基因表达谱
医学
肿瘤科
正电子发射断层摄影术
亚型
无线电技术
基因签名
单变量
比例危险模型
内科学
病理
生物
基因表达
核医学
基因
放射科
多元统计
计算机科学
机器学习
生物化学
程序设计语言
作者
Saveria Mazzara,Laura Lavinia Travaini,Francesca Botta,Consuelo Granata,Giovanna Motta,Federica Melle,Stefano Fiori,Valentina Tabanelli,Anna Vanazzi,Safaa Ramadan,Tommaso Radice,Sara Raimondi,Giuliana Lo Presti,Mahila Ferrari,Barbara Alicja Jereczek‐Fossa,Corrado Tarella,Francesco Ceci,Stefano Pileri,Enrico Derenzini
出处
期刊:Blood Advances
[American Society of Hematology]
日期:2023-02-21
卷期号:7 (4): 630-643
被引量:1
标识
DOI:10.1182/bloodadvances.2022007825
摘要
Emerging evidence indicates that chemoresistance is closely related to altered metabolism in cancer. Here, we hypothesized that distinct metabolic gene expression profiling (GEP) signatures might be correlated with outcome and with specific fluorodeoxyglucose positron emission tomography (FDG-PET) radiomic profiles in diffuse large B-cell lymphoma (DLBCL). We retrospectively analyzed a discovery cohort of 48 consecutive patients with DLBCL treated at our center with standard first-line chemoimmunotherapy by performing targeted GEP (T-GEP)- and FDG-PET radiomic analyses on the same target lesions at baseline. T-GEP-based metabolic profiling identified a 6-gene signature independently associated with outcomes in univariate and multivariate analyses. This signature included genes regulating mitochondrial oxidative metabolism (SCL25A1, PDK4, PDPR) that were upregulated and was inversely associated with genes involved in hypoxia and glycolysis (MAP2K1, HIF1A, GBE1) that were downregulated. These data were validated in 2 large publicly available cohorts. By integrating FDG-PET radiomics and T-GEP, we identified a radiometabolic signature (RadSig) including 4 radiomic features (histo kurtosis, histo energy, shape sphericity, and neighboring gray level dependence matrix contrast), significantly associated with the metabolic GEP-based signature (r = 0.43, P = .0027) and with progression-free survival (P = .028). These results were confirmed using different target lesions, an alternative segmentation method, and were validated in an independent cohort of 64 patients. RadSig retained independent prognostic value in relation to the International Prognostic Index score and metabolic tumor volume (MTV). Integration of RadSig and MTV further refined prognostic stratification. This study provides the proof of principle for the use of FDG-PET radiomics as a tool for noninvasive assessment of cancer metabolism and prognostic stratification in DLBCL.
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