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.

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Available from: Olivier Gevaert
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    • "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. "
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