János Tibor Fekete1, Ágnes Ősz1, Imre Pete2, Gyula Richárd Nagy3, Ildikó Vereczkey2, Balázs Győrffy4. 1. Semmelweis University, Department of Bioinformatics, H-1094 Budapest, Hungary; Research Center for Natural Sciences, Momentum Cancer Biomarker Research Group, Institute of Enzymology, Magyar tudósok körútja 2, H-1117 Budapest, Hungary; Semmelweis University, 2nd Department of Pediatrics, H-1094 Budapest, Hungary. 2. National Institute of Oncology, H-1122 Budapest, Hungary. 3. Research Center for Natural Sciences, Momentum Cancer Biomarker Research Group, Institute of Enzymology, Magyar tudósok körútja 2, H-1117 Budapest, Hungary; Semmelweis University, Department of Obstetrics and Gynecology, H-1085 Budapest, Hungary. 4. Semmelweis University, Department of Bioinformatics, H-1094 Budapest, Hungary; Research Center for Natural Sciences, Momentum Cancer Biomarker Research Group, Institute of Enzymology, Magyar tudósok körútja 2, H-1117 Budapest, Hungary; Semmelweis University, 2nd Department of Pediatrics, H-1094 Budapest, Hungary. Electronic address: gyorffy.balazs@med.semmelweis-univ.hu.
Abstract
OBJECTIVE: The first-line chemotherapy for ovarian cancer is based on a combination of platinum and taxane. To date, no reliable predictive biomarker has been recognized that is capable of identifying patients with pre-existing resistance to these agents. Here, we have established an integrated database and identified the most significant biomarker candidates for chemotherapy resistance in serous ovarian cancer. METHODS: Gene arrays were collected from the GEO and TCGA repositories. Treatment response was defined based on pathological response or duration of relapse-free survival. The responder and nonresponder cohorts were compared using the Mann-Whitney and receiver operating characteristic tests. An independent validation set was established to investigate the correlation between chemotherapy response for the top 8 genes. Statistical significance was set at p < 0.05. RESULTS: The entire database included 1816 tumor samples from 12 independent datasets. From analyzing all the genes for platinum + taxane response, we identified the eight strongest genes correlated to chemotherapy resistance: AKIP1 (p = 1.60E-08, AUC = 0.728), MARVELD1 (p = 2.70E-07, AUC = 0.712), AKIRIN2 (p = 2.60E-07, AUC = 0.704), CFL1 (p = 8.10E-08, AUC = 0.694), SERBP1 (p = 8.10E-07, AUC = 0.684), PDXK (p = 1.30E-04, AUC = 0.634), TFE3 (p = 7.90E-05, AUC = 0.631) and NCOR2 (p = 1.90E-03, AUC = 0.611). Of these, the independent validation confirmed TFE3 (p = 0.012, AUC = 0.718), NCOR2 (p = 0.048, AUC = 0.671), PDXK (p = 0.019, AUC = 0.702), AKIP1 (p = 0.002, AUC = 0.773), MARVELD1 (p = 0.044, AUC = 0.675) and AKIRIN2 (p = 0.042, AUC = 0.676). An online interface was set up to enable future validation and ranking of new biomarker candidates in an automated manner (www.rocplot.org/ovar). CONCLUSIONS: We compiled a large integrated database with available treatment and response information and used this to uncover new biomarkers of chemotherapy response in serous ovarian cancer.
OBJECTIVE: The first-line chemotherapy for ovarian cancer is based on a combination of platinum and taxane. To date, no reliable predictive biomarker has been recognized that is capable of identifying patients with pre-existing resistance to these agents. Here, we have established an integrated database and identified the most significant biomarker candidates for chemotherapy resistance in serous ovarian cancer. METHODS: Gene arrays were collected from the GEO and TCGA repositories. Treatment response was defined based on pathological response or duration of relapse-free survival. The responder and nonresponder cohorts were compared using the Mann-Whitney and receiver operating characteristic tests. An independent validation set was established to investigate the correlation between chemotherapy response for the top 8 genes. Statistical significance was set at p < 0.05. RESULTS: The entire database included 1816 tumor samples from 12 independent datasets. From analyzing all the genes for platinum + taxane response, we identified the eight strongest genes correlated to chemotherapy resistance: AKIP1 (p = 1.60E-08, AUC = 0.728), MARVELD1 (p = 2.70E-07, AUC = 0.712), AKIRIN2 (p = 2.60E-07, AUC = 0.704), CFL1 (p = 8.10E-08, AUC = 0.694), SERBP1 (p = 8.10E-07, AUC = 0.684), PDXK (p = 1.30E-04, AUC = 0.634), TFE3 (p = 7.90E-05, AUC = 0.631) and NCOR2 (p = 1.90E-03, AUC = 0.611). Of these, the independent validation confirmed TFE3 (p = 0.012, AUC = 0.718), NCOR2 (p = 0.048, AUC = 0.671), PDXK (p = 0.019, AUC = 0.702), AKIP1 (p = 0.002, AUC = 0.773), MARVELD1 (p = 0.044, AUC = 0.675) and AKIRIN2 (p = 0.042, AUC = 0.676). An online interface was set up to enable future validation and ranking of new biomarker candidates in an automated manner (www.rocplot.org/ovar). CONCLUSIONS: We compiled a large integrated database with available treatment and response information and used this to uncover new biomarkers of chemotherapy response in serous ovarian cancer.