A DNA Repair Pathway–Focused Score for Prediction of Outcomes in Ovarian Cancer Treated With Platinum-Based Chemotherapy

Abstract

BackgroundNew tools are needed to predict outcomes of ovarian cancer patients treated with platinum-based chemotherapy. We hypothesized that a molecular score based on expression of genes that are involved in platinum-induced DNA damage repair could provide such prognostic information.MethodsGene expression data was extracted from The Cancer Genome Atlas (TCGA) database for 151 DNA repair genes from tumors of serous ovarian cystadenocarcinoma patients (n = 511). A molecular score was generated based on the expression of 23 genes involved in platinum-induced DNA damage repair pathways. Patients were divided into low (scores 0–10) and high (scores 11–20) score groups, and overall survival (OS) was analyzed by Kaplan–Meier method. Results were validated in two gene expression microarray datasets. Association of the score with OS was compared with known clinical factors (age, stage, grade, and extent of surgical debulking) using univariate and multivariable Cox proportional hazards models. Score performance was evaluated by receiver operating characteristic (ROC) curve analysis. Correlations between the score and likelihood of complete response, recurrence-free survival, and progression-free survival were assessed. Statistical tests were two-sided.ResultsImproved survival was associated with being in the high-scoring group (high vs low scores: 5-year OS, 40% vs 17%, P < .001), and results were reproduced in the validation datasets (P < .05). The score was the only pretreatment factor that showed a statistically significant association with OS (high vs low scores, hazard ratio of death = 0.40, 95% confidence interval = 0.32 to 0.66, P < .001). ROC curves indicated that the score outperformed the known clinical factors (score in a validation dataset vs clinical factors, area under the curve = 0.65 vs 0.52). The score positively correlated with complete response rate, recurrence-free survival, and progression-free survival (Pearson correlation coefficient [r2] = 0.60, 0.84, and 0.80, respectively; P < .001 for all).ConclusionThe DNA repair pathway–focused score can be used to predict outcomes and response to platinum therapy in ovarian cancer patients.

Predicting time to ovarian carcinoma recurrence using protein markers

“…In contrast, several recently proposed genedriven models for predicting survival were unable to demonstrate significance for the prediction of PFS particularly in the validation set. (10)(11)(12). OS in ovarian carcinoma is influenced by various factors, such as differences in therapy after progression.…”

“…In Kang et al, a score was developed based on the expression levels of 23 genes in DNA repair pathways (11). Per patient, a point was given for each gene associated with better survival if the expression was higher than the median expression and, on the other hand, a point was given for each gene associated with poorer survival if the expression was lower than the median expression across samples.…”

“…With the development of microarray technologies, several studies have identified genetic markers or gene expression profiles that are associated with the prognosis of high-grade ovarian cancer (9)(10)(11)(12). However, these signatures often contain large numbers of genes, which reduces their applicability in clinical practice.…”

XRCC1 and ADPRT Polymorphisms Associated with Survival in Breast Cancer Cases Treated with Chemotherapy

“…Studies have shown that DNA repair gene polymorphisms may promote cancer progression and thus develop more aggressive tumors (Kudo et al, 2012). These DNA repair gene polymorphisms may lead to different responses to platinum-based chemotherapy by affecting the gene products' capacity to remove anthracycline -DNA adducts (Kang et al, 2012).…”

Gene-expression signatures in ovarian cancer: Promise and challenges for patient stratification

“…Many early generation ovarian cancer gene expression profiling studies focused primarily on the prognostic value of gene expression signatures. Results of these early prognostic gene expression studies [6][7][8][9][10][11][12][13][14] and those following [15][16][17][18][19][20][21][22][23][24][25] are summarized in Table 1 (Table 1). Most of these studies have identified a group of prognostically relevant genes in relatively small training sets but did, to their credit, validate the prognostic relevance of the respective gene signatures in independent cohorts.…”