Background: Epithelial ovarian cancer (EOC) affects nearly 22,000 women annually and is the leading cause of death from gynecologic cancer in the United States. Five year cure rates are <40% and approximately 14,000 will die each year. Large-scale efforts are currently underway to use molecular profiling via next generation sequencing (NGS) technology to guide treatment in cancer patients with poor prognosis, but limited application of NGS in ovarian cancer has been reported. In this report, our previously described Ex Vivo 3D Drug Response Profiling was used to identify response differences between newly diagnosed and relapsed ovarian cancer patients and correlated with NGS of primary tissue. Materials & Methods: Processing of Ovarian Cancers: Under informed consent, ovarian cancer samples were obtained and processed using standard mincing & digestion. 3D spheroids were developed and 3D perfused Ovarian Microtumors were cultured using the 3DKUBE™. Ex Vivo Testing & Analysis: Cultured cells were exposed to clinically relevant concentrations of cytotoxic or targeted agents. Relative IC50s and total percent inhibition were used for ranking compounds. Isolated DNA was sequenced in a CLIA laboratory using a 37-gene NGS panel (GeneTrails®) on the Ion Torrent PGM. Results: Tissues from both newly diagnosed, treatment naive subjects and relapsed subjects were obtained and processed after IRB-approved tissue consent was provided. Spheroid formation was uniform across all malignant tumor types. There was a statistical difference for 3D spheroids treated with Carboplatin formed from either naïve or relapse tissue. The relapse samples had a significantly higher median IC50 than did the naïve samples (70.8 vs. 17.4). This significant difference was not apparent in matched 2D treatment groups. Gemcitabine response varied across tissue type, but did not correlate with traditional biomarkers (i.e. hENT mRNA expression). NGS testing turn-around time was a median of 9 days (range 7-14). Ovarian 3D microtumors were successfully perfuse and tested with targeted agents guided by NGS results, as evidence by a tumor with a mutation of EGFR (p.P265T, clinical significance unknown) with both erlotinib and afatanib demonstrating activity (3.3uM and 0.7uM, respectively). Conclusions: EV3D DRP successfully differentiates carboplatin response and 3D perfusion of microtumors permits ex vivo culture of primary ovarian samples for genotypic-phenotypic response determination. Clinical response data and clinical correlation is ongoing. EV3D DRP permits phenotypic drug response correlation with molecular profiling in real time and may be a clinically relevant functional assay for driver mutation identification and maximal patient response to targeted agent(s). Citation Format: Tessa Desrochers, Stephen Shuford, Christina Mattingly, Lillia Holmes, Matt Gevaert, Jeff Elder, David Orr, Christopher Corless, Larry Puls, Hal E. Crosswell. Ex vivo 3d drug response profiling of primary human ovarian cancer differentiates treatment-naive and relapsed patients and molecular subtypes. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr LB-282. doi:10.1158/1538-7445.AM2015-LB-282

Background: A goal of personalized medicine is to identify the most active and safest therapy for individual patients. One method for employing personalized medicine for cancer patients is through testing of a patient's cultured tumor tissue against a panel of predetermined drug candidates. To date, there is no standard platform by which an individual patient's tumor can be tested ex vivo in order to reliably predict if a certain drug or therapy is effective. We describe optimized methods of ex vivo 3D (EV3D™) culture and testing of primary human ovarian cancer for personalized medicine. Our hypothesis was that 3D spheroid cultures from primary human tumors would demonstrate unique growth parameters and distinct ex vivo testing responses compared to traditional 2D cultures. Materials & Methods: Optimization experiments: Methods for comparing 2D and 3D drug response, rapid spheroid formation across multiple cell types and scaffold/media conditions in both static and perfusion cultures were optimized using human cancer cell lines and reagents. Processing of Ovarian Cancers: Under informed consent, ovarian cancer samples were obtained and processed using standard mincing & digestion. Spheroids and 2D monolayers were established after viability assessment. Ex Vivo Testing & Analysis: cultured cells in 2D and 3D were exposed to a clinically relevant concentration range of cytotoxic or targeted agents. Multiple analytical techniques were applied including imaging, metabolism and dsDNA quantification; relative and absolute IC50s and total percent inhibition were used for ranking compounds. Results: Median subject age was 63(29-82), majority of which had advanced stage adenocarcinomas. Carboplatin & taxane based combination therapy was used in >90% of patients. Clinical response and outcomes data collection are ongoing (median follow up 8 months). Spheroid formation was uniform across all malignant tumor types, but low grade lesions trended towards smaller, looser aggregates. All drug tested ex vivo samples were exposed to at least one agent that the subject received. Growth and response to positive control varied between 2D and 3D platforms. Doxorubicin and LY294002 showed greatest activity (median IC50 = 1, 0.8 uM, respectively) and cisplatin and topotecan the least (median IC50 16, 22). Median CA-125 fold reduction from baseline was 16 (range, 1.9-143). The subject with the greatest response by CA-125 levels (143 fold decrease) was predicted to respond to carboplatin-paclitaxel therapy in 3D culture but not in 2D (2D v 3D, p<0.01). Conclusions: EV3D allows for rapid and high throughput phenotypic profiling of novel small molecules (i.e. PI3K inhibitors) as well as conventional, FDA approved cytotoxic agents against patient-specific tumor samples in a relevant tumor microenvironment. EV3D cultures have reduced metabolism, decreased short term growth, and different drug-response profiles than 2D. Next generation exome sequencing of 39 drugable targets will allow genotypic-phenotypic correlation. Clinical data collection is ongoing to correlate ex vivo response with clinical outcomes. Current efforts are focused on developing EV3D as a novel small molecule phenotypic screen for clinical trials and as an in vitro chemo-sensitivity assay for personalized medicine. Citation Format: Stephen Shuford, Rebecca Widener, Chaitra Cheluvaraju, Teresa Desrochers, christina mattingly, Larry Puls, matt gevaert, David Orr, Hal E. Crosswell. Chemotherapy testing of primary human ovarian cancers in an ex vivo 3D culture platform: A novel method of phenotypic profiling for clinical trial selection and personalized medicine. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr LB-36. doi:10.1158/1538-7445.AM2014-LB-36

