Abstract
Epithelial ovarian cancer patients usually relapse after primary management. We utilized the support vector machine algorithm to develop a model for the chemo-response using the Cancer Cell Line Encyclopedia (CCLE) and validated the model in The Cancer Genome Atlas (TCGA) and the GSE9891 dataset. Finally, we evaluated the feasibility of the model using ovarian cancer patients from our institute. The 10-gene predictive model demonstrated that the high response group had a longer recurrence-free survival (RFS) (log-rank test, p = 0.015 for TCGA, p = 0.013 for GSE9891 and p = 0.039 for NTUH) and overall survival (OS) (log-rank test, p = 0.002 for TCGA and p = 0.016 for NTUH). In a multivariate Cox hazard regression model, the predictive model (HR: 0.644, 95% CI: 0.436–0.952, p = 0.027) and residual tumor size < 1 cm (HR: 0.312, 95% CI: 0.170–0.573, p < 0.001) were significant factors for recurrence. The predictive model (HR: 0.511, 95% CI: 0.334–0.783, p = 0.002) and residual tumor size < 1 cm (HR: 0.252, 95% CI: 0.128–0.496, p < 0.001) were still significant factors for death. In conclusion, the patients of high response group stratified by the model had good response and favourable prognosis, whereas for the patients of medium to low response groups, introduction of other drugs or clinical trials might be beneficial.
Highlights
Ovarian carcinoma is a major cause of cancer death in women [1,2]
To identify a set of genes that could be used to develop a predictive model, we used the genetic algorithm (GA) shown in Figure S1 to select the best combination of 10 genes from the 575 significant genes
A predictive model composed of the 10 selected genes was developed using the SVM algorithm, and its performance was evaluated based on the leave one out cross-validation strategy
Summary
Ovarian carcinoma is a major cause of cancer death in women [1,2]. Due to the lack of initial symptoms and effective screening tools, most patients are diagnosed at an advanced stage with a 5-year survival of less than 50% [3,4]. The clinical prognostic factors include cancer stage, histological subtypes, tumor grade, the residual tumor size after debulking surgery and the response. Most ovarian cancer patients experience tumor recurrence and eventually are resistant to salvage treatments [3,5]. The serum CA-125 level is the current biomarker, but it is not ideal due to its limited specificity. Many potential biomarkers have been evaluated alone or in combination with CA-125, but the majority show disappointing results [6]
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