Transcriptomics Associates Molecular Features with ¹⁸F-Fluorocholine PET/CT Imaging Phenotype and Its Potential Relationship to Survival in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC), the third leading cause of cancer-related death worldwide (1), is rising sharply in incidence and mortality in the United States (2). Efforts aimed at molecularly characterizing this disease, particularly by transcriptomic analysis, have revealed significant heterogeneity across tumors (3–7). This heterogeneity has the potential to confound clinical trials by diluting molecular therapeutic targets among otherwise uniformly selected patients (8, 9) and contributes to the variability in clinical outcomes across all stages of this disease, including early stage (10–12). Accordingly, there is a constant search for biomarkers to provide better molecular target identification and risk-stratification for patients with HCC. However, increasing use of radiologic criteria as the basis for HCC diagnosis in the clinical setting has inadvertently limited the availability of tumor tissue for molecular profiling (13, 14). Although liver biopsies can be pursued to profile tumors, this invasive procedure causes significant morbidity and carries the risks of sampling error and tumor seeding (15). As a potential alternative, molecular imaging techniques such as positron emission tomography/computed tomography (PET/CT) might provide less invasive means to gain insight about tumor heterogeneity and pathobiology.

Fluorine-18 fluorocholine (FCh) is a radiopharmaceutical analogue of choline designed to allow PET to trace the initial steps of phosphatidylcholine biosynthesis (16). It is approved by the European Medicines Agency as an imaging agent for HCC (17, 18). Anomalies in choline metabolism have been found in many different cancers (19), including HCC (20, 21), providing the premise for tumor detection using FCh PET/CT. When performed in tandem with ¹⁸F-fluorodeoxy-d-glucose (FDG) PET/CT, FCh PET/CT is associated with diagnostic sensitivity of 90% or greater and results in improved tumor staging and treatment allocation for HCC (17, 18, 22). In comparison, FDG PET/CT and FCh PET/CT individually are associated with site-based sensitivities of 67% and 84%, respectively (P = 0.01; ref. 17). Failures to detect HCC with FDG are predominantly the result of poor tumor contrast in the liver (17, 23), which was something anticipated by preclinical studies (24). In comparison, HCC can be detected with FCh based on either its increased metabolism relative to the surrounding liver, as found in approximately 75% of patients, or its decreased metabolism, which occurs in another 10%–15% (17, 18). As a diagnostic imaging modality, FCh PET/CT is unique in this capacity to display two divergent “imaging phenotypes” for detecting HCC (examples shown in Fig. 1A and B).

We hypothesized that gene expression differences are associated with these FCh PET/CT imaging phenotypes. Given that a large number of gene signatures have already been developed to characterize HCC, we further hypothesized that a subset of these existing gene signatures will contain components that correspond to an imaging phenotype. To test this hypothesis, we conducted a series of tumor enrichment analyses using collections of gene sets from a public repository to identify published gene signatures associated with tumor FCh uptake. By following this approach, we also sought to examine the clinical potential of FCh PET/CT as a noninvasive image-based molecular marker that could have value for patient risk-stratification and clinical trial enrichment in HCC.

Between February 2012 and March 2016, 51 consecutive patients gave written informed consent to undergo FCh PET/CT prior to the liver tumor resection as participants in an Institutional Review Board–approved, federally registered (NCT01395030) diagnostic trial. This trial was listed prospectively on clinicaltrials.gov in accordance with International Committee of Medical Journal Editors. Briefly, patients were enrolled if they had HCC diagnosed histologically or suspected radiographically [i.e., Liver Imaging Reporting and Data System (LI-RADS) category 3, 4, or 5 lesion detected on a contrast-enhanced CT or MRI examination; ref. 25], or had a liver mass with imaging features of another primary malignancy, and were surgical candidates agreeable to liver tumor resection with Child-Pugh score < 10. Patients were excluded if they weighed greater than 350 pounds, were pregnant or lactating, had a serious underlying medical condition that would make FCh PET/CT intolerable, or previously received chemotherapeutic, molecularly targeted, biological, or radiotherapeutic treatment for HCC. On the basis of these criteria, the study enrolled patients with suspected HCC and other primary liver cancers. Because this study pertains to HCC, 8 patients with final diagnoses of other liver tumors (7 intrahepatic cholangiocarcinoma and 1 primary sarcoma) were not included. Two HCC patients with inadequate tissue for gene expression analysis were also not included, leaving 41 patients for analysis.

