Background: 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 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 [18F]FDG-PET.
Methods: Based on a genetic signature, PETscore, representing [18F]FDG avidity, was developed by imaging data acquired from thirty patient-derived xenografts (PDX). The PETscore was validated by [18F]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 [18F]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.
Keywords: Gastric cancer; Gene signature; Patient-derived xenograft; Positron emission tomography.
Bae SW, Berlth F, Jeong KY, Park JH, Choi JH, Park SH, Suh YS, Kong SH, Park DJ, Lee HJ, Lee C, Kim JI, Youn H, Choi H*, Cheon GJ, Kang KW, Yang HK*.(2021)