Wang, Chen

Abstract Background: Epithelial ovarian cancer (EOC), commonly known as ovarian cancer (OvCa), is the deadliest gynecological malignancy. High tumor expression of either NR3C1 mRNA (encoding the glucocorticoid receptor or GR) or GR protein (IHC) are associated with poor overall survival (OS) in patients (pts) with advanced stage OvCa. We hypothesized that high GR expression and associated GR transcriptional activity favor OvCa cell survival and chemotherapy resistance, thereby reducing OS. Here we investigated whether a GR transcriptome-based gene signature (GR-sig) would be a better prognosticator than tumor NR3C1 mRNA expression alone. Methods: mRNAs up- or downregulated >1.5 fold after glucocorticoid treatment (Dex) in one or both cultured OvCa cell lines (OVSAHO and HEYA8) were defined as differentially expressed genes (DEGs). DEGs were then identified in n=1247 unique OvCa microarrays from 15 publicly available Gene Expression Omnibus (GEO) datasets with matched pt OS (training set). We then used the OS data to train a Cox proportional hazards model. The “best subset selection” (BeSS) algorithm and bootstrap sampling identified those GR-regulated DEGs whose tumor mRNA expression correlated most significantly with pt OS, creating a GR-sig “risk score.” Subsequently, all n=522 microarray and n=374 RNA-seq publicly available samples from the OvCa TCGA were used as two independent validation sets. Microarray and RNA-seq data were normalized using the MAS5 and FPKM-UQ algorithms respectively. The two validation sets were independently stratified relative to the median training set risk score into low- and high-risk score groups. To examine the prognostic ability of NR3C1 mRNA expression alone, the median training set NR3C1 expression value was used to stratify validation set pts into low- and high-expression groups. In all cases, OS was estimated by the Kaplan-Meier method. Results: We identified n=2288 protein-coding DEGs in one or both cell lines. Of these genes, n=1251 were expressed in each GEO microarray sample (training set). A 10-gene GR-signature (GR-Sig) was then identified using the BeSS algorithm and bootstrap sampling, yielding a median training set risk score of 1.02. In both the microarray and RNA-seq validation datasets, the 10-gene GR-sig risk score was associated with significant OS differences between low- and high-risk score groups (microarray p = 0.018; RNA-seq p = 0.00049). In contrast, the OS difference between low- and high- NR3C1 mRNA expression groups did not reach significance (microarray p = 0.12; RNA-seq p = 0.28). Conclusion: In this retrospective analysis, a 10-gene GR-sig derived from OvCa GR-regulated DEGs performed better as a prognosticator than NR3C1 mRNA expression alone. Prospective application of the GR-sig may be useful in stratifying patients for treatment response with novel agents in OvCa clinical trials. Citation Format: Ananth V. Panchamukhi, Lei Huang, Eleanore P. Gray, Yuanhang Liu, Manisha Taya, Jennifer T. Veneris, Xiaonan Hou, S John Weroha, Balázs Győrffy, Gini F. Fleming, Chen Wang, Suzanne D. Conzen. Development of a prognostic ovarian cancer gene signature based on glucocorticoid receptor transcriptional activity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 4739.

Abstract Objective: Endometrial cancer (EC) is the most common gynecologic malignancy and serous histology is the second most common subtype. It is associated with poorer outcomes as recurrent disease tends to be resistant to chemotherapy. Given the dearth of proven targeted therapies in this setting and the tendency for serous ECs to have homologous recombination deficiency (HRD), this study aimed to test the synergy between poly (ADP-ribose) polymerase inhibitors (PARPis) and SN38, an active metabolite of DNA topoisomerase I, irinotecan. PARPis are currently FDA approved in ovarian cancer and have their greatest efficacy in tumors with HRD while topoisomerase inhibitors are a standard chemotherapy option for recurrent cancer. Methods: A genomic instability score (GIS) was derived from low-pass whole genome sequencing-based bioinformatics analysis according to three previously reported HRD metrics: telomeric allelic imbalance, large state transitions, and loss of heterozygosity. GIS was calculated for 34 patient derived xenograft (PDX) tumors, of which eight had serous histology. PARPi, rucaparib, and SN38 were used to treat EC PDX tumors in ex vivo 3D cell culture experiments using RealTime Glo for viability after 4-5 days of drug exposure. Synergy was assessed by calculating the combination index (CI) using Chou-Talalay method. Results: GIS ≥ 42 was noted in 62.5% (5/8) of serous EC PDX tumors and only 19.2% (5/26) of non-serous EC PDX tumors. Synergy (CI < 1.0) between rucaparib and SN38 was demonstrated in 85.7% (6/7) of serous EC PDX ex vivo 3D cell culture experiments, but only 62.5% (10/16) of non-serous EC PDX tumors. Conclusions: Most serous EC PDX tumors had a high GIS, consistent with HRD, when compared to a minority of non-serous histologies. Furthermore, combination therapy of rucaparib and SN38 had a synergistic effect in almost all serous EC PDX models, while still being effective in a majority of non-serous models. Additional PDX tumors need to be assessed to establish the predictive value of a GIS on synergy between rucaparib and SN38. Further in vivo studies are also needed to confirm these ex vivo findings of synergy. Ultimately, the hope is to generate preclinical data which justifies the use of PARPis in serous ECs as monotherapy or in combination with other anticancer therapies. Citation Format: Conway Xu, Xiaonan Hou, Erik Jessen, Chen Wang, S. John Weroha. Combination therapy with poly (ADP-ribose) polymerase inhibitor in endometrial cancer. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5495.