Exploring the Potential of Drug Response Assays for Precision Medicine in Ovarian Cancer

One of the major challenges in the treatment of cancer are differential responses of patients to existing standard of care anti-cancer drugs. These differential responses may, in part, be due to a diverse range of genomic, epigenomic, proteomic, and metabolic alterations among individuals suffering from the same type of cancer. Precision medicine is an emerging approach in cancer therapeutics that takes into account specific molecular alterations, environmental factors as well as lifestyle of individual patients. This approach allows clinicians and researchers to select or predict treatments that would most likely benefit the patient based on their individual tumor characteristics. One class of precision medicine tools are predictive, in vitro drug-response assays designed to test the sensitivity of patient tumor cells to existing or novel therapies. These assays have the potential to rapidly identify the most effective treatments for cancer patients and thus hold great promise in the field of precision medicine. In this review, we have highlighted several drug-response assays developed in ovarian cancer and discussed the current challenges and future prospects of these assays in the clinical management of this disease.


Introduction
Cancer is a major global health problem. According to the World Health Organization (WHO), cancer is the second leading cause of death behind cardiovascular disease, with one out of every six deaths being cancer-related. In the United States, the American Cancer Society estimates that in 2020 alone, there will be 1,806,590 new diagnoses and 606,520 cancer-related deaths [1].
Traditional modalities of cancer therapy such as surgery, radiation therapy, and chemotherapy still remain the standard of care for clinical management of most cancers [2]. Although these modalities have led to improvements in overall survival of many cancer patients, their impact can be somewhat limited to a subset of treatment sensitive tumors. In addition, these therapies can be associated with long term side-effects. Our growing understanding of the biology of cancer over the past few decades has shifted cancer treatment paradigms from organ-centric approaches to genotype-driven precision medicine approaches, leading to the development of more novel therapies for cancer: targeted therapies and immunotherapy [2,3]. While cancer immunotherapy is still in its early phase of development and is not yet widely directed as first-line treatment [4], targeted therapies have opened the door to personalized treatments for cancer patients focused on specific molecular alterations of individual tumors [5]. However, the successful implementation of targeted therapies requires development of assays or biomarkers that would allow clinicians to select and apply effective treatments.
Companion diagnostics that have the ability to identify patients who are most likely to benefit from a particular anti-cancer therapy are being increasingly integrated with drug-development processes and clinical trials [6]. HER2 overexpression by United States Food and Drug Administration (FDA)-approved assays was part of the approval for the use of trastuzumab (HERCEPTIN) in 1998 in selected patients with breast cancer [6]. The PD-L1 immunohistochemistry (IHC) 28-8 PharmDx assay is another FDA-approved companion diagnostic that assesses non-small cell lung cancer (NSCLC) patients to receive nivolumab (OPDIVO) based on overexpression of PD-L1 [6]. Most recently, in 2016, the FDA approved the VENTANA PD-L1 (SP142) assay as a companion diagnostic for selecting patients with NSCLC and bladder cancer for treatment with Atezolizumab (TECENTRIQ) [6]. In 2018, the FDA approved Olaparib (Lynparza) for the maintenance treatment of women with breast cancer susceptibility gene (BRCA)-mutated advanced epithelial ovarian, fallopian tube or peritoneal cancer with favorable response to first line platinum-based chemotherapy [7]. There are currently two FDA-approved companion diagnostics for Olaparib to identify patients with advanced ovarian cancer who harbor BRCA mutations: BRACAnalysis companion diagnostic (CDx) (Myriad Genetic Laboratories, Salt Lake City, UT, USA) and FoundationOne CDx (Foundation Medicine, Cambridge, MA, USA) [8]. Although biomarker-directed patient stratification has substantially improved clinical outcomes for cancer patients, its reach is limited to a small subset of targeted treatments. Moreover, response-predictive biomarkers are not available for most frontline anti-cancer therapies. Hence, alternative approaches are required that would allow the clinician to test the response of patient's tumor cells to various available anti-cancer therapeutics.
Drug-response assays are in vitro platforms wherein live patient tumor cells are exposed to various chemotherapeutic and other agents in order to test their drug sensitivity. Development of such assays are specifically important in the context of ovarian cancer as most patients treated with the standard platinum-based chemotherapy eventually develop recurrent disease that is resistant to this treatment [9]. Due to the lack of effective second-line chemotherapies, the median overall survival of ovarian cancer patients with platinum resistant disease is about one year [9]. Hence, novel therapeutic strategies are an urgent need in the treatment of ovarian cancer patients. In recent years molecular targeted therapies have shown great promise for personalized treatment of ovarian cancer patients. For example, poly (ADP-ribose) polymerase (PARP) inhibitors and anti-angiogenic inhibitors are two FDA approved targeted drugs for ovarian cancer that have shown an encouraging progression free survival benefit [10]. However, these targeted drugs are only effective in a subset of patients. Hence, validated in vitro assays that can test individual tumor responses to chemotherapy or targeted therapies could be employed as a valuable clinical tool for individualized treatment of cancer patients.
The majority of drug-response assays involve culturing primary patient tumor cells in a 2D or 3D cell culture environment where they are exposed to cytotoxic drugs [11]. Performance of these assays may hinge on the type of cell culture technique being used. In this review, we have explored the potential of several 2D as well as 3D cell culture models as drug sensitivity screening tools in ovarian cancer. We have addressed the pitfalls and bottlenecks of these assays wherever possible. We believe that a better understanding of these preclinical models may help in promoting research initiatives focused on the development of assays for effective drug screening and clinical management of ovarian cancer.

