Background
Intrahepatic cholangiocarcinoma (ICC) is the second most common primary liver malignancy, with an increasing incidence and mortality worldwide [
1‐
3]. Currently, surgical resection represents the curative treatment option, but surgery is only possible for selected patients. Even with optimal surgery, the 5-year overall survival (OS) rate is 15–40% [
4,
5]. The incidence of post-surgical recurrence is 50–60% [
3,
4], and recurrence is associated with a poor prognosis. It is thus essential to develop a multidisciplinary strategy for ICC to improve prognosis [
6‐
9].
Recently, new therapies based on the molecular or genetic characteristics of ICC have been developed. In theory, it should be possible to select the appropriate therapy for each patient depending on the molecular or genetic characteristics of their individual tumors. Without surgical resection, however, there is little biological information in ICC, so it is difficult to select treatment before surgery.
In the era of genetic landscape and precision medicine, increasing evidence suggests that genetic mutation profiles allow the evaluation of prognosis of cancer postoperatively [
10‐
14]. ICC has a relatively large number of actionable mutations compared to other gastrointestinal carcinomas. Several studies have demonstrated that
KRAS mutation in ICC could affect prognosis [
12‐
14]. Therefore, assessment of
KRAS mutation status may contribute to the development of treatment strategies. A surrogate marker for
KRAS mutation would provide genomic information without the need for biopsy or surgery. To identify this surrogate marker, it may be helpful to assess the biological effects of
KRAS mutation.
Investigators have reported that tumor cells with
KRAS mutation exhibit enhanced glucose uptake and glycolysis to survive in severe conditions (i.e., low-glucose and hypoxia) [
15‐
17]. Glucose transporter-1 (GLUT-1) is a major glucose transporter in cholangiocarcinoma, and its expression is correlated with higher malignant potential in ICC [
18,
19]. Positron emission tomography (PET) with
18F-fluorodeoxyglucose (
18F-FDG), a glucose analog, is a less invasive modality that determines the glucose metabolism potential of tumors by quantifying
18F-FDG uptake. However, to date, the association between
KRAS mutation and
18F-FDG-PET has not been reported in ICC.
We investigated the presence of KRAS mutation, the expression of GLUT-1, and 18F-FDG-PET parameters in 50 ICC patients, and examined whether there was an association between ICC prognosis and these factors.
18F-FDG-PET study and image analysis
18F-FDG-PET studies were performed using a PET/computed tomography (CT) scanner (Discovery ST Elite or Discovery IQ; GE Healthcare, Milwaukee, WI, USA). Patients fasted for at least 4 h before undergoing 18F-FDG-PET. The plasma glucose level was checked before the injection of 18F-FDG, and there were no patients with blood glucose level > 150 mg/dL in this study. Data acquisition started approximately 60 min after intravenous administration of 18F-FDG (injected dose: approximately 3.7 MBq/kg body weight). Initially, starting at the level of the upper thigh, low-dose CT scans were obtained with the following parameters: 40–60 mA, 120 kV, 0.6-s tube rotation, and 3.75-mm section thickness. The CT images were acquired during shallow breathing, and scanning included the area from the upper thigh to the skull. Immediately after the CT scans were acquired, PET emission scanning was performed with an acquisition time of 2–3 min per bed position. Whole-body PET images were attenuation-corrected using CT data and reconstructed using a 3D ordered-subsets expectation–maximization algorithm called VUE Point Plus (Discovery ST Elite: 14 subsets, two iterations, a matrix size of 128 × 128, a voxel size of 4.7 × 4.7 × 3.3 mm, and post-filtering at 5-mm full width at half maximum; Discovery IQ: 12 subsets, four iterations, a matrix size of 192 × 192, a voxel size of 3.3 × 3.3 × 3.3 mm, and post-filtering at 5-mm full width at half maximum). For quantitative analysis, at least two board-certified radiologists/nuclear medicine physicians assessed 18F-FDG accumulation on a workstation (Advantage Workstation 4.6; GE Healthcare) by calculating the standardized uptake value (SUV) in the regions of interest placed over the suspected lesions using all available clinical information and correlative conventional imaging for anatomic guidance. The SUV was calculated for the quantitative analysis of tumor 18F-FDG uptake as follows: SUV = C (kBq/mL)/ID (kBq)/body weight (kg), where C is the tissue activity concentration measured by PET and ID is the injected dose.
18F-FDG uptake was also quantitatively assessed by SUVs calculated in volumes of interest (VOIs) that were placed over regions of abnormal 18F-FDG uptake. The boundaries of each VOI were checked by comparison with fused CT to exclude adjacent 18F-FDG avid structures. The maximum SUV (SUVmax) within the VOI was recorded for the primary tumor. Metabolic tumor volume (MTV) was defined as the total tumor volume segmented via the threshold SUV. The threshold of the mediastinal blood pool activity was used to define the lesions. For the threshold SUV established using mediastinal blood pool activity, a VOI of more than 5 × 5 × 5 voxels was drawn manually at the aortic arch. The average SUV at the aortic arch plus two standard deviations of the VOI was adopted as the threshold SUV for the tumor using the mediastinal blood pool. Total lesion glycolysis (TLG) was determined as a product of the average SUV (SUVmean) segmented via the threshold SUV multiplied by the number of voxels in the MTV (i.e., SUVmean × MTV). For each patient, we defined the MTV and TLG as the sum of the MTVs and TLGs of all lesions, respectively.
