Prognostic PET F-18-FDG Uptake Imaging Features Are Associated with Major Oncogenomic Alterations in Patients with Resected Non-Small Cell Lung Cancer

Division of Pulmonary & Critical Care Medicine, Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
Cancer Research (Impact Factor: 9.33). 06/2012; 72(15):3725-34. DOI: 10.1158/0008-5472.CAN-11-3943
Source: PubMed


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 (SUV(max)), SUV(variance), and SUV(PCA2)], 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.

Download full-text


Available from: Olivier Gevaert,
  • Source
    • "In clinical practice, this heterogeneity is typically described in nonquantitative terms. More recently, metrics [16] [17] of heterogeneity, such as Shannon entropy, have been developed and can be correlated with tumor molecular features [18] [19] [20] [21] and clinical outcomes [22] [23] [24]. However, metrics that assign a single value to heterogeneity tacitly assume that the tumor is " well mixed " and thus does not capture spatial distributions of specific tumor properties. "
    [Show abstract] [Hide abstract]
    ABSTRACT: We examined pretreatment magnetic resonance imaging (MRI) examinations from 32 patients with glioblastoma multiforme (GBM) enrolled in The Cancer Genome Atlas (TCGA). Spatial variations in T1 post-gadolinium and either T2-weighted or fluid attenuated inversion recovery sequences from each tumor MRI study were used to characterize each small region of the tumor by its local contrast enhancement and edema/cellularity ("habitat"). The patient cohort was divided into group 1 (survival < 400 days, n = 16) and group 2 (survival > 400 days, n = 16). Histograms of relative values in each sequence demonstrated that the tumor regions were consistently divided into high and low blood contrast enhancement, each of which could be subdivided into regions of high, low, and intermediate cell density/interstitial edema. Group 1 tumors contained greater volumes of habitats with low contrast enhancement but intermediate and high cell density (not fully necrotic) than group 2. Both leave-one-out and 10-fold cross-validation schemes demonstrated that individual patients could be correctly assigned to the short or long survival group with 81.25% accuracy. We demonstrate that novel image analytic techniques can characterize regional habitat variations in GBMs using combinations of MRI sequences. A preliminary study of 32 patients from the TCGA database found that the distribution of MRI-defined habitats varied significantly among the different survival groups. Radiologically defined ecological tumor analysis may provide valuable prognostic and predictive biomarkers in GBM and other tumors.
    Translational oncology 02/2014; 7(1):5-13. DOI:10.1593/tlo.13730 · 2.88 Impact Factor
  • Source
    • "These include glucose transporters and specific enzymes that promote the aerobic glycolysis phenotype, such as PKM2 and PDK1 [40–42]. Thus, 18F-FDG PET images depict the complex interplay among gene expression [43], translation, and various signal transduction pathways (reviewed in [44]). The information extracted from these images can provide insights into tumor proliferative activity, aggressiveness [45, 46], and prognosis [47]. "
    [Show abstract] [Hide abstract]
    ABSTRACT: The discovery of the Warburg effect in the early twentieth century followed by the development of the fluorinated glucose analogue 18F-fluorodeoxyglucose (18F-FDG) and the invention of positron emission tomographs laid the foundation of clinical PET/CT. This review discusses the challenges and obstacles in clinical adoption of this technique. We then discuss advances in instrumentation, including the critically important introduction of PET/CT and current PET/CT protocols. Moreover, we provide evidence for the clinical utility of PET/CT for patient management and its potential impact on patient outcome, and address its cost and cost-effectiveness. Although this review largely focuses on 18F-FDG imaging, we also discuss a variety of additional molecular imaging approaches that can be used for cancer phenotyping with PET. Throughout this review we emphasize the critical contributions of CT to the strength of PET/CT.
    09/2013; 1(3). DOI:10.1007/s40134-013-0016-x
  • Source
    • "Diagnostic PET imaging is routinely performed in NSCLC, the most frequent histological type of lung cancer (still the leading cause of cancer death in the world [8]). There is evidence that high glucose metabolism is present in NSCLC, so a role of metabolism as prognostic factor can be hypothesized; in fact this role is actually investigated in lung cancer by the assessment of FDG uptake level [6,9,10]. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Glycolysis in presence of oxygen with high glucose consumption is known to be the metabolism of choice in many tumors. In lung cancer this phenomenon is routinely exploited in diagnostic PET imaging of fluorodeoxyglucose uptake, but not much is known about the prognostic capabilities of glycolysis level assessment in resected lung tumor samples. In this retrospective study, we used real time polymerase chain reaction(RQ-PCR) to assess the expression level of the gene for Glyceraldehyde 3-phosphate dehydrogenase(GAPDH), key enzyme for glucose breakdown, in tumor samples from 82 consecutive early stages resected non small cell lung cancer(NSCLC) patients. We then compared our results in six large publicly available NSCLC microarray datasets collecting data from over 1250 total patients. In our study GAPDH gene over expression was found to be an adverse prognostic factor in early stages NSCLC (n = 82 HR = 1.30 p = 0.050). This result was confirmed in 5 of 6 public datasets analyzed: Shedden et al. 2008: n = 442 HR = 1.54 p < 0.0001; Lee et al. 2008: n = 138 HR = 1.31 p = 0.043; Tomida et al. 2009: n = 117 HR = 1.59 p = 0.004; Roepman et al. 2009: n = 172 (TPI1 gene) HR = 1.51 p = 0.009; Okayama et al. 2012: n = 226 HR = 3.19 p < 0.0001; Botling et al. 2013: n = 196 HR = 1.00 p = 0.97). Furthermore, in the large and clinically well annotated Shedden et al. microarray dataset, GAPDH hazard ratio did not change whether calculated for the whole dataset or for the subgroup of adjuvant naive patients only (n = 330 HR = 1.49 p < 0.0001). GAPDH gene over expression in resected tumor samples is an adverse prognostic factor in NSCLC. Our results confirm the prognostic value of glucose metabolism assessment in NSCLC.
    Molecular Cancer 08/2013; 12(1):97. DOI:10.1186/1476-4598-12-97 · 4.26 Impact Factor
Show more