Commercially Tested Drug-Response Assays in Ovarian Cancer
Chemo-response assays (CRAs) are ex vivo drug-response assays designed to characterize the sensitivity or resistance of a patient's tumor cells to physician-selected, clinically applicable chemotherapy agents [12]. Most CRAs developed thus far share similar principles and procedures which include: (a) collection of primary patient tumor samples; (b) processing of tumor samples into single cells; (c) establishment of in vitro cell culture; (d) treatment with chemotherapeutic agents; (e) assessment of cell survival or death; and (f) statistical analysis and prediction of drug sensitivity [12].
Two commercially tested CRAs in the US include Microculture-Kinetic (MiCK) assay (DiaTech Oncology, Nashville, TN, USA) and ChemoFx assay (Helomics, Pittsburgh, PA, USA). Both assays utilize 2D cell culture systems. The MiCK assay is based on the principles of drug-induced apoptosis in which neoplastic cells are purified from patients' tumor samples, seeded in a 2D cell culture environment and exposed to serially diluted chemotherapeutic drugs causing apoptosis in tumor cells [13]. The optical density (OD) of cells, a surrogate marker of apoptosis, is measured over time to create a density-by-time curve [13]. The extent of drug induced apoptosis is a measure of the tumor cells' sensitivity to the tested drug. This assay has been applied to study the chemo-response of tumor cells of different types of cancers including hematologic [13], breast [14,15], lung [15], and gynecologic malignancies [15][16][17]. The MiCK assay has also been clinically tested in ovarian cancer patients. Some of these trials are outlined in Table 1. The ChemoFx assay is another 2D culture based ex vivo assay designed to predict therapy sensitivity or resistance of a patient's tumor cells to a variety of chemotherapy drugs [20]. After treating tumor cells with increasing doses of selected chemotherapeutic agents, the number of remaining live cells (cells without DAPI-stained nuclei) is quantified microscopically using automated cell-counting software. These data are used to generate dose-response curves and score tumors' drug sensitivity as either "sensitive", "intermediate sensitive", or "resistant" [20]. In contrast to other CRAs, the ChemoFx assay has several advantages that may makes it clinically applicable [20]. First, it utilizes a cell-culture process that supports the growth of epithelial cells, decreasing the confounding effects of other cell types. This process is complimented by incorporating immunocytochemistry steps within its workflow to validate that the majority of cells are epithelial. Second, due to the low cellular volume required for this assay (as low as 35 mm 3 of tissue), it is accessible for testing core needle biopsy samples. Finally, this assay is highly automated allowing use of a wide range of drug concentrations. This assay has been utilized to predict the chemo-response of several types of solid tumors, including ovarian cancer [18,19] and breast cancer [21]. Multiple clinical trials, both retrospective and prospective, have been reported on the clinical validity of this assay in ovarian cancer. Some of these trials are outlined in Table 1.
Beyond these two assays, there are several other 2D cell culture-based approaches that have been clinically evaluated in epithelial ovarian cancer as platforms for testing therapeutic responses. An overview of these assays is outlined in Table 2.

