First published December 2, 2013 - More info
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
Original citation: J Clin Invest. 2010;123(9):3740–3750. doi:10.1172/JCI68509.
Citation for this erratum: J Clin Invest. 2013;123(12):5410. doi:10.1172/JCI74035.
Some expressions and notations related to Equations 1 and 2 were presented incorrectly. The correct text and equations are below.
The coefficients (β) in Cox’s regression model are estimated by maximizing the partial likelihood function subject to a constraint on the L1-norm of the coefficients. The lasso estimator (β̂) maximizes the objective function given below:
Here l(β) is the log partial likelihood in the Cox model; for the exact form of this function, see ref. 41. The tuning parameter, λ in Equation 1, was chosen by 10-fold cross-validation. For the implementation, we used the R package “glmnet” (39).
PROVAR was defined for each of the 222 TCGA samples as the sum of the estimated coefficients multiplied by protein expression levels, as shown below. Here i represents patients (i = 1, ..., 222), j represents proteins with nonzero coefficients (j = 1, ..., m), β̂j is the lasso coefficient of the jth protein marker, and Xij is the expression level of the jth protein for the ith patient.
The JCI regrets the error.