Delay of Aortic Arterial Input Function Time Improves Detection of Malignant Vertebral Body Lesions on Dynamic Contrast-Enhanced MRI Perfusion.

Dynamic contrast-enhanced MRI (DCE) is an emerging modality in the study of vertebral body malignancies. DCE-MRI analysis relies on a pharmacokinetic model, which assumes that contrast uptake is simultaneous in the feeding of arteries and tissues of interest. While true in the highly vascularized brain, the perfusion of the spine is delayed. This delay of contrast reaching vertebral body lesions can affect DCE-MRI analyses, leading to misdiagnosis for the presence of active malignancy in the bone marrow. To overcome the limitation of delayed contrast arrival to vertebral body lesions, we shifted the arterial input function (AIF) curve over a series of phases and recalculated the plasma volume values (Vp) for each phase shift. We hypothesized that shifting the AIF tracer curve would better reflect actual contrast perfusion, thereby improving the accuracy of Vp maps in metastases. We evaluated 18 biopsy-proven vertebral body metastases in which standard DCE-MRI analysis failed to demonstrate the expected increase in Vp. We manually delayed the AIF curve for multiple phases, defined as the scan-specific phase temporal resolution, and analyzed DCE-MRI parameters with the new AIF curves. All patients were found to require at least one phase-shift delay in the calculated AIF to better visualize metastatic spinal lesions and improve quantitation of Vp. Average normalized Vp values were 1.78 ± 1.88 for zero phase shifts (P0), 4.72 ± 4.31 for one phase shift (P1), and 5.59 ± 4.41 for two phase shifts (P2). Mann-Whitney U tests obtained p-values = 0.003 between P0 and P1, and 0.0004 between P0 and P2. This study demonstrates that image processing analysis for DCE-MRI in patients with spinal metastases requires a careful review of signal intensity curve, as well as a possible adjustment of the phase of aortic AIF to increase the accuracy of Vp.

The Intricate Metabolism of Pancreatic Cancers.

Currently, approximately 95% of pancreatic cancers are pancreatic ductal adenocarcinomas (PDAC), which are the most aggressive form and the fourth leading cause of cancer death with extremely poor prognosis [1]. Poor prognosis is primarily attributed to the late diagnosis of the disease when patients are no longer candidates for surgical resection [2]. Cancer cells are dependent on the oncogenes that allow them to proliferate limitlessly. Thus, targeting the expression of known oncogenes in pancreatic cancer has been shown to lead to more effective treatment [3]. This chapter discusses the complexity of metabolic features in pancreatic cancers. In order to comprehend the heterogeneous nature of cancer metabolism fully, we need to take into account the close relationship between cancer metabolism and genetics. Gene expression varies tremendously, not only among different types of cancers but also within the same type of cancer among different patients. Cancer metabolism heterogeneity is often prompted and perpetuated not only by mutations in oncogenes and tumor-suppressor genes but also by the innate diversity of the tumor microenvironment. Much effort has been focused on elucidating the genetic alterations that correlate with disease progression and treatment response [4, 5]. However, the precise mechanisms by which tumor metabolism contributes to cancer growth, survival, mobility, and aggressiveness represent a functional readout of tumor progression (Fig. 1).

Uncovering the Role of N-Acetyl-Aspartyl-Glutamate as a Glutamate Reservoir in Cancer.

N-acetyl-aspartyl-glutamate (NAAG) is a peptide-based neurotransmitter that has been extensively studied in many neurological diseases. In this study, we show a specific role of NAAG in cancer. We found that NAAG is more abundant in higher grade cancers and is a source of glutamate in cancers expressing glutamate carboxypeptidase II (GCPII), the enzyme that hydrolyzes NAAG to glutamate and N-acetyl-aspartate (NAA). Knocking down GCPII expression through genetic alteration or pharmacological inhibition of GCPII results in a reduction of both glutamate concentrations and cancer growth. Moreover, targeting GCPII in combination with glutaminase inhibition accentuates these effects. These findings suggest that NAAG serves as an important reservoir to provide glutamate to cancer cells through GCPII when glutamate production from other sources is limited. Thus, GCPII is a viable target for cancer therapy, either alone or in combination with glutaminase inhibition.

The Intricate Metabolism of Pancreatic Cancers.

KEY POINTS: Currently, approximately 95% of pancreatic cancers are pancreatic ductal adenocarcinoma (PDAC), which is the most aggressive form and the fourth leading cause of cancer death with extremely poor prognosis [1]. Poor prognosis is primarily attributed to the late diagnosis of the disease when patients are no longer candidates for surgical resection [2]. Cancer cells are dependent on the oncogenes that allow them to proliferate limitlessly. Thus, targeting the expression of known oncogenes in pancreatic cancer has been shown to lead to more effective treatment [3]. This chapter will discuss the complexity of metabolic features in pancreatic cancers. To be able to fully comprehend the heterogeneous nature of cancer metabolism, we need to take into account the close relationship between cancer metabolism and genetics. Gene expression varies tremendously, not only among different types of cancers, but also within the same type of cancer among different patients. Cancer metabolism heterogeneity is often prompted and perpetuated not only by genetic mutations in oncogenes and tumor suppressor genes but also by the innate diversity of the tumor microenvironment. Much effort has been focused on elucidating the genetic alterations that correlate with disease progression and treatment response [4]. However, the precise mechanism by which tumor metabolism contributes to cancer growth, survival, mobility, and aggressiveness represents a functional readout of tumor progression.