ABSTRACT
BACKGROUND: Breast cancer is the most frequent female malignancy in Thailand. Prolactin (PRL) and prolactin receptor (PRLR) play an important role in normal breast development and carcinogenesis of breast cancer. There are two major isoforms of PRLR, consisting of long-form (LF-PRLR) and short-form (SF-PRLR) that stimulate different signaling pathways. This study aims to explore the associations between all PRLR isoforms (all-PRLR) and LF-PRLR with clinicopathological parameters in breast cancer patients. METHODS: A total of 340 patients were recruited from January 2009 to December 2015. Expressions of PRLR in breast cancer tissue were determined by immunohistochemistry using specific antibodies that recognize different domains of PRLR (B6.2 for all-PRLR and H-300 for LF-PRLR). The associations between all-PRLR and LF-PRLR expressions with clinicopathological parameters were evaluated. RESULTS: Expression of all-PRLR was observed in 86.2% of all patients while LF-PRLR expression was observed in 54.4%. All-PRLR was co-expressed with estrogen receptor (ER) and progesterone receptor (PR). LF-PRLR expression was associated with high grade tumor and human epidermal growth factor receptor-2 (HER2) overexpression (P=0.010 and <0.001, respectively). Subgroup analysis revealed that LF-PRLR expression was the independent predictor for lower disease-free survival (DFS) in node-negative breast cancer patients with high expression of all-PRLR [hazard ratio (HR): 5.224, 95% confidence interval (CI): 1.089-25.064, P=0.039]. CONCLUSIONS: The presence of LF-PRLR in the patients with high expression of all-PRLR was associated with adverse outcome. Evaluation of all-PRLR and LF-PRLR might be used as novel prognosticators in node-negative breast cancers.
ABSTRACT
BACKGROUND AND PURPOSE: Web-based prognostic calculators have been developed to inform about the use of adjuvant systemic treatments in breast cancer. CancerMath and PREDICT are two examples of web-based prognostic tools that predict patient survival up to 15 years after an initial diagnosis of breast cancer. The aim of this study is to validate the use of CancerMath and PREDICT as prognostic tools in Thai breast cancer patients. PATIENTS AND METHODS: A total of 615 patients who underwent surgical treatment for stage I to III breast cancer from 2003 to 2011 at the Division of Head Neck and Breast Surgery, Department of Surgery, Siriraj Hospital, Mahidol University, Thailand were recruited. A model-predicted overall survival rate (OS) and the actual OS of the patients were compared. The efficacy of the model was evaluated using receiver-operating characteristic (ROC) analysis. RESULTS: For CancerMath, the predicted 5-year OS was 88.9% and the predicted 10-year OS was 78.3% (p<0.001). For PREDICT, the predicted 5-year OS was 83.1% and the predicted 10-year OS was 72.0% (p<0.001). The actual observed 5-year OS was 90.8% and the observed 10-year OS was 82.6% (p<0.001). CancerMath demonstrated better predictive performance than PREDICT in all subgroups for both 5- and 10-year OS. In addition, there was a marked difference between CancerMath and observed survival rates in patients who were older as well as patients who were stage N3. The area under the ROC curve for 5-year OS in CancerMath and 10-year OS was 0.74 (95% CI; 0.65-0.82) and 0.75 (95% CI; 0.68-0.82). In the PREDICT group, the area under the ROC curve for 5-year OS was 0.78 (95% CI; 0.71-0.85) and for 10-year OS, it was 0.78 (95% CI; 0.71-0.84). CONCLUSION: CancerMath and PREDICT models both underestimated the OS in Thai breast cancer patients. Thus, a novel prognostic model for Thai breast cancer patients is required.
ABSTRACT
BACKGROUND AND PURPOSE: Magee Equations have been developed as accurate tools for predicting response and clinical outcomes in breast cancer patients treated with adjuvant systemic therapy using basic clinicopathological parameters. This study aims to evaluate the alternative application of Magee Equation 2 score in predicting pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) in hormone receptor (HR)-positive, HER2-negative breast cancer. PATIENTS AND METHODS: Patients with HR-positive, HER2-negative breast cancer who received NAC from January 2010 to May 2018 at Siriraj Hospital, Mahidol University, Thailand, were recruited. Pre-treatment status of HR and HER2 was used to calculate the Magee Equation 2 scores. The pCR rates among different clinicopathological parameters were analyzed. Survival analysis was performed by Log-rank test. Kaplan-Meier survival curves were analyzed. RESULTS: A total of 215 patients were eligible. The pCR rates for low, intermediate, and high scores were 4.8%, 3.6%, and 23.8%, respectively. Patients with high scores had significantly higher size reduction and pCR rates compared to those with intermediate or low scores (p<0.001). Those with high scores had higher rates of locoregional recurrence and death. The patients with high score had significantly lower overall survival (p=0.034). CONCLUSION: Among patients with HR-positive and HER2-negative breast cancer treated with NAC, Magee Equation 2 might be used as a tool for predicting the pCR and clinical outcome.
ABSTRACT
BACKGROUND: Prolactin (PRL) is a polypeptide hormone secreted by the anterior pituitary to stimulate growth and differentiation of the normal mammary gland. Together with its receptor, prolactin receptor (PRLR) have been shown to play a role in breast cancer. This study aimed to examine the roles of PRL and PRLR polymorphisms and expression in breast cancer risk and aggressiveness in Thai patients. METHODS: PRL (rs3756824 C/G and rs2244502 T/A) and PRLR (rs37364 G/T and rs249537 A/G) polymorphisms were genotyped by real-time PCR and PRLR expression was assessed by immunohistochemistry (IHC) in breast cancer tissues. The correlations between PRL and PRLR polymorphisms and breast cancer susceptibility/aggressiveness as well as the associations between PRLR expression and clinicopathological parameters were determined. RESULTS: Two hundred and twenty-seven breast cancer patients and 119 matched controls were recruited at the Division of Head Neck and Breast Surgery, Department of Surgery, Faculty of Medicine, Siriraj Hospital, Thailand from 2010-2014. PRL and PRLR polymorphisms were not correlated with breast cancer susceptibility and there was no association between PRLR polymorphisms and PRLR expression. PRLR was frequently overexpressed in breast cancer with positive hormone receptors. High expression of PRLR was significantly related to the presence of axillary nodal metastasis and lymphovascular invasion and showed a trend towards poorer outcome. CONCLUSIONS: There was a correlation between high PRLR expression and aggressive features of breast cancer. PRLR expression might be utilized as a prognostic factor for identification of luminal breast cancer with poorer outcome.