Liver imaging was performed preoperatively within two weeks of surgery using a Gemini TF-64 PET/CT scanner (Philips). An in-line X-ray CT transmission scan of the torso was performed without intravenous contrast and completed in the supine position using these parameters: 64 channels, 120 kV, 50 mA/slice, rotation time 0.75 seconds, slice thickness/interval 5.0 mm. For positron emission scanning, a dose of 2.2 to 3.0 MBq/kg of FCh was administered intravenously followed by emission scans of liver at 30 to 40 minutes postinjection. PET image reconstruction was completed using the manufacturer's protocol, with corrections for radioactive decay, dead-time, random coincidences, and scatter. Emission data were corrected for nonuniform attenuation using CT data. FCh doses were synthesized on the day of scanning using an automated chemical process control unit (CPCU, CTI/Siemens) in accordance with an FDA Investigational New Drug Application. All doses passed assays for radiochemical purity, radionuclide identity, chemical purity, and nonpyrogenicity before use.

Tumor tissue samples were collected upon resection of the affected hepatic segment. Sampling was performed only if it did not interfere with surgical pathology evaluation, and limited to areas distant from the resection margin, avoiding any capsular, necrotic, hemorrhagic, or adjacent liver tissue. For radiopathologic correspondence, gross pathologic tumor size and sampling locations relative to the tumor–liver interface and surgical margin were recorded. The samples were preserved in RNALater (Thermo Fisher Scientific) and stored at −80 C. RNA was extracted using AllPrep DNA/RNA Kit (Qiagen), checked for integrity using an Agilent Bioanalyzer with RNA 6000 Nano Chips (Agilent Technologies, Inc.), processed using whole-genome direct annealing, selection, extension, and ligation (WG-DASL, Illumina), and hybridized onto Human HT-12 v4 Expression BeadChips (Illumina) covering 24,000 transcripts of genes, gene candidates, and splice variants. The final data arrays consisted of 20,818 gene expression values for each patient. Formalin-fixed sections from each specimen also underwent histopathologic review by a board-certified pathologist to confirm tumor cellularity and exclude the presence of benign liver tissue, hemorrhage, necrosis, and fibrosis.

FCh PET/CT images were analyzed by an experienced reader (S.A. Kwee, 15 years) in an unblinded fashion, alongside clinically available contrast-enhanced CT or MRI examinations. Areas of tumor sampling were then visually classified as demonstrating increased (FCh_HIGH), decreased (FCh_LOW), or iso-intense (FCh_ISO) tracer activity relative to the adjacent liver on FCh PET/CT images. Tracer uptake in these areas was also quantified using a 1.2-cm diameter region of interest (ROI) to measure the mean and maximum standardized uptake value (SUV_mean and SUV_max; ref. 26). SUV was defined as the ratio of the voxel to the injected radioactivity, normalized to body weight. SUV_mean of the liver was also measured using an ROI placed over tumor-adjacent liver. All ROIs excluded areas of hemorrhage or necrosis found on any of the imaging examinations. Tumor to adjacent liver ratios of SUV_mean (TLR_SUV) were calculated by dividing tumor SUV_mean by liver SUV_mean.

Gene set enrichment analysis (GSEA; v3.0, Broad Institute) was used to determine whether a priori defined sets of genes showed statistically significant concordant differences between tumors demonstrating the FCh_HIGH and FCh_LOW PET phenotypes (27). All gene sets used for analysis were downloaded from the Broad Institute Molecular Signature Database (mSigDB v6.2, obtained September 7, 2018 from software.broadinstitute.org/gsea/msigdb).

To test the study hypothesis that previously developed gene sets related to HCC were associated with different FCh PET/CT imaging phenotypes, a custom collection of HCC-related gene sets was created using mSigDB. Using search operations, a list of gene sets, whose descriptions contained the keyword “hepatocellular,” was generated and limited by species to Homo sapiens, and by sample type to human tumor tissue, resulting in a user-defined collection of 70 HCC-related gene sets. GSEA was then performed with 1,000 phenotype permutations using FDR q-value of 0.05 as the significance threshold based on the number of gene sets tested (27).

