Evaluation of 11C-Acetate and 18 F-FDG PET/CT in mouse multidrug resistance gene-2 deficient mouse model of hepatocellular carcinoma

Abstract

BackgroundHepatocellular carcinoma (HCC) remains a global health problem with unique diagnostic and therapeutic challenges, including difficulties in identifying the highest risk patients. Previous work from our lab has established the murine multidrug resistance-2 mouse (MDR2) model of HCC as a reasonable preclinical model that parallels the changes seen in human inflammatory associated HCC. The purpose of this study is to evaluate modalities of PET/CT in MDR2−/− mice in order to facilitate therapeutic translational studies from bench to bedside.Methods18F-FDG and 11C-acetate PET/CT was performed on 12 m MDR2−/− mice (n = 3/tracer) with HCC and 12 m MDR2−/+ control mice (n = 3/tracer) without HCC. To compare PET/CT to biological markers of HCC and cellular function, serum alpha-fetoprotein (AFP), lysophosphatidic acid (LPA), cAMP and hepatic tumor necrosis factor α (TNFα) were quantified in 3-12 m MDR2−/− (n = 10) mice using commercially available ELISA analysis. To translate results in mice to patients 11C-acetate PET/CT was also performed in 8 patents suspected of HCC recurrence following treatment and currently on the liver transplant wait list.ResultsHepatic18F-FDG metabolism was not significantly increased in MDR2−/− mice. In contrast, hepatic 11C-acetate metabolism was significantly elevated in MDR2−/− mice when compared to MDR2−/+ controls. Serum AFP and LPA levels increased in MDR2−/− mice contemporaneous with the emergence of HCC. This was accompanied by a significant decrease in serum cAMP levels and an increase in hepatic TNFα. In patients suspected of HCC recurrence there were 5 true positives, 2 true negatives and 1 suspected false 11C-acetate negative.ConclusionsHepatic 11C-acetate PET/CT tracks well with HCC in MDR2−/− mice and patients with underlying liver disease. Consequently 11C-acetate PET/CT is well suited to study 1) HCC emergence/progression in patients and 2) reduce animal numbers required to study new chemotherapeutics in murine models of HCC.

Metabolic activity of brown, “beige,” and white adipose tissues in response to chronic adrenergic stimulation in male mice

“…Our results confirm the ability to iWAT to undergo browning after chronic CL and cold and to exhibit increased mtDNA content. However, our results do not demonstrate that the browning of iWAT was paralleled by a major increase in its metabolic activity as assessed with a triad of PET tracers that have been proven to readily detect any metabolic changes in BAT (3,4,19,33) as well as in other metabolically active tissues such as heart or liver (2,8,21,29,46). The oxidative activity ([ 11 C]acetate) of iWAT following chronic CL and cold was not only trivial compared with that of iBAT, but it was also not higher than that of eWAT, which resisted browning.…”

“…11 C]acetate has proven to be a very reliable tool for detecting metabolic activity changes in the heart (2, 29), liver (8,46), skeletal muscle (21), cancer cells (28,34), and, more recently, in brown fat (3,4,19,33), even in humans, where the density of brown fat adipocytes appears not higher than that of beige fat in laboratory mice (55). One can also argue that either the mouse strain that we used was not a good responder to cold or the cold stimulus that we used did not sufficiently enhance iWAT thermogenic capacity.…”

Angiogenesis for tumor vascular normalization of Endostar on hepatoma 22 tumor-bearing mice is involved in the immune response

“…cAMP has previously been associated with HCC ( 26 ). The serum cAMP level was markedly decreased in H22-bearing mice from a peak on day 1 of 0.51±0.07 nmol/ml to a low of 0.07±0.01 nmol/ml on day 12 (P<0.05).…”

Automatic and fast segmentation of breast region-of-interest (ROI) and density in MRIs

“…As a result, each pixel in the images are classified as classification (true positive (TP) and true negative (TN)) and misclassification (false positive (FP) and false negative (FN)). Based on these predictions, the performance of our algorithm is compared in terms of Accuracy (Acc), Precision (P), Sensitivity (Se) or Recall, Specificity (Sp), Area under ROC curve (AUC), Misclassification rate (MR), Dice similarity coefficient (DSC) and Jaccard Coefficient [66], [67], [70], [27]. These performance metrics are defined as shown in Eq.…”

“…Acc is the measure of the total number of correctly classified pixels (sum of true positives and true negatives) to the number of total pixels in an image [63], [64]. Precision is the proportion of correctly predicted positive observations to the total predicted positive observations [67]. Although both accuracy and precision depict the closeness of measurement to an actual value, precision reflects the reproducible measurements even if they are far from accepted value.…”