit ovarian tumor aggressiveness

The tumor microenvironment (TME) plays a key role in the poor prognosis of many cancers. However, there is a knowledge gap concerning how multicellular communication among the critical players within the TME contributes to such poor outcomes. Using epithelial ovarian cancer (EOC) as a model, we show how crosstalk among cancer cells (CC), cancer associated fibroblasts (CAF), and endothelial cells (EC) promotes EOC growth. We demonstrate here that co-culturing CC with CAF and EC promotes CC proliferation, migration, and invasion in vitro and that co-implantation of the three cell types facilitates tumor growth in vivo. We further demonstrate that disruption of this multicellular crosstalk using a gold nanoparticle (GNP) inhibits these pro-tumorigenic phenotypes in vitro as well as tumor growth in vivo. Mechanistically, GNP treatment reduces expression of several tumor-promoting cytokines and growth factors, resulting in inhibition of MAPK and PI3K-AKT activation and epithelial-mesenchymal transition - three key oncogenic signaling pathways responsible for the aggressiveness of EOC. The current work highlights the importance of multicellular crosstalk within the TME and its role for the aggressive nature of EOC, and demonstrates the disruption of these multicellular communications by self-therapeutic GNP, thus providing new avenues to interrogate the crosstalk and identify key perpetrators responsible for poor prognosis of this intractable malignancy.

Personalized models of heterogeneous 3D epithelial tumor microenvironments: Ovarian cancer as a model

Intractable human diseases such as cancers, are context dependent, unique to both the individual patient and to the specific tumor microenvironment. However, conventional cancer treatments are often nonspecific, targeting global similarities rather than unique drivers. This limits treatment efficacy across heterogeneous patient populations and even at different tumor locations within the same patient. Ultimately, this poor efficacy can lead to adverse clinical outcomes and the development of treatment-resistant relapse. To prevent this and improve outcomes, it is necessary to be selective when choosing a patient's optimal adjuvant treatment. In this review, we posit the use of personalized, tumor-specific models (TSM) as tools to achieve this remarkable feat. First, using ovarian cancer as a model disease, we outline the heterogeneity and complexity of both the cellular and extracellular components in the tumor microenvironment. Then we examine the advantages and disadvantages of contemporary cancer models and the rationale for personalized TSM. We discuss how to generate precision 3D models through careful and detailed analysis of patient biopsies. Finally, we provide clinically relevant applications of these versatile personalized cancer models to highlight their potential impact. These models are ideal for a myriad of fundamental cancer biology and translational studies. Importantly, these approaches can be extended to other carcinomas, facilitating the discovery of new therapeutics that more effectively target the unique aspects of each individual patient's TME. STATEMENT OF SIGNIFICANCE: In this article, we have presented the case for the application of biomaterials in developing personalized models of complex diseases such as cancers. TSM could bring about breakthroughs in the promise of precision medicine. The critical components of the diverse tumor microenvironments, that lead to treatment failures, include cellular- and extracellular matrix- heterogeneity, and biophysical signals to the cells. Therefore, we have described these dynamic components of the tumor microenvironments, and have highlighted how contemporary biomaterials can be utilized to create personalized in vitro models of cancers. We have also described the application of the TSM to predict the dynamic patterns of disease progression, and predict effective therapies that can produce durable responses, limit relapses, and treat any minimal residual disease.

Impact of ERCC1, XPF and DNA Polymerase β Expression on Platinum Response in Patient-Derived Ovarian Cancer Xenografts