Additional analyses using gene set collections from mSigDB were also conducted. To identify biological themes and pathways associated with imaging phenotype, GSEA was performed using a canonical collection containing 50 gene sets (mSigDB Hallmarks Collection v6.2). An exploratory analysis for gene set enrichment across a variety of clinical diseases and biological conditions was also performed using the entire mSigDB Chemical and Genetic Perturbations collection (CGP v6.2), a curated collection of 2,701 gene sets encompassing a broad range of molecular signatures published in biomedical journals. This latter analysis was performed with an exploratory FDR q threshold of 0.25 (27).

Molecular signatures corresponding to the highest scoring HCC-related gene sets on GSEA were used to classify tumors into subgroups according to the referenced literature. Classes were assigned on the basis of correlation with class genes (28). FDR q < 0.05 was used to define confidence in class-based subgroup assignments.

Genes differentially expressed between FCh_HIGH and FCh_LOW tumors were identified at FDR q < 0.05 (GenePattern 3.9.10, genepattern.broadinstitute.org). Functional profiling of these results was performed using the Gene Functional Classification and Annotation Clustering Tools of the Database for Annotation, Visualization, and Integrated Discovery (DAVID v6.8, https://david.ncifcrf.gov/; refs. 29, 30).

Other statistical analyses were performed using JMP Pro 14 (SAS Institute) and MedCalc (v18.9, MedCalc bvba, Ostend, Belgium). Differences between groups were tested using Wilcoxon–Mann–Whitney, Fisher exact, or χ² test as appropriate. Survival was analyzed using the Kaplan–Meier method and log-rank test, followed by calculation of mortality HRs with 95% confidence intervals (CI) by Cox univariate and multivariate proportional hazards regression. Multivariate analysis was adjusted for age and gender. Deaths occurring within 30 days of surgery were censored. Unless noted, the significance was based on a two-tailed P < 0.05.

The patient characteristics are summarized in Table 1. Regions of abnormal FCh uptake corresponding to LI-RADS category 3, 4, or 5 liver lesions found on diagnostic CT or MRI examinations were identified on PET/CT images in all 41 patients. In 31 (76%) patients, sampled tumor regions were assigned FCh_HIGH phenotype based on visually increased FCh uptake relative to the liver background. In the remaining 10 (24%) patients, tumor regions demonstrated decreased FCh uptake and were assigned FCh_LOW phenotype. No tumor regions were assigned FCh_ISO phenotype. No significant association was noted between FCh PET/CT imaging phenotype and LI-RADS category (P = 0.54).

TLR_SUV ranged from 1.21 to 2.00 (mean 1.47) for FCh_HIGH tumors and from 0.12 to 0.78 (mean 0.52) for FCh_LOW tumors. There were no TLR_SUV values between 0.78 and 1.21. Therefore, visual classification and phenotype assignment based on the TLR_SUV threshold of 1.0 classified the tumors identically (Supplementary Fig. S1). Well and moderately differentiated tumors (i.e., Edmondson–Steiner grade 1 and 2), lower alpha-fetoprotein (AFP) values, and male gender were significantly associated with FCh_HIGH phenotype (P = 0.027, P = 0.002, and P = 0.001, respectively). There were no significant associations with the patient age, body mass index, Model for End-stage Liver Disease (MELD) score, tumor size, presence of microvascular invasion, or HCC risk factor.

From among 70 tumor-derived HCC-related gene sets available from mSigDB, 10 demonstrated enrichment by FCh_HIGH tumors relative to FCh_LOW tumors at FDR < 0.05 (Table 2). The list of significant gene sets included those from previously developed gene signatures for predicting overall survival or recurrence in early-stage HCC (italicized in Table 2; refs. 31–33). The list also included gene sets corresponding to 3 different molecular classification systems for HCC. The enrichment score plots for these gene sets are shown in Fig. 2. The top scoring gene set, HOSHIDA_LIVER_CANCER_SUBCLASS_S3, defines an S3 subtype of HCC based on a molecular classification system developed by Hoshida and colleagues (5). Applying the complete Hoshida gene signature for classification, all tumor samples were successfully categorized into different subtypes at FDR q < 0.05. Mirroring previous cohorts (5), the S3 subtype comprised the largest subgroup with 24 tumors. Consistent with the GSEA results, all tumors classified as S3 displayed FCh_HIGH phenotype. The S2 subtype comprised the smallest subgroup with 4 tumors, with all displaying FCh_LOW phenotype, while the S1 subtype corresponded to 6 FCh_LOW and 7 FCh_HIGH tumors. S3 was significantly associated with FCh_HIGH phenotype (P < 0.001) and lower AFP levels (435.8 vs. 2662.7 for non-S3, P < 0.005).

