As discussed above, ascites is a source of tumor material from which valuable information can be extracted not only to understand the pathophysiology of ovarian cancer progression but also for the development of markers which will predict prognosis and monitor the progression of the disease. The frequent presence of ascites at first presentation, and subsequent relapses, provides an accessible pool of tumor material that can be studied to determine the molecular characteristics of cells as the disease progresses. With the establishment of methods which can separate the different soluble and cellular components of the ascites (60), it may now be possible to identify and differentiate the true molecular perturbations that exist between the chemonaive, chemoresistant, and recurrent status of the disease. Isolated cellular components of the ascites can be preserved as paraffin embedded blocks for immunohistochemical analysis (121, 122), or can be frozen for molecular analysis at the RNA and protein levels (60, 122). Moreover, ascites provides a substantial amount of biological material which can be obtained to design studies which require relatively larger amounts of tumor material, which previously were only limited to genome-based studies due to the scarce availability of primary and metastatic tumors leftover after pathological diagnosis. These studies include methods to elucidate the protein profile of ascites-derived tumor and associated cells by proteomic methods such as matrix-assisted laser desorption and ionization (MALDI), surface enhanced laser desorption and ionization (SELDI), and liquid chromatography followed by mass spectroscopy (MS) (2, 123), all of which require larger amounts of samples than that used by genomic methods. In addition, highthroughput automated array-based proteomics techniques such as reverse phase protein arrays (RPAs) can be used to understand the differential expression of proteins in the isolated ascites cellular components from chemonaive and chemoresistant patients. A recent study which used the RPA analysis on ascites samples and pleural effusions obtained from ovarian cancer patients showed significantly higher expression of AKT, cAMP-responsive element binding protein (CREB), and Jun-N-terminal kinase (JNK) in malignant ascites compared to benign effusions (124). Given that deregulation of PI3 kinase and the downstream AKT pathway has been demonstrated in ovarian cancer (125, 126), and high levels of p38 and an increase in the ratio of phosphorylated EGFR and phosphorylated JNK were associated with bad prognosis in ovarian cancer patients (124), it seems that the proteomic profile of the ascites environment may imitate the protein expression profile of the original tumors (2). These observations suggest the enormous potential of using ascites samples for diagnostic, prognostic, and therapeutic endpoints.

Accessibility to ascites also provides a means of comparing the secretory components of the chemonaive and chemoresistant patients. A recent study has determined the cytokine expression profile of the ascites of ovarian cancer patients. Out of 120 cytokines analyzed OPG, IL-10, and leptin was found to be associated with worst prognosis in ovarian cancer patients (24). The concept that the damage of tumor cells in response to chemotherapy treatment can activate autocrine and paracrine secretory responses of residual tumor cells (69, 127, 128), suggest that the soluble component of the ascites microenvironment of chemonaive and chemoresistant patients may be significantly different. In addition, the tumor growth promoting effect of exosomes released by ovarian tumors has been reported (129). Malignant ascites-derived

exosomes of ovarian carcinoma patients have been shown to contain CD24 and EpCAM (130). The exosome-associated proteolytic activity in the tumor vicinity has been suggested to augment tumor invasion into the stroma (130).  In addition, ascites have been shown to contain pro-survival factors which compromised the therapeutic effects of TRAIL and were shown to be associated with shorter disease-free intervals in ovarian cancer patients (131). These data suggest that the signals derived from the soluble ascites microenvironment plays a crucial role in regulating ovarian tumor cells and targeting the survival promoting activity of the soluble component of ascites may be mandatory for the development of efficient therapies for ovarian cancer patients.

## **FUTURE DIRECTIONS**

From a clinical perspective, our understanding of ascites and its associated cellular and soluble components are of utmost importance to understand the advanced-stage disease. The central component of such investigations would be ascites obtained from patients pre- and post-chemotherapy and understanding both the soluble and cellular components individually and/or in association with each other. These studies can be performed using microfluidic systems to investigate the impact of ascites on resident and non-resident cell systems either individually or in combination (20). Microfluidic platforms have been used to investigate the morphological parameters and migratory potentials of immune cells in response to external stimulus (132). Other studies have used cell-on-chip based platforms to investigate the interaction of tumor cells with endothelial cells (133). Recently, a simple cell-on-chip platform was developed to investigate the crosstalk between immune cells and cancer (89). Using this approach, which consisted of three wide parallel chambers interconnected via an array of short and narrow capillary migration channels, it was possible to visualize under the microscope the interaction between the immune and cancer cells (89). Hence, customized microfluidic platforms may be helpful to study and mimic the events of ascites-derived

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microenvironment. This can also provide helpful clinical information as understanding the crosstalk between cancer cells with associated surrounding cells in the native ascites microenvironment will result in the improvement of therapies for ovarian cancer.

## **CONCLUSION**

The accessibility of ascites undeniably provides a rich source of tumor samples to monitor the course of chemotherapy treatment in patients. In addition, it also provides an opportunity for the identification of prognostic and treatment-monitor markers, as well as options for molecular profiling of both the cellular and soluble components. The cellular and molecular profile of individual ascites is a subject of inter-patient variations which will differ not only with the treatment protocol but also how each patient responds to a particular therapy. Hence, to provide a molecular characterization which would fit into a defined pattern to design appropriate targeted therapies would be challenging. Hence, long-term, longitudinal studies within the same patient cohorts, starting with chemonaive status and periodic evaluations of molecular and cellular characterization of the ascites components as the disease progresses would be useful to develop an individualized predictive profile which will be crucial for designing targeted therapies. The interrogation of soluble and cellular variations in ascites during the treatment regimen in patients may guide clinical decision making for patient management (134). This may form a basis for informed and effective personalized treatment approaches. Hence, with the advances in our understanding of the pathophysiology of ascites and the development of new methods which can delineate the cross talk between the different cellular components it is anticipated that more effective and targeted strategies for the management of ascites and ovarian cancer patients will be available in near future.

## **ACKNOWLEDGMENTS**

This work was supported by the Victorian Government's Operational Infrastructure Support Program (Australia). The authors wish to thank Women's Cancer Foundation for supporting this work.

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 25 July 2013; accepted: 11 September 2013; published online: 25 September 2013.*

*Citation: Ahmed N and Stenvers KL (2013) Getting to know ovarian cancer ascites: opportunities for targeted therapy-based translational research. Front. Oncol. 3:256. doi: 10.3389/fonc.2013.00256*

*This article was submitted to Women's Cancer, a section of the journal Frontiers in Oncology.*

*Copyright © 2013 Ahmed and Stenvers. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

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

#### **CurtisW. McCloskey 1,2, Reuben L. Goldberg1,2, Lauren E. Carter 1,2, Lisa F. Gamwell 1,2, Ensaf M. Al-Hujaily 1,2 , Olga Collins 1,2, Elizabeth A. Macdonald1,2, Kenneth Garson1,2, Manijeh Daneshmand2,3, Euridice Carmona4 and Barbara C. Vanderhyden1,2,5\***

1 Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON, Canada

2 Centre for Cancer Therapeutics, Ottawa Hospital Research Institute, Ottawa, ON, Canada

3 Department of Pathology and Laboratory Medicine, University of Ottawa, Ottawa, ON, Canada

4 Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Institut du Cancer de Montréal, Montreal, QC, Canada

5 Department of Obstetrics and Gynecology, University of Ottawa, Ottawa, ON, Canada

#### **Edited by:**

Viive Maarika Howell, University of Sydney, Australia

#### **Reviewed by:**

Viive Maarika Howell, University of Sydney, Australia Jim Petrik, University of Guelph, Canada

#### **\*Correspondence:**

Barbara C. Vanderhyden, Centre for Cancer Therapeutics, Ottawa Hospital Research Institute, 501 Smyth Road, Box 926, Ottawa, ON K1H 8L6, Canada

e-mail<EMAIL_ADDRESS>
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.

 Epithelial ovarian cancer (EOC) is the most common type, which is further divided into endometrioid, clear cell, mucinous, low-grade serous, and high-grade serous (HGSC). HGSC is the most common and aggressive subtype of EOC, accounting for the majority of new cases (2). With a 5-year survival rate of only 40%, a greater understanding of HGSC is essential to improve patient outcome (1). The high mortality rate is due, at least in part, to a lack of screening methods to detect the disease before it metastasizes within the peritoneal cavity (3). The main reason for this inability to detect and diagnose early stage ovarian cancer is a lack of understanding

of disease initiation, made even more challenging due to the current debate over the origin of HGSC. HGSC was long thought to arise from the ovarian surface epithelium (OSE) or inclusion cysts derived from them (2, 4, 5), but recent evidence has identified the distal fimbrial epithelium of the fallopian tube as the source for at least a subset of HGSC (2, 6–8).

To establish experimental models for the study of the initiation of EOC, much effort has been dedicated to the genetic modification of cells from an OSE or fimbrial origin, either in tissue culture or *in vivo*. Attempts to model HGSC have been particularly challenging and have yielded inconsistent results (5, 9, 10). Transgenic approaches have generally involved targeting specific genes known to be associated with human HGSC. This targeted approach to tumorigenesis may not be fully reflective of human disease for a number of reasons. First, it is unclear, in human disease, whether commonly mutated genes are normally involved in disease initiation and/or progression. In addition, the expression of the designed genetic changes using developmentally regulated promoters may introduce founder effects that are not reflective of human disease (9). Furthermore, it has been shown that murine cells require fewer genetic alterations than human cells to undergo transformation, again making it difficult to draw conclusions on the origin of cancer in humans from transgenic murine models (11, 12) For this reason, spontaneous models of EOC would be helpful to better understand the origins of this disease, but these models are rare and limited to the spontaneous development of ovarian cancer in hens (13, 14). New spontaneous models of HGSC are clearly needed to provide opportunities to determine the molecular basis of ovarian and fallopian tube epithelial transformation.

There is growing evidence to support the contribution of cancer stem cells (CSC) to the initiation and recurrence of cancer. The CSC theory posits that tumors arise from cells with stemlike characteristics and these cells underlie tumor heterogeneity and recurrence (15–17). Stem cells are slowly dividing cells with drug efflux mechanisms that allow them to escape the effects of chemotherapeutics that commonly target rapidly dividing cells. Another characteristic of a stem cell is the ability to generate multi-lineage progeny. Recurrent cases of HGSC maintain the heterogeneity of the original tumor suggesting that a cell with multi-lineage potential underlies tumorigenesis, instead of a single clone with a survival advantage (15). A cell with all the characteristics of CSCs is still elusive in ovarian cancer but cells with some of these CSC characteristics, identified by their expression of CD44, CD133, CD117, CD24, and ALDH1 (3), have been reported. These CSC-like cells are referred to as tumor-initiating cells (TICs) due to their increased tumorigenic capacity. The role and identification of TICs in ovarian cancer is a rapidly growing area of study.

We recently reported the first stem cell marker that identifies a subpopulation of mouse OSE cells with progenitor cell characteristics. A population of cells expressing stem cell antigen 1 [SCA1; aka lymphocyte antigen 6 complex, locus A (LY6A)] is regulated by ovulation-associated factors present in the follicular fluid and possesses a number of features of stem cells, including slow growth and capacity for self-renewal (18). After several years of establishing and growing cultures of mouse OSE cells, one cell line that was grown for a prolonged period appeared to spontaneously transform. The following body of work describes the characterization of this spontaneously transformed OSE (STOSE) cell line, demonstrating that it reliably forms syngeneic HGSC tumors. Testing of the SCA1+ cells in the parental and transformed cell lines enabled us to compare the characteristics of these stem cell-like populations, as well as determine the relative malignant potential of SCA1+ vs. SCA1− STOSE cells.

## **MATERIALS AND METHODS**

#### **EXPERIMENTAL ANIMALS**

Severe combined immunodeficient (SCID) and FVB/N mice were obtained from The Jackson Laboratory and housed with a 12 h light:12 h dark photoperiod. The animals had free access to food and water and experiments were done in accordance with the Canadian Council on Animal Care's*Guidelines for the Care and Use of Animals*. Protocols were approved by the University of Ottawa Animal Care Committee.

#### **MOUSE OSE CELL ISOLATION AND CULTURE**

The M0505 OSE cell line was isolated and established in 2005 according to the protocol described in Gamwell et al. (18). Upon long-term passage of the cells in adherent cultures on tissue culture plates (Becton Dickinson) using MOSE media (18), the M0505 cell line spontaneously transformed and were from that point on labeled STOSE cells, which were also maintained in MOSE medium. The M1107 OSE cell line was established and maintained using the same methods as the M0505 cell line and is used as an independent control for mouse OSE cells.

#### **PROLIFERATION ASSAY**

M0505 and STOSE cell proliferation was assessed from 1 to 3 days after seeding 2 × 104 cells in 24-well tissue culture dishes (Becton Dickinson) in MOSE medium. The number of viable cells was determined using the Vi-CELL XR cell viability analyzer (Beckman Coulter).