Platinum resistance is an unmet medical need in ovarian carcinoma. Molecular biomarkers to predict the response to platinum-based therapy could allow patient stratification and alternative therapeutic strategies early in clinical management. Sensitivity and resistance to platinum therapy are partially determined by the tumor’s intrinsic DNA repair activities, including nucleotide excision repair (NER) and base excision repair (BER). We investigated the role of the NER proteins—ERCC1, XPF, ERCC1/XPF complex—and of the BER protein DNA polymerase β, as possible biomarkers of cisplatin (DDP) response in a platform of recently established patient-derived ovarian carcinoma xenografts (OC-PDXs). ERCC1 and DNA polymerase β protein expressions were measured by immunohistochemistry, the ERCC1/XPF foci number was detected by proximity ligation assay (PLA) and their mRNA levels by real-time PCR. We then correlated the proteins, gene expression and ERCC1/XPF complexes with OC-PDXs’ response to platinum. To the best of our knowledge, this is the first investigation of the role of the ERCC1/XPF complex, detected by PLA, in relation to the response to DDP in ovarian carcinoma. None of the proteins in the BER and NER pathways studied predicted platinum activity in this panel of OC-PDXs, nor did the ERCC1/XPF foci number. These results were partially explained by the experimental evidence that the ERCC1/XPF complex increases after DDP treatment and this possibly better associates with the cancer cells’ abilities to activate the NER pathway to repair platinum-induced damage than its basal level. Our findings highlight the need for DNA functional assays to predict the response to platinum-based therapy.

Mesenchymal Stem Cells As Guideposts for Nanoparticle-Mediated Targeted Drug Delivery in Ovarian Cancer

Nanocarriers have been extensively utilized for the systemic targeting of various solid tumors and their metastases. However, current drug delivery systems, in general, suffer from a lack of selectivity for tumor cells. Here, we develop a novel two-step targeting strategy that relies on the selective accumulation of targetable synthetic receptors (i.e., azide moieties) in tumor tissues, followed by delivery of drug-loaded nanoparticles having a high binding affinity for these receptors. Mesenchymal stem cells (MSCs) were used as vehicles for the tumor-specific accumulation of azide moieties, while dibenzyl cyclooctyne (DBCO) was used as the targeting ligand. Biodistribution and antitumor efficacy studies were performed in both orthotopic metastatic and patient-derived xenograft (PDX) tumor models of ovarian cancer. Our studies show that nanoparticles are retained in tumors at a significantly higher concentration in mice that received azide-labeled MSCs (MSC-Az). Furthermore, we observed significantly reduced tumor growth (p < 0.05) and improved survival in mice receiving MSC-Az along with paclitaxel-loaded DBCO-functionalized nanoparticles compared to controls. These studies demonstrate the feasibility of a two-step targeting strategy for efficient delivery of concentrated chemotherapy for treating solid tumors.

Engineered 3D Model of Cancer Stem Cell Enrichment and Chemoresistance

Intraperitoneal dissemination of ovarian cancers is preceded by the development of chemoresistant tumors with malignant ascites. Despite the high levels of chemoresistance and relapse observed in ovarian cancers, there are no in vitro models to understand the development of chemoresistance in situ. Method: We describe a highly integrated approach to establish an in vitro model of chemoresistance and stemness in ovarian cancer, using the 3D hanging drop spheroid platform. The model was established by serially passaging non-adherent spheroids. At each passage, the effectiveness of the model was evaluated via measures of proliferation, response to treatment with cisplatin and a novel ALDH1A inhibitor. Concomitantly, the expression and tumor initiating capacity of cancer stem-like cells (CSCs) was analyzed. RNA-seq was used to establish gene signatures associated with the evolution of tumorigenicity, and chemoresistance. Lastly, a mathematical model was developed to predict the emergence of CSCs during serial passaging of ovarian cancer spheroids. Results: Our serial passage model demonstrated increased cellular proliferation, enriched CSCs, and emergence of a platinum resistant phenotype. In vivo tumor xenograft assays indicated that later passage spheroids were significantly more tumorigenic with higher CSCs, compared to early passage spheroids. RNA-seq revealed several gene signatures supporting the emergence of CSCs, chemoresistance, and malignant phenotypes, with links to poor clinical prognosis. Our mathematical model predicted the emergence of CSC populations within serially passaged spheroids, concurring with experimentally observed data. Conclusion: Our integrated approach illustrates the utility of the serial passage spheroid model for examining the emergence and development of chemoresistance in ovarian cancer in a controllable and reproducible format.

