Direct and indirect assessment of cancer metabolism explored by MRI.

Abstract

Magnetic resonance-based approaches to obtain metabolic information on cancer have been explored for decades. Electron paramagnetic resonance (EPR) has been developed to pursue metabolic profiling and successfully used to monitor several physiologic parameters such as pO2 , pH, and redox status. All these parameters are associated with pathophysiology of various diseases. Especially in oncology, cancer hypoxia has been intensively studied because of its relationship with metabolic alterations, acquiring treatment resistance, or a malignant phenotype. Thus, pO2 imaging leads to an indirect metabolic assessment in this regard. Proton electron double-resonance imaging (PEDRI) is an imaging technique to visualize EPR by using the Overhauser effect. Most biological parameters assessed in EPR can be visualized using PEDRI. However, EPR and PEDRI have not been evaluated sufficiently for clinical application due to limitations such as toxicity of the probes or high specific absorption rate. Hyperpolarized (HP) 13 C MRI is a novel imaging technique that can directly visualize the metabolic profile. Production of metabolites of the HP 13 C probe delivered to target tissue are evaluated in this modality. Unlike EPR or PEDRI, which require the injection of radical probes, 13 C MRI requires a probe that can be physiologically metabolized and efficiently hyperpolarized. Among several methods for hyperpolarizing probes, dissolution dynamic nuclear hyperpolarization is a widely used technique for in vivo imaging. Pyruvate is the most suitable probe for HP 13 C MRI because it is part of the glycolytic pathway and the high efficiency of pyruvate-to-lactate conversion is a distinguishing feature of cancer. Its clinical applicability also makes it a promising metabolic imaging modality. Here, we summarize the applications of these indirect and direct MR-based metabolic assessments focusing on pO2 and pyruvate-to-lactate conversion. The two parameters are strongly associated with each other, hence the acquired information is potentially interchangeable when evaluating treatment response to oxygen-dependent cancer therapies.