There were also three significant gene sets that belonged to an HCC molecular classification system developed by Chiang and colleagues (6). The gene sets identified as significant contained overexpressed genes corresponding to a “polysomy 7” class, an “unannotated” class, and under-expressed genes corresponding to a “proliferation” class (Table 2). The complete classification signature successfully classified all 41 tumor samples at FDR q < 0.05. Because 32 of the under-expressed genes of the “proliferation” class were also overexpressed genes of a CTNNB1 (catenin beta-1) class, two “Chiang groups” were created for the purpose of making study comparisons: Chiang group 1, containing CTNNB1, polysomy 7, and unannotated classes; and Chiang group 2, containing the “interferon” and “proliferation” classes described in the original publication (6). Chiang group 1 was significantly associated with FCh_HIGH phenotype (P = 0.001) as well as with lower AFP levels (807.1 ng/mL vs. 2064.6 ng/mL, P < 0.05).

GSEA also identified two significant gene sets belonging to a molecular classification system developed by Boyault and colleagues (4). The significant gene sets identified by GSEA comprised the under-expressed genes corresponding to groups termed G3 and G123 (Table 2). Applying the corresponding molecular classification signature, 38 of 41 tumor samples were confidently classified at FDR q < 0.05. There were no significant associations between imaging phenotype and groups defined by this signature.

Gene sets belonging to two prognostic signatures for HCC were also significantly associated with the FCh_HIGH phenotype. The first, LEE_LIVER_CANCER_SURVIVAL_UP, comprises overexpressed genes of the gene signature developed by Lee and colleagues to predict survival after hepatic resection (31). This signature successfully classified 35 of 41 tumor samples at FDR q < 0.05, assigning 22 to a good survival group and 13 to a poor survival group. AFP levels were significantly lower for the good survival group compared with the poor survival group (471.0 ng/mL vs. 2763.7 ng/mL, P < 0.01). The other significant survival-related gene set was KIM_LIVER_CANCER_POOR_SURVIVAL_DN. It is comprised of under-expressed genes from a survival signature developed by Villanueva and colleagues (32). This signature successfully classified 35 of 41 tumors, assigning 21 into good survival and 14 into poor survival groups. AFP levels also differed significantly between good and poor survival groups defined by this signature (435.8 ng/mL vs. 2,662.7 ng/mL; P < 0.005). Good survival groups defined by both signatures were significantly associated with the FCh_HIGH phenotype (Table 1). Gene signatures corresponding to the remaining gene sets shown in Table 2 were unable to classify enough samples at FDR < 0.05 for statistical comparisons.

A total of 20 patients died at follow-up. Excluding 3 patients that died within 30 days of surgery, the median duration of censored follow-up was 1,463 days (range 814–2,200 days). Significantly longer overall survival was observed in patients whose tumors demonstrated the FCh_HIGH phenotype compared with the FCh_LOW phenotype (Fig. 3). Although significantly more males had FCh_HIGH tumors, gender was not significantly associated with overall survival. Age, HCC risk factor, AFP level and Edmondson–Steiner grade were also not significantly associated with overall survival (all P > 0.05). Tumor size > 4 cm was significantly associated with shorter overall survival and was a significant univariate predictor of increased mortality based on Cox regression analysis (Table 3). FCh_HIGH imaging phenotype, Hoshida subtype S3 (vs. S1/S2), and the good survival groups of both Lee and Villanueva survival signatures were significant univariate predictors of lower mortality based on Cox regression analysis. FCh_HIGH imaging phenotype and the Villanueva signature remained significant on multivariate regression analysis (Table 3). No subgroups based on Chiang and Boyault signatures were associated with significantly lower mortality, consistent with these signatures not being intended as prognostic classifiers.

