Jen-Jane Liu

Metabolic reprogramming ensures cancer cell survival despite oncogenic signaling blockade

There is limited knowledge about the metabolic reprogramming induced by cancer therapies and how this contributes to therapeutic resistance. Here we show that although inhibition of PI3K-AKT-mTOR signaling markedly decreased glycolysis and restrained tumor growth, these signaling and metabolic restrictions triggered autophagy, which supplied the metabolites required for the maintenance of mitochondrial respiration and redox homeostasis. Specifically, we found that survival of cancer cells was critically dependent on phospholipase A2 (PLA2) to mobilize lysophospholipids and free fatty acids to sustain fatty acid oxidation and oxidative phosphorylation. Consistent with this, we observed significantly increased lipid droplets, with subsequent mobilization to mitochondria. These changes were abrogated in cells deficient for the essential autophagy gene ATG5 Accordingly, inhibition of PLA2 significantly decreased lipid droplets, decreased oxidative phosphorylation, and increased apoptosis. Together, these results describe how treatment-induced autophagy provides nutrients for cancer cell survival and identifies novel cotreatment strategies to override this survival advantage.

MP22-19 ALLOGENEIC BLOOD TRANSFUSION IS ASSOCIATED WITH INCREASED MORTALITY AND INFECTIOUS COMPLICATIONS AFTER NEPHROURETERECTOMY

INTRODUCTION AND OBJECTIVES: The incidence of brain metastases in patients with renal cell carcinoma (RCC) is hypothesized to have increased in the last two decades. Our objective was to provide an overview of recent incidence trends in patients with primary renal cell carcinoma (RCC) and brain metastases at diagnosis using a nationally representative cancer cohort originating from the United States. Secondary objectives include developing a tool for prediction of brain metastases at diagnosis and to assess their oncological outcomes, and externally validating it in second database. METHODS: All patients with a primary diagnostic confirmation of RCC within the Surveillance, Epidemiology, and End Results (SEER, 2010-2013) database and the National Cancer Database (NCDB, 20102012) were abstracted. The incidence proportions (%IP) and 95% confidence intervals (CI) of brain metastases were calculated overall and according to patient, sociodemographic, and disease characteristics. A 1000-bootstrap corrected multivariable logistic regression models was developed for prediction of brain metastases at diagnosis using the SEER cohort (development). Backward variable selection was conducted to identify the most parsimonious model. Model performance was evaluated via measures of predictive accuracy in the NCDB cohort (validation). RESULTS: The overall %IP of brain metastases at RCC diagnosis was 1.51% (95% CI: 1.39-1.64) in the SEER and 1.37% (95% CI: 1.29-1.45%) in the NCDB. The odds of harbouring brain metastases at RCC diagnosis varied significantly according to sociodemographic and clinical characteristics. Following backward variable selection within the SEER database, only histology, tumor size, and cN stages were retained in the final model. Predictive accuracy was adequate in the external validation cohort (C-statistic: 0.778). Median time to any death was 6.37 months in patients with brain metastases. After adjusting for confounders, patients with brain metastases were more likely to succumb to any death than those without brain metastases at diagnosis (hazard ratio: 1.87, 95% CI: 1.71-2.05, P<0.001). CONCLUSIONS: The incidence of brain metastases in patients with RCC is increasing. The oncological outcomes of such patients remain poor and their treatment management variable. A clinical risk model including cN-stage, histology and tumor size can predict the presence of brain metastases at diagnosis and may justify baseline imaging in asymptomatic but high-risk patients.