Although 2[18F]fluoro-2-deoxy-d-glucose (FDG) uptake during positron emission tomography (PET) predicts post-surgical outcome in patients with non–small cell lung cancer (NSCLC), the biologic basis for this observation is not fully understood. Here, we analyzed 25 tumors from patients with NSCLCs to identify tumor PET-FDG uptake features associated with gene expression signatures and survival. Fourteen quantitative PET imaging features describing FDG uptake were correlated with gene expression for single genes and coexpressed gene clusters (metagenes). For each FDG uptake feature, an associated metagene signature was derived, and a prognostic model was identified in an external cohort and then tested in a validation cohort of patients with NSCLC. Four of eight single genes associated with FDG uptake (LY6E, RNF149, MCM6, and FAP) were also associated with survival. The most prognostic metagene signature was associated with a multivariate FDG uptake feature [maximum standard uptake value (SUVmax), SUVvariance, and SUVPCA2], each highly associated with survival in the external [HR, 5.87; confidence interval (CI), 2.49–13.8] and validation (HR, 6.12; CI, 1.08–34.8) cohorts, respectively. Cell-cycle, proliferation, death, and self-recognition pathways were altered in this radiogenomic profile. Together, our findings suggest that leveraging tumor genomics with an expanded collection of PET-FDG imaging features may enhance our understanding of FDG uptake as an imaging biomarker beyond its association with glycolysis. Cancer Res; 72(15); 3725–34. ©2012 AACR.
Non–small cell lung cancer (NSCLC) remains the number one cause of cancer-related mortality for men and women in the United States, and its prevalence continues to increase worldwide (1). Despite potentially curative resection in early-stage NSCLCs, survival remains suboptimal and recurrence rates are high (2).
2[18F]Fluoro-2-deoxy-d-glucose (FDG) positron emission tomographic (PET) imaging is currently the standard of care for preoperative staging of disease in NSCLC, and multiple investigations suggest that the intensity of FDG uptake in the tumor before surgery is a useful biomarker for tumor aggressiveness and patient outcome postoperatively (3–5). This is an important finding as PET imaging is noninvasive, cost-effective, and routinely conducted for patients preoperatively (6–9).
PET-FDG uptake is governed by GLUT transporter uptake and metabolized to an inert intracellular product by a key regulatory enzyme in glycolysis, hexokinase-2 (HK2; ref. 10). The biologic basis for the use of FDG as a biomarker is not fully understood, but upregulated glycolysis that results in increased FDG uptake has been associated with tumor growth, metastasis, and immune evasion (11–14). Recently, a key driver that seemingly promotes a less favorable cellular energy profile was discovered [pyruvate kinase isoenzyme M2 (PKM2); ref. 15–17] and has been implicated with other major genes involved in oncogenesis that may help to explain a mechanistic switch to a glycolytic phenotype (18).
To date, no studies have examined differential genome-wide expression across varying FDG uptake levels in NSCLCs. We explored this relationship in a cohort of patients with NSCLCs to identify individual genes and gene expression signatures associated with prognostically relevant FDG uptake features.
Patients and Methods
We used a novel biocomputational approach to associate gene expression with prognostic FDG uptake features (Fig. 1) using 3 cohorts (study, external, and validation cohorts). Use of all 3 cohorts allowed us: (i) to associate FDG uptake features to gene expression, (ii) to generate a model of image features in terms of their gene expression (study cohort), (iii) to identify prognostic gene signatures from this model (external cohort), and finally (iv) to examine whether image features associated with prognostic gene sets were predictive of clinical outcome (validation cohort). To build prognostic gene expression signatures associated with FDG uptake image features, we applied our previously described radiogenomics strategy (19).
Study, external, and validation cohorts
For the study cohort, a group of patients with surgically resected NSCLC between 2008 and 2010 from 2 medical centers were retrospectively identified. All patients had preoperative PET/computed tomographic (CT) scans analyzed for tumor FDG uptake features matched with excised tumor specimens subjected to global gene expression analysis. Patients receiving neoadjuvant therapy were excluded, and follow-up data were not available for the study cohort, as these cases were taken from recent operative specimens. For the external cohort, we used data from a previous study that modeled gene expression and outcome in patients with NSCLCs (GSE8894; ref. 20). For the validation cohort, we examined patients with resected, limited stage NSCLCs from 2003 to 2010 who underwent treatment-naive, preoperative PET imaging. Death was assessed using the National Death Index (www.cdc.gov/nchs/ndi.htm), and patients not dead were assumed to be alive at the time of data extraction (June 2011). All work was conducted with the Institutional Review Board authorization of both participating centers.
