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38 records · Page 3

PDK4-dependent hypercatabolism and lactate production of senescent cells promotes cancer malignancy

Abstract Senescent cells remain metabolically active, but their metabolic landscape and resulting implications remain underexplored. Here, we report upregulation of pyruvate dehydrogenase kinase 4 (PDK4) upon senescence, particularly in some stromal cell lines. Senescent cells display a PDK4-dependent increase in aerobic glycolysis and enhanced lactate production but maintain mitochondrial respiration and redox activity, thus adopting a special form of metabolic reprogramming. Medium from PDK4 + stromal cells promotes the malignancy of recipient cancer cells in vitro, whereas inhibition of PDK4 causes tumor regression in vivo. We find that lactate promotes reactive oxygen species production via NOX1 to drive the senescence-associated secretory phenotype, whereas PDK4 suppression reduces DNA damage severity and restrains the senescence-associated secretory phenotype. In preclinical trials, PDK4 inhibition alleviates physical dysfunction and prevents age-associated frailty. Together, our study confirms the hypercatabolic nature of senescent cells and reveals a metabolic link between cellular senescence, lactate production, and possibly, age-related pathologies, including but not limited to cancer.

Endocrinology & Metabolism↗

Cancer genomics predicts disease relapse and therapeutic response to neoadjuvant chemotherapy of hormone sensitive breast cancers

Several studies provide insight into the landscape of breast cancer genomics with the genomic characterization of tumors offering exceptional opportunities in defining therapies tailored to the patient’s specific need. However, translating genomic data into personalized treatment regimens has been hampered partly due to uncertainties in deviating from guideline based clinical protocols. Here we report a genomic approach to predict favorable outcome to treatment responses thus enabling personalized medicine in the selection of specific treatment regimens. The genomic data were divided into a training set of N = 835 cases and a validation set consisting of 1315 hormone sensitive, 634 triple negative breast cancer (TNBC) and 1365 breast cancer patients with information on neoadjuvant chemotherapy responses. Patients were selected by the following criteria: estrogen receptor (ER) status, lymph node invasion, recurrence free survival. The k-means classification algorithm delineated clusters with low- and high- expression of genes related to recurrence of disease; a multivariate Cox’s proportional hazard model defined recurrence risk for disease. Classifier genes were validated by Immunohistochemistry (IHC) using tissue microarray sections containing both normal and cancerous tissues and by evaluating findings deposited in the human protein atlas repository. Based on the leave-on-out cross validation procedure of 4 independent data sets we identified 51-genes associated with disease relapse and selected 10, i.e. TOP2A, AURKA, CKS2, CCNB2, CDK1 SLC19A1, E2F8, E2F1, PRC1, KIF11 for in depth validation. Expression of the mechanistically linked disease regulated genes significantly correlated with recurrence free survival among ER-positive and triple negative breast cancer patients and was independent of age, tumor size, histological grade and node status. Importantly, the classifier genes predicted pathological complete responses to neoadjuvant chemotherapy (P < 0.001) with high expression of these genes being associated with an improved therapeutic response toward two different anthracycline-taxane regimens; thus, highlighting the prospective for precision medicine. Our study demonstrates the potential of classifier genes to predict risk for disease relapse and treatment response to chemotherapies. The classifier genes enable rational selection of patients who benefit best from a given chemotherapy thus providing the best possible care. The findings encourage independent clinical validation.

59 BASIC BIOLOGICAL SCIENCES↗