Abstract
Simple SummaryObesity and overweight, considered the pandemic disease in the 21st century, is highly related to breast cancer. Leptin receptor (Ob-R) and its ligand leptin display an important role in driving this connection. Nowadays, translational cancer research is mainly focused on the identification of new biomarkers able to discriminate those patients deriving greater efficacy from a given therapy. In this regard, our study examines the role of Ob-R, namely, the correlation between Ob-R and pathological complete Response (pCR) in early breast cancer patients receiving neoadjuvant systemic therapy. Here, we decoded the correlation of Ob-R with certain clinical features such as breast cancer subtype, age, body mass index (BMI), menopausal status, and mammogram breast density. The study provides further support for the potential value of Ob-R for the first time as a possible role for predicting pCR. Moreover, we would like to highlight the importance of Ob-R as independent predictive factors for pCR in breast cancer patients.The primary aim of this retrospective study was to investigate the correlation between the immunohistochemical expression of Ob-R (leptin receptor) with pCR (pathological complete response) in early breast cancer patients receiving neoadjuvant systemic treatment (NST). A total of 100 women with breast cancer receiving NST (2017–2020) followed by surgical resection were retrospectively obtained. Demographic parameters and clinicopathological factors (e.g., treatment modalities, immunohistochemistry (IHC), and cancer subtype) were obtained from the patient’s clinical records. In the analyzed breast cancer cohort, high expression of Ob-R was found in 52% of tumors and there was a significantly higher incidence in the HER2+ and TNBC subgroups. Overall, a significantly greater percentage of patients with Ob-R positive tumors achieved pCR compared with Ob-R negative patients (57.7% vs. 27.1%; p = 0.002). This result was observed in most breast cancer subtypes. In patients with HER2+ breast cancer, there was no difference in Ob-R expression in relation to the HR status. Ob-R cell positivity was significantly higher in younger breast cancer patients (p = 0.008), those who were premenopausal (p = 0.011), and in those with a BMI > 25 kg/m2 (p = 0.019). A significantly greater percentage of early breast cancer patients with Ob-R positive tumors achieved pCR compared with Ob-R negative patients. Furthermore, breast cancer patients with positive Ob-R expression were significantly younger than those with negative Ob-R expression. This association was not explained by differences in BMI between young and old patients.
Highlights
Neoadjuvant systemic treatment (NST) is one of the most common strategies for treating early breast cancer
Key histopathological data for this group of patients are presented in Table 1 and 80% had mammographic breast density (MBD) type C or D; almost equal numbers of patients had breast cancer subtypes human epidermal growth factor receptor 2 (HER2)+ (35%), triple negative (31%), or luminal (34%); 96% had histological Grade 2 or 3; 83% had a T size of 1–2; 97% had a nodal status of N0 (50%) or N1 (47%); and 52% of patients had an Ob-R positivity of >50% (+2, +3) vs. 48% of patients with an Ob-R positivity of ≤50% (0, +1) (Figure 1)
80% had MBD type C or D; almost equal numbers of patients had breast cancer subtypes HER2+ (35%), triple negative (31%), or luminal (34%); 96% had histological Grade 2 or 3; 83% had a T size of 1–2; 97% had a nodal status of N0 (50%) or N1 (47%); and 526%of 14 of patients had an Ob-R positivity of >50% (+2, +3) vs. 48% of patients with an Ob-R positivity of ≤50% (0, +1) (Figure 1)
Summary
Neoadjuvant systemic treatment (NST) is one of the most common strategies for treating early breast cancer. It still continues to be the primary objective endpoint in the majority of clinical trials in the neoadjuvant setting due to its correlation with long-term patient outcomes and, adjuvant therapy choice may differ based on the pCR status [3]. As an increasing number of breast cancer patients are treated with NST, identification of biomarkers to predict the probability of pCR in individual cases is a high priority, since these could provide insights into breast cancer pathogenesis and support personalized cancer management. While we are making steady improvements with biomarkers in breast cancer, there is still an unmet need for more accurate predictors of factors that impact pCR, especially in the neoadjuvant setting
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