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Gene Signatures Identified by Meta-Analysis Predict Survival and Suboptimal Debulking in Late-Stage Ovarian Cancer

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Key Points

  • A survival gene signature differentiated high and low risk with a hazard ratio of 2.19, outperforming a TCGA signature.
  • The sum of immunohistochemistry intensities for three proteins provided an index that classified 92.8% of samples correctly in high- and low-risk groups for suboptimal debulking.

As reported in the Journal of the National Cancer Institute, Riester et al have identified gene-expression signatures predictive of survival and debulking status in late-stage ovarian cancer.

Study Details

The study involved meta-analytic techniques using integrated data from 13 publicly available datasets including 1,525 patients. Prediction models were trained using a meta-analysis variation on the compound covariable method, and models were tested by a “leave-one-dataset-out” procedure.

The survival signature was intended to identify long- and short-term survivors among patients with early-stage and late-stage serous ovarian cancer. Tested models each included 200 genes. The second signature was intended to identify advanced-stage serous tumors that cannot be optimally debulked to ≤ 1 cm of residual tumor.

Survival Signature in Late-Stage Disease

The identified survival gene signature stratified patients into high- and low-risk groups exhibiting significant or near-significant differences in overall survival in each of six training data sets and seven validation data sets, including patients with early-stage disease. Use of the gene signature stratified patients with late-stage disease into high- and low-risk groups with median overall survival of 29.6 vs 60.1 months (hazard ratio [HR] = 2.19, 95% confidence interval [CI] =1.84–2.61).

The hazard ratio represented an increase of 0.36 (P = .04) over the hazard ratio for low vs high risk on an initial prognostic The Cancer Genome Atlas (TCGA) consortium gene signature (HR = 1.83, 95% CI = 1.54–2.17). The original TCGA signature and an updated TCGA signature had similar performance in distinguishing low and high risk (HR difference = 0.00, 95% CI = −0.33 to 0.34). Pathway analysis of the signature showed enrichment of TGF-β and PDGF signaling in poor-prognosis patients.

Debulking Status Signature

The transcriptional network found to be associated with suboptimal debulking leads to upregulation of genes that support tumor dissemination, decreasing the likelihood of total surgical removal and optimal debulking. From an initial group of identified genes in a debulking signature, six were validated by immunohistochemistry and quantitative reverse-transcription polymerase chain reaction (PCR) in two independent cohorts of 78 and 179.

POSTN, CXCL14, FAP, NUAK1, PTCH1, and TGFBR2 were validated by quantitative reverse-transcription PCR (all P < .05) as independent predictors of debulking status in one cohort, with a model using all genes classifying 76.9% of all samples correctly (area under the curve [AUC] = 0.76, 95% CI = 0.66–0.87). Protein expression of three of the associated proteins, POSTN, CXCL14, and pSmad2/3 (a surrogate marker of TGF-β pathway activation) was validated by immunohistochemistry as an independent predictor of debulking status in the other cohort. The sum of immunohistochemistry intensities for these three proteins provided an index that classified 92.8% of samples correctly in high- and low-risk groups for suboptimal debulking (AUC = 0.89, 95% CI = 0.84–0.93).

The investigators concluded, “Our survival signature provides the most accurate and validated prognostic model for early- and advanced-stage high-grade, serous ovarian cancer. The debulking signature accurately predicts the outcome of cytoreductive surgery, potentially allowing for stratification of patients for primary vs secondary cytoreduction.”

Michael J. Birrer, MD, PhD, Massachusetts General Hospital Cancer Center, is the corresponding author for the Journal of the National Cancer Institute article.

The study was supported by grants from the National Cancer Institute, Dana-Farber/Harvard Cancer Center, and National Science Foundation.

The content in this post has not been reviewed by the American Society of Clinical Oncology, Inc. (ASCO®) and does not necessarily reflect the ideas and opinions of ASCO®.


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