Current Clinical Use of CRAs
According to the American Society of Clinical Oncology (ASCO) guidelines (2011) the use of chemotherapy sensitivity and resistance assays is not recommended outside of the clinical trial setting [26]. Most currently available CRAs utilize 2D cell culture models for testing drug sensitivity. Growing evidence demonstrates that cancer cells in 2D culture behave radically different than actual tumor cells in vivo [27]. Given limitations with existing 2D cell culture-based assays, many investigators have shifted their focus to utilization of 3D cell culture models which may be more representative of tumor architecture compared to 2D models.

Emergence of 3D Cell Culture Models for Drug-Testing in Ovarian Cancer
The importance of interaction between cells and an extracellular matrix (ECM) was pioneered by Mina Bissell in the early 1980s where she and her team postulated that a reciprocal and dynamic interaction between cells and their surrounding ECM can modulate gene expression [28]. Over time, investigations into this model led to the emergence of 3D cell culture techniques and 3D organoids became a preferred model for studying complex malignant tumors. In 3D culturing technique, cells are able to interact with each other and with the ECM to form organoids [29]. There are multiple lines of evidence that suggest the response of tumor cells to cytotoxic agents dramatically differs in 2D vs. 3D cell culture models [30][31][32]. Hence, these 3D culture models are being widely adopted in drug screening and drug toxicity assays.
Organoids, resembling mini organ structures, are 3D multicellular aggregates grown in an ECM and utilized to model human organ development and disease in a dish [29,33]. Organoids can be derived from tissues, primary tumors, cancer cell lines, and normal stem cells including embryonic stem cells and induced pluripotent stem cells when embedded within ECM hydrogels [29,[33][34][35]. Due to their intrinsic ability of self-organization, they are thought to retain the identity of their tissue of origin [29,33]. There is some evidence supporting that genomic alterations are recapitulated in matrix-dependent organoid cultures [36,37]. As reviewed by Tibbit and Anseth [38], ECM hydrogels play an important role in the generation of organoids as they act as a scaffold for cell growth, promote cell adhesion, and enable proper transport of nutrients, gases, and growth factors to cells (Table  3). Table 3. Types of extracellular matrix (ECM) gels used in 3D organoid culture.

Natural ECM Gels Synthetic ECM Gels
Formed from extracellular matrix components or materials derived from biological sources such as chitosan and alginate.
Made of non-natural molecules  Similar to organoids, spheroids are compact 3D multicellular structures. Although these terms are often used interchangeably, they represent two distinct models of 3D culture. Spheroids are cultured under non-adherent conditions in serum-free media [39] and may be generated from tumor tissue [40] or cancer cell lines [41]. Compared to 2D cell culture, spheroids may more closely resemble tumors due to their 3D structure [42]. In addition, spheroids are thought to be enriched for cancer stem cells (CSCs) [39,43]. Spheroid cultures may be relevant in ovarian cancer as such cell aggregates are naturally found in ascites, free-floating tumor cells found in the abdomen of patients, an important mode of distant metastasis [44,45]. In experimental models, ovarian cancer spheroids may contribute to disease progression, metastasis, and chemotherapy resistance [46,47]. Spheroid cultures have been used to investigate conditions in which CSCs can be grown, and also in evaluating the efficacy of therapies targeting ovarian cancer. Some of the applications of spheroid culture in ovarian cancer are outlined in Table 4. Developed an ex vivo patient derived 3D spheroid model for drug testing. A correlation between clinical response to therapy and in vitro response was seen in some patients. Cell adhesion molecule Nectin-4 may be involved in ovarian cancer spheroid formation [62,63] Similar to spheroids, organoid 3D cultures can be utilized as tools for drug sensitivity assays because they also better recapitulate cell-cell interaction in a compact, self-organized structure compared to 2D cultures. Each model (spheroids vs. organoids) has its own advantages and disadvantages as reviewed by Gilazieva et al. [64]. For example, spheroid models are thought to be enriched for a cell population with a CSC phenotype; however, these models may not fully recapitulate the tumor architecture [64]. On the other hand, organoid models are more architecturally and functionally similar to their parental tumors and can be maintained in culture long term through passaging [64]. For use in drug testing assays, particular consideration should be given to the limitations associated with each model. For example, due to their compact 3D structure tumor spheroids may create a drug penetration gradient, while drug responses of organoids may be impacted by the ECM in which cells are embedded [64].