PET acquisition and FDG uptake feature extraction in study and validation cohorts
PET/CT images were acquired using either an LS PET/CT (slice thickness, 3–5 mm) at Stanford or GE Discovery VCT (slice thickness, 3.75 mm) at the Veterans Administration Palo Alto Health Care System (VAPAHCS). At Stanford, patients fasted for a minimum of 8 hours, a dose of 12 to 17 millicuries (mCi) of FDG was administered, and patients were scanned from the skull base to mid-thigh using multiple bed positions every 5 minutes approximately 45 to 60 minutes after injection. Before injection of FDG, patients who had a blood glucose level of >180 mg/dL were excluded. At the VAPAHCS, patients fasting for 6 hours had FDG injected to a target of 15 mCi at time of scan, which ranged from 60 to 120 minutes after injection and those patients who had a blood glucose of level of >200 mg/dL were rescheduled. Patients were scanned from skull to mid-thigh using multiple bed positions every 2 to 3 minutes. CT-attenuated data were reconstructed using ordered subset expectation maximization for both scanner sites.
FDG uptake was quantified using the maximum standard uptake value (SUVmax) by a certified nuclear medicine physician (A. Quon). The region of interest (ROI) for each nodule was drawn using the trans-axial image that was thought to represent the most FDG-avid portion around the entire lesion, and the maximum SUV pixel within the ROI (SUVmax) was recorded. Partial volume correction was not used for this interpretation. All DICOM images for both study and validation cohorts were then imported into an imaging feature extraction program, RT_Image (http://rtimage.sourceforge.net). FDG uptake metrics were calculated after co-registration with CT images of the tumor and then by defining an automated ROI on the PET image using a region-growing algorithm bounded by a standard threshold uptake above background, with a lower bound set at 50% of the maximum value within the ROI (21).
Fourteen metrics of interest related to SUV were recorded: SUV minimum, maximum, mean, median and percentile (75 and 90) features were extracted to define the intensity of FDG uptake in tumor; SUV standard deviation, variance, skew, and kurtosis features were extracted to measure the variation in FDG uptake across the tumor; and metabolic tumor volume (MTV), registration points (number of voxels used to define the MTV), area and total glycolytic volume (TGV) quantified the spatial extent of FDG uptake. TGV is represented in this study as the product of SUVmean and metabolic volume and represents the “integrated” metabolic uptake across the tumor (22).
Comparison of study and validation cohorts
Basic descriptive clinical, pathologic, and imaging characteristics were tabulated for study and validation cohorts. Continuous variables with median and interquartile range or categorical variables with percentage were calculated. Differences between the study and validation cohorts were assessed using the Student t test for continuous variables or a χ2 or Fisher exact test (for <5 data points in a level) for categorical variables.
Gene expression microarray data
Tumors from the study cohort were processed from a 3 to 5 mm cross-section after removal of fibrotic or necrotic areas during surgical excision (C.D. Hoang and J.B. Shrager). Tumor tissue was snap-frozen within 30 minutes and extracted to RNA using standard commercial kits. Genome-wide arrays were processed by the Stanford Functional Genomics Facility using Illumina Whole Genome Bead Chips (Human HT-12). Microarray data were filtered on the basis of a significant detection call in at least 60% of the samples and log-transformed using quantile normalization to account for array variation. Microarray data were submitted to Gene Expression Omnibus (GEO) under accession number GSE28827.
Basic descriptive clinical, pathologic, and imaging characteristics were tabulated for study and validation cohorts. Continuous variables with median and interquartile range or categorical variables with percentage were calculated. Differences between the study and validation cohorts were assessed using the Student's t test for continuous variables or a χ2 or Fisher exact test (for <5 data points in a level) for categorical variables.
Association of FDG uptake features and gene expression
Basic correlations among FDG uptake features and for FDG uptake features with gene expression were conducted using a Spearman rank correlation test. Significance analysis of microarrays (SAM) was conducted to define genes significantly associated with FDG uptake metrics. Because of the potential for false-positive associations due to multiple comparisons, we used the false discovery rate (FDR; q < 0.05) to assess statistical significance (23). In addition, because single gene associations with image features are more prone to noise, a clustering method to reduce the dimensionality of the microarray data was used in addition to univariate SAM analysis (19). We clustered the microarray data using an iterative k-means clustering algorithm with 200 iterations and a coherence of 0.75; these settings were determined such that the average cluster homogeneity in external data sets was maximal (20, 24), where cluster homogeneity was defined as the average correlation between each member of the cluster within the cluster centroid. This clustering algorithm resulted in 102 clusters that were then filtered on the basis of a homogeneity of at least 0.30 in 1 of 2 external data sets (19, 24), to yield 56 high-quality clusters, defined in this study as metagenes, for further analysis (19).
