HER2 Expression in Patients With Ovarian Cancer Linked to Poor Prognosis
Human epidermal growth factor receptor 2 (HER2) may be a potential marker of poor prognosis in patients with unclassified ovarian cancer, according to a meta-analysis published in PLOS One (online January 30, 2018; doi:10.1371/journal.pone.0191972).
Numerous studies have investigated the prognostic role of HER2 expression in ovarian cancer. However, the results remain controversial and further review is needed to better define this relationship.
Xueqiong Zhu, department of obstetrics and gynecology, the Second Affiliated Hospital of Wenzhou Medical University (China), and colleagues conducted a meta-analysis to systematically review the association between HER2 expression and ovarian cancer diagnosis. Researchers analyzed patient data (n = 5180) from 34 observational studies published through July 2017 that examined the role of HER2 in ovarian cancer. Studies were collected from PubMed, Embase, and Cochrane library databases.
Hazard ratios for survival with 95% confidence intervals, subgroup analysis, and publication bias as well as sensitivity analyses were implemented. Researchers estimated overall survival (OS), progression-free survival (PFS), and disease-free survival (DFS).
Results of the meta-analysis showed that expression of HER2 was negatively correlated with clinical prognosis of overall survival (HR, 1.57; 95% CI, 1.31-1.89; P < .001) and DFS/PFS (HR, 1.26; 95% CI, 1.06-1.49) in ovarian cancers.
Additionally, researchers found that the association between HER2 expression and poor ovarian cancer prognosis in OS was statistically significant in subgroups of unclassified ovarian cancer, Caucasian patients, and Asian patients. These results were maintained irrespective of detection method.
“Our study suggests that HER2 may be a potential marker to predict the poor prognosis of ovarian cancer patients, especially for patients with unclassified ovarian cancer and Caucasian region,” authors of the study wrote.
Researchers acknowledged a few limitations in the meta-analysis, including a reliance on population-based data and a potential for bias of outcomes due to some of the hazard ratios and confidence intervals being extracted indirectly from a growth curve of formula computing. Further investigations are needed to address these shortcomings, they concluded.—Zachary Bessette