### **CHROMOSOMAL ANALYSIS**

G-band karyotyping of 5-metaphase spreads each of M0505 and STOSE cells was carried out by the Cytogenomics and Genome Resource Facility at SickKids Hospital, Toronto, ON, Canada. Briefly, cells were harvested and colcemid (10µg/mL) was added for 30 min and incubated at 37°C. Cells were washed, trypsinized, and a single-cell suspension was made. Following washing, a 0.075 M KCl hypotonic solution was added for 15 min and incubated at 37oC, and banding patterns were visualized.

#### **CELL CYCLE ANALYSIS**

The percentages of cells in G1/G0, S-phase, and G2/M phases as well as the percentage of apoptotic cells were determined for M0505 and STOSE cell lines using flow cytometry. Cells were trypsinized (0.05% trypsin/0.53 mM EDTA, Corning Cellgro), washed in phosphate-buffered saline (PBS), and 1 × 106 cells from three independent isolations of each cell line were resuspended in 300µL of cold PBS. Cells were fixed in 70% ethanol for 2 h,washed, and resuspended in 250µL of PBS and 5µL of RNAse A (Sigma Aldrich) for 1 h. The cell suspension was then incubated for 30 min with 10µL of propidium iodide (Sigma Aldrich) and the cell cycle was assessed by flow cytometry using a Beckman Coulter Epics XL and analyzed by ModFit LT software (Verity Software Inc.). Cell doublets were identified using fluorescence pulse height vs. area measurements and excluded from cell cycle analysis.

#### **MICROARRAY ANALYSIS**

RNA was extracted from M0505 and STOSE cells (*n* = 3) using RNeasy Mini Kit (Qiagen) and cDNA was made using the OneStep RT-PCR kit (Qiagen). Whole genome expression was determined using Affymetrix GeneChip Mouse Gene 1.0 ST arrays. Genes were annotated using T4-MEV software (Dana Farber Cancer Institute, Boston) and linear fold change was determined from robust multi-array average (RMA) normalized expression values. Ingenuity pathway analysis software (Ingenuity Systems, Qiagen) was used to determine functionally relevant clusters of differential gene expression. Microarray data are publicly accessible from the GEO database at record GSE54633.

### **QUANTITATIVE RT-PCR**

RNA was extracted using the RNeasy Mini Kit (Qiagen) and cDNA was made using the OneStep RT-PCR Kit (Qiagen). Quantitative-PCR was then performed on an ABI 7500 FAST qRT-PCR machine (Applied Biosystems) using the Taqman gene expression assay (Life Technologies) and SsoFast gene expression assay (Bio-rad). Probe (2.5 nmol) and primer (5 nmol) sequences are listed in **Table 1**. The level of *Tbp* was used as an endogenous control in the Taqman assay and *Ppia* was used as an endogenous control in the SsoFast assay.

## **INTRAPERITONEAL (IP) AND INTRABURSAL (IB) INJECTIONS OF STOSE CELLS**

M0505 and STOSE cells were released from adherent cultures using trypsin (0.05% trypsin/0.53 mM EDTA), washed with PBS, and resuspended in PBS. 1 × 107 M0505 cells in 500µL of PBS were injected into the peritoneal cavity of FVB/N mice. 1 × 107 STOSE cells in 500µL of PBS were injected into the peritoneal cavity of both SCID and FVB/N mice using a 25-gauge needle (Becton Dickinson). Disease progression was monitored until humane endpoint was reached, which included 15% weight gain and/or abdominal distension. Necropsies were performed at endpoint and tumors were fixed in 10% buffered formalin for 24 h and then paraffin embedded and sectioned at 5µm for immunohistochemical analysis.

To perform intrabursal injections of STOSE cells, FVB/N mice were anesthetized using 3% isoflurane gas and 1% oxygen. A dorsal incision was made and ovaries were externalized. STOSE cells (4 × 104 ) were resuspended in 2µL of PBS and injected under the bursal membrane using a 33-gauge needle and dispensing repeater (Hamilton). Tumor initiation was monitored every 2 days by palpation of the ovaries by someone blinded to the experimental groups. Disease progression was monitored until humane endpoint was reached, at which point tumors were fixed, embedded in paraffin blocks, and 5µm sections were made for immunohistochemistry.

## **IMMUNOHISTOCHEMISTRY**

Assessment of the histopathology of IP and IB STOSE tumors was performed by staining sections with hematoxylin and eosin (H and E) and by immunohistochemical analysis. Following deparaffinization in xylenes and rehydration in an ethanol gradient, antigen unmasking (antigen unmasking solution, Dako) was performed, followed by blocking endogenous peroxidase activity using 3% hydrogen peroxide in dH2O. Sections were then rinsed in PBS. Immunostaining for mouse cytokeratin (pan-CK; pre-diluted, Abcam), mouse WT1 (1:100, Dako), and mouse inhibin (1:100, Dako) was performed according to the mouse-onmouse kit (Vector). Immunostaining for rabbit PAX8 (1:400, Santa Cruz Biotechnology) was done by incubating sections with the PAX8 antibody overnight at 4oC, followed by anti-rabbit horseradish peroxidase-labeled polymer (Dako) for 30 min at room temperature. All sections were counterstained using hematoxylin

#### **Table 1 | Quantitative RT-PCR probe and primer sequences.**

and developed using diaminobenzidine. Following dehydration in an ethanol gradient, sections were mounted using Permount (Fisher Scientific). Images were acquired using the ScanScope CS2 (Aperio).

## **DNA SEQUENCING**

Genomic DNA was extracted from STOSE cells using QIAamp DNA Blood Mini Kit (Qiagen) and PCR amplified using custom primers designed to cover each of the 11 exons in the mouse p53 gene. Following electrophoresis on a 1% agarose gel, bands pertaining to each exon were individually excised under UV light. DNA was extracted from the agarose gel pieces using the QIAquick Gel Extraction Kit (Qiagen). Extracted DNA was then diluted to a concentration of 1 ng/µL and mixed with the appropriate custom primer (2µM) mapping to each exon. Individual exons were sequenced using the 3730 DNA analyzer (Applied Biosystems). Sequences were aligned using the DNA Dynamo program (BlueTractorSoftware).

## **FLOW CYTOMETRY FOR SCA1 EXPRESSION**

M0505 and STOSE cells were trypsinized and a single-cell suspension was made using a 40µm cell strainer. Cells were resuspended in a flow buffer (4% fetal calf serum in 1× PBS) and incubated with anti-SCA1 allophycocyanin fluorophore-conjugated antibody (Miltenyi Biotec) for 15 min at 4oC. Following washing and resuspension in flow buffer, cells were sorted for SCA1 expression using the MoFlo cell sorter (Beckman Coulter).

## **COLONY FORMATION IN SOFT AGAR**

Cells were released from adherent cultures using trypsin, washed with PBS, and a single-cell suspension was achieved by passing cells through a 40-µm cell strainer. A base layer 1:1 mix of 2× Ham's F-12:MOSE medium (Sigma Aldrich) and ultrapure LMP agarose (Life Technologies) was solidified at 4°C for 30 min and then warmed to 37°C prior to the addition of the top layer. The top layer consisting of a 1:1:1 mix of 2.5 × 104 cells from singlecell suspension, 2× Ham's F-12:MOSE medium, and ultrapure LMP agarose was added. The top layer was solidified at 4°C for 30 min and then incubated at 37°C for 7 days. Colonies were visualized using the EVOS XL imaging system (Life Technologies) and counted using ImageJ software.

## **WESTERN BLOT ANALYSIS**

Protein was extracted from M0505 and STOSE cells using M-PER mammalian protein extraction reagent (GE Healthcare). Tumor tissue from SCA1+ and SCA1− tumors was homogenized and protein was extracted using M-PER mammalian protein extraction reagent. Protein extracts were run on a precast Nupage 4–12% bis–tris gradient gel (Life Technologies) and transferred to a nitrocellulose membrane. Following 1 h blocking in 5% non-fat milk, membranes were incubated with mouse monoclonal PAX8 (1:500, Santa Cruz Biotechnology) or mouse monoclonal P53 (1:1000, Cell Signaling) overnight at 4°C. Following washing, the membranes were incubated with rabbit anti-mouse IgG–HRP (1:5000, Abcam) for 1 h and developed using Select™ western blotting detection reagent (GE Healthcare). The same protocol was used for β-actin using mouse monoclonal anti-β-actin (1:40,000, Sigma Aldrich) and rabbit anti-mouse IgG-HRP (1:15,000, Abcam).

## **STATISTICAL ANALYSIS**

All experiments were performed at least three times. A Student's *t*-test was used to determine significant differences between two experimental conditions. Analysis of variance (ANOVA) with Tukey's post-test was used to identify significant differences between more than two experimental groups. Statistical significance was assumed at *p* < 0.05.

## **RESULTS**

## **CHARACTERIZATION OF M0505 AND STOSE CELL LINES**

Early passage M0505 cells grow slowly, having a doubling time of 48 h. The growth rate increases as M0505 cells reach >35 passages and cells begin to lose the epithelial "cobblestone" morphology that is characteristic of early passage M0505 cells (data not shown), and has been reported by others studying spontaneous transformation of epithelial cells (19). Continual passage of late passage M0505 cells led to the establishment of the STOSE cell line. STOSE cells have lost the epithelial "cobblestone" morphology and have transitioned to a more mesenchymal morphology (**Figure 1A**). To determine the malignant potential of STOSE cells *in vitro*, STOSE

cells were assessed for colony forming efficiency in soft agar, a measure of anchorage independent growth that is characteristic of transformed cells (20). STOSE cells formed colonies while early passage M0505 cells did not (**Figure 1B**). Another characteristic of transformed cells is rapid growth (20). STOSE cells have a doubling time of 13 h, almost four times faster than their untransformed M0505 counterpart. The growth rate of STOSE cells in comparison to early passage (passage 18–20) M0505 cells over 72 h is shown in **Figure 1C**. Since a greatly increased growth rate might be explained by aberrant cell cycle regulation, cell cycle analysis was used to determine if there were differences in the percentage of M0505 and STOSE cells in each phase of the cell cycle. Cell cycle analysis of the M0505 cells (monomers) revealed a large G1 peak (59.6 ± 1.0%), a minor S-phase population (10.1 ± 0.3%), and a surprisingly prominent, putative G2/M peak (28.8 ± 0.8%) (**Figure 1D**). Interestingly, the presence of a small percentage (1.5%) of hyperploid cells was detected in the analysis by the ModFit program. The presence of a small population of cells with abnormal DNA content was then confirmed by karyotype analysis that identified near-tetraploid M0505 cells (**Figure 2B**). In addition, the small number of diploid cells in S-phase was consistent with the observed slow proliferation of this cell line. In contrast, STOSE cells have a significantly increased proportion of cells in S-phase (45.2 ± 0.7%) and, a reduced proportion in the G1 phase (46.7 ± 0.7%). The small G2/M population and greatly increased S-phase population suggests that STOSE cell cycle checkpoints may be compromised, which could lead to the observed acceleration in the rate of proliferation.

Due to the role of aneuploidy in transformation and cancer and the abnormalities found in the cell cycle analysis, chromosomal analysis was performed on M0505 and STOSE cells to determine if aneuploidy is present. G-band karyotyping of five metaphase spreads revealed aneuploidy in both M0505 and STOSE cell lines; two representative karyotypes are shown for each cell line (**Figure 2**). STOSE cells have a high degree of aneuploidy with the majority of the population near-triploid (**Figure 2C**) and a smaller polyploid population (**Figure 2D**). All STOSE cells analyzed have an addition at the terminal end of chromosome 4. All near-triploid cases have a loss of chromosome 3, 5, and 8, and all polyploid cases are also hypoploid for chromosomes 3, 5, and 8 (**Figures 2C,D**). Surprisingly, chromosomal analysis of early passage (passage 15) M0505 cells also revealed some degree of aneuploidy with 2/5 near-tetraploid M0505 cells (**Figure 2B**), while 3/5 M0505 cells were near-diploid (**Figure 2A**). This presence of a near-tetraploid subset of M0505 cells is in agreement with the presence of M0505 cells with increased DNA content seen in the cell cycle analysis (**Figure 1D**). All M0505 cells analyzed have terminal deletions in chromosomes 1 and 4. All near-diploid cases have a loss of one chromosome 3, 8, and 12, and all near-tetraploid M0505 cells are hypoploid for chromosomes 3, 8, and 12 (**Figures 2A,B**).