GSEA performed using 50 Hallmark gene sets identified 6 as significantly comprised of genes enriched in FCh_HIGH tumors relative to FCh_LOW tumors, including gene sets representing oxidative phosphorylation [normalized enrichment score (NES) 2.00; FDR 0.018)], fatty acid metabolism (NES 1.96; FDR 0.011), peroxisome (NES 1.90; FDR 0.013), bile acid metabolism (NES 1.89; FDR 0.011), xenobiotic metabolism (NES 1.85; FDR 0.013), and adipogenesis (NES 1.84; FDR 0.013). Correspondingly, 540 differentially expressed genes were identified between FCh_HIGH and FCh_LOW tumors at FDR q < 0.05. The top DAVID functional clusters associated with these differentially expressed genes were also related to oxidative, mitochondrial, fatty acid, lipid, and bile acid metabolism in agreement with the GSEA results (Supplementary Table S1). Thus, FCh_HIGH tumors appear to have more transcriptional features of hepatocyte differentiation than FCh_LOW tumors.

In an exploratory analysis that included 2,701 gene sets covering a broad variety of diseases and conditions, 68 gene sets demonstrated enrichment by FCh_HIGH tumors at FDR q < 0.25 (Supplementary Table S2). Consistent with the findings obtained by GSEA using the Hallmarks Collection, the top scoring gene sets included those related to oxidative phosphorylation and mitochondrial metabolism. In addition, 33 HCC or liver-related gene sets were identified, including the ones found significant by GSEA using only the 70 HCC-related gene sets.

Molecular imaging holds the potential to noninvasively identify the molecular vulnerabilities of a tumor to possibly enable greater precision in treatment. In this study, we tested the hypothesis that an imaging phenotype defined by FCh PET/CT identifies a subset of HCC tumors already characterized by previously published molecular signatures. To test this hypothesis, we applied GSEA to identify sets of genes that were significantly enriched in tumors displaying high FCh uptake from a collection of a priori defined gene sets. This approach obviated the need to develop new molecular signatures to explain the imaging phenotypes and instead leveraged the work of many who have previously developed and validated gene signatures for HCC. By conducting analyses at the gene set level, rather than the gene level, statistical power was also conserved and biological interpretation streamlined.

This study is the first to report significant associations between tumor FCh avidity and enrichment by sets of genes comprising multiple clinically relevant molecular signatures for HCC. The gene set associated with the highest degree of enrichment in our study was HOSHIDA_LIVER_CANCER_SUBCLASS_S3. As part of a tumor classification signature defining 3 HCC subtypes (S1, S2, and S3; ref. 5), this gene set defines a subtype (S3) that, in 8 different patient cohorts, was found to be the most common subtype and the one associated with the most favorable clinical features (5, 8). Consistent with these findings, patients with S3 tumors formed the largest subgroup and experienced the longest survival in this study. Ironically, S3 tumors have proven the most difficult to recapitulate in preclinical models. One analysis of 25 different HCC cell lines found all to be subtype S1 or S2, suggesting either that S3 cells cannot be immortalized or that they change into S1 or S2 in vitro (34, 35). Therefore, noninvasive techniques such as FCh PET/CT may have value for studying this predominant HCC subtype in vivo.

When the Hoshida signature was applied, it was found that all S3 tumors displayed FCh_HIGH phenotype. Interestingly, all four of the S2 tumors in this study displayed FCh_LOW phenotype. While inferences should not be made based on the small number of tumors, it is worth noting that S2 is associated with the most aggressive biological traits and poorest survival of the tumor subtypes (35). Because S2 tumors may be selectively vulnerable to specific molecularly targeted agents (8, 35), the ability to noninvasively identify this subtype could have clinical importance. However, S1 tumors were found capable of demonstrating either low or high FCh uptake, limiting the specificity of FCh_LOW phenotype as an indicator of tumor subtype. As a potential explanation for why S1 tumors may be associated with either imaging phenotype, it is possible that the Hoshida molecular classification system does not incorporate every gene influencing tumor choline metabolism (or alternatively, FCh uptake). S1 may also be the most molecularly heterogeneous among the subtypes (5), with heatmaps from our analysis also showing the greatest heterogeneity in gene expression corresponding to S1 tumors (Supplementary Figs. S2–S4). Conceivably, it may be possible to further subdivide the S1 subtype using additional defining genes including those relevant to FCh or choline metabolism. The performance of FCh PET/CT for tumor classification based on the Hoshida system will hopefully be refined by further studies.