Principal components analysis of FDG uptake features
We examined the principal components defining FDG uptake for the 14 features extracted to determine which features accounted for most of the variability of FDG uptake. Each principal component was defined as a linear combination of the 14 original features and could be interpreted on the basis of the weights associated with each of these features. We restricted our analysis to the first three principal components and incorporated these new features to the study cohort data set for further analyses.
Predicted FDG uptake features and their association with overall survival
Individual genes associated with FDG uptake in the study cohort were directly analyzed in the external cohort for their association to clinical outcomes. In addition, a multivariate model of FDG uptake features in the study cohort for each of the 14 features studied was built by a linear combination of metagenes to further examine the likely underlying biology of the features and their association with outcome in the external cohort. The accuracy of gene signatures to predict FDG uptake features was determined by examining the absolute difference in the predicted FDG uptake feature with the actual imaging feature value (19). Only signatures with an accuracy of greater than 0.70 were carried forward for further analysis and analyzed with outcome in the external data set. We refer to these features, now defined in terms of a gene signature, as “predicted FDG uptake features” and denote them with a prefix “p” (i.e., pSUVmax represents the prognostic gene signature associated with SUVmax). Kaplan–Meier curves were dichotomized at the median gene expression value, and unadjusted Cox proportional hazards (CPH) testing was conducted to assess the prognostic significance–predicted FDG uptake features and individual genes that were highly correlated to FDG features. We also conducted Lasso CPH modeling (25) with 10-fold cross-validation to identify a multivariate signature of multiple predicted FDG uptake features whose gene expression signature was associated with outcome, creating the “multivariate-SUV” model.
Validation of prognostic FDG uptake features
We studied FDG uptake features associated with prognostic gene signatures (i.e., pFDG features) from the external cohort in a validation cohort that consisted of 84 patients with PET imaging and survival data. We analyzed extracted FDG uptake features, computed their principal components, and derived the multivariate-SUV model by Lasso CPH analysis. To describe outcomes for these features, Kaplan–Meier curves were dichotomized at the median FDG uptake feature value and unadjusted CPH testing was conducted. Finally, we analyzed the prognostic significance of age, tumor size, and stage for the external and validation cohorts separately, followed by multivariate analyses with univariately derived prognostic imaging features to determine whether these imaging features added statistically significant independent information to prognosis in both cohorts.
Gene enrichment analysis of predicted FDG uptake features
We used a hypergeometric test with multiple testing correction and FDR (26) for metagene enrichment analysis using gene set collections from GeneSigDb (http://compbio.dfci.harvard.edu/genesigdb/), the NIH Database for Annotation, Visualization and Integrated Discovery (DAVID; http://david.abcc.ncifcrf.gov; ref. 27), MSigDb (http://www.broadinstitute.org/gsea/msigdb/index.jsp; refs. 28, 29), and Reactome (http://www.reactome.org/ReactomeGWT/entrypoint.html; ref. 30). We then framed the biologic context of these genes signatures by mapping them to known molecular pathways [Ingenuity pathway analysis (IPA)]. For this study, gene expression magnitude and direction for network visualization was derived using the z-score from univariate SAM analysis in the external cohort with survival as the dependent variable.
All analyses were conducted using MATLAB (MathWorks Inc.), R (v. 2.11.1), SAS™ (v9.2, SAS), and IPA (v9.0, Ingenuity).
Study, external, and validation cohorts
Twenty-five tumors from 25 patients with a median age of 71 years (range, 50–86), who had predominantly early-stage adenocarcinoma with lobectomy conducted were identified for the study cohort (Table 1). The median time to operation from PET acquisition was 27 days, and median tumor diameter (in the largest measured dimension) was 2.3 cm. The external cohort consisted of 63 patients with adenocarcinoma and a median age of 60 years. For the external cohort, 81% of patients were stage I–II with a median tumor diameter of 3.5 cm, follow-up of 42 months, and 24 (38%) deaths in the follow-up (Supplementary Data S1). The validation cohort consisted of 84 patients who had similar patient characteristics to the study cohort discounting gender (Table 1), SUVmedian and SUVmin, for which significant differences existed among variables (Table 1). Median follow-up time in the validation cohort was 38 months, during which time, 21 (25%) patients died.
