Abstract
Ovarian cancer is the leading cause of gynecologic cancer death among women. Regardless of the development made in the past two decades in the surgery and chemotherapy of ovarian cancer, most of the advanced-stage patients are with recurrent cancer and die. The conventional treatment for ovarian cancer is to remove cancerous tissues using surgery followed by chemotherapy, however, patients with such treatment remain at great risk for tumor recurrence and progressive resistance. Nowadays, new treatment with molecular-targeted agents have become accessible. Bevacizumab as a monotherapy in combination with chemotherapy has been recently approved by FDA for the treatment of epithelial ovarian cancer (EOC). Prediction of therapeutic effects and individualization of therapeutic strategies are critical, but to the authors’ best knowledge, there are no effective biomarkers that can be used to predict patient response to bevacizumab treatment for EOC and peritoneal serous papillary carcinoma (PSPC). This dataset helps researchers to explore and develop methods to predict the therapeutic effect of patients with EOC and PSPC to bevacizumab.
Highlights
Background & SummaryOvarian cancer is the fifth most common cause of cancer death worldwide among women, accounting for more deaths than any other gynecologic cancer
Despite the surgery and chemotherapy of ovarian cancer, more than 70% of the patients suffer from progres sive resistance and tumor recurrence to treatment
High-grade serous ovarian cancer (HGSOC) is the most common subtype of Epithelial ovarian cancer (EOC), representing for over 70% of the EOCs4,5
Summary
Background & SummaryOvarian cancer is the fifth most common cause of cancer death worldwide among women, accounting for more deaths than any other gynecologic cancer. Despite the surgery and chemotherapy of ovarian cancer, more than 70% of the patients suffer from progres sive resistance and tumor recurrence to treatment.
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