Predicting time to ovarian carcinoma recurrence using protein markers
Ji-Yeon Yang, … , Gordon B. Mills, Roel G.W. Verhaak
Ji-Yeon Yang, … , Gordon B. Mills, Roel G.W. Verhaak
Published August 15, 2013
Citation Information: J Clin Invest. 2013;123(9):3740-3750. https://doi.org/10.1172/JCI68509.
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Research Article Oncology

Predicting time to ovarian carcinoma recurrence using protein markers

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 BRCA mutation, PROVAR may provide clinically useful predictions of time to tumor recurrence.

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

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Figure 4

The heat map of RPPA data for the TCGA set (222 samples and 172 proteins).

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The heat map of RPPA data for the TCGA set (222 samples and 172 proteins...
The row dendrogram indicates unsupervised hierarchical clustering of 172 proteins. Colored bars next to the dendrogram represent (a) 8 unique markers found by using the TCGA set; (b) the overlap, AR; and (c) 6 unique markers found by using the validation set. The clusters were cut into 4 groups as indicated by different colors. Ward’s linkage method and absolute distance were used as a group linkage method and a distance measure, respectively. The column dendrogram indicates unsupervised hierarchical clustering of 222 patients. Red bars below the dendrogram indicate the high-risk group. The sample size of each cluster and a comparison with existing molecular subtypes are provided in Supplemental Table 2.