FDG uptake measurements
Blood glucose (mg/dL), injected dose (mCi), and time to scan (minutes) were similar between the study and validation cohorts except for time to scan, which was longer in the study cohort (Table 1). ROI segmentation and feature extraction was fully automated for all patients in the discovery cohort using RT_Image, but 9 of 84 patients (11%) in the validation group required manual override due to improper segmentation from the RT_Image algorithm.
In the study cohort, median calculated SUVmax from RT_Image was 3.2 (range, 0.98–30), which agreed well with human observation by a certified nuclear medicine physician (A. Quon, r = 0.91). FDG uptake features between study and validation cohorts were reasonably similar in distribution but differed significantly for SUVmedian and SUVmin (Table 1). SUVmax was highly correlated (r > 0.8) with mean, median, sigma, variance, total glycolytic volume, and percentile metrics, moderately correlated (r > 0.6) with minimum, area, volume and points metrics, and modestly correlated with skew and kurtosis features (r = 0.48 and 0.32, respectively).
Principal components analysis of FDG uptake features
The first three principal components explained 96% of the image feature variance for the study cohort and defined new FDG uptake features. The first principal component was dominated by traditional point estimates of FDG uptake features (SUVmax, SUVmean, and SUVmedian) and spatial metrics (SUVpoints, SUVMTV, and SUVarea). The second and third components were associated with SUVkurtosis and SUVskew, measures of the shape of FDG uptake distribution, as well as SUVmin. These three principal components were added into a subsequent analysis with the initial 14 imaging features to examine their association with survival in the external cohort.
Gene expression and FDG uptake feature associations in the study cohort
In the study cohort, 37,798 assayed genes were filtered according to a 60% call rate (i.e., only those genes appearing in > 60% of samples were carried forward) to yield 8,238 present genes. After applying a variance filter described in the Patients and Methods, approximately half of these genes were included for further analysis. Eight of these 4,261 single genes were strongly associated with 7 different FDG uptake features by SAM analysis (Table 2). SUVmean was significantly associated with 5 genes and SUVmean and SUVskew were the only FDG uptake features uniquely associated with single genes.
Fifty-six high-quality gene clusters (representing 2,300 individual genes, per Patients and Methods) were correlated to the FDG uptake features using SAM analysis. Eight of these metagenes consisting of 240 individual genes were significantly associated with 7 different FDG uptake features (FDR = 0), and SUVmax and SUVmean features were enriched in extracellular matrix components in the study cohort (Table 2). Imaging features SUVskew, SUVmin, and SUVTGV were uniquely associated with metagenes enriched in protein and nucleic acid catabolism, as well as tumor suppressor pathways.
Predicted FDG uptake features and their association with survival
Predicted FDG uptake imaging features with an acceptable accuracy were carried forward to subsequent survival analysis (Table 3). All features passed this quality control except the third PCA (SUVPCA3). Predicted pSUVmax, pSUVmean, pSUVmin, pSUVvariance, and pSUVPCA2 were significantly associated with survival in the external cohort (Table 4). A multivariate model of predicted FDG uptake features associated with survival identified pSUVmax, pSUVvariance, and pSUVPCA2 as the top 3 prognostic FDG uptake features for predicting poor outcomes, with associated weights of 0.260, −0.281, and 0.148 respectively; we refer to this model as the multivariate-SUV model. Compared with the univariate FDG uptake features, the multivariate-SUV model yielded greatest prognostic value in the external cohort [HR, 5.87; confidence interval (CI), 2.59–13.8]. For single genes, 4 of 8 genes associated with FDG uptake from the study cohort were significantly associated with survival in the external data set (Table 4).
Validation of the prognostic FDG uptake features
We validated the significance of the predicted FDG uptake features that were associated with prognostic gene signatures on outcome in an additional 84 patient cohort with image data and survival data (Table 4). Both SUVmax (HR, 1.05; CI, 1.00–1.10) and the multivariate-SUV model (HR, 6.12; CI, 1.08–34.8) were significantly associated with survival by univariate CPH analysis and Kaplan–Meier plot (Fig. 2). The prognostic significance of SUVmean (P = 0.08), SUVPCA2 (P = 0.08), SUVvariance (P = 0.16), and SUVmin (P = 0.62) were not confirmed in the validation cohort, although the first 2 features maintained a strong trend with outcome (P < 0.10).