## **MICROARRAY ANALYSIS OF STOSE CELLS**

To determine the molecular mechanisms by which M0505 cells transformed into the STOSE cells, whole genome microarray analysis was performed on M0505 and STOSE cells and linear fold changes were calculated for STOSE cells relative to M0505 cells. The top 10 up- and down-regulated genes in STOSE compared

to M0505 cells are presented in **Table 2**. Interestingly, *Ddr2*, *Ereg*, *Glipr1*, *Calcr*, and *Ankrd1*, all up-regulated in STOSE cells, have been shown to be up-regulated in primary tumors and ovarian cancer cells (21–24). *Igfbp4* has been shown to be down-regulated in primary tumors (25, 26). The other up-regulated genes in STOSE cells: *Serpinb8*, *Epb41l4a*, *Aif1l*, and *Mgll* have no known links to ovarian cancer. Five of the 10 most down-regulated genes, *Aldh1a2*, *Enpp2*, *Lgfbp5*, *Thbd*, and *Uchl1*, have been previously implicated in ovarian cancer (25, 27–34). The remaining genes among these down-regulated candidates have no previous association with ovarian cancer: *Gpr64*, *Gpr126*, *Cybrd1*, *Star*, *Ncf2*. In accord with the more rapid proliferation of STOSE cells, two negative regulators of Cdk4, *Cdkn2b* and *Cdkn2a*, are down-regulated in STOSE cells 13.4- and 5.8-fold, respectively, and both *Ccna2* and *Ccnd1* are up-regulated (2.02- and 6.2-fold).

The Cancer Genome Atlas (TCGA) ovarian carcinoma array is a whole genome array database with analysis of 570 human HGSC tumors. The TCGA array dataset was analyzed by the Cancer Genome Research Analysis Network and two of the top gene changes in the STOSE cell microarray were among those reported in the pathways most frequently altered in ovarian carcinomas (35): downregulation of *Cdkn2a* (−5.8) and overexpression of *Ccnd1* (+6.2). Overexpression of *Ccnd1* is strongly correlated to decreased progression free survival (36) and loss of *Cdkn2a* through mutation or hypermethylation has also been shown in human ovarian carcinomas (35, 37–39). Ingenuity pathway analysis (IPA) was used to identify functionally related clusters of gene expression differences from the microarray data. IPA analysis revealed possible aberrant Wnt/β-catenin and Nf-κB signaling in STOSE cells. The expression of multiple genes associated with Wnt signaling are significantly altered including *Cdkn2a* and downregulation of Wnt signaling inhibitors *Sfrp1* and *Frzb.* Genes differentially expressed in the Nf-κB pathway include *Spp1*, *S100a4*,*IkBk*ε, and *Ccnd1*. Interestingly, *Ccdn1* is associated with both Wnt/βcatenin and Nf-κB signaling. Validations of *Cdkn2a* and *Ccnd1*, as well as Wnt/β-catenin and Nf-κB-related genes were performed by quantitative RT-PCR on three microarray-independent samples of M0505 and STOSE cells (**Figure 3**).

## **STOSE CELLS PRODUCE HGSC TUMORS IN BOTH SCID AND SYNGENEIC FVB/N MICE**

Given that STOSE cells exhibit transformed characteristics*in vitro*, their *in vivo* tumorigenicity was assessed using immunocompromised SCID mice and the syngeneic strain of mice, FBV/N. When STOSE cells (1 × 107 ) were injected IP into four SCID mice, tumors formed in all mice (4/4) with a median endpoint of 47 days. Tumors were collected from most organs within the peritoneal cavity and the average total tumor burden was 2.22 ± 0.21 g

per mouse. All SCID mice had ascites with an average volume of 5.25 ± 0.63 mL. Following IP injection of STOSE cells into immunocompetent syngeneic hosts, STOSE cells were tumorigenic in all FVB/N mice (4/4) with a median endpoint of 48 days. Necropsy revealed tumors throughout the peritoneal cavity and an average total tumor burden of 3.06 ± 0.21 g per mouse, not different from the tumors in SCID mice. All STOSE-injected FVB/N mice had ascites with an average volume of 3.08 ± 0.92 mL, also not significantly different from SCID mice (*n* = 4, *p* = 0.98). Intraperitoneal injection of 1 × 107 M0505 cells into FVB/N mice did not result in tumor formation in 107 days (0/6 mice).

Spontaneously transformed OSE-derived tumors from both SCID and FVB/N mice were analyzed by H and E staining for morphological classification (**Figure 4A**) and immunohistochemistry for expression of markers commonly found in human ovarian cancers (**Figure 4B**). Tumor morphology was mixed including regions of mucinous, undifferentiated, and papillary serous structures. The most common morphologies are presented in **Figure 4A**. To confirm an epithelial origin, tumors were stained for epithelial cytokeratins using a pan-CK antibody. Both SCID and FVB/N tumors have strong positive pan-CK staining. Wilms tumor-1 (WT1) positivity is a hallmark of HGSC (40), and all STOSE tumors stained strongly for WT1. Given the WT1 positivity, the tumors were examined for expression of another marker of HGSC, PAX8. All STOSE tumors had strong PAX8 expression. To exclude a

granulosa cell origin of STOSE tumors, the expression of the granulosa cell marker inhibin was determined. No tumors expressed inhibin. Thus, STOSE-derived tumors have a pan-CK+, WT1+, inhibin−, PAX8+ profile, indicating that the STOSE tumors resemble HGSC. Since almost 100% of HGSC cases present with p53 mutations (9), DNA sequencing was performed on all 11 exons of the p53 gene in STOSE cells and no mutations were found (data not shown).

## **STOSE CELLS RETAINED A POPULATION OF SCA1**+ **CELLS THAT EXHIBIT GREATER MALIGNANT POTENTIAL**

We have recently identified SCA1 as a marker of a defined stemlike population in the OSE (18). Flow cytometry confirmed that the parental M0505 cell line contains an average SCA1+ population of 14.5 ± 1.4% (*n* = 6). Interestingly, STOSE cells have retained a smaller SCA1+ population, on average 5.8 ± 0.8% (*n* = 11, **Figure 5A**). To determine if SCA1+ and SCA1− cells exhibit a difference in malignant potential *in vitro*, M0505 and STOSE cells were sorted for SCA1 expression and assayed for colony forming efficiency in soft agar. SCA1+ STOSE cells formed significantly more colonies than SCA1− STOSE cells (*p* < 0.05, **Figure 5B**).

Since SCA1+ STOSE cells exhibit a more malignant phenotype *in vitro*, SCA1+ STOSE cell malignancy was tested *in vivo*. To determine if SCA1 marks cells with enhanced ability to initiate

tumors, SCA1+ and SCA1− STOSE cells (4 × 104 ) were injected IB into 29 FVB/N mouse ovaries, 15 with SCA1− cells and 14 with SCA1+ cells. SCA1+ STOSE cells initiated tumorigenesis faster than SCA1− STOSE cells with the median times to a palpable tumor of 19 (*n* = 15) and 27 days (*n* = 14), respectively (*p* < 0.01, **Figure 6A**). There was no difference in total tumor burden between the two groups when the mice were euthanized 116 days after STOSE cell injection, with mice having a tumor burden of 2.70 ± 0.53 g (*n* = 7) for SCA1− tumors vs. 2.72 ± 0.32 g (*n* = 6) for SCA1+ tumors. At that time point, SCA1+ and SCA1− STOSE tumors also showed a similar degree of tumor dissemination, metastasizing consistently to the uterus, stomach, diaphragm, small and large intestines, spleen, and pancreas.

To determine if the increased initiation rate in SCA1+ compared to SCA1− STOSE tumors resulted in different histological presentation, immunohistochemistry was performed using markers of HGSC. Both SCA1+ and SCA1− STOSE tumors are pan-CK+, WT1+, inhibin−, and PAX8+ (**Figure 6B**), with no gross histological differences evident between SCA1+ and SCA1− tumors. Western blot analysis confirmed strong PAX8 positivity in both SCA1+ and SCA1− STOSE tumors (**Figure 6C**), relative to the positive control, normal uterine tissue, and to the low level of expression seen in M0505 and STOSE cells cultured *in vitro*. An independent non-tumorigenic normal OSE cell line, M1102, was used as a negative control. Expression of p53 in SCA1+ and SCA1− STOSE-derived tumors was determined using western blot analysis. SCA1+ and SCA1− tumors were positive for p53 expression (**Figure 6D**).

## **DISCUSSION**

There is substantial need for new models of HGSC that have similar expression profiles, chromosomal aberrations, and histological features characteristic of human HGSC. These models should also account for the multiple origins of HGSC in order to effectively narrow down screening targets based on the tissue of origin. The body of work presented here describes the production and characterization of a STOSE cell line. STOSE cells have lost characteristic epithelial "cobblestone" morphology, have a greatly increased proliferation rate, and form colonies in soft agar. Interestingly, there is aneuploidy in both M0505 and STOSE cells, suggesting that aneuploidy may have preceded transformation. Linear fold

changes calculated from M0505 and STOSE cell microarray data revealed that STOSE cells have differentially expressed genes that are consistent with human HGSC tumor samples and previous studies on ovarian cancer cell lines. Tumors with an immunohistochemical profile of HGSC formed in all immunocompromised SCID and syngeneic FVB/N mice following IP STOSE cell injections, confirming the potential for STOSE cells to be used as a syngeneic model of HGSC. Finally, STOSE cells that express SCA1 appear to be more aggressive, with increased colony forming efficiency *in vitro* and faster tumor initiation *in vivo*.

fallopian tube (oviduct) positive control. Scale bars = 50µm.

**FIGURE 5 | A SCA1**+ **population is present in STOSE cells. (A)** Percentage of SCA1+ cells in M0505 (n = 6) and STOSE (n = 11) cells as assessed by flow cytometry. \*p < 0.01, Student's t-test. **(B)** Quantification of colony formation in soft agar by SCA1+ and SCA1− M0505 and STOSE cells. Colonies were counted using ImageJ software 7 days after seeding 2.5 × 104 cells in soft agar. The average number of colonies in five fields of view is presented (n = 3). ANOVA was used to determine significance; bars with different letters are significantly different.

Recent reviews have discussed the pros and cons of current models of HGSC (5, 9, 10). Current models have focused on the use of transgenics, xenografts of human cancer cells, and OSE cells transformed by genetic engineering in attempts to model HGSC. These models have had some success in modeling HGSC as well as low-grade serous, endometrioid, and granulosa cell-derived tumors, although results of these studies are highly variable and commonly have strain-dependent phenotypes (5). Most transgenics have focused on the use of the anti-Mullerian hormone type II receptor (*Amhr2* or MISIIR) promoter to drive tumor suppressor knockout or oncogene activation, but its expression in granulosa cells as well as both ovarian epithelium and fimbria can confound the results and make the origins of such cancers unclear (5). Human xenografts into immune-compromised mice have provided much knowledge on the metastasis and chemoresistance of human tumors. The lack of an immune system can limit some uses of these models, which do not accurately represent the human tumor microenvironment in which the immune system has a critical role in tumor progression and response to treatment (9, 10). Genetically engineered OSE cells have provided much insight into genes that are sufficient to transform OSE cells (41, 42), but their involvement in HGSC initiation or progression

and STOSE-derived SCA1+ and SCA1− tumors. Lane 1 is uterus from a wild-type FVB/N mouse as a positive control. Lane 2 is the normal M1102 OSE cell line as a negative control. Lanes 3–4 are passage 14 and 22 M0505 cells and lane 5 is STOSE cells. Lanes 6–7 and 8–9 represent tumors derived from SCA1− and SCA1+ STOSE cells, respectively. β-actin (42 kDa) was used as a loading control. **(D)** P53 (53 kDa) expression in STOSE-derived SCA1+ and SCA1− tumors. Lane 1 is a T-antigen expressing CAg–TAg tumor as a positive control.  Lane 3–4 represents STOSE-derived SCA1− and SCA1+ tumors, respectively.

**FIGURE 6 | SCA1**+ **STOSE cells initiate HGSC tumorigenesis faster than SCA1**− **STOSE cells.** Flow cytometric sorting was used to separate SCA1+ and SCA1− STOSE cells. SCA1+ (n = 14) and SCA1− (n = 15) STOSE cells were injected intrabursally into FVB/N mouse ovaries (4 × 104 cells/ovary). **(A)** The first day ovarian tumors were palpable after day of injection (day 0) is presented and represents the initiation of tumorigenesis. Black lines represent median values. \*p < 0.01, Student's t-test. **(B)** Hematoxylin and eosin (H&E) staining and immunohistochemical staining of pan-cytokeratin (CK), WT1, inhibin, and PAX8, all commonly used markers to subtype ovarian carcinoma. Scale bars = 50µm. **(C)** PAX8 (48 kDa) expression in cell lines

is unknown and manipulating such genes may not represent the natural progression of disease.

The STOSE cells reported here join a number of other spontaneously transformed rat (ROSE) (43, 44) and mouse OSE cell lines that have been previously reported. Syngeneic mouse models include ID8, IF5, IG10, L-MOSE, and MOSEC cells (45–48). These models are all tumorigenic in immunocompetent mice and allow the study of immunologic parameters as well as serve as a resource to test immunotherapies in ovarian cancer (48). Spontaneous models are beneficial since they arise from specific cell types, so their origins are clear (49). All of the models derived from spontaneously transformed OSE cell lines have yielded poorly

differentiated epithelial carcinomas, but have not been examined further to confirm their histologic identity as it compares with human tumors. Those lines tested have shown gene expression profiles similar to human (3, 50).

