Abstract
BackgroundA high-quality risk prediction model is urgently needed for the clinical management of ovarian cancer. However most existing models are solely based on clinical parameters, and molecular classifications in recent reports are still being debated. This study aimed to establish a risk prediction model by using both clinicopathological and molecular factors (the synthetic model) for epithelial ovarian cancer.MethodsA retrospective cohort study was conducted in epithelial ovarian cancer patients (n = 161) treated with primary debulking surgery and adjuvant chemotherapy. The expression level of 15 selected molecular markers were measured using immunohistochemistry. A risk model was developed using COX regression analysis with overall survival as the primary outcome. A simplified scoring system for each prognostic factor was based on its coefficient. Independent validation (n = 40) was conducted to evaluate the performance of the model.ResultsA total of 10 out of 15 molecular markers were significantly associated with clinical characteristics and overall survival. The synthetic model performed better than the clinicopathological risk model or the molecular risk model alone, as assessed by analysis of the receiver-operating characteristics curve area and the Youden index. The synthetic model included parity (>3), peritoneal metastasis, stage, tumor type, residual disease, and expression of human epidermal growth factor receptor 2 (HER2), epidermal growth factor receptor (EGFR), breast cancer 1 (BRCA1), murine sarcoma viral oncogene homolog B (BRAF) and Kirsten rat sarcoma viral oncogene homolog (KRAS).ConclusionsOur synthetic risk model may more accurately predict survival of epithelial ovarian cancer patients than current models.Electronic supplementary materialThe online version of this article (doi:10.1186/s13048-015-0195-6) contains supplementary material, which is available to authorized users.
Highlights
A high-quality risk prediction model is urgently needed for the clinical management of ovarian cancer
Two of the most striking findings were that (a) highly significant associations were discovered between the overexpression of human epidermal growth factor receptor 2 (HER2) (26/ 36 resistant patients, P = 0.013), Kirsten rat sarcoma viral oncogene homolog (KRAS) (32/36 resistant patients, P = 0.004), low expression of phosphatase and tensin homolog (PTEN) (36/36 resistant patients, P = 0.043) and platinum resistance, and (b) platinum resistance (P = 0.043), residual disease (P < 0.001), the expression of vascular endothelial growth factor (VEGF) (P = 0.031) and HER2(P = 0.008), the expression of breast cancer 1 (BRCA1)(P = 0.05) and KRAS (P = 0.021) were significantly associated with overall survival in patients with the same stages and treated with uniform therapies
There were no associations between clinicopathological characteristics and the expression of VEGF, Notch homolog 3 (NOTCH3), and BRCA2
Summary
A high-quality risk prediction model is urgently needed for the clinical management of ovarian cancer. This study aimed to establish a risk prediction model by using both clinicopathological and molecular factors (the synthetic model) for epithelial ovarian cancer. The development of a high-quality risk prediction model for ovarian cancer to guide personalized therapy is a primary research focus in the field. A series of studies reported that the prognostic model, which is based on clinical characteristics including advanced age, higher stage and grade of tumor, presence of ascites, poorer performance status and residual disease (>1 cm), was able to stratify patients with poor survival [7,8,9]. Ovarian cancer patients with similar clinical characteristics exhibit difference in prognosis, which
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