Incorporating clinical variables with prognostic FDG uptake features
In a univariate analysis, tumor size (cm) and stage (I–IV) were prognostically significant in both the external and validation cohorts (Supplementary Data S6). After adjusting for the clinical variables tumor size, stage, and age, pSUVmax was found to be independently significant for predicting worse outcome in the external cohort (Supplementary Data S6). Adjusted point estimates for the HRs associated with SUVmax in the validation cohort, as well as the multivariate-SUV feature in the external and validation cohorts, were all greater than 1.0 but not statistically significant (Supplementary Data S6).
Gene network analysis
We further used network analysis and gene enrichment databases to inform us of important functional relationships between imaging features and gene expression for pSUVmax and the multivariate-pSUV model. pSUVmax, comprising 15 metagenes and 508 individual genes, was enriched in cell cycle (CDK2NA–p16) and acetylation (histone) pathways with network analysis showing a prominent NF-κB node (Supplementary Data S7). Of the 1,367 genes comprising the multivariate-pSUV model, 484 were duplicated between features, leaving 883 unique genes to map for network analysis. Multiple important oncogenic pathways including cell proliferation (STAT1, PKA, FGF), cell cycle (CCNB, CCND, CEPBA), apoptosis (BAX, caspase, BIRC5), endocytosis (COP–Clathrin pathways), cell recognition (HLA-MHC), and oxidative phosphorylation (SLC25, cytochrome C, COX) were distinct nodes in this network (Supplementary Data S7).
Aberrant cell signaling, proliferation, and immortality are well-known hallmarks of cancer (31). We show here that gene expression related to prognostic FDG uptake features was enriched in these canonical pathways. Emerging hallmarks of cancer, including cell bioenergetics, inflammation, and immune evasion, may also play an important role in defining FDG uptake as a global marker of poor prognosis in patients with resected NSCLCs according to our analysis. Finally, increasing SUVmax is well established to be associated with poor patient outcome (3–5), and our analysis suggests that NF-κB signaling is a key molecular correlate for this imaging biomarker. This is a provocative finding, as NF-κB signaling is activated downstream by lactate production from glycolysis and—like FDG uptake—is increased in inflammatory and malignant diseases (32).
By using a computational design for this study, we examined multiple prognostic FDG uptake imaging features with genome-wide expression from NSCLC tumors. After accounting for multiple comparisons with FDR analysis (q < 0.05), we show that novel FDG uptake features were associated with distinct genes and gene signatures, less related (i.e., correlated) features were associated with different genes and gene signatures, and in combination features provided a more prognostic model than any one feature alone.
One previous study has investigated gene expression across varying degrees of FDG uptake in breast cancer, as defined by SUVmax, and glycolytic genes were remarkably absent from the most highly significant associations—as was the case in our study (Supplementary Data S8; ref. 33). A follow-up to that study focusing on cell bioenergetics found that glycolytic pathways are upregulated in the context of other major tumor pathways but are not the most highly enriched pathways (34). Interestingly, this follow-up study focused on another important driver of oncogenesis, c-MYC, and related it to FDG uptake. In contrast, our study's prognostic multivariate-SUV feature was associated with other major drivers of oncogenesis, although we also found that MYC-related pathways were enriched during ontology analysis (Supplementary Data S3 and S4).
Models such as these may deepen the use, and our understanding, of FDG uptake as a biomarker and may provide additional insight at the genomic level for a tumor phenotype defined by heterogeneous FDG uptake at the imaging level. The Warburg effect was initially described more than 80 years ago and postulates that tumors undergo glycolysis preferentially despite adequate intracellular oxygen tension (35, 36). Although Warburg believed that this was a result of mitochondrial dysfunction, we now know that tumor glycolysis can proceed with functional cellular mitochondria and in fact may be an adaptive response for tumor survival (10, 11, 37). Furthermore, studies have recently linked glycolysis in cancer to more widespread dysregulation of cell bioenergetics (38–40), suggesting that FDG uptake may be a surrogate for more than glycolysis alone and perhaps a lens through which one can view global tumor bioenergetics.