Statistical analysis
Continuous values are expressed as median (range) and were compared using the Mann–Whitney U test. Categorical variables were compared using Fisher’s exact test. The prognostic values of clinicopathological factors for survival were assessed using a Cox proportional hazard regression model for univariate and multivariate analyses. Hazard ratios with Wald 95% confidence intervals (CIs) were provided for the Cox regression models. OS was calculated from the date of surgery to the date of death or last follow-up according to the Kaplan–Meier method and analyzed by the log-rank test. Cut-off values of 18F-FDG-PET parameters to discriminate KRAS mutation status were determined by receiver operating characteristic (ROC) curve analysis. The optimal cut-off values, sensitivities, and specificities of 18F-FDG-PET parameters were determined using the Youden index. All analyses were two-sided, and differences were considered significant when P was < 0.05. All statistical analyses were performed using the JMP statistical software package (SAS Institute Inc., Cary, NC, USA).
Discussion
In this study, we investigated the KRAS mutation status in a cohort of 50 consecutive ICC patients who underwent radical hepatectomy and identified KRAS mutations in 32.0% of cases. The following features of our study are significant: (1) correlation between KRAS mutation and glucose uptake is recapitulated in ICC tumors, and (2) metabolic tumor volume on 18F-FDG-PET may provide useful information as a surrogate for prognosis, reflecting the impact of KRAS mutation on survival.
Malignant tumors can enhance tumor cell survival by genetic changes or modify glucose metabolism by cellular responses [
25]. A number of genetic mutations in ICC have been identified [
10‐
14]. In particular,
KRAS mutation has been reported as a representative factor indicative of poor prognosis in ICC [
12‐
14], and our patients with mutated
KRAS showed significantly worse survival compared to those with wild-type
KRAS. Regarding glucose metabolism, Warburg discovered that, even in the presence of oxygen, cancer cells undergo aerobic glycolysis rather than the normal oxidative phosphorylation. Aerobic glycolysis produces just two molecules of adenosine triphosphate (ATP) per molecule of glucose, while 36 ATP molecules are produced by oxidative phosphorylation. Cancer cells have an accelerated metabolism and increased requirements for ATP production. The reason why cancer cells, which need high ATP levels, take this inefficient pathway is not clear. To maintain high ATP levels for energy utilization, cancer cells may increase glucose transport through overexpression of GLUT-1. In this study, 52.0% of patients with ICC showed high expression of GLUT-1, which was associated with
KRAS mutation. Whether the upregulation of GLUT-1 is attributable to
KRAS mutation or whether GLUT-1 expression contributes to
KRAS mutation remains to be established in ICC. High expression of GLUT-1 is associated with multiple tumors and tumor stage, and has been reported as a prognostic factor [
26‐
28]. Patients with high GLUT-1-expressing tumors have a significantly poorer survival compared to patients with low GLUT-1 expression.
It has been reported that
18F-FDG accumulation reflects the
KRAS mutational status of cancers [
29‐
31]. We assessed three parameters measured by
18F-FDG-PET (SUV
max, MTV, and TLG). In practice, SUV
max is the most commonly assessed parameter of
18F-FDG-PET, and previous reports have suggested that this parameter is associated with survival in patients with various cancers [
32,
33]. Recently, several reports suggested that the volumetric parameters of tumors measured by
18F-FDG-PET, such as the MTV and TLG, are more accurate prognostic factors than SUV
max in patients with various malignancies [
34,
35]. In this study, we have shown that
KRAS mutations were significantly associated with high
18F-FDG uptake as calculated by the MTV and TLG, while SUV
max was comparable between the mutated
KRAS group and wild-type group. Volumetric parameters measured by
18F-FDG-PET have advantages in terms of predicting
KRAS mutation status. First,
18F-FDG-PET is non-invasive and harmless compared to performing a liver tumor biopsy. Second, volumetric parameters reflect the metabolic activity of the entire tumor mass in a three-dimensional manner. There was no association between SUV
max and
KRAS mutation in this study, but this could be due to the intratumoral heterogeneity of the
KRAS mutation status [
13,
36]. SUV
max exhibits only the highest intensity of
18F-FDG uptake in the tumor and cannot reflect the metabolic activity of the entire tumor. Of the three parameters, the MTV and TLG were associated with
KRAS mutation, and ROC analysis showed that the MTV was the best predictor of
KRAS mutation.
In this study, the median SUVmax of the tumor lesions was 5.8 (range, 2.9–14.7). No patient with ICC had too little 18F-FDG uptake to detect the tumor lesion by FDG-PET despite of detectable tumor by CT or magnetic resonance imaging (MRI). When it was difficult to determine boundaries for obscure tumors, the tumor margin was identified using preoperative imaging, such as CT and/or MRI. Morphological size measured by CT or MRI was not significantly different between tumors with mutated KRAS and those with wild-type KRAS. In addition, it is well known that ICC tumors are frequently accompanied by central necrosis as they increase in size. Metabolic volume measured by 18F-FDG-PET more accurately reflects tumor viability than does radiographic volume, particularly as it takes into account tumor activity.
The current study has a limitation. It was a retrospective design conducted in a single institutional cohort of patients and involved a small study population of 50 consecutive ICC patients, including only 16 patients with KRAS mutation. This weakened the statistical power of our analysis. In addition, patient-selection bias might have influenced the statistical results. However, this study focused exclusively on ICC, rather than on biliary tract cancer, contributing to a better understating of KRAS-related molecular biology. Therefore, this should be considered a preliminary report, and further prospective studies with larger patient cohorts are required to validate the combination of 18F-FDG-PET parameters in association with somatic mutations and prognosis for patients with ICC.
Authors’ contributions
YI, SS, and KI designed the study, interpreted the results, and wrote the manuscript. YI, SS, and YN were responsible for the collection and assembly of data. All authors are responsible for the provision of study material or patients. All authors have made a substantial contribution to the study. All authors read and approved the final manuscript.