The STOSE model is the first spontaneous HGSC model, as confirmed by the expression of immunohistochemical markers (pan-CK+, WT1+, inhibin−, PAX8+), consistent with human ovarian carcinomas (2, 40). The expression of WT1 and PAX8, commonly used to diagnose HGSC, help to confirm that OSE cells have the ability to spontaneously transform into HGSC. PAX8 positivity in human HGSC is one of the characteristics used to support a fimbrial origin of HGSC (2). It is well-established as a marker of fimbrial epithelium and, due to its expression in HGSC, much research has now focused on the fimbrial epithelium (2, 4). Recently, a report has shown that PAX8+ tumors can be produced from transformed hilum cells that originate in the ovary, providing additional evidence that the OSE cells can be an origin of HGSC (4). Although OSE cells have little to no PAX8 expression, our results show that both the untransformed M0505 cells as well as the STOSE cells had a low level of expression of PAX8+ (**Figure 6C**), suggesting that early acquisition of PAX8 expression in the M0505 cells may have facilitated the transformation of these cells. Further study of PAX8 and its function in M0505 and STOSE cells will help delineate its role in the transformation process.

The STOSE model is also the first syngeneic ovarian cancer model in the FVB/N strain of mice. All previous spontaneously transformed mouse OSE cells have been derived from C57Bl/6 mice (32, 45, 46, 48). Most spontaneous models have been produced by IP injection into syngeneic hosts, abrogating the ability to study metastasis from a specific site. The ovarian bursa is a controlled and distinct microenvironment and we have previously shown that, while tumor histology is not different when cells are injected into this location, it is an effective means to identify more invasive cells, as only aggressive cells can invade the ovary and/or breach the bursal membrane (51). Injecting cells under the bursal membrane also provides the ability to study the immune parameters associated with metastasis that could enable the production of immune therapies to prevent metastasis. The spontaneous ID8 model has produced peritoneal metastases following IB injection into their syngeneic C57Bl/6 strain of origin (52). The STOSE model also forms extensive peritoneal metastases following IB injection, making STOSE the first metastatic HGSC model in the FVB/N strain. Having spontaneous models in multiple strains is an important resource to enable investigators to show that the efficacy of a therapeutic strategy is independent of strain background, greatly improving the translation of therapeutic strategies.

Spontaneously transformed OSE cells are aneuploid and have gene expression changes consistent with human ovarian cancer. Aneuploidy is common in many cancers including ovarian cancers (19, 39, 45, 46, 53). Aneuploidy is a prognostic determinant in HGSC since severe aneuploidy is associated with poor outcome (53). STOSE cells have a high degree of aneuploidy, characterized by triploid and polyploid cells. Furthermore, the loss of genomic stability in both M0505 and STOSE cells as seen by aneuploidy may have been an early event leading to transformation that may explain the tumorigenic capacity of STOSE cells. Loss of chromosome 3, which contains many tumor suppressors, is seen in both M0505 and STOSE cells. Haploinsufficiency of chromosome 3 tumor suppressors such as *Lrrc3b* (fold change of −2.69 in STOSE cells) may underlie transformation (54). Similarly, chromosome 8 is lost in M0505 and STOSE cells and it has been shown to contain ovarian cancer susceptibility loci, allelic loss of which may have contributed to transformation (55, 56). Three downregulated genes in STOSE cells, *Enpp2, Sfrp1,* and *Star* are all located on chromosome 8. Loss of chromosome 8 in M0505 cells may have been an early event in transformation (30, 57).

Ingenuity pathway analysis of microarray data revealed gene expression changes related to Wnt/β-catenin signaling in STOSE cells suggesting signaling in the Wnt pathway might be aberrant. Many of the down-regulated genes in STOSE cells are associated with Wnt/β-catenin signaling and have been associated with loss of heterozygosity or promoter methylation in ovarian cancer, including *Fzd4*, *Sfrp1*, and *Axin2* (58–61). Interestingly, *Cdkn2a* is down-regulated in 30% of HGSC cases and *Ccnd1* is amplified in 4% of the cases, according to TCGA ovarian carcinoma array (35). STOSE cells have a similar expression pattern of *Cdkn2a* and *Ccnd1*. *Cdkn2a* and *Ccnd1* are both associated with Wnt/β-catenin signaling. *Ccnd1* is a well-established target gene of β-catenin signaling and has a role in promoting cell cycle progression, while *Cdkn2a* encodes a cell cycle inhibitor that is suppressed by βcatenin (35, 62, 63). Due to the association of these two genes with human HGSC and aberrant Wnt signaling in STOSE cells, further study of the role of *Cdkn2a*, *Ccnd1*, and Wnt/β-catenin signaling is needed to understand the role Wnt/β-catenin signaling in the transformation of M0505 cells into STOSE cells or in the tumorigenic capacity of STOSE cells. A greater understanding of this pathway may translate to greater knowledge on the initiation and progression of HGSC.

Interestingly, *Aldh1a2* is the most down-regulated gene in STOSE cells (−170.58 fold). *Aldh1a2* is involved in retinoic acid (RA) biosynthesis and has been shown to have ubiquitous expression in the human ovarian surface epithelium (2, 29). The RAreceptor β (*Rar*β) is also down-regulated in STOSE cells (−10.80 fold) suggesting multiple aspects of RA signaling are lost. RA signaling has been shown to crosstalk with Wnt/β-catenin signaling and *Aldh1a2* has also been identified as a tumor suppressor in prostate cancer, loss of which is an early event in the disease (64, 65). Further study of *Alhd1a2*, RA signaling, and the crosstalk between RA and Wnt/B-catenin signaling may help determine the mechanisms leading to transformation and tumorigenesis in HGSC.

Investigation of a potential TIC population in the STOSE revealed that STOSE cells have retained a SCA1+ population that appears to have a more malignant phenotype than SCA1− STOSE cells. TICs have been thought to be key contributors to HGSC etiology based on the heterogeneity and recurrence that are characteristic of the disease (3, 15, 50). TICs have been identified in both human and murine ovarian cancers by sorting for CD44, CD133, CD117, CD24, ALDH1, and SCA1 expression alone or in combination (3, 15). SCA1 has also been used for the enrichment of a stem cell population in leukemia, prostate, and breast cancers (15). STOSE cells were found to contain a SCA1+ population that exhibits increased malignancy both *in vitro* as assessed by colony

formation and *in vivo* as assessed by initiation of tumorigenesis. Interestingly, SCA1+ and SCA1− STOSE-derived tumors were positive for p53 expression by western blot analysis. DNA sequencing showed no mutations in the p53 gene, suggesting pathways that lead to p53 stabilization might also be aberrant in STOSE cells. Our findings that the SCA1+ population exhibits TIC characteristics is in line with a recent study on SCA1+ cells in the T2 mouse model of ovarian cancer, which showed that these cells have TIC characteristics that allow them to escape chemotherapy and produce heterogeneous tumors following treatment (15). The retention of a SCA1+population with TIC characteristics allows us to compare tumorigenic SCA1+ STOSE cells with non-tumorigenic SCA1+ M0505 cells.

In summary, this study has led to the development of a spontaneously transformed syngeneic model of HGSC in the FVB/N mouse, the first spontaneous murine model with defined features of HGSC. The STOSE model has characteristics of human disease such as aneuploidy, gene expression, and the presence of a TIC population. This model also produces extensive metastases in the peritoneal cavity following IB injection allowing for the study of tumor dissemination. Further investigation is required to understand the contribution of Wnt/β-catenin signaling in STOSE cells. The STOSE model offers vast potential for testing of novel therapeutics, including immune therapies. This model will also allow for the discovery of new screening targets that are involved in the transition of normal cells to HGSC.

## **AUTHOR CONTRIBUTIONS**

Experiments were designed by Curtis W. McCloskey and Barbara C. Vanderhyden. Olga Collins derived the cell line and performed initial validation of the transformation. All surgeries, IHC, microarray, and cell culture were done by Curtis W. McCloskey; western blotting was done by Reuben L. Goldberg; qPCR was done by Curtis W. McCloskey and Lauren E. Carter; Lisa F. Gamwell and Curtis W. McCloskey performed the flow cytometry; Kenneth Garson and Curtis W. McCloskey performed the cell cycle analysis. Elizabeth A. Macdonald and Curtis W. McCloskey performed the IP injections and necropsies. Manijeh Daneshmand performed the pathology assessment and Euridice Carmona did the IPA analysis. Curtis W. McCloskey and Ensaf M. Al-Hujaily performed DNA sequencing. The manuscript was written by Curtis W. McCloskey and Barbara C. Vanderhyden and edited by Manijeh Daneshmand and Euridice Carmona.

## **ACKNOWLEDGMENTS**

This work was supported by grants from the Cancer Research Society, the Canadian Institutes of Health Research (#111194), and the Canadian Cancer Society Research Institute (#2011-700869).

## **REFERENCES**

in ovarian cancer. *Clin Cancer Res* (2008) **14**:6924–32. doi:10.1158/1078-0432. CCR-07-5189

epithelial ovarian cancer in the Fischer 344 rat. *Am J Obstet Gynecol* (1996) **175**:593–9. doi:10.1053/ob.1996.v175.a73595

65. Touma SE, Perner S, Rubin MA, Nanus DM, Gudas LJ. Retinoid metabolism and ALDH1A2 (RALDH2) expression are altered in the transgenic adenocarcinoma mouse prostate model. *Biochem Pharmacol* (2009) **78**:1127–38. doi:10.1016/j.bcp.2009.06.022

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 30 September 2013; accepted: 04 March 2014; published online: 18 March 2014.*

*Citation: McCloskey CW, Goldberg RL, Carter LE, Gamwell LF, Al-Hujaily EM, Collins O, Macdonald EA, Garson K, Daneshmand M, Carmona E and Vanderhyden* *BC (2014) A new spontaneously transformed syngeneic model of high-grade serous ovarian cancer with a tumor-initiating cell population. Front. Oncol. 4:53. doi: 10.3389/fonc.2014.00053*

*This article was submitted to Women's Cancer, a section of the journal Frontiers in Oncology.*

*Copyright © 2014 McCloskey, Goldberg , Carter, Gamwell, Al-Hujaily, Collins, Macdonald, Garson, Daneshmand, Carmona and Vanderhyden. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# Culture models to define key mediators of cancer matrix remodeling

## **Emily Suzanne Fuller and Viive Maarika Howell \***

Bill Walsh Translational Cancer Research Laboratory, Kolling Institute of Medical Research, Royal North Shore Hospital, University of Sydney, St. Leonards, NSW, Australia

#### **Edited by:**

Elise Kohn, National Cancer Institute, USA

#### **Reviewed by:**

Elise Kohn, National Cancer Institute, USA Ben Davidson, Oslo University Hospital, Norway Christina Annunziata, National Cancer Institute, USA

#### **\*Correspondence:**

Viive Maarika Howell, Bill Walsh Translational Cancer Research Laboratory, Kolling Institute of Medical Research, Royal North Shore Hospital, University of Sydney, Level 8, Kolling Building, St Leonards, NSW 2065 Australia

e-mail<EMAIL_ADDRESS>
High grade serous epithelial ovarian cancer (HG-SOC) is one of the most devastating gynecological cancers affecting women worldwide, with a poor survival rate despite clinical treatment advances. HG-SOC commonly metastasizes within the peritoneal cavity, primarily to the mesothelial cells of the omentum, which regulate an extracellular matrix rich in collagens type I, III, and IV along with laminin, vitronectin, and fibronectin. Cancer cells depend on their ability to penetrate and invade secondary tissue sites to spread, however a detailed understanding of the molecular mechanisms underlying these processes remain largely unknown. Given the high metastatic potential of HG-SOC and the associated poor clinical outcome, it is extremely important to identify the pathways and the components of which that are responsible for the progression of this disease. In vitro methods of recapitulating human disease processes are the critical first step in such investigations. In this context, establishment of an in vitro "tumor-like" micro-environment, such as 3D culture, to study early disease and metastasis of human HG-SOC is an important and highly insightful method. In recent years, many such methods have been established to investigate the adhesion and invasion of human ovarian cancer cell lines.The aim of this review is to summarize recent developments in ovarian cancer culture systems and their use to investigate clinically relevant findings concerning the key players in driving human HG-SOC.

 Typically treatment consists of surgical debulking, followed by platinum/taxol chemotherapy regimens (1, 2). Treatment fails in up to 70% of patients, and patients with platinum resistant disease have a median survival of 6–12 months (1, 3). Some success has been observed in clinical trials for the palliative management of ascites accumulation using targeted antibody treatment (4), and while this symptom based therapy is clinically important, disease modifying/halting treatments are lacking. Other treatments have shown varied success, including those that target tumor angiogenesis such as bevacizumab alone or in combination with platinum agents and gemcitabine. Many other approaches have been taken including tyrosine kinase inhibitors, angiopoietin inhibitors, histone deacetylase inhibition, and EGF receptor targeting (5). The role of immune cells and interactions with tumor stroma are under intense investigation and may improve the future prospects for immunotherapy based regimes (5). However, response to treatment varies between patients and therefore, the development of personalized care through discovery of predictive molecular or protein markers becomes imperative for effective disease treatment.

