NF-κB protein expression associates with (18)F-FDG PET tumor uptake in non-small cell lung cancer: a radiogenomics validation study to understand tumor metabolism.

INTRODUCTION We previously demonstrated that NF-κB may be associated with (18)F-FDG PET uptake and patient prognosis using radiogenomics in patients with non-small cell lung cancer (NSCLC). To validate these results, we assessed NF-κB protein expression in an extended cohort of NSCLC patients. METHODS We examined NF-κBp65 by immunohistochemistry (IHC) using a Tissue Microarray. Staining intensity was assessed by qualitative ordinal scoring and compared to tumor FDG uptake (SUVmax and SUVmean), lactate dehydrogenase A (LDHA) expression (as a positive control) and outcome using ANOVA, Kaplan Meier (KM), and Cox-proportional hazards (CPH) analysis. RESULTS 365 tumors from 355 patients with long-term follow-up were analyzed. The average age for patients was 67±11 years, 46% were male and 67% were ever smokers. Stage I and II patients comprised 83% of the cohort and the majority had adenocarcinoma (73%). From 88 FDG PET scans available, average SUVmax and SUVmean were 8.3±6.6, and 3.7±2.4 respectively. Increasing NF-κBp65 expression, but not LDHA expression, was associated with higher SUVmax and SUVmean (p=0.03 and 0.02 respectively). Both NF-κBp65 and positive FDG uptake were significantly associated with more advanced stage, tumor histology and invasion. Higher NF-κBp65 expression was associated with death by KM analysis (p=0.06) while LDHA was strongly associated with recurrence (p=0.04). Increased levels of combined NF-κBp65 and LDHA expression were synergistic and associated with both recurrence (p=0.04) and death (p=0.03). CONCLUSIONS NF-κB IHC was a modest biomarker of prognosis that associated with tumor glucose metabolism on FDG PET when compared to existing molecular correlates like LDHA, which was synergistic with NF-κB for outcome. These findings recapitulate radiogenomics profiles previously reported by our group and provide a methodology for studying tumor biology using computational approaches.