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

Abstract

this study investigated the associations between image features extracted from tumor 18 F-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, merging medical imaging data and genomic information, has great potential in the era of personalized medicine 1-4. Genomic information could enable physicians to select appropriate management strategies according to the genetic alterations in an individual patient with cancer 5. Nevertheless, the application of genomic medicine in the field of oncology has shortcomings because tumors have genetic and phenotypic diversities even within a single mass 5-7. Such intra-tumor heterogeneity eventually drives treatment failure and disease progression 7-9. To overcome it, multiple and sequential biopsies should be conducted to identify all the genetic alterations within the whole tumor throughout the course of disease progression within a patient, which is not always feasible in routine clinical practice. Therefore, the search for noninvasive techniques that reflect genetic alterations in multiple sites and at multiple time points is important because such techniques could improve patient care. Various features from medical images of a tumor could be surrogate markers that predict the alteration of particular genetic pathways. A known association between image features and genetic alterations has a potential as a useful additional information to improve decision making of biopsies, which could create new, accessible management strategies for patients with cancer 4,6,10 .

Genomic and Glycolytic Entropy Are Reliable Radiogenomic Heterogeneity Biomarkers for Non-Small Cell Lung Cancer

“…We also found that glycolytic entropy was significantly higher in patients with an advanced staging status, whereas genomic heterogeneity showed no significant difference. As tumor glycolysis is associated with oncogenic signaling pathway alterations [ 8 , 28 ], glycolytic features are associated with more aggressive behavior and metastatic potential [ 5 , 28 ]. Herein, the intensity, volumetric and heterogeneity glycolytic features were positively linked to higher cancer staging, suggesting that glycolysis is a biomarker of tumor aggressiveness.…”

“…These features also reportedly predict pathological response after neoadjuvant chemoradiotherapy in patients with NSCLC [ 4 , 5 , 6 ]. The combination of gene tests and metabolic radiomics of 18 F-FDG PET (radiogenomics) has gained increasing attention in NSCLC research [ 7 , 8 ].…”

A review of radiomics and genomics applications in cancers: the way towards precision medicine

“…The literature on radiogenomics has greatly expanded in the past decade. Radiogenomics has been applied in various types of cancer, including lung cancer [70][71][72], breast cancer [73][74][75], genitourinary cancer [76][77][78], digestive system cancer [79][80][81], and brain cancer [82][83][84]. The comprehensive analysis method of radiogenomics can be used to diagnose and predict patient outcomes such as recurrence, metastasis, and survival of patients with cancer; predict the tumour microenvironment; identify potential molecular biomarkers and targets for clinical diagnosis and treatment; and establish a molecular typing system of variable cancers.…”

Radiomics-guided radiation therapy: opportunities and challenges

“…Considering this issue (Bai et al 2016, Bodalal et al 2019. For example, in a study by Kim et al (2020), FDG PET/CT imaging features were associated to genetic alterations in patients with lung cancer. In this study, association between PET features and alterations in oncogenic signaling pathways such as Cell cycle, WNT, p53 and TGFβ were determined and association between SUVpeak and SUVmax and mutation in TGFβ signaling pathway was found.…”