Modeling HG-SOC as closely as possible to human disease to facilitate clinically relevant treatment testing is the "holy-grail" in research. A plethora of immortalized ovarian cancer cells and *in vitro* and *in vivo* model systems that utilize these cell lines have been described. Early disease events are arguably the most

therapeutically relevant targets of preventative treatments and here, we discuss recently used model systems to identify pathways involved in the development of invasive malignancy.

## **ESTABLISHED EPITHELIAL OVARIAN CANCER CELL LINES AS MODEL SYSTEMS: A CONTROVERSIAL CHOICE**

High grade serous epithelial ovarian cancer has long been thought to arise from the epithelial layer surrounding the ovary (6, 7). However, studies point to a different site of origin, the secretory cells of the fallopian tube fimbria. This highlights the lack of understanding of the histogenesis and molecular signature of this heterogeneous disease (8–14). Anglesio et al. suggested that the biomarker and molecular signatures of ovarian cancer cell lines may be a more accurate and relevant way of grouping "histotypes" over previously determined histological subtypes (15). However, discrepancies between the molecular profile of ovarian cancer cell lines and the tumor types they model have been identified. In fact, these profiles show more similarity between the cell lines themselves, despite differing tissues of origin (8, 16). Further, these reports have raised doubt on the use of a number highly cited ovarian cancer cell lines as models of clinically relevant HG-SOC, in particular A2780 and SKOV3 (8, 15). Cancer cell lines derived from patients who have undergone treatment will represent a population of cells that is intrinsically different from that of the original tumor due to the development of resistance. However, it has been suggested that cell lines derived from untreated tumors are enriched for resistant cells with up-regulation of multi

drug resistance associated genes via activation of stress responses during the primary culture process (16).

Immortalized normal ovarian epithelial cells and normal fallopian epithelial cells are increasingly being used to model early stages of cancer development (10, 11, 17–21). While the use of primary cancer cell cultures avoids issues associated with multiple passages (16), this is a labor intensive method, and differences between individual primary cultures leading to lack of reproducibility, may be a significant confounder. Immortalized cell lines offer the advantage of increased stable survival over longer periods in culture and can be manipulated to include many genetic alterations to mimic the disease of interest. Studies using immortalized cells derived from non-transformed normal human fallopian epithelial secretory cells, along with the induction of relevant genetic alterations, have been shown to successfully model human high grade serous cancer biology (10, 11, 19). The use of virally induced immortalization of cells is common; however this may also induce unappreciated effects on tumor development and virally induced tumor initiation is irrelevant to the pathogenesis of ovarian cancer. Non-viral methods using shRNA technology have also successfully targeted relevant genetic factors resulting in transformed cells (11).

Along with the method of cell line derivation, site of origin, and continuous passaging, culture conditions (monolayer, various 3D culture models, organ-like culture models) are also significant effectors of the characteristics of established ovarian cancer cell lines (8, 15, 16, 22). These issues are inherently difficult to address and there is likely no ideal way to completely control for all these changes. To date, particular HG-SOC cell lines have not been reported as being more relevant to 3D culture compared to 2D culture systems*.* SKOV3 and A2780 are the most commonly cited but may not be the best representations of HG-SOC with their use in 3D likely reflecting their popularity in 2D systems*.* Therefore at this stage there are no specific criteria for cell line selection for 3D systems and progression from 2D to 3D experiments with the same cell line can be a useful strategy. However, consistent use at a low passage number, of an appropriate cell line to model HG-SOC (via histological and molecular markers) is extremely important.

## **IN VITRO CULTURE MODEL SYSTEMS OF HGSEOC**

## **2D VERSUS 3D CULTURE METHODS**

Although it is well known that culturing cancer cell lines can drastically alter their genetic characteristics over multiple passages immortalized cancer cell lines remain the gold standard in cancer research and pre-clinical drug testing (22). This is largely because these cell lines display a consistent and relatively homogeneous phenotype over long periods of time, notwithstanding reports of minor side populations with cancer stem-like characteristics in some cell lines (23, 24). Evidence is accumulating that culturing these cells in 3D matrices is far more representative of disease than traditional 2D systems, as they provide structurally similar conditions for cell growth encompassing the ability to manipulate oxygen and growth factor/cytokine gradients as well as the material properties of the matrix (22, 25–30).

Common methods for assessing ovarian cancer cell proliferation/migration/invasion have included 2D culture growth studies, "scratch"wound healing assays, and penetration through transwell

inserts. Scratch wound assays are relatively easy to set up, and very cheap to run and there are now many options for tracking and quantitating cell growth and migration, including the MetaMorph™ and Incucyte™ real-time Imaging systems (31). Migration assays through transwell inserts are more expensive and do not allow for real-time monitoring. Microfluidic assays have the advantage that cells can be grown in controlled chemotactic gradients (31). However, these systems have not to date been utilized widely for ovarian cancer cell culture studies. Cell spreading assays, in which a plastic culture surface is coated with various extracellular matrix (ECM) components (fibronectin or collagen type I) and cells are allowed to spread under serum free conditions for a short period of time, have been used to assess migration of ovarian cancer cells (32). While these methods may provide some useful information regarding the characteristics of certain cancer cell lines and their responses to stimuli (drug treatment, signaling molecules),they lack a 3D micro-environment to accurately mimic pathophysiological conditions. 3D environments containing relevant structural proteins (collagens, laminin, elastin) (**Figure 1A**), as well as defined tissue organization appropriate to site of tumor growth *in vivo,* are important considerations for recapitulating tumor cell behavior (**Figure 1B**).

Spread of ovarian cancer cells is complex with cells responding to stimuli from neighboring cells and ECM components and their ability to invade connective tissue is crucial for successful metastasis. In the absence of a requirement for ECM interactions and matrix degradation, 2D systems primarily evaluate the motility of cells, rather than a true invasive barrier removal (29). Care must also be taken when interpreting results based on incomplete 3D representations of a *bona fide* tumor/metastatic site ECM. For example, only a partial understanding of the involvement of proteases/MMPs in the spread and invasion of ovarian cancer cells can be drawn from experiments using matrices that lack structural properties of a relevant ECM. For example, matrigel is substantially less cross-linked and differs in overall composition compared to many tissues (29, 31, 33).

Omental models have been used, in which a primary culture of fibroblasts is grown in 2D with a confluent layer of mesothelial cells grown on top before fluorescently labeled ovarian cancer calls are seeded on a final layer to form a "mock" peritoneal environment. Invasion is typically measured by fluorescent microscopy after the cell layers are cultured in transwell inserts placed over growth promoting media. (26, 34–36). These models provide a more accurate representation of the tissue structure encountered by tumor cells, by supplying a barrier to test "metastatic" invasion of cells in presence of other cells such as fibroblasts that are important to disease processes. However, primary tumor development and the "metastatic cascade" are highly complex processes, and the 2D platforms that are currently used do not typify pathways involved, likely contributing to the unsuccessful translation of findings into *in vivo* systems and eventual failure of many treatments under clinical trial (37).

## **NATURAL VERSUS SYNTHETIC 3D PLATFORMS**

The importance of recapitulating tumor ECM in model systems was highlighted by Infanger and others in their review (25). These authors stated that interactions between tumor cells and their

surrounding micro-environment are as pivotal to tumorigenicity as oncogenic mutation (25). Normal homeostatic process and tissue structural properties control the dormancy required after malignant transformation of epithelial cells and when these pathways fail, along with the presence of certain genetic mutations, cells grow uncontrollably and tumors develop (25). Currently, there is a definite lack of studies that evaluate the combined effect of cell–cell, cell–ECM interactions as well as biochemical, biomechanical, and the specific processes that occur during the metastatic processes of ovarian cancer (25, 38).

Hydrogels, such as Matrigel, are commonly used for *in vitro* studies of ovarian cancer cell growth and invasion (29, 32, 39). Other substrates such as collagen gels (40), polyhydroxyethylmethacrylate coated plastics (22), algimatrix, and geltrex are also used to model ECM (16). Natural alternatives include human amniotic membranes (HAM) and chick chorioallantoic membranes (CAM). 3D culture systems incorporating amniotic membranes have been used to assess the spreading and invasive capacities of ovarian cancer cells. These offer the advantage of a physiologically relevant tissue barrier for assessment of cell behavior (41–43). Limitations of these materials are the batch to batch variation, presence of confounding growth factors and other biological components whose effects on culturing experiments are not well known (25, 44). Other non-biological considerations in these model systems, which to date have been largely ignored, are the tissue structural properties as well as

gradients of oxygen tension and effects from external physical stimuli (compression, shear stress) (25, 41).

Semi-synthetic matrices such as polyethylene glycol (PEG), hyaluronan, alginate-based, and peptide-based (Puramatrix™) hydrogels are amenable to experimental determination of matrix stiffness and integration of different binding sites and protease cleavage sites (31, 45). Matrix stiffness has been shown to influence endothelial cell behavior independently of matrix molecular composition, highlighting the relevance of matrix material properties in tumor modeling (46). PEG based hydrogels have been used to investigate the role of proteases in the migration of fibroblasts (47) and more recently to investigate cell–ECM interactions and drug resistance of epithelial ovarian cancer cells (48).

Semi-synthetic or synthetic matrices offer the greatest levels of experimental reproducibility due to the control that investigators have in the makeup of the ECM. The study by Loessner et al. is, to date, the most relevant study using a synthetic 3D scaffold to comprehensively investigate ovarian cancer cell growth and response to drugs in an anisotropic biomimetic hydrogel (48). This method enables combination of designed binding sites, protease substrates, other proteins including growth factors and an easily adjustable matrix stiffness. Cells seeded uniformly in the liquid scaffold precursor are exposed to similar levels of biomechanical and biochemical stimuli in all directions (48).

While these models are highly relevant, the addition of other cell types found in the cancer micro-environment (stromal cells, immune cells) would make these models more complete. The immune response has been shown to be clinically relevant in ovarian cancer. Traditionally, immune–cancer cell interactions have been studied in 2D cultures by the addition of immune components or immune stimulatory factors. The establishment of a physiologically relevant tumor micro-environment would enable all cells present (cancer, stromal, immune) to phenotypically resemble those found in disease (49–52). This would create a unique and powerful *in vitro* situation for testing the effects of different immune components and inflammatory responses relevant to disease. For example, TNF-β is known to effect ECM stability, and could therefore influence the capacity of tumor cells to migrate and invade (53). A biologically relevant *in vitro* representation of a tumor is also central for accurately testing drug efficacy, as the interaction of different cell types contributes to the drug response (54). Various 3D models (spheroid cultures, scaffold based 3D cultures, organotypic cultures) would be amenable to the addition of immune factors/cytokines, and although not yet in development, 3D co-culture of many cell types found in ovarian cancer including immune cells should be possible (55, 56).

Heterotypic culture to simulate the micro-environment of ovarian cancer has been shown to be a promising and representative method for investigating stromal–epithelial interactions during disease (57). It has been suggested that modeling ovarian cancer by using 3D cultures of fallopian tube secretory epithelial cells would be more relevant to early stage HG-SOC (58). Combining synthetic matrices, in heterotypic culture with the relevant cells that drive the initiation processes of disease to investigate potential therapeutic targets, would be ideal. A collaborative effort between the NIH, FDA, and the Defense Advanced Research Projects Agency has been instigated to develop and refine methods for functional organ microphysiological systems aimed at drug screening (59). These may also have potential for use in cancer biology. For example, a human liver-like model has been developed to study breast cancer metastases (60). It is possible that such models may, in the future, be adapted to investigate metastases to the liver in ovarian cancer. **Table 1** summarizes some of the factors to consider when choosing a method to model cancer cell growth.

3D modeling of early stage ovarian cancer, which the aforementioned systems aim to achieve, may be the most relevant for identifying potential targets for disease modifying therapies. The second stage of disease involves the spread of ovarian cancer cells from the primary tumor into the peritoneal space. Experiments to capture the behavior of ovarian cancer cells during metastasis focus on anchorage-independent models of cell migration (68–71). Multicellular aggregate, or spheroid formation is critical for shedding of cancer cells from the primary tumor, and it has recently been shown that the culture of ovarian cancer cells as spheroids in a biomimetic ECM, recapitulates the metastatic niche (72). Further, the biomechanical environment of the peritoneal space plays an important role on cancer cell behavior and spread, and so incorporation of physiological fluid mechanics are appropriate in these systems (41, 69). While the development of oxygen tension gradients limits the size of the multicellular spheroids in culture; it mimics the structure of solid tumors and the potential development of necrotic cores (73, 74). This representation of the physiological micro-environment is relevant and appropriate for the screening of drugs, as penetration into the tumor/spheroid is very different to 2D systems and consequently, the response will also be very different (75). A recent study by Jaeger et al. describes the development of a 3D culture system incorporating an oxygen permeable polymer and micro pillars, to mimic gas delivery via vessels (76). This system offers the potential of larger growth of organotypic models and more realistically represents vascularized tumors *in vivo*.