This study has limitations. To identify the prognostic significance of FDG features and their associated gene signatures, the ideal cohort would typically consist of hundreds of patients with: (i) genomic profiling of their resected tumor, (ii) PET imaging of the tumor before resection, and (iii) long-term follow-up. Because such large cohorts are not yet commonly available, we present and implemented a novel technique that integrates the three data types (namely, gene expression, imaging, and survival data) from three different cohorts.
We did not apply a correction of PET-FDG signal that may sometimes be required for tumors that approach the resolution of the PET scanner (∼1.5–2.0 cm)—known as partial volume effect correction—to our data (41, 42). While some studies have shown that this can have a significant effect on traditionally used metrics of uptake, such as SUVmax and SUVmean,(43), the effect of correcting for more novel features, such as SUVMTV and SUVskew, is unresolved to date (44). In addition to the above intrascan variation, interscan variation between PET scanners exists, can add to imprecision for feature quantification, and should be accounted for in future multicenter studies (45).
Although our sample was predominantly adenocarcinoma, there was some heterogeneity in histology, which is well known to affect both FDG uptake and gene expression (46, 47). We may not have exploited the full gamut of prognostic imaging features we derived using RT_Image as we were interested in examining only those FDG uptake features that were associated with prognostic gene signatures. Finally, the added significance of prognostic imaging features and their associated gene profiles compared with traditional clinical variables of prognosis was marginal, possibly due to the sample size and heterogeneity of the cohorts examined. Yet, our results indicate that larger studies are warranted to evaluate the prognostic significance of a broader characterization of FDG uptake features and to assess the relationship of FDG uptake to molecular processes beyond glycolysis.
Using gene leveraging techniques can harness the power of the public domain for expediting studies of radiogenomic biomarkers. A computational approach to understanding gene expression correlates of aggressive NSCLC as defined by prognostic 18F-FDG uptake features may offer new insights into tumor biology. Our methods require further study in other human cancers and larger, homogeneous cohorts of patients with NSCLC with standardized gene expression, imaging, and clinical data.
Disclosure of Potential Conflicts of Interest
No potential conflicts of interest were disclosed.
Conception and design: V.S. Nair, O. Gevaert, S. Napel, E.E. Graves, A. Quon, D.L. Rubin, S.K. Plevritis
Development of methodology: V.S. Nair, O. Gevaert, S. Napel, E.E. Graves, J.B. Shrager, A. Quon, S.K. Plevritis
Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): V.S. Nair, G. Davidzon, C.D. Hoang, J.B. Shrager, A. Quon, S.K. Plevritis
Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): V.S. Nair, O. Gevaert, G. Davidzon, A. Quon, S.K. Plevritis
Writing, review, and/or revision of the manuscript: V.S. Nair, O. Gevaert, S. Napel, E.E. Graves, J.B. Shrager, A. Quon, D.L. Rubin, S.K. Plevritis
Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): V.S. Nair, C.D. Hoang, J.B. Shrager, D.L. Rubin, S.K. Plevritis
Study supervision: V.S. Nair, S. Napel, S.K. Plevritis
This research was supported by the Information Sciences in Imaging at Stanford and the Center for Cancer Systems Biology (U54 CA149145 to O. Gevaert and S.K. Plevritis; R01 CA16025 to S. Napel and S.K. Plevritis) and the Division of Pulmonary & Critical Care Medicine at Stanford University School of Medicine (T32 HL007948 to V.S. Nair). Additional support to V.S. Nair was provided by the Lung Cancer Research Foundation and the CHEST Foundation California Chapter Clinical Research Award. O. Gevaert is a fellow of the Fund for Scientific Research Flanders (FWO-Vlaanderen), an Honorary Fulbright Scholar of the Commission for Educational Exchange between the United States of America, Belgium and Luxembourg, and a Henri Benedictus Fellow of the King Baudouin Foundation and the Belgian American Educational Foundation (BAEF). Database support for this project was provided by the Stanford NIH/NCRR CTSA UL1 RR025744 and NCI 5P30CA124435 Awards.
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.
The authors thank Yue Xu for processing tissues for genomic analysis, Sanjiv S. Gambhir for his insights and comments relating to this work, as well as Frezghi Habte and Khun Visith Keu for providing data relating to inter-calibration of PET scanners. They also thank Dr. S.H. Kim for sharing additional clinical data related to his gene expression data set.
Note: Supplementary data for this article are available at Cancer Research Online (http://cancerres.aacrjournals.org/).
- Received December 12, 2011.
- Revision received April 18, 2012.
- Accepted May 4, 2012.
- ©2012 American Association for Cancer Research.