Identification of pathway‐based recurrence‐associated signatures in optimally debulked patients with serous ovarian cancer. Issue 10 (20th August 2018)

• Record Type: Journal Article
• Authors: Deng, Kui; Zhang, Fan; Song, Wei; Zhao, Weiwei; Rong, Zhiwei; Cai, Yuqing; Xu, Huan; Lu, Mingliang; Wang, Wenjie; Li, Ang; Hou, Yan; Li, Zhenzi; Li, Kang
• Is Part Of: Journal of cellular biochemistry. Volume 119:Issue 10(2018)
• Page Start: 8564
• Page End: 8573
• Publication Date: 2018-08-20
• Subjects: ovarian cancer -- pathway -- predictive model -- recurrence; Cytochemistry -- Periodicals; 572
• DOI: 10.1002/jcb.27098
• Languages: English
• ISSNs: 0730-2312

Abstract

Abstract: Serous ovarian cancer (SOC) is the most common form of the histological subtype of epithelial ovarian cancer, with the worst clinical outcome. Despite improvements in surgery and chemotherapy, most patients with SOC experience recurrence within 12‐18 months of first‐line treatment. Current studies are unable to robustly predict the recurrence of SOC, and more accurate predictive models are urgently required. We have, therefore, developed a novel pathway‐structured model to predict the recurrence of SOC. We trained the model on a set of 333 patients and validated it in 3 diversified validation datasets of 403 patients. Genes significantly associated with recurrence within each pathway were identified using a Cox proportional hazards model based on LASSO estimation in the training dataset. Next, a pathway‐structured scoring matrix was obtained after computation of the prognostic score for each pathway by fitting to the Cox proportional hazards model. With the pathway‐structure scoring matrix as an input, the pathway‐based recurrent signatures were identified using the Cox proportional hazards model based on LASSO estimation and the significant pathway‐based signatures were externally validated in 3 independent datasets. Meanwhile, our pathway‐structured model was compared with a commonly used gene‐based model. Our results revealed that our 12 pathway‐based signatures successfully predicted the recurrence of SOC with high accuracy in the training dataset and in the 3 validation datasets. Moreover, our pathway‐structured model was superior to the gene‐based model in 4 datasets. The pathways selected in our study will provide new insights into the pathogenesis and clinical treatments of SOC. Abstract : We developed a novel pathway‐structured model consisting of 12 pathways that can robustly predict the recurrence of serous ovarian cancer (SOC). Our pathway‐structured model was superior to the commonly used gene‐based model. The identified 12 pathways will provide new insights into the underlying mechanisms of SOC recurrence with the aim of developing more personalized treatment strategies.