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

BACKGROUND

Although FDG-PET is widely used in cancer, its role in gastric cancer (GC) is still controversial due to variable [F]fluorodeoxyglucose ([F]FDG) uptake. Here, we sought to develop a genetic signature to predict high FDG-avid GC to plan individualized PET and investigate the molecular landscape of GC and its association with glucose metabolic profiles noninvasively evaluated by [F]FDG-PET.

METHODS

Based on a genetic signature, PETscore, representing [F]FDG avidity, was developed by imaging data acquired from thirty patient-derived xenografts (PDX). The PETscore was validated by [F]FDG-PET data and gene expression data of human GC. The PETscore was associated with genomic and transcriptomic profiles of GC using The Cancer Genome Atlas.

RESULTS

Five genes, PLS1, PYY, HBQ1, SLC6A5, and NAT16, were identified for the predictive model for [F]FDG uptake of GC. The PETscore was validated in independent PET data of human GC with qRT-PCR and RNA-sequencing. By applying PETscore on TCGA, a significant association between glucose uptake and tumor mutational burden as well as genomic alterations were identified.

CONCLUSION

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.