Background: In 2017, there will be 107,000 cases of gynecologic cancer diagnosed in the US with an overall survival of around 70%-most occurring in post-menopausal individuals. In this study, we have examined a younger (≤ 40 years of age) subpopulation of these women with high grade/ high stage gynecologic malignancies, attempting to identify unique genetic abnormalities or combinations thereof through tissue block specimens. This information was then analyzed in light of known target therapies to see if genetic analysis in this setting would yield significant therapeutic advantage. Methods: We retrospectively evaluated patients with high grade/high stage gynecologic cancers (≤ 40 years of age), examined the presence and status of 400 oncogenes and tumors suppressor genes from Formalin-fixed, Paraffin-embedded (FFPE) tissue and functionally classified mutations by SIFT and Polyphen. Results: Twenty women were identified and stratified into positive and negative outcomes. No demographic, clinicopathologic or treatment factors were significant between these groups. Of the 400 genes evaluated, twelve mutations were significant between the groups, six with targeted therapies. Mutations associated with negative outcomes within histologies/locations were evaluated: ERBB3 in epithelial (ovarian), ALK/GPR124/KMT2D in neuroendocrine (ovarian/endometrial), ROS1/EGFR, ROS1/ERBB3/KMT2D/NIRK1 and GPR124 in sarcoma. All negative outcomes were void of mutations in APC/ABL2. A predictive model for negative outcomes in our cohort was developed from these data: AKAP9-/MBD1-/APC-/ABL2-with a mutation load of > 20.5. Conclusions: Unique multi-gene and mutational outcome correlations were identified in our cohort. Resulting complex mutational profiles in distinctly aggressive gynecologic cancers suggested potential for novel therapeutic treatment. Future larger scale studies will be needed to correlate the genotypic and phenotypic features identified here.

Personalized medicine in cancer typically refers to the use of genetics and/or biomarkers to direct the use of targeted therapy or predict overall prognosis based on statistical probability. Therapy selection and predictions are not based on any physical interaction between a patient’s tumor cells to clinically relevant therapies based on their disease indication. We have developed an assay using 3D cell culture that exposes a patient’s tumor cells to standard of care chemotherapies for the purpose of predicting their clinical response to potential treatment options prior to treatment. We have analytically validated this assay enabling its performance under CLIA regulations as a Laboratory Developed Test (LDT) and prospectively validated it against clinical patient outcome in ovarian cancer. The test utilizes excess fresh patient tissue acquired during standard of care surgical debulking or biopsy and returns results within 7 business days of tissue receipt, typically well before the start of chemotherapy. Previously, we have shown in newly diagnosed ovarian cancer, the test has an accuracy of 87% with a specificity of 100% and a sensitivity of 84% in the prediction of standard first-line carboplatin/taxol combination therapy using the biomarker CA-125 and CT imaging as clinical readouts. We have similar results for the prediction of response to neoadjuvant therapy following laparoscopic biopsy using RECIST criteria. We have now analytically validated the assay in glioblastoma (GBM) and rare tumors. Preliminary clinical validation in GBM has shown the ability of the test to accurately predict response to standard first-line temozolomide using RANO criteria as the clinical readout. Rare tumor validation has included a panel of 12 drugs covering those used as standard of care for most rare tumors. Aspects of validation have included examining inter- and intra-assay variability and drug panel response in a defined number of rare tumors including sarcomas, neuroendocrine, and other tumors such as Sertoli-Leydig. In breast cancer, we have validated the assay for the use of a single diagnostic biopsy core as the tissue source and established preliminary clinical validation against standard of care such as doxorubicin and paclitaxel with pathologic complete response (pCR) as the clinical readout. We are further validating the predictive ability of the test in newly diagnosed and relapsed ovarian cancer and GBM patients (clinical trial NCT03561207). With demonstrated accurate prediction of patient specific response, the transition to cancer therapy selection based on physical evidence vs statistical probability would significantly improve patient outcomes and benefit economic stakeholders. Citation Format: Stephen Shuford, Christine Wilhelm, Ashley M. Smith, Melissa Rayner, Jeremy Stuart, Lillia Holmes, Matt Gevaert, Howland E. Crosswell, Teresa M. DesRochers. Redefining personalized medicine by drug response profiling of patient-derived spheroids [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2240.

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