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Inhibitor of apoptosis (IAP) proteins are frequently upregulated in ovarian cancer, resulting in the evasion of apoptosis and enhanced cellular survival. Birinapant, a synthetic second mitochondrial activator of caspases (SMAC) mimetic, suppresses the functions of IAP proteins in order to enhance apoptotic pathways and facilitate tumor death. Despite on-target activity, however, pre-clinical trials of single-agent birinapant have exhibited minimal activity in the recurrent ovarian cancer setting. To augment the therapeutic potential of birinapant, we utilized a high-throughput screening matrix to identify synergistic drug combinations. Of those combinations identified, birinapant plus docetaxel was selected for further evaluation, given its remarkable synergy both in vitro and in vivo. We showed that this synergy results from multiple convergent pathways to include increased caspase activation, docetaxel-mediated TNF-&alpha, upregulation, alternative NF-kB signaling, and birinapant-induced microtubule stabilization. These findings provide a rationale for the integration of birinapant and docetaxel in a phase 2 clinical trial for recurrent ovarian cancer where treatment options are often limited and minimally effective.

Disease recurrence is the major cause of morbidity and mortality of ovarian cancer (OC). In terms of maintenance therapies after platinum-based chemotherapy, PARP inhibitors significantly improve the overall survival of patients with BRCA mutations but is of little benefit to patients without homologous recombination deficiency (HRD). The stem-like tumor-initiating cell (TIC) population within OC tumors are thought to contribute to disease recurrence and chemoresistance. Therefore, there is a need to identify drugs that target TICs to prevent relapse in OC without HRD. RNA sequencing analysis of OC cells grown in TIC conditions revealed a strong enrichment of genes involved in drug metabolism, oxidative phosphorylation and reactive oxygen species (ROS) pathways. Concurrently, a high-throughput drug screen identified drugs that showed efficacy against OC cells grown as TICs compared to adherent cells. Four drugs were chosen that affected drug metabolism and ROS response: disulfiram, bardoxolone methyl, elesclomol and salinomycin. The drugs were tested in vitro for effects on viability, sphere formation and markers of stemness CD133 and ALDH in TICs compared to adherent cells. The compounds promoted ROS accumulation and oxidative stress and disulfiram, elesclomol and salinomycin increased cell death following carboplatin treatment compared to carboplatin alone. Disulfiram and salinomycin were effective in a post-surgery, post-chemotherapy OC relapse model in vivo, demonstrating that enhancing oxidative stress in TICs can prevent OC recurrence.

Quality control (QC) metrics are critical in high throughput screening (HTS) platforms to ensure reliability and confidence in assay data and downstream analyses. Most reported HTS QC metrics are designed for plate level or single well level analysis. With the advent of high throughput combination screening there is a need for QC metrics that quantify the quality of combination response matrices. We introduce a predictive, interpretable, matrix-level QC metric, mQC, based on a mix of data-derived and heuristic features. mQC accurately reproduces the expert assessment of combination response quality and correctly identifies unreliable response matrices that can lead to erroneous or misleading characterization of synergy. When combined with the plate-level QC metric, Z’, mQC provides a more appropriate determination of the quality of a drug combination screen. Retrospective analysis on a number of completed combination screens further shows that mQC is able to identify problematic screens whereas plate-level QC was not able to. In conclusion, our data indicates that mQC is a reliable QC filter that can be used to identify problematic drug combinations matrices and prevent further analysis on erroneously active combinations as well as for troubleshooting failed screens. The R source code of mQC is available at http://matrix.ncats.nih.gov/mQC.

Sphere forming cells persist in the ascitic fluid of patients with high-grade serous ovarian cancer after first-line therapy and likely contribute to relapse and metastasis. These residual tumor spheres, which are enriched for cancer cells by up to 95% based on detectable TP53 mutations, are slow growing, resistant to platinum-based chemotherapy, and remain a large obstacle towards durable remission. Screening established, rapidly dividing monolayer cell lines in proliferation assays has failed to produce chemotherapeutics capable of eradicating this slow growing population. To identify a consolidation therapy that targets these residual tumor cells we screened nearly 2000 mechanistically annotated, approved and investigational drugs in three cell lines (PEO1, PANC1, A375M) cultured in spheroid and conventional monolayer. To elucidate targetable genes and pathways responsible for spheroid maintenance we performed a whole-genome RNAi screen against PEO1 in both culture conditions. Our pharmacological and genetic profiling provided mechanistic insight into the baseline changes that occur when cells are grown in three dimensional, anchorage independent conditions and identified susceptibilities specific to spheroid populations. Consistent with residual disease, cultured spheres were resistant to many common chemotherapeutics, most notably proteasome inhibitors, but were highly sensitive to the pro-oxidant elesclomol. We verified elesclomol's activity in ex vivo cancer spheroids extracted from the ascitic fluid of patients with advanced high-grade serous ovarian cancer using a simple, culture-free technique. Expression profiling of cultured and ex vivo spheres revealed broad downregulation of genes involved in cell cycle signaling (AURKA, AURKB, PLK1, CDK1, CCNA2, CCNB1, CCNB2, GMNN, CHEK1). Treatment with elesclomol induced expression of chaperone proteins (HSP6, HSP7, HSPA1A, HSPA1B, DNAJA4, DNAJB1), metallothionein proteins (MT1F, MT1M, MT1P2, MT1X, MT2A), and genes involved with the oxidative stress response (HMOX1, ABCB1, SLC7A11) consistent with increased reactive oxygen species caused by high intracellular Cu2+. To pursue this drug as a consolidation therapy in vivo, we evaluated elesclomol's toxicity, pharmacokinetic properties, and efficacy in a murine model for recurrent high-grade serous ovarian cancer. By targeting residual tumor cells with elesclomol after successful treatment with platinum-based therapeutics we hope to prevent recurrence. Citation Format: Ian S. Goldlust, Kelli Wilson, Ludmila Szabova, Xiaohu Zhang, Lesley Mathews-Griner, Maria Vias, Anna Piskorz, Rory Stark, Lee Mendil, Monica Kasbekar, John Braisted, Rajarshi Guha, Crystal McKnight, Paul Shinn, Donna Michelle-Smith, Zoe Weaver Ohler, Mindy Davis, Udo Rudloff, Sam Michael, Madhu Lal-Nag, Scott Martin, Christina Annunziata, Marc Ferrer, James D. Brenton, Craig Thomas. No cell left behind: Residual ovarian spheroids drive recurrence and are sensitive to the pro-oxidant elesclomol. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 278.