Tissue chips are a relatively new area of research aimed at incorporating as many components as possible to recapitulate the living tissue and study biological responses to many factors in concert (77, 78). Tissue chips allow the modeling of organ systems in a highly functional and controlled manner. They can incorporate many components relevant to tumor biology such as various 3D matrix components and hydrogels. These systems have the potential as tools for measuring metastatic potential, response to various growth stimulators or inhibitors, immune interactions, and drug responses. However, optimization of parameters such as endpoint data collection is still required in order to use these systems for complex tumor modeling (77, 78).

## **CONCLUSION AND FUTURE PERSPECTIVES**

Many advances have been made in recent years in the development of representative 3D models to mimic ovarian cancer relevant to human HG-SOC. However, these systems are still limited and none to date combine all factors, biomechanical, and biological, to create a complete experimental culture system. This is compounded by recent controversy regarding the molecular characterization of HG-SOC cell lines, with several that are commonly used for research, being shown to be non-representative of this

#### **Table 1 | Summary of factors contributing to the choice of model system for ovarian cancer cell culture.**

grade of ovarian cancer. It has become clear that when modeling the micro-environment, it is particularly important to create an ECM that closely mimics that relevant to ovarian cancer, and so considerations of the origin of the cell line are important. For example, an ECM relevant to a primary tumor derived cell line may be different from that of a cell line derived from ascites. Likewise, generation of an appropriate ECM for early disease modeling may have different requirements for epithelial cells derived from the fallopian tube to those derived from the ovarian surface. Only through a comprehensive understanding of physiological tumor behavior will it be possible to identify key players in tumor progression, whether these are ECM proteins (MMPs, TIMPs), immune regulators or cytokines or upstream genetic changes in the cancer cells themselves.

While the sophisticated 3D culture models developed in the last few years have circumvented many problems associated with traditional methods, the use of these systems is still in its infancy in part due to the complex nature, cost, and specialized equipment that is often required. Thus these methods are not yet amenable for highthroughput experimentation and pre-clinical testing. However, technological progress in the coming years will hopefully reduce these limitations and see the widespread use of high-throughput screening using 3D culture systems that accurately recapitulate the tumor micro-environment.

#### **REFERENCES**

1. Saladino E, Fleres F, Irato S, Famulari C, Macrí A. The role of cytoreductive surgery and hyperthermic intraperitoneal chemotherapy in the treatment of ovarian cancer relapse. *Updates Surg* (2013) **65**(4):265–70. doi:10.1007/s13304- 013-0229-9

and ERK1/2 lead to anoikis resistance of ovarian cancer spheroids. *Exp Cell Res* (2014) **320**(2):329–42. doi:10.1016/j.yexcr.2013.11.011

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 21 January 2014; paper pending published: 16 February 2014; accepted: 11 March 2014; published online: 25 March 2014.*

*Citation: Fuller ES and Howell VM (2014) Culture models to define key mediators of cancer matrix remodeling. Front. Oncol. 4:57. doi: 10.3389/fonc.2014.00057*

*This article was submitted to Women's Cancer, a section of the journal Frontiers in Oncology.*

*Copyright © 2014 Fuller and Howell. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

## Recent technological advances in using mouse models to study ovarian cancer

## **Carrie Danielle House, Lidia Hernandez and Christina Messineo Annunziata\***

Women's Malignancies Branch, National Cancer Institute, Bethesda, MD, USA

#### **Edited by:**

Viive Maarika Howell, University of Sydney, Australia

#### **Reviewed by:**

Tamara Louise Kalir, Mount Sinai School of Medicine, USA Judy Luborsky, Rush University Medical Center, USA

#### **\*Correspondence:**

Christina Messineo Annunziata, Women's Malignancies Branch, National Cancer Institute, 10 Center Drive, Room 12N226, Bethesda, MD 20892-1906, USA e-mail<EMAIL_ADDRESS>
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.

 Through the validation of *in vitro* findings, cancer progression, regression, and relapse in the physiological setting is better understood. The continued high mortality associated with serous epithelial ovarian cancer (SEOC) underscores a need for tailored disease models and improved technology to study such models. Several recent advancements promise to facilitate the success of preclinical models in refining our understanding and treatment of SEOC. This mini-review will focus on the latest mouse models of ovarian cancer and improved technologies for using these models to study SEOC initiation, progression, relapse, and therapeutic response.

Ovarian cancer is the most common cause of gynecological mortality in the United States, accounting for more than 14,000 deaths annually (1). Most patients initially respond favorably to platinum-based therapy, however, there is a high incidence of recurrent, chemoresistant disease. Our knowledge of the clinical and molecular attributes of epithelial ovarian cancer has improved greatly over the last few decades, but survival rates for women with this disease remain low. This is partially explained by the inability of clinical trials to replicate the therapeutic response observed in animal models. So far, about one-third of highly cited animal studies saw success in human trials, however, improvements in study design and data interpretation should increase that figure (2, 3). Animal models in the personalized medicine era highlight the availability of affordable genomic sequencing and molecular profiling. As the pharmaceutical industry relies heavily on mouse models, such new refinements will be critical for producing reliable preclinical data on personalized ovarian cancer therapeutic approaches.

In order to generate accurate models, the biology of the disease must be understood. High grade SEOC is thought to arise in a rapid fashion *de novo* from the surface epithelium of the ovary or from the mucosa of the fallopian tube (4, 5). The remaining ovarian carcinomas, categorized as low grade, follow a stepwise adenoma-carcinoma sequence (4, 6). Whether high or low grade, SEOC usually does not reach clinical detection until late stage where it has spread well beyond the ovaries. This feature has hampered efforts to identify the site of origin and understand the pathophysiology of SEOC. Most existing mouse models of SEOC present a disseminated abdominal phenotype, which closely resembles late metastatic disease, and therefore may only provide a good model for therapeutic response in the "average" patient. Some recent mouse models provide a phenotype of early progressive disease coming from defined genetic abnormalities identified from patient subtypes.

## **MOUSE MODELS OF EOC**

An extensive analysis of every mouse model is beyond the scope of this mini-review, however, a summary of recent advances in mouse models of ovarian cancer to place the technological advances in context is presented here. Several recent reviews are available detailing epithelial ovarian cancer experimental models (7–9). Mouse models of ovarian cancer generally fall into one of three categories (xenograft, syngeneic, genetically engineered), the most suitable being dependent on the information being sought.

## **XENOGRAFT MODELS**

A human tumor xenograft is the most widely used mouse model in which human tumor cells are transplanted under the skin (subcutaneous), into the abdominal cavity (intraperitoneal), or into the organ of origin (orthotopic) of an immune-compromised host. While intraperitoneal and orthotopic injections can mimic metastatic dissemination, subcutaneously injected cells are largely limited to tumor formation at the site of injection. The unique presence of a bursa, a sac encapsulating the ovaries and fallopian tubes, allows for intra-bursal injections in mice (10, 11). This technique permits the study of early, localized disease, tumor cell invasion, and dissemination in a more biologically relevant order of events (12).

Xenograft models are particularly useful for evaluating tumorigenesis in a timely fashion (13, 14). Within a few weeks, tumor formation can be measured *in vivo* with histology representative of the tumor of origin (12, 15, 16). Importantly, the pattern of spread to the ascites, liver, and spleen, typical in human disease, is replicated in many of these models and depending on the cell line used, tumors representing the different histological subtypes of epithelial ovarian cancer can be produced (8, 12). Xenografts are versatile and often used in parallel with *in vitro* studies to generate a majority of preclinical data.

Although quite valuable, xenografts carry important limitations. One major disadvantage is the lack of immune response inherent in these models. Nude mice are athymic and therefore have a limited T cell response, and severe combined immunodeficiency (SCID) strains lack both T and B cell responses. Because tumors can promote anti-tumor responses such as lymphocyte and macrophage infiltration, these models may not accurately represent disease progression and therapeutic response observed in otherwise immune-competent individuals (17–19). Furthermore, these models are not suitable for studying immunotherapy or mechanisms involving host–cell interactions. Cell linederived xenografts have had little success in predicting therapeutic response in patients, thereby emphasizing a need for improvements to current models.

An alternative to traditional cell line-derived xenograft models involves the direct transfer of tumor fragments from individual patients. Minced fragments are delivered via orthotopic or intraperitoneal injection into immune-deficient mice to create "xenopatients" or tumor grafts (8, 20). Successful engraftment is higher in SCID mice compared to nude mice, likely due to the suppression of both cellular and humoral immunity (20–23). Several reports have demonstrated that tumor grafts stably maintain the histopathology, immunophenotype, and heterogeneity of the original tumor through multiple passages (21–26). Moreover, these models have the capacity to recapitulate the same therapeutic properties observed in patients (20, 25, 26). The better predictive response value makes these models superior to traditional cell line-based xenografts generated using a suspension of mostly homogenous cells. Engraftment of the native stromal extracellular matrix that would normally accompany a tumor graft may provide the most suitable microenvironment for replicating the biology of the original tumor. This feature renders tumor grafts more suitable for studying early metastasis, as it relies on dissemination of cells from a tumor fragment rather than dispersion of

cells from a suspension (8). Thus, patient-derived tumor grafts provide a means to model inter-patient heterogeneity known to exist across high grade SEOC, and to study tumor evolution through exposure to therapy. Tumor grafts, although promising, are not without their own challenges. Generating a mouse model using a tumor graft is labor intensive and expensive and, as in traditional xenografts, the mice are immunocompromised; consequently immune responses cannot be studied. Although well suited for clinicians and personalized medicine, access to patient tumor samples can be challenging for many basic and translational investigators. Some research teams have generated banks of tumor grafts to make these models more accessible (20, 23, 24).

## **SYNGENEIC MODELS**

Some challenges of xenograft models can be overcome using syngeneic mouse models, wherein tumors are established in immunecompetent mice using cells from the same strain. In one of the most widely used syngeneic models, generated by Roby et al., ovarian surface epithelial cells isolated from immune-competent mice were repeatedly passaged *in vitro* until transformation occurred, and subsequently injected back into the same strain (27). Other syngeneic models have been created using genetically modified cells (28, 29) and highly metastatic cell lines stably expressing luciferase for monitoring disease (30). The histopathologic characteristics observed in the tumors of these models including the presence of papillary structures, nuclear atypia, and malignant ascites, closely resemble those seen in humans (29, 31).

The major advantage of this model is that the mice have an intact immune system; therefore the anti-tumor immune response can be examined and the risk of infection is minimized (19, 32). Syngeneic models provide the opportunity to study the tumor microenvironment, epithelial–stromal cell interactions, tumorsecreting factors, immune cell infiltration, and vasculature (28, 29, 31, 33). This model, however, is completely derived from the animal system and therefore may not mirror every element of human cancer. Although human and mouse tumors share similar features, the complexity of human disease coupled with the heterogeneity of cancer make it difficult to translate findings (34).

## **GENETICALLY ENGINEERED MODELS**

Genetically engineered mouse models (GEMMs) are immunecompetent mice with genetic defects introduced using RNA interference,inducible gene expression, viruses, or DNA recombination techniques. GEMMs provide a means for investigating the role of genetic alterations in cancer development. These models allow researchers to control and direct gene expression, which can be limited to the tissue of interest using a tissue-specific promoter to introduce the desired genetic alteration, or expressed throughout the organism using germ-line mutations (35). Furthermore, regulation of gene expression in the presence or absence of tetracycline and its receptor allows for inducible gene expression systems and provides the flexibility to turn on or off gene(s). For example, transgenic mice carrying both the tetracycline-regulated transcriptional transactivator and its respective binding site linked to a gene of interest permits amplified expression of that gene. If mice are provided with the tetracycline antibiotic in their drinking water, this expression is reversibly suppressed. Thus, GEMMs provide

opportunities to identify which genes are necessary for disease progression, regression, and/or resistance to treatment.

Extensive analyses of human ovarian cancer specimens have identified several genetic alterations associated with malignancy including *TP53*, *C-MYC, K-RAS, AKT*, and *BRCA1* and *BRCA2* (36–38). Subsequently, several genetically modified ovarian cancer models, summarized in**Table 1**, have been developed to explore the contribution of these different aberrations to ovarian cancer development (39–44).

Although GEMMs are labor-, time-, and resource-intensive, they provide information that cannot be attained in xenograft or syngeneic models. Early tumorigenesis and genetic events leading to tumor initiation, maintenance, and relapse can be analyzed. The flexibility provided by genetic manipulation permits the study of different mutation combinations. These models are ideal for target validation, treatment response, and chemoprevention (45). The major challenge with this model is the scarcity of tissue-specific promoters in ovarian surface epithelium or distal fallopian tube. It is also challenging to accurately replicate the contribution of genetic elements given that genes over-expressed in mice are often at non-physiological levels or deleted throughout the organism (46). GEMMs may fail to recapitulate the genetic complexity of human SEOC, and the varied genetic background of different mouse strains can influence findings and conclusions (8).