Screening a library of approved drugs reveals that prednisolone synergizes with pitavastatin to induce ovarian cancer cell death

The survival rate for patients with ovarian cancer has changed little in the past three decades since the introduction of platinum-based chemotherapy and new drugs are needed. Statins are drugs used for the treatment and prevention of cardiovascular diseases. Recent work from our laboratory has shown that pitavastatin has potential as a treatment for ovarian cancer if dietary geranylgeraniol is controlled. However, relatively high doses of statins are required to induce apoptosis in cancer cells, increasing the risk of myopathy, the most common adverse effect associated with statins. This makes it desirable to identify drugs which reduce the dose of pitavastatin necessary to treat cancer. A drug-repositioning strategy was employed to identify suitable candidates. Screening a custom library of 100 off-patent drugs for synergistic activity with pitavastatin identified prednisolone as the most prominent hit. Prednisolone potentiated the activity of pitavastatin in several assays measuring the growth, survival or apoptosis in several ovarian cancer cells lines. Prednisolone, alone or in some cases in combination with pitavastatin, reduced the expression of genes encoding enzymes in the mevalonate pathway, providing a mechanistic explanation for the synergy.

Prognostic values of GMPS, PR, CD40, and p21 in ovarian cancer

Early detection and prediction of prognosis and treatment responses are all the keys in improving survival of ovarian cancer patients. This study profiled an ovarian cancer progression model to identify prognostic biomarkers for ovarian cancer patients. Mouse ovarian surface epithelial cells (MOSECs) can undergo spontaneous malignant transformation in vitro cell culture. These were used as a model of ovarian cancer progression for alterations in gene expression and signaling detected using the Illumina HiSeq2000 Next-Generation Sequencing platform and bioinformatical analyses. The differential expression of four selected genes was identified using the gene expression profiling interaction analysis (http://gepia.cancer-pku.cn/) and then associated with survival in ovarian cancer patients using the Cancer Genome Atlas dataset and the online Kaplan–Meier Plotter (http://www.kmplot.com) data. The data showed 263 aberrantly expressed genes, including 182 up-regulated and 81 down-regulated genes between the early and late stages of tumor progression in MOSECs. The bioinformatic data revealed four genes (i.e., guanosine 5′-monophosphate synthase (GMPS), progesterone receptor (PR), CD40, and p21 (cyclin-dependent kinase inhibitor 1A)) to play an important role in ovarian cancer progression. Furthermore, the Cancer Genome Atlas dataset validated the differential expression of these four genes, which were associated with prognosis in ovarian cancer patients. In conclusion, this study profiled differentially expressed genes using the ovarian cancer progression model and identified four (i.e., GMPS, PR, CD40, and p21) as prognostic markers for ovarian cancer patients. Future studies of prospective patients could further verify the clinical usefulness of this four-gene signature.

Recent Insights into Mucinous Ovarian Carcinoma

Ovarian mucinous tumors represent a group of rare neoplasms with a still undefined cell of origin but with an apparent progression from benign to borderline to carcinoma. Even though these tumors are different from the other histological subtypes of epithelial ovarian neoplasms, they are still treated with a similar chemotherapeutic approach. Here, we review its pathogenesis, molecular alterations, (differential) diagnosis, clinical presentation and current treatment, and how recent molecular and biological information on this tumor might lead to better and more specific clinical management of patients with mucinous ovarian carcinoma.