GSEA was also performed using canonical gene set collections curated by the Broad Institute to provide a wider survey of molecular features and traits. Significant gene sets identified from the Hallmarks collection conveyed particular metabolic traits to FCh_HIGH tumors, including those associated with oxidative, fatty acid, bile acid, and xenobiotic metabolism. GSEA using the CGP collection also identified significant gene sets associated with oxidative and mitochondrial metabolism while showing that HCC-related gene sets prevailed over gene sets of other diseases with regards to their associations with this imaging phenotype. Functional annotation clustering based on the differential gene expression between FCh_HIGH and FCh_LOW tumors implicated these metabolic features as well, which notably are also constitutively expressed by hepatocytes. This appears to indicate that cellular differentiation is a prevailing characteristic of FCh_HIGH tumors. Interestingly, the S3 tumor subtype defined by Hoshida and colleagues is also distinctly associated with hepatocyte differentiation (5). Consistent with these observations, tumors displaying FCh_HIGH phenotype were found on histopathology to be significantly comprised of moderate and well differentiated tumors.

Another notable result was the finding of poorer overall survival in patients with FCh_LOW tumors compared to patients with FCh_HIGH tumors (Fig. 3). Consistent with this finding, a previous pilot study of patients who underwent FCh PET/CT prior to hepatectomy reported earlier tumor recurrence among patients with tumors showing low FCh uptake (36). Similarly, a study that used 11C-acetate, another lipid metabolism tracer, found that tumors demonstrating high uptake of this tracer required a lower radiation dose (152–174 Gy vs. 262 Gy) to achieve good therapeutic response following yttrium-90 glass microsphere radioembolization (37). The possibility of enabling clinical risk stratification and further optimizing treatment for HCC using FCh PET/CT will hopefully be studied prospectively in larger cohorts.

Several previously published gene signatures corresponding to the gene sets enriched by FCh_HIGH tumors also discriminated survival in our cohort, including interestingly the molecular classification signature developed by Hoshida and colleagues (5). Patients with S3 tumors identified by this signature experienced significantly lower mortality in our study (Table 3). Although S3 tumors are known to harbor fewer aggressive features compared with S1 or S2 tumors (34) to the best of our knowledge, the S3 subtype has not before been associated with better survival in a prospective study. However, Hoshida and colleagues did report that S3 tumors were coenriched by genes from a previously reported clinical prognostic signature for HCC (5). In our study, overexpressed genes from that coenriched signature (LEE_LIVER_CANCER_SURVIVAL_UP; ref. 31), as well as genes from another HCC survival signature (KIM_LIVER_CANCER_POOR_SURVIVAL_DN) developed by Villanueva and colleagues (32), demonstrated significant enrichment by FCh_HIGH tumors (Table 2). Furthermore, univariate survival analyses revealed significantly lower mortality among good survival groups defined by these signatures (Table 3). These associations with survival, in addition to supporting speculation that FCh PET/CT has a prognostic value for HCC, constitute an additional validation of these previously published prognostic signatures in an independent albeit relatively small cohort of patients with HCC.

Phosphatidylcholines are bilayer-forming phospholipids comprising the most abundant constituent of cell and subcellular membranes (38). Both cell and mitochondrial proliferation depend on its supply (20). Phosphatidylcholine synthesis from choline via the intermediate phosphorylcholine (called the Kennedy pathway) involves esterification with two fatty acids, linking this pathway to lipid metabolism. Approximately 50% of de novo synthesized phosphiatdylcholine is consumed by mitochondria where membrane function plays a critical role in oxidative phosphorylation and electron transport (20, 38). In keeping with these roles, gene set enrichment by tumors exhibiting the FCh_HIGH phenotype was associated with oxidative phosphorylation, lipid metabolism, and mitochondrial function, implying that oxidative phosphorylation is active in the tumors exhibiting high FCh uptake. Gene sets associated with the FCh_HIGH phenotype also overlapped significantly with gene sets from MSigDB associated with fatty acid metabolism, oxidative phosphorylation, and mitochondrial function (Supplementary Tables S3–S8).

