EP236/#587 Proteomic profiling of protein signatures associated with response to PARP inhibitor maintenance therapy in ovarian cancer. (4th December 2022)

• Record Type: Journal Article
• Authors: Kim, Se Ik; Lee, Cheol; Kim, Hee Seung; Chung, Hyun Hoon; Kim, Jae-Weon; Park, Noh Hyun; Song, Yong-Sang; Lee, Maria
• Is Part Of: International journal of gynecological cancer. Volume 32(2022)Supplement 3
• Page Start: A145
• Page End: A146
• Publication Date: 2022-12-04
• Subjects: Generative organs, Female -- Cancer -- Periodicals; 616.99465
• DOI: 10.1136/ijgc-2022-igcs.327
• Languages: English
• ISSNs: 1048-891X

Abstract

Abstract : Objectives: Despite the substantial clinical use of PARP inhibitors, the development of resistance contributes to mortality. Thus, we aimed to discover protein signatures associated with response to PARP inhibitors in high-grade serous ovarian carcinoma (HGSOC) through proteomic analysis. Methods: We conducted an in-depth proteomic analysis of FFPE tissues of patients with platinum-sensitive recurrent HGSOC who received PARP inhibitor maintenance therapy (n=24). The proteomic strategy was as follows: removal of paraffin, isolation of tumor via examination by a pathologist, protein extraction, filter-aided sample preparation, tandem mass tags based labeling, off-line high-pH peptide fractionation, and high-resolution quadruple Orbitrap LC-MS/MS. Patients who discontinued PARP inhibitors due to disease progression within nine months were assigned to the poor prognosis group (n=9). Dysregulated proteins between the good and poor response groups were investigated. Results: In total, 7, 825 proteins were quantified. There were 56 proteins significantly expressed in the good response group, whereas 131 proteins were in the poor response group. Proteins significantly upregulated in the good response group included ribosomal- and infection-related proteins. Proteins significantly upregulated in the poor response group included extracellular matrix receptor- and coagulation-related proteins. To identify a protein signature that stratifies good and poor responders to PARP inhibitors, we performed four feature selection algorithms with leave-one-out cross-validation to improve the accuracy. High expression of Proteins A and B were associated with worse and better progression-free survival, respectively. Conclusions: We successfully identified protein signatures associated with response to PARP inhibitors. This study was the most extensive proteomic analysis to predict PARP inhibitor response in ovarian cancer.