Platinum sensitivity and DNA repair in a recently established panel of patient-derived ovarian carcinoma xenografts

A xenobank of patient-derived (PDX) ovarian tumor samples has been established consisting of tumors with different sensitivity to cisplatin (DDP), from very responsive to resistant. As the DNA repair pathway is an important driver in tumor response to DDP, we analyzed the mRNA expression of 20 genes involved in the nucleotide excision repair, fanconi anemia, homologous recombination, base excision repair, mismatch repair and translesion repair pathways and the methylation patterns of some of these genes. We also investigated the correlation with the response to platinum-based therapy. The mRNA levels of the selected genes were evaluated by Real Time-PCR (RT-PCR) with ad hoc validated primers and gene promoter methylation by pyrosequencing. All the DNA repair genes were variably expressed in all 42 PDX samples analyzed, with no particular histotype-specific pattern of expression. In high-grade serous/endometrioid PDXs, the CDK12 mRNA expression levels positively correlated with the expression of TP53BP1, PALB2, XPF and POLB. High-grade serous/endometrioid PDXs with TP53 mutations had significantly higher levels of POLQ, FANCD2, RAD51 and POLB than high-grade TP53 wild type PDXs. The mRNA levels of CDK12, PALB2 and XPF inversely associated with the in vivo DDP antitumor activity; higher CDK12 mRNA levels were associated with a higher recurrence rate in ovarian patients with low residual tumor. These data support the important role of CDK12 in the response to a platinum based therapy in ovarian patients.

IGF system targeted therapy: Therapeutic opportunities for ovarian cancer

The insulin-like growth factor (IGF) system comprises multiple growth factor receptors, including insulin-like growth factor 1 receptor (IGF-1R), insulin receptor (IR) -A and -B. These receptors are activated upon binding to their respective growth factor ligands, IGF-I, IGF-II and insulin, and play an important role in development, maintenance, progression, survival and chemotherapeutic response of ovarian cancer. In many pre-clinical studies anti-IGF-1R/IR targeted strategies proved effective in reducing growth of ovarian cancer models. In addition, anti-IGF-1R targeted strategies potentiated the efficacy of platinum based chemotherapy. Despite the vast amount of encouraging and promising pre-clinical data, anti-IGF-1R/IR targeted strategies lacked efficacy in the clinic. The question is whether targeting the IGF-1R/IR signaling pathway still holds therapeutic potential. In this review we address the complexity of the IGF-1R/IR signaling pathway, including receptor heterodimerization within and outside the IGF system and downstream signaling. Further, we discuss the implications of this complexity on current targeted strategies and indicate therapeutic opportunities for successful targeting of the IGF-1R/IR signaling pathway in ovarian cancer. Multiple-targeted approaches circumventing bidirectional receptor tyrosine kinase (RTK) compensation and prevention of system rewiring are expected to have more therapeutic potential.

Delineation of retroperitoneal metastatic lymph nodes in ovarian cancer with near-infrared fluorescence imaging

Lymph node metastasis occurs in early-stage and late-stage ovarian cancers. Systematic lymphadenectomy is frequently conducted in an attempt to prevent disease progression. However, this method is associated with multiple complications. Therefore, it is necessary to develop a less invasive and more sensitive method for detecting lymphatic metastasis in ovarian cancer. The aim of the present study was to develop an appropriate fluorescent label for the analysis of lymphatic metastasis in vivo. To this end, epithelial ovarian cancer cells with high potential for lymph node metastasis were labeled using mCherry fluorescence. The cells were then imaged in vitro to determine the expression of mCherry, and in a mouse xenograft model in vivo. The data demonstrated the successful identification of metastatic retroperitoneal lymph nodes by co-localization with lymph nodes labeled by near-infrared fluorescence nanoparticles in vivo. These data provided important insights into the further development of methods for intra-operative identification of lymphatic metastasis and the mechanisms underlying lymphatic metastasis.

Orthotopic Model of Ovarian Cancer

Epithelial ovarian cancer (EOC) is the fifth commonest cancer-related cause of female death in the developed world. In spite of current surgical and chemotherapeutic options the vast majority of patients have widely metastatic disease and the survival rate has not much changed over the last years. The anti-angiogenic drugs are driving the field of agents targeting the tumor microenvironment in ovarian cancer. Preclinical models that accurately reproduce the molecular and biological features of ovarian cancer patients are a valuable means of producing reliable data on personalized medicine and predicting the therapeutic response in clinical trials.In this methodological chapter we describe the orthotopic model of ovarian cancer implanted under the ovarian bursa of mice. In spite of anatomical differences between the rodent and human bursa-fallopian tube, the appropriate primary tumor microenvironment at the site of the implant allows investigation of tumor-stroma interactions (e.g., angiogenesis), and is well suited for studying the tumor dissemination and metastasis typical of this disease.This model-although fairly labor intensive-may be useful for assessing novel, more selective therapeutic interventions and for biomarker discovery, reflecting the behavior of this disease.