Applications of Organoid Culture Models in Ovarian Cancer
Tumor-derived organoids are patient-specific cancer organoids that are capable of partially preserving different tumor cell types as seen in vivo [65]. These organoids are generated from tumors obtained after surgical excision, biopsy, ascites, or pleural effusion samples. Primary tumor-derived organoids offer several advantages as preclinical models for drug testing: they are less resource intensive, can be readily expanded long term compared to patient-derived xenografts (PDXs) and are compatible with high throughput drug screening [66,67]. Tumor-derived organoids faithfully retain some of the biological properties of their parental tumors, even in long-term ex vivo expansion, making them a potentially better model compared to traditional cancer cell lines [36,68]. Hence, tumor-derived organoid models can help fill the gaps in cancer treatment research by complementing cell line and xenograft-based drug studies (Figure 1). To fully utilize the potential of primary tumor-derived organoids as a platform for personalized medicine in cancer patients, scientists and drug companies are exploring the potential of large biobanks of organoids that may be used in predicting treatment response. Biobanks of patient-derived tumor organoids have been established for many tumors including colorectal [69], prostate [70], and breast cancer [71]. In these studies, patient-derived organoids were established to capture the heterogeneity and genetic landscape of their parental tumors and were further utilized as a platform to investigate therapeutic drug responses.

Int
One short-term patient-derived tumor organoid study of high grade serous ovarian cancer was reported by Hill and colleagues wherein their research group established 33 organoid lines from 22 ovarian cancer patients [72]. These organoids were tested for defects in homologous recombination and replication fork protection and were used as a platform to predict therapeutic responses to PARP inhibitors [72]. Kopper et al. utilized tumor samples from 32 patients to establish 56 organoid lines, including the main subtypes of ovarian cancer [36]. They optimized protocols allowing these organoid lines to be genetically manipulated and transplanted in mice. This study also provided a proof of concept that organoid-derived xenografts can be used for testing anti-cancer drug sensitivity [36]. Maenhoudt and colleagues developed yet another ovarian cancer biobank derived from predominantly high grade serous ovarian cancer patients as preclinical models for drug screening [73]. Hoffmann et al. established 15 organoid lines from HGSOC tumor specimens that recapitulated the mutational profile and phenotype of their original tumors [74]. Using organoid cell culture models, several investigators have evaluated the therapeutic response of ovarian cancer cells to various chemotherapeutic agents. Some of these studies published in the past decade are outlined in Table 5.  Figure 1. Overview of potential applications of tumor-derived organoids in ovarian cancer.
To fully utilize the potential of primary tumor-derived organoids as a platform for personalized medicine in cancer patients, scientists and drug companies are exploring the potential of large biobanks of organoids that may be used in predicting treatment response. Biobanks of patient-derived tumor organoids have been established for many tumors including colorectal [69], prostate [70], and breast cancer [71]. In these studies, patientderived organoids were established to capture the heterogeneity and genetic landscape of their parental tumors and were further utilized as a platform to investigate therapeutic drug responses.
One short-term patient-derived tumor organoid study of high grade serous ovarian cancer was reported by Hill and colleagues wherein their research group established 33 organoid lines from 22 ovarian cancer patients [72]. These organoids were tested for defects in homologous recombination and replication fork protection and were used as a platform to predict therapeutic responses to PARP inhibitors [72]. Kopper et al. utilized tumor samples from 32 patients to establish 56 organoid lines, including the main subtypes of ovarian cancer [36]. They optimized protocols allowing these organoid lines to be genetically manipulated and transplanted in mice. This study also provided a proof of concept that organoid-derived xenografts can be used for testing anti-cancer drug sensitivity [36]. Maenhoudt and colleagues developed yet another ovarian cancer biobank derived from predominantly high grade serous ovarian cancer patients as preclinical models for drug screening [73]. Hoffmann et al. established 15 organoid lines from HGSOC tumor specimens that recapitulated the mutational profile and phenotype of their original tumors [74]. Using organoid cell culture models, several investigators have evaluated the therapeutic response of ovarian cancer cells to various chemotherapeutic agents. Some of these studies published in the past decade are outlined in Table 5.  With vast experience in ovarian cancer research, we have collaborated with colleagues and contributed to the development of a 3D organoid based platform for high throughput drug screening [77]. In this assay, tumors retrieved from patients through institutional review board (IRB)-approved protocols are mechanically and enzymatically dissociated. These cells can either be cryopreserved or used freshly in the assay. Dissociated tumor cells are suspended in Matrigel and plated around the rim of a well in a 96-well plate to form a miniring, allowing the growth and formation of multiple organoids within the same well [77]. In this assay, tumor organoids are allowed to grow for two days followed by treatment with drugs in a dose-dependent manner for three days [77]. Following treatment, organoids are released from the Matrigel using dispase and cell viability is assessed by an ATP-based cell viability assay [77]. A workflow for assessing drug response in this assay using samples obtained from ovarian cancer patients through image-guided biopsies, surgical specimens, and ascites is shown in Figure 2. With vast experience in ovarian cancer research, we have collaborated with colleagues and contributed to the development of a 3D organoid based platform for high throughput drug screening [77]. In this assay, tumors retrieved from patients through institutional review board (IRB)-approved protocols are mechanically and enzymatically dissociated. These cells can either be cryopreserved or used freshly in the assay. Dissociated tumor cells are suspended in Matrigel and plated around the rim of a well in a 96well plate to form a miniring, allowing the growth and formation of multiple organoids within the same well [77]. In this assay, tumor organoids are allowed to grow for two days followed by treatment with drugs in a dose-dependent manner for three days [77]. Following treatment, organoids are released from the Matrigel using dispase and cell viability is assessed by an ATP-based cell viability assay [77]. A workflow for assessing drug response in this assay using samples obtained from ovarian cancer patients through image-guided biopsies, surgical specimens, and ascites is shown in Figure 2. As a drug testing platform, this approach offers several advantages [77]. First, this assay is clinically accessible as it allows formation of organoids from a variety of tumor samples including solid tumors, ascites, and pleural effusion. Second, due to the low cellular input needed, biopsy samples can also be tested using this assay. Third, assay results are obtained within a week from collection of the clinical sample making it compatible

