The Role of Lactate Metabolism in Prostate Cancer Progression and Metastases Revealed by Dual-Agent Hyperpolarized 13C MRSI

Abstract

This study applied a dual-agent, ¹³C-pyruvate and ¹³C-urea, hyperpolarized ¹³C magnetic resonance spectroscopic imaging (MRSI) and multi-parametric (mp) ¹H magnetic resonance imaging (MRI) approach in the transgenic adenocarcinoma of mouse prostate (TRAMP) model to investigate changes in tumor perfusion and lactate metabolism during prostate cancer development, progression and metastases, and after lactate dehydrogenase-A (LDHA) knock-out. An increased Warburg effect, as measured by an elevated hyperpolarized (HP) Lactate/Pyruvate (Lac/Pyr) ratio, and associated <i>Ldha</i> expression and LDH activity were significantly higher in high- versus low-grade TRAMP tumors and normal prostates. The hypoxic tumor microenvironment in high-grade tumors, as measured by significantly decreased HP ¹³C-urea perfusion and increased PIM staining, played a key role in increasing lactate production through increased <i>Hif1α</i> and then <i>Ldha</i> expression. Increased lactate induced <i>Mct4</i> expression and an acidic tumor microenvironment that provided a potential mechanism for the observed high rate of lymph node (86%) and liver (33%) metastases. The <i>Ldha</i> knockdown in the triple-transgenic mouse model of prostate cancer resulted in a significant reduction in HP Lac/Pyr, which preceded a reduction in tumor volume or apparent water diffusion coefficient (ADC). The <i>Ldha</i> gene knockdown significantly reduced primary tumor growth and reduced lymph node and visceral metastases. These data suggested a metabolic transformation from low- to high-grade prostate cancer including an increased Warburg effect, decreased perfusion, and increased metastatic potential. Moreover, these data suggested that LDH activity and lactate are required for tumor progression. The lactate metabolism changes during prostate cancer provided the motivation for applying hyperpolarized ¹³C MRSI to detect aggressive disease at diagnosis and predict early therapeutic response.