The promise and challenge of ovarian cancer models

The complexity and heterogeneity of ovarian cancer cases are difficult to reproduce in in vitro studies, which cannot adequately elucidate the molecular events involved in tumor initiation and disease metastasis. It has now become clear that, although the multiple histological subtypes of ovarian cancer are being treated with similar surgical and therapeutic approaches, they are in fact characterized by distinct phenotypes, cell of origin, and underlying key genetic and genomic alterations. Consequently, the development of more personalized treatment methodologies, which are aimed at improving patient care and prognosis, will greatly benefit from a better understanding of the key differences between various subtypes. To accomplish this, animal models of all histotypes need to be generated in order to provide accurate in vivo platforms for research and the testing of targeted treatments and immune therapies. Both genetically engineered mouse models (GEMMs) and xenograft models have the ability to further our understanding of key mechanisms facilitating tumorigenesis, and at the same time offer insight into enhanced imaging and treatment modalities. While genetic models may be better suited to examine oncogenic functions and interactions during tumorigenesis, patient-derived xenografts (PDXs) are likely a superior model to assess drug efficacy, especially in concurrent clinical trials, due to their similarity to the tumors from which they are derived. Genetic and avatar models possess great clinical utility and have both benefits and limitations. Additionally, the laying hen model, which spontaneously develops ovarian tumors, has inherent advantages for the study of epithelial ovarian cancer (EOC) and recent work champions this model especially when assessing chemoprevention strategies. While high-grade ovarian serous tumors are the most prevalent form of EOC, rarer ovarian cancer variants, such as small cell ovarian carcinoma of the hypercalcemic type and transitional cell carcinoma, or non-epithelial tumors, including germ cell tumors, will also benefit from the generation of improved models to advance our understanding of tumorigenic mechanisms and the development of selective therapeutic options.

Advances in tumor screening, imaging, and avatar technologies for high-grade serous ovarian cancer

The majority of high-grade serous ovarian carcinoma cases are detected in advanced stages when treatment options are limited. Surgery is less effective at eradicating the disease when it is widespread, resulting in high rates of disease relapse and chemoresistance. Current screening techniques are ineffective for early tumor detection and consequently, BRCA mutations carriers, with an increased risk for developing high-grade serous ovarian cancer, elect to undergo risk-reducing surgery. While prophylactic surgery is associated with a significant reduction in the risk of cancer development, it also results in surgical menopause and significant adverse side effects. The development of efficient early-stage screening protocols and imaging technologies is critical to improving the outcome and quality of life for current patients and women at increased risk. In addition, more accurate animal models are necessary in order to provide relevant in vivo testing systems and advance our understanding of the disease origin and progression. Moreover, both genetically engineered and tumor xenograft animal models enable the preclinical testing of novel imaging techniques and molecularly targeted therapies as they become available. Recent advances in xenograft technologies have made possible the creation of avatar mice, personalized tumorgrafts, which can be used as therapy testing surrogates for individual patients prior to or during treatment. High-grade serous ovarian cancer may be an ideal candidate for use with avatar models based on key characteristics of the tumorgraft platform. This review explores multiple strategies, including novel imaging and screening technologies in both patients and animal models, aimed at detecting cancer in the early-stages and improving the disease prognosis.