Organoid Growth in Matrigel
Drug Treatment

Cell viability assessed
Media Media + Drug ECM ECM Figure 2. A workflow for testing drug response of ovarian cancer organoids using a high-throughput drug assay.
As a drug testing platform, this approach offers several advantages [77]. First, this assay is clinically accessible as it allows formation of organoids from a variety of tumor samples including solid tumors, ascites, and pleural effusion. Second, due to the low cellular input needed, biopsy samples can also be tested using this assay. Third, assay results are obtained within a week from collection of the clinical sample making it compatible with the timeline for therapeutic decision making [77]. The histology of organoids can also be studied when plated in larger culture dish wells. [82]. Examples of such organoids derived from patients diagnosed with platinum-resistant high-grade ovarian cancer and from a platinum-resistant ovarian cancer cell line are shown in Figure 3.
with the timeline for therapeutic decision making [77]. The histology of organoids can also be studied when plated in larger culture dish wells. [82]. Examples of such organoids derived from patients diagnosed with platinum-resistant high-grade ovarian cancer and from a platinum-resistant ovarian cancer cell line are shown in Figure 3.

Advanced 3D Cell Culture Models as Preclinical Drug Screening Platforms
The tumor microenvironment (TME) plays a very important role in disease progression and metastasis [83]. Ovarian cancer cells are known to metastasize to the peritoneal cavity and spread to the omentum [84]. During this process, cancer cells typically interact with non-transformed cells like mesothelial cells and omental fibroblasts. Hence, to design or screen new therapeutics targeting ovarian cancer cells, it is imperative that drug testing models mimic the TME. Significant progress has been made in the development of 3D culture models to replicate the metastatic niche of ovarian cancer cells (Figure 4).

