Imaging Cancer Metabolism

Abstract

It is widely accepted that altered metabolism contributes to cancer growth and has been described as a hallmark of cancer. Our view and understanding of cancer metabolism has expanded at a rapid pace, however, there remains a need to study metabolic dependencies of human cancer in vivo. Recent studies have sought to utilize multi-modality imaging (MMI) techniques in order to build a more detailed and comprehensive understanding of cancer metabolism. MMI combines several in vivo techniques that can provide complementary information related to cancer metabolism. We describe several non-invasive imaging techniques that provide both anatomical and functional information related to tumor metabolism. These imaging modalities include: positron emission tomography (PET), computed tomography (CT), magnetic resonance imaging (MRI), magnetic resonance spectroscopy (MRS) that uses hyperpolarized probes and optical imaging utilizing bioluminescence and quantification of light emitted. We describe how these imaging modalities can be combined with mass spectrometry and quantitative immunochemistry to obtain more complete picture of cancer metabolism. In vivo studies of tumor metabolism are emerging in the field and represent an important component to our understanding of how metabolism shapes and defines cancer initiation, progression and response to treatment. In this review we describe in vivo based studies of cancer metabolism that have taken advantage of MMI in both pre-clinical and clinical studies. MMI promises to advance our understanding of cancer metabolism in both basic research and clinical settings with the ultimate goal of improving detection, diagnosis and treatment of cancer patients.

Keywords: Mass spectrometry; Optical imaging; Pet imaging; Tumor metabolism.

Fig. 1.

Overview of major metabolic pathways that can be detected using different imaging modalities. Glucose uptake can be detected using PET with ¹⁸F-FDG; using SIRM or MRS with ¹³C-Glucose; using 2DG probes for optical imaging. Contribution of pyruvate and lactate to TCA cycle can be measured using SIRM or MRS with ¹³C-Pyruvate and ¹³C-Lactate. Glutamine uptake can be detected using PET with ¹⁸F-Glutamine or ¹¹C-Glutamine; using SIRM with ¹³C-Glutamine. Additional probes based on amino acids can be used to detect contribution of amino acids to cellular biomass synthesis. Fatty acid uptake by tumor cells can be evaluated using PET probe ¹¹C-Acetate or using optical imaging with luciferase-tagged free fatty acid. PET probes ¹⁸F-FLT, ¹⁸F-FAC, ¹⁸F-FMAU can be used to determine reliance of tumors on thymidine kinase (TK) and/or deoxycitidine kinase (dCK) and/or cytidine deamidase (CDA). Combining imaging modalities with qIHC allows for quantification of total protein levels as well as phosphorylation.

Fig. 2.

Pre-clinical studies in mice using multi-modality imaging of tumor metabolism to guide targeted therapies (A) Overview of longitudinal ¹⁸F-FDG PET/CT and BLI imaging of genetically engineered mouse model of lung cancer at 2, 6, 8 and 10 weeks post tumor induction. (B) Representative histology performed on mice imaged with PET/CT and BLI. Tumors were stained with H&E or Glut1 as a surrogate biomarker of glucose uptake or P-S6, a surrogate biomarker of mTORC1 signaling. (C) Representation of the PI3K/AKT/mTOR signaling pathway and its regulation of cellular growth and metabolism. In pink are metabolic based drugs and targeted therapies that target this pathway. (D) Representation of ¹⁸F-FDG PET/CT guided studies in mice measuring metabolic response to the catalytic mTOR kinase inhibitor (mTORi). Top panel shows representative ¹⁸F-FDG PET/CT images of mice untreated vs those treated with mTORi. Bottom panel shows representative immunohistochemical staining of lung tumors for the mTORC1 substrate.

Fig. 3.

Comprehensive profiling of tumor metabolism. Following non-invasive imaging with PET, MRI, MRS, CT patients undergo resection or biopsy of the tumor. Tissue analysis of the resected or biopsied tumor includes metabolomics, qIHC and gene expression analysis. Comprehensive tumor profiling identified preference for glucose in poorly vascularized areas of the tumor, compared to well vascularized tumors that used both glucose and alternative metabolites to fuel their growth.