Background: Inhibitors of apoptosis (IAPs) are attractive anti-cancer therapeutic targets as a mechanism to sensitize cancer cells to apoptosis. IAPs are overexpressed in ovarian cancer and portend a poor prognosis. SMAC- mimetics are IAP antagonists that have shown in vitro and in vivo antitumor activity in ovarian cancer, but preclinical and clinical studies suggest SMAC mimetics may be more effective in combination therapies. Methods: Birinapant (B) was initially screened in combination with each of 1,912 compounds using an automated acoustic drug dispensation system in 6×6 matrices. Seventy compounds were chosen from the initial screen, based on synergistic characteristics, and verified in 10×10 matrices. The most clinically rational drug combinations with B were further evaluated in 8 high-grade serous ovarian cancer cell lines (CAOV3, CAOV4, OV-90, OVCAR4, OVCAR5, OVCAR8, PEO1, PEO4), focusing on 3 synergistic (docetaxel, panobinostat, volasertib) and 2 antagonistic combinations (cisplatin, olaparib) for exploration of mechanism. Combination index was determined using Compusyn software. Results: Specific classes of drugs were identified to have synergism with B, including taxanes, HDAC inhibitors, and PLK1 inhibitors, while platinum agents and PARP inhibitors were found to be antagonistic. Four of the 8 cell lines were sensitive to B. Caspase-Glo 3/7 assay was performed in B-sensitive cell lines and confirmed increased caspase-3 activity in the combination treatments. Ongoing studies include genomic analysis, mechanistic interrogation, and in vivo xenograft studies. Conclusions: B is a promising novel therapy for use in combination treatment for ovarian, fallopian tube, or primary peritoneal cancers. Our findings provide insight on optimal therapies to combine with B, and constitute a foundation for further evaluation of predictive and pharmacodynamic biomarkers to select the patient population most likely to respond to this targeted therapeutic approach. Citation Format: Kristen Bunch, Ian Goldlust, Craig Thomas, Lidia Hernandez, Rajarshi Guha, Christina Annunziata. High-throughput drug matrix screen with SMAC (second mitochondria-derived activator of caspases)-mimetic birinapant in high-grade serous ovarian cancer cell lines identifies synergism. [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-254. doi:10.1158/1538-7445.AM2015-LB-254

Drug combinations are used commonly to treat patients with cancer. Combination studies are conducted in vitro, to support advancement to in vivo and/or clinical research. In order to interpret in vitro data, mathematical models and numerical methods are used to quantify degrees of drug synergism. Several traditional approaches exist but they have been questioned with regard to how resulting synergy or antagonism levels should be interpreted. Our aim was to develop a robust method to analyse in vitro drug combination data, avoiding the issues related to traditional models and methods. To facilitate its implementation, software to enable rigorous and systematic assessment of combinations in vitro was also developed. We have derived a new mathematical model to identify synergy distributions based on drug combination dose response surfaces. Our new model and the classical Bliss, Loewe and HSA models were incorporated into the newly developed Combenefit software. Combenefit enables systematic quantification of high-throughput screening data, incorporating statistical assessment, metrics to describe synergy distributions and advanced graphical visualisation. Using multiple simulations of combination data with expected outcomes, our approach showed that it always provided appropriate evaluation while classical models failed to do so. We showed how our new model is in principle better aligned with in vivo requirements of identifying combinations which provide improvements compared to single agents. We then proceeded to analyse a large high-throughput combination screen. We first demonstrated that dataset assessment is strongly model-sensitive. We then demonstrated that our new model identified the best combinations in terms of dose response, thus confirming our simulated cases. Therefore, we have developed a new mathematical model that solves fundamental issues of model relevance that are encountered with classical models in the context of drug combinations. The newly developed Combenefit software enables easily performing rigorous and systematic quantification of in vitro combinations based on this novel approach. (Combenefit is freely available on our institutional website http://www.cruk.cam.ac.uk/combenefit). The resulting procedure is robust and better aligned with our perception of in vivo requirements. Citation Format: Giovanni Y. Di Veroli, Duncan I. Jodrell, Frances M. Richards, Ian Goldlust. A new mathematical model and software, to enable improved quantification and interpretation of combinatorial drug effects. [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 2540. doi:10.1158/1538-7445.AM2015-2540