## **TECHNOLOGICAL ADVANCES IN USING ANIMAL MODELS REPORTERS**

Most ovarian cancer cell lines can be stably transfected with a fluorescent and/or bioluminescent reporter for monitoring tumor cell growth and dissemination, pathway activity, and receptor interactions.

This technology has been adapted to xenograft and syngeneic mouse models of ovarian cancer. For example, NF-κB activity was tracked in a syngeneic model of SEOC to confirm that activation correlated with progression and influenced immune cells of the microenvironment (47). Similarly, reporter-tagged tumor cells can be used to monitor tumor response in real-time using digital imaging following systemic targeted therapy (48–50). Using reporters in live animals to track tumor cell dissemination allows for studying cancer progression and therapeutic response, especially in syngeneic models where the immune response is integral.

Luciferase complementation-based assays measure receptor activation and protein interactions using monomeric enzyme components that have enzymatic activity only when complementation is induced by the interaction of binding partners or small molecules (51). Activation is proportional to the production of light that occurs upon complementation. The flexibility of this technology allows detailed quantitative measurements of complexes, assessment of nuclear translocation, and identification of pathway modulators (52). For example, this assay was successfully implemented for live imaging of the chemokine, CXCL12, interacting with its receptor, CXCR4, in animal models of ovarian cancer (53, 54).

#### **IMAGING**

Quantitative measurements of late-stage disease in ovarian cancer models are challenging due to the presence of varying levels of ascites and the poor correlation between total body weight gain and tumor burden. Diagnostic imaging is a reproducible means to

#### **Table 1 | GEMMs for ovarian cancer.**

quantify tumor mass, monitor tumor progression, and interrogate the tumor microenvironment. Imaging techniques used in the clinic [e.g.,magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), ultrasound] have been adapted for use in animals (55, 56). These modalities are especially informative as they can be performed in intact living animals. Interval imaging reduces the number of animals needed for experiments as measurements are taken without sacrificing the animal. The major challenge to imaging ovarian cancer in animal models, as in humans, is the difficulty in detecting early disease; by the time mice begin to show signs of morbidity the cancer has often spread beyond the ovaries and throughout the peritoneum.

Positron emission tomography imaging is a standard diagnostic radiological technique commonly used to monitor drug action in cancer patients. This modality allows the measurement of metabolic activity in cancer cells and is especially useful in quantitative monitoring of tumor response to anti-cancer therapies (56). PET imaging can assess targeted therapies in both transgenic (57) and xenograft (58) models of ovarian cancer.

Ultrasound imaging is another common tool used in small animal models and is often combined with other imaging techniques for a more comprehensive analysis (57). Ultrasound is cost-effective and convenient for measuring individual tumors in live animals (59). Doppler ultrasonography can measure changes in blood flow and angiogenesis associated with disease progression or response to anti-angiogenic therapy (59, 60).

Magnetic resonance imaging with gadolinium-based contrast agent permits high-resolution serial imaging with minimum scanning duration, allowing quantification of tumor volume over time. MRI data are comparable to caliper-based measurements taken at necropsy. This longitudinal imaging protocol is well suited for monitoring therapeutic response (61). MRI can also be combined with fluorescence molecular tomography (FMT) to monitor tumor-specific biology, such as protease and integrin activity (62). When coupled with a reporter gene such as ferritin heavy chain (FHC), MRI can evaluate recruitment of other cell types, such as fibroblasts, to the tumor site (63). Alternatively, MRI combined with magnetic resonance spectroscopy (MRS) can characterize tumor physiology and metabolic profiles over time (64).

### **METABOLIC PROFILING**

Measurement of metabolites and their intermediates can illustrate the response of an organism to a genetic manipulation or therapy. Metabolites are small, low molecular weight analytes and include amino acids, oligopeptides, sugars, fatty acids, and various intermediates of biochemical pathways, in contrast to large proteins and nucleotides that are assessed using proteomics and genomics, respectively (65). Nuclear magnetic resonance (NMR) spectroscopy, liquid and gas chromatography, and mass spectrometry (MS) are generally used to analyze serum, urine, or tissue extracts. Such measurements provide insight into drug mechanisms and toxicities. Metabolic profiles represent a snapshot of the biochemical reactions occurring at a point functionally downstream of genome, transcriptome, and proteome (65).

Commonly used in human studies, (66, 67) this technology was adopted in a GEMM of SEOC. The metabolic profile overlapped with human SEOC and showed a temporal correlation with disease progression (44, 68), highlighting the feasibility of metabolic profiling for identifying biomarkers and monitoring treatment response in animal models (44).

#### **TUMOR-INITIATING CELLS**

The cancer stem cell (CSC) or tumor-initiating cell (TIC) hypothesis suggests that a small population of chemoresistant cells reside in the tumor, capable of reconstituting the tumor. These cells share properties of normal stem cells, such as self-renewal and multipotency. Given the high recurrence of ovarian cancer, the TIC hypothesis is an attractive model for explaining ovarian cancer relapse.

Mouse models have been especially useful in evaluating TICs. When injected into mice, these cells must recapitulate the heterogeneity of the original tumor. Animal models are essential for defining TICs and for evaluating drugs and pathways important for eradicating these cells. Patient-derived xenografts might allow further characterization of the frequency of TICs in human tumors, and their relevant biomarkers.

A number of markers have been used to identify and isolate ovarian cancer TICs including CD133, CD44, CD117, and ALDH activity; however it is unlikely that a single marker defines ovarian TICs (69, 70). Several studies have demonstrated heterogeneous tumor formation in xenograft mice after subcutaneous injection of sorted ovarian cancer cells from primary tumors, cell lines, or ascites (71–74). TICs have also been propagated *in vitro* using low attachment culture plates and specialized serum-free media to enhance the formation of multicellular spheroids with stem-like features (69, 73).

Although much research has focused on characterizing tumorigenesis of human TICs in xenograft models, recent studies evaluated endogenous TICs in mice (75–77). Syngeneic or GEMMs offer the possibility of studying the role of the immune system in TIC biology. Furthermore, with direct or indirect labeling of the TICs, each of these models can facilitate tracking of the cells to monitor tumor initiation and dissemination.

#### **IMMUNE THERAPIES**

The role of the immune system in ovarian cancer is studied extensively using animal models (19). Representing a robust predictor of outcome, tumor-infiltrating lymphocytes are associated with better survival for ovarian cancer patients (78, 79). Immune therapies involving vaccines, dendritic cell therapy, engineered T cells, and immune modulators thus hold promise for ovarian cancer treatment (80–86).

Current goals aim to enhance the anti-tumor immune response through increased immune activation and decreased immune suppression. Programed death-1 (PD-1) and CTL antigen-4 (CTLA-4) signals silence the immune response in tumors. A syngeneic mouse model of ovarian cancer showed that simultaneously blocking these pathways enhanced T cell infiltration into the tumor and increased long-term survival (81). A related model found that the therapeutic effect of gemcitabine is limited because of the immunosuppressive network of CTLA-4 (83). Gemcitabine plus anti-CTLA-4 antibody exhibited synergy in a strong anti-tumor immune response. Likewise, anti PD-1 therapy shows synergism

with a variety of immunotherapies or vaccines (86). These findings have translated well and are currently under evaluation in the clinic.

Genetically modified T cells engineered to over-express receptors for tumor-associated antigens have shown great success in mouse models of ovarian cancer (87, 88). This emerging technology is a logical avenue for ovarian cancer, an apparently immunogenic disease where T cell infiltration is associated with improved survival (19, 88).

## **CONCLUSION**

Despite our progress in understanding ovarian cancer biology, there remains a high mortality associated with this disease. Exciting advances in reporter assays, live imaging, metabolomics, TICs, and immune therapies, provide new information about the tumor microenvironment and further our understanding of SEOC development, progression, and recurrence (**Figure 1**). Further refinement of mouse models of ovarian cancer, an awareness of the limitations each model presents, and taking advantage of the technologies available to study these models will undoubtedly expedite the success of new treatments.

## **REFERENCES**

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 18 October 2013; accepted: 28 January 2014; published online: 13 February 2014.*

*Citation: House CD, Hernandez L and Annunziata CM (2014) Recent technological advances in using mouse models to study ovarian cancer. Front. Oncol. 4:26. doi: 10.3389/fonc.2014.00026*

*This article was submitted to Women's Cancer, a section of the journal Frontiers in Oncology.*

*Copyright © 2014 House, Hernandez and Annunziata. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited andthatthe original publication inthis journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# Patient-derived xenograft models to improve targeted therapy in epithelial ovarian cancer treatment

#### **Clare L. Scott 1 , Marc A. Becker 2 , Paul Haluska2 and Goli Samimi 3\***

1 Walter and Eliza Hall Institute of Medical Research, Royal Melbourne Hospital and The Royal Women's Hospital, Melbourne, VIC, Australia

2 Division of Medical Oncology, Mayo Clinic, Rochester, MN, USA

3 The Kinghorn Cancer Centre, St Vincent's Clinical School, Garvan Institute of Medical Research, Sydney, NSW, Australia

#### **Edited by:**

Viive Maarika Howell, University of Sydney, Australia

#### **Reviewed by:**

Shaveta Vinayak, Stanford University, USA Simon Langdon, University of Edinburgh, UK

#### **\*Correspondence:**

Goli Samimi, The Kinghorn Cancer Centre, Garvan Institute of Medical Research, 370 Victoria Street, Darlinghurst, Sydney, NSW 2010, Australia e-mail<EMAIL_ADDRESS>
Despite increasing evidence that precision therapy targeted to the molecular drivers of a cancer has the potential to improve clinical outcomes, high-grade epithelial ovarian cancer (OC) patients are currently treated without consideration of molecular phenotype, and predictive biomarkers that could better inform treatment remain unknown. Delivery of precision therapy requires improved integration of laboratory-based models and cuttingedge clinical research, with pre-clinical models predicting patient subsets that will benefit from a particular targeted therapeutic. Patient-derived xenografts (PDXs) are renewable tumor models engrafted in mice, generated from fresh human tumors without prior in vitro exposure. PDX models allow an invaluable assessment of tumor evolution and adaptive response to therapy. PDX models have been applied to pre-clinical drug testing and biomarker identification in a number of cancers including ovarian, pancreatic, breast, and prostate cancers. These models have been shown to be biologically stable and accurately reflect the patient tumor with regards to histopathology, gene expression, genetic mutations, and therapeutic response. However, pre-clinical analyses of molecularly annotated PDX models derived from high-grade serous ovarian cancer (HG-SOC) remain limited. In vivo response to conventional and/or targeted therapeutics has only been described for very small numbers of individual HG-SOC PDX in conjunction with sparse molecular annotation and patient outcome data. Recently, two consecutive panels of epithelial OC PDX correlate in vivo platinum response with molecular aberrations and source patient clinical outcomes. These studies underpin the value of PDX models to better direct chemotherapy and predict response to targeted therapy. Tumor heterogeneity, before and following treatment, as well as the importance of multiple molecular aberrations per individual tumor underscore some of the important issues addressed in PDX models.

**Keywords: ovarian cancer, patient-derived xenografts, pre-clinical models, targeted therapy, clinical trials**

## **INTRODUCTION**

Cell lines and archival tumor tissue have provided a platform for discovery and validation of novel therapeutic targets in epithelial ovarian cancer (OC). However, despite increasing evidence that precision therapy targeted to the molecular driver(s) of a tumor has the potential to impact overall survival (1), patients with high-grade epithelial OC are currently treated with a "one-size fits all" approach, without consideration of molecular phenotype or biomarkers of response that could better inform treatment. Preclinical models to predict those patients who will benefit from targeted therapy are imperative to implement effective precision therapy strategies. Classic cell line-derived xenograft models have provided invaluable mechanistic insight toward the key signaling pathways and oncogenic drivers of OC tumorigenesis, malignant progression, and chemotherapeutic resistance. The translational potential of models, generated from either human OC, many years ago, with scant histo-pathologic data about the source OC tumor from which they were derived or from human ovarian surface epithelial (OSE) cell lines, remains questionable (2). In both cases, the cell lines used to generate xenografts have been expanded *in vitro*, and as such, have likely acquired significant alterations in morphology, motility, and proliferation that do not necessarily reflect the physiologic state of the tumor (3). More importantly, recent evidence suggests that the ovarian surface may not be the origin of "ovarian cancer" (4, 5).

The majority of epithelial OC are serous in sub-type (50% of OC) and display Fallopian tube-like or "endosalpingeal" characteristics. Endometrioid OC (20% of OC) and mucinous OC (10% of OC) represent additional epithelial sub-types displaying features of epithelia from other Mullerian tract (developmental female genital tract) organs, resembling endometrial and endocervical epithelia, respectively (6). Two main phenotypic groupings of human EOC have been described. Type I EOC includes low-grade, mucinous, and clear cell cancers, with progression identifiable from adenoma-borderline-cancer. Type II EOC comprises lethal, high-grade serous (HG-SOC), endometrioid, and undifferentiated EOC. Although previously thought not to have recognizable precursor lesions (7), the current consensus indicates an association

with early lesions being found in the distal Fallopian tube in carriers of *BRCA1/2* mutations (5, 8). The Fallopian tubes are derived from the Mullerian ducts (also of mesodermal origin) and consist of muscular, ciliated, and secretory epithelia (9). Fallopian tube cancer and primary Peritoneal cancer (the latter is derived from the coelomic epithelium, as is the OSE), behave in a clinically similar fashion to serous EOC and while often studied together, distinct molecular differences are evident (10, 11).