Murine model for non-invasive imaging to detect and monitor ovarian cancer recurrence

Epithelial ovarian cancer is the most lethal gynecologic malignancy in the United States. Although patients initially respond to the current standard of care consisting of surgical debulking and combination chemotherapy consisting of platinum and taxane compounds, almost 90% of patients recur within a few years. In these patients the development of chemoresistant disease limits the efficacy of currently available chemotherapy agents and therefore contributes to the high mortality. To discover novel therapy options that can target recurrent disease, appropriate animal models that closely mimic the clinical profile of patients with recurrent ovarian cancer are required. The challenge in monitoring intra-peritoneal (i.p.) disease limits the use of i.p. models and thus most xenografts are established subcutaneously. We have developed a sensitive optical imaging platform that allows the detection and anatomical location of i.p. tumor mass. The platform includes the use of optical reporters that extend from the visible light range to near infrared, which in combination with 2-dimensional X-ray co-registration can provide anatomical location of molecular signals. Detection is significantly improved by the use of a rotation system that drives the animal to multiple angular positions for 360 degree imaging, allowing the identification of tumors that are not visible in single orientation. This platform provides a unique model to non-invasively monitor tumor growth and evaluate the efficacy of new therapies for the prevention or treatment of recurrent ovarian cancer.

Patient-derived ovarian tumor xenografts recapitulate human clinicopathology and genetic alterations

Epithelial ovarian cancer (EOC) is the most lethal gynecologic malignancy. On the basis of its histopathology and molecular-genomic changes, ovarian cancer has been divided into subtypes, each with distinct biology and outcome. The aim of this study was to develop a panel of patient-derived EOC xenografts that recapitulate the molecular and biologic heterogeneity of human ovarian cancer. Thirty-four EOC xenografts were successfully established, either subcutaneously or intraperitoneally, in nude mice. The xenografts were histologically similar to the corresponding patient tumor and comprised all the major ovarian cancer subtypes. After orthotopic transplantation in the bursa of the mouse ovary, they disseminate into the organs of the peritoneal cavity and produce ascites, typical of ovarian cancer. Gene expression analysis and mutation status indicated a high degree of similarity with the original patient and discriminate different subsets of xenografts. They were very responsive, responsive, and resistant to cisplatin, resembling the clinical situation in ovarian cancer. This panel of patient-derived EOC xenografts that recapitulate the recently type I and type II classification serves to study the biology of ovarian cancer, identify tumor-specific molecular markers, and develop novel treatment modalities.

A new spontaneously transformed syngeneic model of high-grade serous ovarian cancer with a tumor-initiating cell population

Improving screening and treatment options for patients with epithelial ovarian cancer has been a major challenge in cancer research. Development of novel diagnostic and therapeutic approaches, particularly for the most common subtype, high-grade serous ovarian cancer (HGSC), has been hampered by controversies over the origin of the disease and a lack of spontaneous HGSC models to resolve this controversy. Over long-term culture in our laboratory, an ovarian surface epithelial (OSE) cell line spontaneously transformed OSE (STOSE). The objective of this study was to determine if the STOSE cell line is a good model of HGSC. STOSE cells grow faster than early passage parental M0505 cells with a doubling time of 13 and 48 h, respectively. STOSE cells form colonies in soft agar, an activity for which M0505 cells have negligible capacity. Microarray analysis identified 1755 down-regulated genes and 1203 up-regulated genes in STOSE compared to M0505 cells, many associated with aberrant Wnt/β-catenin and Nf-κB signaling. Upregulation of Ccnd1 and loss of Cdkn2a in STOSE tumors is consistent with changes identified in human ovarian cancers by The Cancer Genome Atlas. Intraperitoneal injection of STOSE cells into severe combined immunodeficient and syngeneic FVB/N mice produced cytokeratin+, WT1+, inhibin-, and PAX8+ tumors, a histotype resembling human HGSC. Based on evidence that a SCA1+ stem cell-like population exists in M0505 cells, we examined a subpopulation of SCA1+ cells that is present in STOSE cells. Compared to SCA1- cells, SCA1+ STOSE cells have increased colony-forming capacity and form palpable tumors 8 days faster after intrabursal injection into FVB/N mice. This study has identified the STOSE cells as the first spontaneous murine model of HGSC and provides evidence for the OSE as a possible origin of HGSC. Furthermore, this model provides a novel opportunity to study how normal stem-like OSE cells may transform into tumor-initiating cells.

