Glucose metabolic profiles evaluated by PET associated with molecular characteristic landscape of gastric cancer

Although FDG-PET is widely used in cancer, its role in gastric cancer (GC) is still controversial due to variable [18F]fluorodeoxyglucose ([18F]FDG) uptake. Here, we investigate the molecular landscape of GC and its association with glucose metabolic profiles noninvasively evaluated by [18F]FDG PET. Based on a genetic signature, PETscore, representing [18F]FDG avidity, was developed by imaging data acquired from thirty patient-derived xenografts (PDX). Five genes, PLS1, PYY, HBQ1, SLC6A5, NAT16, were identified for the PETscore, which was validated in independent cohorts by qRT-PCR and RNA-sequencing. By applying the PETscore on the Cancer Genome Atlas (TCGA), a significant association between glucose uptake and tumor mutational burden as well as genomic alterations was identified in GC. Our findings suggest that molecular characteristics are underlying the diverse metabolic profiles of GC. Diverse glucose metabolic profiles may apply to precise diagnostic and therapeutic approaches for GC. RNA sequencing(RNA-seq) data of thirty gastric cancer PDXs (training set) were analyzed for identification of a genetic signature representing [18F]FDG avidity. RNA-seq data of fifteen patients who were paired with 15 PDX cases in the training set were utilized for estimation of SUVmax prediction based on the genetic signature. RNA-seq data of eight patients (validation set) with PET results were used for validation of FDG avidity prediction.