PhD Scientific Days 2019

Budapest, April 25–26, 2019

Predictive biomarkers of ovarian cancer

Fekete, János Tibor

János Fekete1, Ágnes Ősz1,2, Imre Pete3, Ildikó Vereczkey3, Attila Marcell Szász4, Balázs Győrffy1,2

1 Semmelweis University, 2nd Department of Pediatrics, H-1094, Budapest, Hungary
2MTA TTK Lendület Cancer Biomarker Research Group, Institute of Enzymology, Hungarian Academy of Sciences, Magyar tudósok körútja 2., H-1117, Budapest, Hungary
3National Institute of Oncology, H-1122, Budapest, Hungary
4Semmelweis University Center of Oncology, H-1083, Budapest, Hungary

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Text of the abstract

Introduction: Systemic therapy of ovarian cancer can include chemotherapy and targeted therapy. Prognostic biomarkers are capable to predict survival and predictive biomarkers are capable to predict therapy response. To date, multiple online tools were established to identify prognostic biomarkers, but no platform is yet available for predictive biomarkers.
Aims: Our goal was to develop an online tool to validate gene expression based predictive biomarkers using transcriptomic data of a large set of ovarian cancer patients.
Method: Published gene expression data of 13 independent datasets was integrated with treatment data into a unified database. The classification is based on either author-reported pathological complete response (n=1,022) or relapse-free survival status at six months (n=1,347) or relapse-free survival status at twelve months (n=1,282). Treatment data includes chemotherapy (platin, taxol, docetaxel, paclitaxel, gemcitabine, topotecan) and targeted therapy (avastin). The transcriptomic database includes 54,675 probe sets corresponding to 20,089 distinctive genes. Finally, we performed a sample collection at the National Institute of Oncology (NIO cohort), and used these patient samples to validate the top genes in ovarian cancer patients. Gene expression and therapy response were compared using receiver operating characteristics and Mann-Whitney tests.
Results: In the validation of the tool we focused on paclitaxel-resistance associated genes. We selected the top genes after running the analysis across all samples and validated these by PCR in the NIO cohort of patients (n=80). The best performing paclitaxel-resistance biomarker candidates were ARAF (AUC=0.743, p=4.1E-09), TFE3 (AUC=0.725, p=4.2E-05) and NCOR2 (AUC=0.762, p=5.6E-06).
Conclusion: The analysis pipeline enables to validate and rank predictive biomarker nominees. By analyzing the candidate genes in a large set of independent patients, we can select the most reliable candidate and abolish those which are most likely to fail in a clinical setting. The registration-free interface of the online analysis platform is accessible at

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Doctoral School: Basic Medicine
Program: Oncology
Supervisor: Dr. Balázs Győrffy
E-mail address:
poster presentation