First in-mouse development and application of a surgically relevant xenograft model of ovarian carcinoma

Purpose

Preclinical models of epithelial ovarian cancer have not been exploited to evaluate the clinical standard combination therapy of surgical debulking with follow-up chemotherapy. As surgery is critical to patient survival, here we establish a combined surgical/chemotherapy xenograft model of epithelial ovarian cancer and demonstrate its translational relevance.

Experimental Design

SKOV-3^luc+ ovary cancer cells were injected topically into the ovaries of immunodeficient mice. Disease development and effect of clinical standard treatment including hysterectomy, bilateral salpingoophorectomy and removal of metastasis with follow up chemotherapy (carboplatin 12 mg/kg + paclitaxel 15 mg/kg) was evaluated by clinical parameters. Tumor burden was quantified by bioluminescence imaging (BLI).

Results

The xenograft ovarian tumors developed were poorly differentiated and multicystic and the disease disseminated into the peritoneal cavity. When compared to the controls with a mean survival time of 4.9 weeks, mice treated with surgery and chemotherapy, surgery or chemotherapy demonstrated significantly improved mean survival of 16.1 weeks (p = 0.0008), 12.7 weeks (p = 0.0008), or 10.4 weeks (p = 0.008), respectively.

Conclusion

Combined surgical intervention and adjuvant chemotherapy was demonstrated for the first time in an orthotopic xenograft model of ovarian cancer. Similar to observation in human studies the combined approach resulted in the longest medial survival time, advocating application of this strategy in future preclinical therapeutic development for this disease.

Recent technological advances in using mouse models to study ovarian cancer

Serous epithelial ovarian cancer (SEOC) is the most lethal gynecological cancer in the United States with disease recurrence being the major cause of morbidity and mortality. Despite recent advances in our understanding of the molecular mechanisms responsible for the development of SEOC, the survival rate for women with this disease has remained relatively unchanged in the last two decades. Preclinical mouse models of ovarian cancer, including xenograft, syngeneic, and genetically engineered mice, have been developed to provide a mechanism for studying the development and progression of SEOC. Such models strive to increase our understanding of the etiology and dissemination of ovarian cancer in order to overcome barriers to early detection and resistance to standard chemotherapy. Although there is not a single model that is most suitable for studying ovarian cancer, improvements have led to current models that more closely mimic human disease in their genotype and phenotype. Other advances in the field, such as live animal imaging techniques, allow effective monitoring of the microenvironment and therapeutic efficacy. New and improved preclinical mouse models, combined with technological advances to study such models, will undoubtedly render success of future human clinical trials for patients with SEOC.

Methodologies for the establishment of an orthotopic transplantation model of ovarian cancer in mice

This study used different methods to establish an animal model of orthotopic transplantation for ovarian cancer to provide an accurate simulation of the mechanism by which tumor occurs and develops in the human body. We implanted 4T1 breast cancer cells stably-transfected with luciferase into BALB/c mice by using three types of orthotopic transplantation methodologies: (1) cultured cells were directly injected into the mouse ovary; (2) cell suspension was initially implanted under the skin of the mouse neck; after tumor mass formed, the tumor was removed and ground into cell suspension, which was then injected into the mouse ovary; and (3) a subcutaneous tumor mass was first generated, removed, and cut into small pieces, which were directly implanted into the mouse ovary. After these models were established, in vivo luminescence imaging was performed. Results and data were compared among groups. Orthotopic transplantation model established with subcutaneous tumor piece implantation showed a better simulation of tumor development and invasion in mice. This model also displayed negligible response to artificial factors. This study successfully established an orthotopic transplantation model of ovarian cancer with high rates of tumor formation and metastasis by using subcutaneous tumor pieces. This study also provided a methodological basis for future establishment of an animal model of ovarian cancer in humans.