Abstract
Patients with ovarian cancer are at high risk of tumor recurrence. Prediction of therapy outcome may provide therapeutic avenues to improve patient outcomes. Using reverse-phase protein arrays, we generated ovarian carcinoma protein expression profiles on 412 cases from TCGA and constructed a PRotein-driven index of OVARian cancer (PROVAR). PROVAR significantly discriminated an independent cohort of 226 high-grade serous ovarian carcinomas into groups of high risk and low risk of tumor recurrence as well as short-term and long-term survivors. Comparison with gene expression–based outcome classification models showed a significantly improved capacity of the protein-based PROVAR to predict tumor progression. Identification of protein markers linked to disease recurrence may yield insights into tumor biology. When combined with features known to be associated with outcome, such as
Authors
Ji-Yeon Yang, Kosuke Yoshihara, Kenichi Tanaka, Masayuki Hatae, Hideaki Masuzaki, Hiroaki Itamochi, Masashi Takano, Kimio Ushijima, Janos L. Tanyi, George Coukos, Yiling Lu, Gordon B. Mills, Roel G.W. Verhaak
Figure 1
Flow chart for construction and validation of PROVAR and for comparison with gene-driven models from Konstantinopoulos et al. (
Copyright © 2021 American Society for Clinical Investigation
ISSN: 0021-9738 (print), 1558-8238 (online)