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Development and Validation of an Individualized Immune Prognostic Signature in 3005 Ovarian Cancer Patients

62 Pages Posted: 28 Aug 2018

See all articles by Sipeng Shen

Sipeng Shen

Nanjing Medical University - Department of Biostatistics

Guanrong Wang

Jiangsu Institute of Planned Parenthood Research - National Health and Family Planning Commission Contraceptives Adverse Reaction Surveillance Center

Ruyang Zhang

Nanjing Medical University - China International Cooperation Center for Environment and Human Health; Nanjing Medical University - Department of Biostatistics

Yang Zhao

Nanjing Medical University - Department of Biostatistics

Yongyue Wei

Nanjing Medical University - State Key Laboratory of Reproductive Medicine; Nanjing Medical University - Department of Biostatistics

Feng Chen

Nanjing Medical University - State Key Laboratory of Reproductive Medicine; Nanjing Medical University - China International Cooperation Center for Environment and Human Health; Nanjing Medical University - Department of Biostatistics

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Abstract

Ovarian cancer (OV) is the most lethal gynecological cancer in women. We aim to develop a robust, individualized immune prognostic signature that can stratify and predict overall survival for ovarian cancer. The gene expression profiles of ovarian cancer tumor tissue samples were collected from 20 public cohorts, including 3005 cases totally. We used the immune genes provided by ImmPort database to develop an immune-based prognostic signature for OV (IPSOV) in the training set (n = 409). The signature was validated in seven independent validation sets (n = 606, 519, 634, 415, 194, 128, 100). Further, we compared IPSOV with nine reported ovarian cancer prognostic signatures as well as the clinical characteristics including stage, grade and debulking status. The IPSOV significantly stratified patients into low- and high-immune risk groups in the training set (HR = 2.52; 95% CI: 1.92-3.30; P = 1.88×10-11) and in the 7 validation sets (HR range: 1.70 [95%CI: 1.32-2.10; P = 1.40×10-5] to 2.20 [95%CI: 1.24-3.91; P = 0.007]). Significant interaction effects were identified for IPSOV and platinum, Gemcitabine and Topotecan chemotherapy. The IPSOV achieved the highest mean C-index (0.631) compared with the other signatures (0.516 to 0.602) and clinical characteristics (0.550 to 0.576). Further, we integrated IPSOV with stage, grade and debulking, which showed improved prognostic accuracy than clinical characteristics only. The proposed clinical-immune signature is a promising biomarker for estimating overall survival in ovarian cancer. Prospective studies are needed to further validate its analytical accuracy and test the clinical utility.
Funding: This study was supported by the National Natural Science Foundation of China (81530088 and 81473070 to F.C.), National Key Research and Development Program of China (2016YFE0204900 to F.C.), Natural Science Foundation of the Jiangsu Higher Education Institutions of China (14KJA310002 to F.C.).
Conflict of Interest: None.
Ethical Approval Statement: This study has been proved by the Nanjing Medical University institutional committee.

Keywords: ovarian cancer; immune; gene expression; prognostic signature

Suggested Citation

Shen, Sipeng and Wang, Guanrong and Zhang, Ruyang and Zhao, Yang and Wei, Yongyue and Chen, Feng, Development and Validation of an Individualized Immune Prognostic Signature in 3005 Ovarian Cancer Patients (June 7, 2018). Available at SSRN: https://ssrn.com/abstract=3209541 or http://dx.doi.org/10.2139/ssrn.3209541

Sipeng Shen (Contact Author)

Nanjing Medical University - Department of Biostatistics ( email )

Nanjing
China

Guanrong Wang

Jiangsu Institute of Planned Parenthood Research - National Health and Family Planning Commission Contraceptives Adverse Reaction Surveillance Center ( email )

China

Ruyang Zhang

Nanjing Medical University - China International Cooperation Center for Environment and Human Health ( email )

Nanjing
China

Nanjing Medical University - Department of Biostatistics ( email )

Nanjing
China

Yang Zhao

Nanjing Medical University - Department of Biostatistics ( email )

Nanjing
China

Yongyue Wei

Nanjing Medical University - State Key Laboratory of Reproductive Medicine ( email )

Nanjing
China

Nanjing Medical University - Department of Biostatistics ( email )

Nanjing
China

Feng Chen

Nanjing Medical University - State Key Laboratory of Reproductive Medicine ( email )

Nanjing
China

Nanjing Medical University - China International Cooperation Center for Environment and Human Health ( email )

Nanjing
China

Nanjing Medical University - Department of Biostatistics ( email )

Nanjing
China

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