The OSE has long been postulated to be the source of putative cancer initiating cells for epithelial "ovarian" cancer (12), with an alternative origin postulated to derive from the distal ends of the Fallopian tube and malignant or pre-malignant cells migrating to and settling on contiguous OSE (5, 8). A proportion of malignant lesions originate in the Fallopian tube and may potentially metastasize to the OSE (13). The OSE persists as a single layer of squamous to cuboidal epithelium that covers the ovary (14), is derived from coelomic epithelium (of mesenchymal origin), has a basement membrane and, unusual for a surface epithelium, can undergo epithelial-mesenchymal transition (EMT) (15). The OSE has been described as a facultative stem cell niche, with cells retained within this niche maintaining pluripotency and expressing markers typical of a stem cell-like quiescent state (12). However, it is possible that the OSE provides a suitable "niche" for the development of "ovarian" cancer and that the majority of HG-SOC in fact derive from secretory cells from the fallopian tube (16). Many reports of OC xenografts and patient-derived xenografts (PDXs) have mixed together OC sub-types, which may have very different implications for cell of origin or treatment approaches. By not taking into account sub-type, the likelihood of deriving useful information is greatly diminished.

Furthermore, traditional *in vivo* pre-clinical models do not accurately recapitulate the complexity and heterogeneity of patient tumors (17). As each tumor's molecular phenotype impacts prognosis and response to treatment, detailed genomic annotation of each xenograft is necessary for comprehensive evaluation of targeted therapies. Xenografts derived from a cell line originating from OC have to undergo extensive selection. More often these lines reflect the *in vitro* culture system and are devoid of the complex pathology and molecular attributes of the original patient tumor. A compelling example of this discordant phenomenon was reported in a study involving 41 cell lines, each of which rarely contained *BRCA1* and/or *BRCA2* mutations (18). The authors concluded that the use of these cell lines for xenograft studies would not accurately reflect the patient population. Moreover, xenografts derived from cell lines cultured from potentially irrelevant tissues (e.g., non-surface epithelium), may be even more flawed as models of human HG-SOC.

Xenografts may be derived directly from patient tissue without prior *in vitro* culture (PDXs), in which tumor tissue excised at the time of surgery is immediately transplanted into immunedeficient mice. Importantly, digestion of tumor material using protocols known to involve harsh cell dissociation buffers may inadvertently strip the cell surface of molecules integral toward *in vivo* cell–cell interactions. Alternatively, methods such as mincing of tumor fragments or the use of whole fragments may more closely model the heterogeneity of clinical disease. PDXs derived from non-digested OC can provide extremely flexible models for pre-clinical analysis of novel therapeutics (19). Primary OC and resultant serial PDX can be histologically assessed for known diagnostic and prognostic markers and characterized by molecular techniques including genome sequencing. These PDX models can therefore be extensively annotated and serve as powerful models for pre-clinical studies of targeted therapeutic strategies, thus bridging the gap between lab bench discoveries and clinical translation. As such, there has been an increase in characterization and application of PDX models for drug screening across a range of cancers [reviewed in Ref. (17)].

Thus, major concerns regarding OC PDX literature to date are as follows: numerous papers lack detail regarding histologic subtype, molecular phenotype, a detailed description of the methods used to generate and maintain the PDX, limited genomic characterization has been performed (e.g., CGH or CNV analysis), and the stability of various phenotypes over successive generations is noteworthy. As a result, a substantial barrier to the study of OC is the paucity of translationally (e.g., transient *in vitro* primary cell lines) and clinically (e.g., archived tissues from retrospective analyses) relevant models, thereby highlighting the salient need for an alternative, clinically relevant means to rapidly translate results from bench-to-bedside. The development of personalized PDX models, with each patient having a PDX generated across her disease progression (primary tumor, metastasis, recurrence) and stage of treatment (prior to treatment, at relapse), with availability of source biospecimens (germline DNA, serum, frozen, and FFPE tissue, etc.) and prospective clinical annotations could overcome many of the current hurdles (e.g., the dependence on isolation/digestion and subsequent amplification *in vitro* prior to establishment and testing in animals). These PDX models recapitulate primary patient tumors (e.g., formation of bowel metastases, obstruction, ascites, etc.), reproducibly engraft, retain the molecular and gross phenotypic characteristics of the donor OC patient, can be accurately monitored for tumor detection and progression (e.g., gross tumor palpation, calipers, ultrasound-guided imaging, etc.) and represent a practical and highly translatable medium to study the effects of both standard chemotherapy and precision targeted therapeutics.

## **METHODOLOGY**

As previously noted, standard OC xenografts are derived from established, highly annotated, and widely available cell lines. While OSE models are commonly utilized and have become a mainstay workhorse to investigators, their uncommon origin (e.g., murinederived) brings into question functional significance. For example, the ID8 cell line was originally developed as a syngeneic mouse model to study the early molecular and immune events related to ovarian carcinogenesis (20). As a result, greater attention toward patient-derived OC models has been expended.

Patient-derived xenograft (digested) have played a key role toward the study of the cancer stem cell (CSC) niche and identified tumor-initiating cells (TICs) as key players in primary patient ovarian xenografts (21). The frequency of TICs represents an intrinsic property of the primary patient tumor. However, the integrity of the TIC landscape (e.g., proportion of CD133 positive versus negative cells) is altered in PDX models. It is plausible that the extensive *ex vivo* digestion prior to PDX engraftment is the confounding source of TIC PDX discrepancies.

Patient-derived xenograft (fragments) are generated by sectioning of fresh tumor tissue and engrafting (1–2 × 1–3 mm3 ) pieces either subcutaneously or orthotopically into immuno-deficient mice (e.g., NOD-SCID IL2Rγ −/−). Engraftment rates for this generation (T1) range from 25 to 80% depending on tumor type (22), and growth usually takes 2–6 months. Once the T1 PDX tumor has reached ∼700–1500 mm3 (23), it is harvested and directly re-transplanted for expansion in later serial generations (T2, T3) which are used for *in vivo* drug response, biomarker studies and generating cell lines for additional drug response and molecular studies. Alternatively, the fresh patient tumor can be minced and cryo-preserved in DMSO for later thawing and transplantation, thus ensuring the renewability of the resource. For molecular comparisons, the original patient tumor and the PDX models can undergo extensive histo-pathological and genomic analysis. In addition, for OC PDX models, the T1 PDX can be analyzed for platinum response and homologous recombination (HR) activity, which are key clinical indicators of drug response.

#### **PDX MODELS IN OVARIAN CANCER**

Patient-derived xenograft models have been applied to pre-clinical drug testing and biomarker identification in a number of cancers including pancreatic cancer (24), NSCLC (25, 26), melanoma (27), breast cancer (28, 29), and prostate cancer (30). As reviewed by Tentler et al. comprehensive genomic analysis including sequencing, expression, and copy number, have demonstrated that PDX models maintain overall global gene expression and activity as the source tumor (17). In OC, PDX models have been developed that accurately reflect the patient tumor and have successfully been used to examine drug response and effects of targeted treatment (22).

Some of the earliest applications of OC PDXs in studies of drug response were reported by the Repasky group. They developed 20 different PDX models in severe combined immuno-deficient (SCID) mice. Histo-pathologic and *in situ* hybridization analyses were carried out to confirm similarity to the source tumor. While all implanted PDXs eventually formed tumors, 65% (13/20) of them reached 1–2 cm within 2–6 months and were further expanded. Three of the later generation PDX models developed metastases and two developed ascites, representing clinical progression of the disease (31). The group then applied their subcutaneous PDX models in two separate studies to examine the effects of IL-12 and Flt-3 ligand on ovarian tumor growth. Following engraftment, PDX mice were treated with either placebo or IL-12 (32), or placebo or Flt-3 ligand (33), and tumor volume was measured over time. Treatment with IL-12 or Flt-3 ligand resulted in decreased tumor growth compared to control-treated mice, with increased NK cells and necrosis in the tumors of IL-12 or Flt-3 ligand treated mice. These findings suggest an immunologic reaction in response to IL-12 and Flt-3 ligand, supporting their potential therapeutic roles in the treatment of OC (32, 33).

Ghamande et al. followed up these studies by examining the effect of CD40 ligand therapy, previously shown to decrease growth in OC cells, on CD40 receptor-positive PDX serous OC models (34). PDX mice with subcutaneous or intra-abdominal tumors were treated with vehicle or increasing concentrations of recombinant CD40 ligand and tumor growth was assessed over time. Tumor growth in both locations was decreased following as little as one cycle of treatment,regardless of concentration. In addition, once tumors were excised following treatment, histological analysis revealed disruption of tissue architecture and increased fibrosis and apoptosis, providing further insight into the mechanism of therapy. Furthermore, the authors utilized these PDX models to examine the effect of combination therapy using standard chemotherapeutic agents and CD40 ligand therapy, further demonstrating an augmented effect when both drugs were used in treatment of CD40-positive tumors (34). These studies highlight the utility of PDX models in evaluating drug efficacy and mechanism of action.

While a majority of HG-SOC patients initially respond to first-line treatment (generally, a platinum drug in combination with a taxane), a large proportion eventually relapse and develop platinum-resistant disease. OC PDX models can be useful for screening drug sensitivity, which in turn provides guidance for clinical management of the patient who presents with recurrent disease. Kolfschoten et al. established 15 subcutaneous OC PDX models and examined sensitivity to standard chemotherapy (35). They reported that response rates in the PDX models correlated with those in OC patients (e.g., 40% of PDXs responded to cisplatin while 48% of patients respond). As detoxification by glutathiones has been demonstrated to render cells resistant to platinum treatment, the authors also investigated the glutathionebased mechanisms involved in the development of resistance. They measured levels of glutathione and glutathione-related enzymes in the PDX models and related them to drug response. They identified a correlation between glutathione reductase activity and efficacy of chemotherapeutic agents cisplatin and cyclophosphamide, suggesting that glutathione-related enzymes may be useful as predictors of drug sensitivity (35). These findings speak to the value of PDX models for expanding *in vitro* findings of drug response and relating them to patient tumors.

Because HG-SOC patients frequently develop resistance to platinum-based chemotherapy, it is imperative to identify novel therapies with efficacy toward tumors with *de novo* or acquired resistance. In an effort to investigate the efficacy of lurbinectedin, a new DNA binding drug, Vidal et al. generated serous PDX models by engrafting primary tumor tissue directly onto the mouse ovary surface (36). They included tumors with cisplatin sensitivity, as well as a tumor selected for acquired cisplatin resistance by repeated *in vivo* exposure. They reported a high correlation of histo-pathologic features between the patient and the platinumsensitive and -resistant PDX tumors. The platinum-sensitive PDX displayed a dose-dependent response to cisplatin treatment, characterized by significant tumor volume reduction. Interestingly, 30–50% of treated mice relapsed at 6 months following treatment, and histo-pathologic features of the relapsed tumors were similar to the un-treated xenografts (36). As expected, cisplatin treatment did not significantly inhibit tumor growth in the cisplatinresistant PDX model compared to control-treated mice. However, lurbinectedin treatment alone significantly decreased tumor growth in both cisplatin-sensitive and -resistant PDX models, and lurbinectedin in combination with cisplatin was more effective

than either drug alone (36). Additional studies demonstrated an increase in apoptosis and mitotic catastrophe in lurbinectedintreated PDX mice, providing further insight into its mechanism of action. Thus, drug-resistant PDX models can be used to identify therapies that may be effective in patients with tumors resistant to standard agents.

In addition to subcutaneous and intra-peritoneal (IP) engraftments, OC PDX models have been established in mice by sub-renal capsule xenografts, allowing for follicle maturation. Lee et al. have demonstrated a high take-rate (95%) in sub-renal capsule PDX models, including low- or moderate-grade OC tissues that are typically difficult to engraft in subcutaneous or IP models (37). The authors compared histo-pathologic features in the original patient tumor, pre-graft tissue, and post-graft tissue and found no architectural or cytological differences, nor any major differences in immunomarker expression including CK20, CK7, or WT-1 (87–91% overall concordance). This group then investigated five individual sub-renal PDX models for drug response and genetic stability over subsequent passages (38). The authors analyzed the primary tumor and corresponding PDX by array CGH and reported similar gene copy numbers, with the primary tumors consistently clustering with their matching PDX. Furthermore, there was no significant difference in copy number changes between the primary tumor and corresponding PDX (38). These findings further support the accurate reflection of the patient tumor in PDX models. Furthermore, the high engraftment rate of sub-renal capsule PDX models may provide the opportunity to investigate the differences in tumor progression between low- and high-grade ovarian tumors.