Advanced 3D Cell Culture Models as Preclinical Drug Screening Platforms
The tumor microenvironment (TME) plays a very important role in disease progression and metastasis [83]. Ovarian cancer cells are known to metastasize to the peritoneal cavity and spread to the omentum [84]. During this process, cancer cells typically interact with non-transformed cells like mesothelial cells and omental fibroblasts. Hence, to design or screen new therapeutics targeting ovarian cancer cells, it is imperative that drug testing models mimic the TME. Significant progress has been made in the development of 3D culture models to replicate the metastatic niche of ovarian cancer cells (Figure 4). Co-culture based models are advanced cell culture techniques where tumor cells are grown together with cells such as fibroblasts or stromal cells that support their growth and development [85]. Co-cultures may be of two types: direct and indirect [86]. In the direct type, different cell types are mixed prior to plating and cultured together; whereas, in the indirect type, two cells types are separated by a physical barrier. As reviewed by Hoarau-Véchot et al., co-cultures are feasible using either 2D or 3D models [87]. However, co-cultures may also present challenges in downstream analyses due to the presence of more than one cell type [87]. Some of the notable co-culture 3D models established in ovarian cancer are outlined in Table 6. Developed a 3D co-culture model using human endothelial cells and ovarian cancer cells to mimic the interaction between the two types of cells in cancer. Utilized this model for anti-cancer drug testing using paclitaxel and cisplatin. Co-culture based models are advanced cell culture techniques where tumor cells are grown together with cells such as fibroblasts or stromal cells that support their growth and development [85]. Co-cultures may be of two types: direct and indirect [86]. In the direct type, different cell types are mixed prior to plating and cultured together; whereas, in the indirect type, two cells types are separated by a physical barrier. As reviewed by Hoarau-Véchot et al., co-cultures are feasible using either 2D or 3D models [87]. However, co-cultures may also present challenges in downstream analyses due to the presence of more than one cell type [87]. Some of the notable co-culture 3D models established in ovarian cancer are outlined in Table 6.  Developed a novel dynamic cell culture platform using an orbital shaker for generating ovarian cancer spheroids by mimicking in vivo dynamic tumor microenvironment using fluid shear stress. [96] Flont et al. (2020) Developed a microfluidic based co-culture model with fibroblasts and ovarian cancer cells. This platform was utilized to evaluate the potential cytotoxic effects of photosensitisers. [97] Dynamic 3D models are another clinically relevant cell culture system capable of simulating the ovarian cancer TME. Some of these models utilize microfluidics to continuously feed cultured cells with fresh media and remove toxic waste products [98]. Compared to 3D static cultures, this technique may be more representative for drug screening as it can simulate circulation rates and perfusion observed in human tissues. Microfluidic set ups can be used for long term tumor organoid/spheroid cultures and are generally coupled with miniaturized features that require a smaller number of cells and reduced amount of drugs to perform the assays [98,99]. Some of the key dynamic 3D cell culture platforms developed in ovarian cancer are outlined in Table 6.

Concluding Remarks and Future Directions
One of the biggest challenges in cancer drug development is the selection of appropriate models for preclinical research. A preclinical model should ideally recapitulate the heterogeneity and genetic landscape of the tumor as closely as possible and should also be compatible with high throughput drug screening. The three main preclinical models for drug testing include cancer cells grown in 2D culture, organoids/spheroids grown in 3D culture, and in vivo patient-derived xenograft models. 3D cell culture models may emerge as the most promising tool in preclinical research as they combine two strengths: (i) the ability to recapitulate tumor architecture, and (ii) compatibility with timely high throughput drug screening.
In this review we have highlighted the significance of chemotherapy-response assays in ovarian cancer. However, no ideal model (either 2D or 3D cell culture based) has been established or FDA-approved for clinical use yet. There are inherent challenges associated with developing these assays including (a) intra-tumoral heterogeneity, (b) intertumor heterogeneity between primary and metastatic sites from the same patient, and (c) absence of a tumor microenvironment. Intra-tumoral heterogeneity is known to play an important role in metastasis, invasion, therapy-resistance and tumor relapse [100]. The existence of multiple cell populations with differential therapeutic drug responses within the same tumor site may decrease the predictive accuracy of CRAs. Similarly, inter-tumor heterogeneity may also lead to differential therapeutic responses especially in diseases such as ovarian cancer where tumor recurrences tend to be metastatic and therapy resistant. The predictive value of these assays may be further limited by the absence of a tumor microenvironment. A vast source of existing literature suggests that other tumor components, such as fibroblasts, endothelial cells, ECM components, and infiltrating immune cells are important in disease progression, and hence should potentially be incorporated into drug response assays to make more accurate predictions on the chemosensitivity profile of tumor cells. In this context, the emergence of co-culture methods and dynamic 3D cell culture approaches hold great promise as pre-clinical tools for disease modeling, drug screening, and cytotoxicity testing.
In summary, this article examines existing chemo-response assays and their potential in improving the clinical outcome of ovarian cancer patients by predicting therapeutic responses ex vivo. To be adopted for widespread clinical use, these assays should employ cell culture models that recapitulate actual tumor architecture, incorporate supporting tumor microenvironment cells for better modeling of disease in vitro, and be compatible with automation and high throughput analysis in a cost-effective manner. For clinical translation, it is also important that these assays are validated through well-designed prospective and blinded multi-center clinical trials. We believe that a reliable bioassaydirected selection of treatment not only could improve patient quality of life but also reduce the economic burden due to costs associated with administration of less effective treatment regimens. Hence, we hope that continued optimization of these assays may help provide an effective precision medicine approach for individualized treatment of cancer patients.

Conflicts of Interest:
The authors declare no conflict of interest.