Development and validation of an immune gene-set based Prognostic signature in ovarian cancer

Abstract

Background: Ovarian cancer (OV) is the most lethal gynecological cancer in women. We aim to develop a generalized, individualized immune prognostic signature that can stratify and predict overall survival for ovarian cancer.

Methods: The gene expression profiles of ovarian cancer tumor tissue samples were collected from 17 public cohorts, including 2777 cases totally. Single sample gene set enrichment (ssGSEA) analysis was used for the immune genes from ImmPort database to develop an immune-based prognostic score for OV (IPSOV). The signature was trained and validated in six independent datasets (n = 519, 409, 606, 634, 415, 194).

Findings: The IPSOV significantly stratified patients into low- and high-immune risk groups in the training set and in the 5 validation sets (HR range: 1.71 [95%CI: 1.32-2.19; P = 4.04 × 10^-5] to 2.86 [95%CI: 1.72-4.74; P = 4.89 × 10^-5]). 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 achieved the highest mean C-index (0.625) compared with the other signatures (0.516 to 0.602) and clinical characteristics (0.555 to 0.583). Further, we integrated IPSOV with stage, grade and debulking, which showed improved prognostic accuracy than clinical characteristics only.

Interpretation: 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. FUND: This work was supported by National Key Research and Development Program of China, National Natural Science Foundation of China and Natural Science Foundation of the Jiangsu Higher Education Institutions of China.

Keywords: Gene expression; Immune; Ovarian cancer; Prognostic signature.

Fig. 1

Flowchart of the study. 17 public ovarian cancer datasets containing 2777 cases were included and categorized into 6 independent datasets according to the microarray platform. We developed the IPSOV in the training set and validated in the other 5 datasets. Further, we integrated IPSOV with stage, grade and debulking status to improve the prognostic value.

Fig. 2

Kaplan-Meier survival analyses of IPSOV. (a) Patients are stratified into low- (red) and high-immune (blue) risk groups with a cutoff of the median value in the training set. (b-f) Further, the prognostic signature IPSOV is validated in five independent validation sets. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 3

IPSOV distribution with survival status in the combined dataset. Upper half panel: IPSOV distribution with patient survival status. The X axis is sorted by IPSOV values. Red color indicates the patients are dead while blue color indicates survive. Lower half panel: Heatmap showing the corresponding 15 immune categories enrichment scores. The score of each immune category is normalized to mean = 0 and standard deviation = 1. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 4

(a) Immune scores are calculated based on the coefficients of IPSOV in antimicrobials, cytokines and cytokine receptors immune processes. We dichotomize the scores into low- and high-immune risk group by the median value. Median survival time is compared using log-rank test. (b) Mean C-index of IPSOV, stage, grade, debulking and 9 reported signatures. (c) P value comparison of IPSOV and 9 reported signatures. Red block indicates the model is significant (P ≤ 0.05) while black indicates unsignificant (P > 0.05). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 5

Integration of IPSOV and clinical characteristics. (a) Training set. (b) Validation set 1. (c) Validation set 2. (d) Validation set 3. Restricted mean survival (RMS) curve for IPSOV and integrated model. In addition, we provided the C-index comparison of the model with clinical characteristics only and the integrated model. P-value represents the difference between the two models in term of C-index.