A novel genomic signature predicting FDG uptake in diverse metastatic tumors

Characterizing cancer metabolism from bulk and single-cell RNA-seq data using METAFlux

Cells often alter metabolic strategies under nutrient-deprived conditions to support their survival and growth. Characterizing metabolic reprogramming in the tumor microenvironment (TME) is of emerging importance in cancer research and patient care. However, recent technologies only measure a subset of metabolites and cannot provide in situ measurements. Computational methods such as flux balance analysis (FBA) have been developed to estimate metabolic flux from bulk RNA-seq data and can potentially be extended to single-cell RNA-seq (scRNA-seq) data. However, it is unclear how reliable current methods are, particularly in TME characterization. Here, we present a computational framework METAFlux (METAbolic Flux balance analysis) to infer metabolic fluxes from bulk or single-cell transcriptomic data. Large-scale experiments using cell-lines, the cancer genome atlas (TCGA), and scRNA-seq data obtained from diverse cancer and immunotherapeutic contexts, including CAR-NK cell therapy, have validated METAFlux's capability to characterize metabolic heterogeneity and metabolic interaction amongst cell types.

Prognostic value of metabolic signature on 18F-FDG uptake in breast cancer patients after radiotherapy

Radiotherapy (RT) is a major modality of postoperative treatment in breast cancer. The maximal standardized value (SUVmax) is ¹⁸FDG-PET/CT derived parameter that reported to be a valuable prognostic factor in cancer patients. Herein, we aimed to identify a prognostic gene signature associated with glucose uptake for breast cancer patients after RT by leveraging the mRNA expression profiling on public datasets. The glucose uptake signature was constructed using the single sample gene set enrichment analysis (ssGSEA) algorithm and evaluated in GSE21217 where SUVmax value was measured by PET-CT directly. The prognostic value was validated in three post-RT breast cancer cohorts (GSE103744, NKI, and FUSCC databases). The patients were stratified into glucose uptake signature score-high and low groups. Patients with a higher score had worse survival than those with a lower score. Mechanistically, the glucose uptake signature was calculated in each cell type of a single-cell RNA-seq database from five breast cancer patients. Glucose uptake signature score was significantly elevated in the malignant epithelial cells compared with normal ones. The immunosuppression markers including PDCD1, TIGIT, LAG3, and HAVCR2 were significantly upregulated in the T cells bearing a high glucose uptake signature score. Collectively, our results demonstrated the potential prognostic value of a glucose uptake signature in the post-RT breast cancer patients.

Metabolic radiogenomics in lung cancer: associations between FDG PET image features and oncogenic signaling pathway alterations

This study investigated the associations between image features extracted from tumor ¹⁸F-fluorodeoxyglucose (FDG) uptake and genetic alterations in patients with lung cancer. A total of 137 patients (age, 62.7 ± 10.2 years) who underwent FDG positron emission tomography/computed tomography (PET/CT) and targeted deep sequencing analysis for a tumor lesion, comprising 61 adenocarcinoma (ADC), 31 squamous cell carcinoma (SQCC), and 45 small cell lung cancer (SCLC) patients, were enrolled in this study. From the tumor lesions, 86 image features were extracted, and 381 genes were assessed. PET features were associated with genetic mutations: 41 genes with 24 features in ADC; 35 genes with 22 features in SQCC; and 43 genes with 25 features in SCLC (FDR < 0.05). Clusters based on PET features showed an association with alterations in oncogenic signaling pathways: Cell cycle and WNT signaling pathways in ADC (p = 0.023, p = 0.035, respectively); Cell cycle, p53, and WNT in SQCC (p = 0.045, 0.009, and 0.029, respectively); and TGFβ in SCLC (p = 0.030). In addition, SUV_peak and SUV_max were associated with a mutation of the TGFβ signaling pathway in ADC (FDR = 0.001, < 0.001). In this study, PET image features had significant associations with alterations in genes and oncogenic signaling pathways in patients with lung cancer.

Radiogenomics Based on PET Imaging

Radiogenomics or imaging genomics is a novel omics strategy of associating imaging data with genetic information, which has the potential to advance personalized medicine. Imaging features extracted from PET or PET/CT enable assessment of in vivo functional and physiological activity and provide comprehensive tumor information non-invasively. However, PET features are considered secondary to features on conventional imaging, and there has not yet been a review of the radiogenomic approach using PET features. This review article summarizes the current state of PET-based radiogenomic research for cancer, which discusses some of its limitations and directions for future study.