Prolonged progression-free survival in a patient with triple-negative breast cancer metastatic to the liver after chemotherapy and local radiation therapy

Knockdown of Pyruvate Kinase M Inhibits Cell Growth and Migration by Reducing NF-kB Activity in Triple-Negative Breast Cancer Cells

Altered genetic features in cancer cells lead to a high rate of aerobic glycolysis and metabolic reprogramming that is essential for increased cancer cell viability and rapid proliferation. Pyruvate kinase muscle (PKM) is a rate-limiting enzyme in the final step of glycolysis. Herein, we report that PKM is a potential therapeutic target in triple-negative breast cancer (TNBC) cells. We found that PKM1 or PKM2 is highly expressed in TNBC tissues or cells. Knockdown of PKM significantly suppressed cell proliferation and migration, and strongly reduced S phase and induced G2 phase cell cycle arrest by reducing phosphorylation of the CDC2 protein in TNBC cells. Additionally, knockdown of PKM significantly suppressed NF-kB (nuclear factor kappa-light-chain-enhancer of activated B cells) activity by reducing the phosphorylation of p65 at serine 536, and also decreased the expression of NF-kB target genes. Taken together, PKM is a potential target that may have therapeutic implications for TNBC cells.

Relationship between Clinicopathologic Variables in Breast Cancer Overall Survival Using Biogeography-Based Optimization Algorithm

Breast cancer is the most common cancer among women and is considered a major public health concern worldwide. Biogeography-based optimization (BBO) is a novel metaheuristic algorithm. This study analyzed the relationship between the clinicopathologic variables of breast cancer using Cox proportional hazard (PH) regression on the basis of the BBO algorithm. The dataset is prospectively maintained by the Division of Breast Surgery at Kaohsiung Medical University Hospital. A total of 1896 patients with breast cancer were included and tracked from 2005 to 2017. Fifteen general breast cancer clinicopathologic variables were collected. We used the BBO algorithm to select the clinicopathologic variables that could potentially contribute to predicting breast cancer prognosis. Subsequently, Cox PH regression analysis was used to demonstrate the association between overall survival and the selected clinicopathologic variables. C-statistics were used to test predictive accuracy and the concordance of various survival models. The BBO-selected clinicopathologic variables model obtained the highest C-statistic value (80%) for predicting the overall survival of patients with breast cancer. The selected clinicopathologic variables included tumor size (hazard ratio [HR] 2.372, p = 0.006), lymph node metastasis (HR 1.301, p = 0.038), lymphovascular invasion (HR 1.606, p = 0.096), perineural invasion (HR 1.546, p = 0.168), dermal invasion (HR 1.548, p = 0.028), total mastectomy (HR 1.633, p = 0.092), without hormone therapy (HR 2.178, p = 0.003), and without chemotherapy (HR 1.234, p = 0.491). This number was the minimum number of discriminators required for optimal discrimination in the breast cancer overall survival model with acceptable prediction ability. Therefore, on the basis of the clinicopathologic variables, the survival prediction model in this study could contribute to breast cancer follow-up and management.