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
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
Submitter: Haifeng Qiu | email@example.com
Shanghai Jiaotong University
Published August 20, 2013
In the August issue of JCI, Yang et al. announced their advance in prediction of the time to recurrence in ovarian carcinoma using a panel of protein markers. Their model successfully discriminated serous ovarian carcinomas into two groups with different risk of tumor recurrence, which showed significant advantages compared to several gene expression–based outcome classification models. However, we have some concerns about it.
First, this study presented a large range of patient age (26-87 years old), given that the proteomics of ovary differs during different peroids of the lifespan, and serous epithelial carcinoma mainly occurred after menopause, a derivation might happen if not taking the menstruation status into consideration. Improved sensitivity and specificity can be expected with reanalyzing their data by removing these noise.
Another one is the population bias between the two datasets. In the TCGA group, there were much more patients with G3 (poorly differentiated, 85% verse 60%), and more patients had the optimal cytoreductive surgery (60% verse 40%). Therefore, it seems hard to conclude that “more advanced grade was associated with a decrease in PFS in TCGA samples but not with others. Surgery status was found to be predictive of PFS or OS in the validation set but not in the TCGA set.”
Moreover, as there were additional Japanese patients in the validation group, we believe this dataset should be analyzed separately, rather than combined with the American one.