Radiogenomic analysis of primary breast cancer reveals [18F]-fluorodeoxglucose dynamic flux-constants are positively associated with immune pathways and outperform static uptake measures in associating with glucose metabolism

Abstract

Background PET imaging of 18F-fluorodeoxygucose (FDG) is used widely for tumour staging and assessment of treatment response, but the biology associated with FDG uptake is still not fully elucidated. We therefore carried out gene set enrichment analyses (GSEA) of RNA sequencing data to find KEGG pathways associated with FDG uptake in primary breast cancers. Methods Pre-treatment data were analysed from a window-of-opportunity study in which 30 patients underwent static and dynamic FDG-PET and tumour biopsy. Kinetic models were fitted to dynamic images, and GSEA was performed for enrichment scores reflecting Pearson and Spearman coefficients of correlations between gene expression and imaging. Results A total of 38 pathways were associated with kinetic model flux-constants or static measures of FDG uptake, all positively. The associated pathways included glycolysis/gluconeogenesis (‘GLYC-GLUC’) which mediates FDG uptake and was associated with model flux-constants but not with static uptake measures, and 28 pathways related to immune-response or inflammation. More pathways, 32, were associated with the flux-constant K of the simple Patlak model than with any other imaging index. Numbers of pathways categorised as being associated with individual micro-parameters of the kinetic models were substantially fewer than numbers associated with flux-constants, and lay around levels expected by chance. Conclusions In pre-treatment images GLYC-GLUC was associated with FDG kinetic flux-constants including Patlak K, but not with static uptake measures. Immune-related pathways were associated with flux-constants and static uptake. Patlak K was associated with more pathways than were the flux-constants of more complex kinetic models. On the basis of these results Patlak analysis of dynamic FDG-PET scans is advantageous, compared to other kinetic analyses or static imaging, in studies seeking to infer tumour-to-tumour differences in biology from differences in imaging. Trial registration NCT01266486, December 24th 2010.

Radiogenomics analysis reveals the associations of dynamic contrast-enhanced–MRI features with gene expression characteristics, PAM50 subtypes, and prognosis of breast cancer

“…For instance, the tumor mutational burden risk can be predicted in both primary and liver-metastatic colorectal cancer (AUCs: 0.732 and 0.812) by using radiogenomics analysis based on computed tomography (CT) images ( 57 ). Radiomics features from positron emission tomography (PET) imaging of 18 F-fluorodeoxyglucose (FDG) markedly related to the activation and alteration of mTOR pathway genes in hepatocellular carcinoma ( 58 ), and similar results were also reported in BC that some immune-related pathways were associated with FDG-PET features, such as flux constants and static uptake ( 59 ), and some researchers also aimed to predict Ki-67 status from multiparametric MRI images (AUC: 0.79) in BC ( 60 ). In addition, integration of radiomics and genomic features is also a promising area, such as the radiogenomics model (AUC: 0.87) showed much better performance than the radiomics-only models (AUCs: 0.71 and 0.73) in the prediction of pathological complete response of TNBC ( 61 ).…”

“…Radiogenomics is a promising approach to realizing precision medicine by using non-invasive imaging technology to monitor the molecular behavior of the tumor, as the latest studies reported (56)(57)(58)(59)(60). For instance, the tumor mutational burden risk can be predicted in both primary and livermetastatic colorectal cancer (AUCs: 0.732 and 0.812) by using radiogenomics analysis based on computed tomography (CT) images (57).…”

Clinical significance of 18F-FDG PET/CT imaging in 32 cases of gastrointestinal stromal tumors

“…Among them, 18 F-FDG PET/CT is a non-invasive method to study biochemical and metabolic changes in tumor tissues, which provides useful functional information for identifying surviving tumor tissues [11]. 18 F-FDG metabolic imaging expresses the metabolic level of glucose molecules in the cell [12][13][14]. Tumor cells have increased nucleus division, active cell proliferation, and increased glucose metabolism, which is manifested by increased FDG metabolism and an increased SUV at the lesion.…”

Lymph node metastasis prediction and biological pathway associations underlying DCE-MRI deep learning radiomics in invasive breast cancer

“…In addition, the radiomics signature and GoogLeNet signature were classified into high and low risk groups with median values, and their correlation with different modules was analyzed. Further, the biological significance of the associated modules was analyzed using the Kyoto Gene and Genome Encyclopedia (KEGG) database [ 21 ]. Gene set variation analysis (GSVA) calculated genes of the same significance or function grouped into a single enrichment score in order to assess changes in the activity of the pathway/function in which the genes were located [ 22 ].…”