Objective This study aimed to compare clinical features and overall survival (OS) between patients with primary peritoneal serous carcinoma (PPSC) and those with advanced serous ovarian carcinoma (ASOC) and to identify prognostic factors. Methods Patients diagnosed with PPSC and ASOC from 2010 to 2015 from the Surveillance, Epidemiology, and End Results (SEER) database were enrolled. Pearson’s chi-square test was used to compare clinical features. The primary endpoint was OS. The Kaplan–Meier method and log–rank test were used to perform the survival analysis. Propensity score matching was also conducted. Univariate, multivariate and subgroup analyses were performed using the Cox proportional hazards model. Results A total of 708 PPSC patients and 7610 ASOC patients were enrolled. The clinical features of PPSC patients were noticeably different from those of ASOC patients. The survival analysis showed that PPSC patients had poorer outcomes than ASOC patients. Even after the clinical features were balanced, PPSC patients still had poorer survival. Univariate and multivariate analyses indicated that older age, higher tumor grade and advanced American Joint Committee on Cancer stage were adverse prognostic factors in both groups, while surgery and chemotherapy were protective factors. A subgroup analysis demonstrated that most factors favored ASOC patients. The total distant metastasis rates of PPSC and ASOC were similar. Liver or lung metastasis was common, but bone and brain metastases were rare. A higher proportion of liver metastasis was observed in the ASOC group. Conclusion The clinical features and survival outcomes between PPSC patients and ASOC patients are clearly different, and PPSC is more aggressive than ASOC.

Breast cancer is one of the leading causes of mortality worldwide. Genomic studies have provided key insights into how breast cancer develop, evolve, metastasize, recurrence and respond to treatment. The advance of next-generation sequencing (NGS) and bioinformatics has led to an unprecedented view of the breast cancer genome and its evolution, and made the dynamic monitoring of recurrence and selective evolution of breast cancer during therapy a reality. Here we illustrate a proposed preclinical strategy of personalized and dynamic therapy for breast cancer based on the technologies of NGS and patient-derived tumor xenograft (PDX) model. The goal of this strategy is to provide a preclinical model of personalized and dynamic therapy for breast cancer to improve patients' outcomes.