With regards to energy metabolism, cancers can also turn to aerobic glycolysis (i.e., the Warburg phenomenon), which can potentially be monitored by FDG PET/CT (39). In contrast to the favorable association between tumor FCh uptake and survival, tumor FDG uptake has been associated with a worse prognosis in HCC (39). Although FDG uptake was not investigated in this study, previous diagnostic trials comparing FDG PET/CT and FCh PET/CT for HCC revealed discordance between these tracers (i.e., high tumor FDG uptake was associated with low FCh uptake and vice versa). Pathobiologically, decreased phosphatidylcholine biosynthesis might interfere with mitochondrial function, turning tumors toward aerobic glycolysis and high FDG uptake. Alternatively, aerobic glycolysis may not support the Kennedy pathway, leading Warburg phenotype tumors to demonstrate low FCh uptake. This radiologic discordance has led some centers to adopt clinical protocols involving two PET/CT scans for HCC, one performed with a lipid-based tracer such as FCh and the second performed with FDG (17, 18, 22, 23).

At present, no second cohort is available to confirm our reported associations between imaging, gene signatures, and clinical outcomes. Despite its increasing incidence, HCC is still relatively uncommon in the US compared with other countries, and this study was conducted at only one institution. This constitutes an important limitation of our study. Another potential limitation stems from the inclusion of only early-stage surgically treated HCC patients, as it is possible that genomic and other molecular alterations will be more varied in patients with advanced tumors. However, previous studies that involved only patients with early-stage HCC have been successful at linking different clinical outcomes to specific molecular profiles (5, 31–33, 40). Although several predictors of survival were identified, this study was also limited in its power to detect and compare significant predictors due to its sample size. It is important to note, however, that this study was adequately powered for GSEA (27). While additional studies are needed, especially to confirm the prognostic associations, this study did accomplish its goal of providing a biologically cohesive framework for interpreting the two imaging phenotypes of HCC detected on FCh PET/CT.

To our knowledge, few studies have reconciled molecular imaging with genomic findings to the extent presented, even though the bioinformatics tools and resources necessary are widely accessible, well-established, and reproducible. We believe that the methods of this study form an under-utilized approach to gaining insight on tumor pathobiology. Such an approach may also aid in identifying and developing new clinical applications for emerging or established molecular imaging techniques. Although further studies are needed, the multiple significant associations with previously validated gene signatures found in this study support FCh PET/CT as a noninvasive tool for molecularly categorizing HCC. The associations between FCh_HIGH tumor phenotype and favorable molecular and clinical features have a potential to impact clinical decision-making. For example, using FCh PET/CT to determine tumor-associated prognosis may help in identifying the most favorable candidates for surgery and liver transplantation or the need for adjuvant treatments or heightened surveillance. Noninvasive tumor phenotyping based on this form of molecular imaging may also be of value in selecting patients for targeted therapies and clinical trials.

Transcriptome-based signatures for molecularly classifying HCC and other cancers abound. This study is the first in providing a cohesive biological framework that explains specific molecular imaging findings of HCC through the analysis of tumor enrichment for genes from previously reported gene signatures. FCh PET/CT is currently being used in Europe as a clinical diagnostic and staging tool for HCC. Following the wake of failures of large clinical trials for HCC (41, 42), there is a growing interest in tumor molecular subclassification to better match patients to potential treatments (8, 34, 43). Our findings, if further validated, suggest that FCh PET/CT may be useful as an in vivo imaging-based biomarker for molecular tumor classification and patient risk stratification in the support of precision medicine and biomarker-based clinical trial enrichment.

L.L. Wong reports receiving a speakers bureau honoraria from Eisai. No potential conflicts of interest were disclosed by the other authors.

Conception and design: S.A. Kwee, L.L. Wong

Development of methodology: S.A. Kwee, M. Tiirikainen, L.L. Wong

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): S.A. Kwee, M. Tiirikainen, J.D. Acoba, L.L. Wong

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): S.A. Kwee, M. Tiirikainen, J.D. Acoba, L.L. Marchand, L.L. Wong

Writing, review, and/or revision of the manuscript: S.A. Kwee, M. Tiirikainen, M.M. Sato, J.D. Acoba, R. Wei, W. Jia, L.L. Marchand, L.L. Wong

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): S.A. Kwee, M. Tiirikainen, M.M. Sato, R. Wei, W. Jia, L.L. Wong

Study supervision: S.A. Kwee, R. Wei, W. Jia

This work was supported by NIH grant R01CA161209-06.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.