Case Western Reserve University, Cleveland, Ohio, USA.
Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA.
Neuro Oncol. 2021 Feb 25;23(2):251-263. doi: 10.1093/neuonc/noaa231.
Recent epidemiological studies have suggested that sexual dimorphism influences treatment response and prognostic outcome in glioblastoma (GBM). To this end, we sought to (i) identify distinct sex-specific radiomic phenotypes-from tumor subcompartments (peritumoral edema, enhancing tumor, and necrotic core) using pretreatment MRI scans-that are prognostic of overall survival (OS) in GBMs, and (ii) investigate radiogenomic associations of the MRI-based phenotypes with corresponding transcriptomic data, to identify the signaling pathways that drive sex-specific tumor biology and treatment response in GBM.
In a retrospective setting, 313 GBM patients (male = 196, female = 117) were curated from multiple institutions for radiomic analysis, where 130 were used for training and independently validated on a cohort of 183 patients. For the radiogenomic analysis, 147 GBM patients (male = 94, female = 53) were used, with 125 patients in training and 22 cases for independent validation.
Cox regression models of radiomic features from gadolinium T1-weighted MRI allowed for developing more precise prognostic models, when trained separately on male and female cohorts. Our radiogenomic analysis revealed higher expression of Laws energy features that capture spots and ripple-like patterns (representative of increased heterogeneity) from the enhancing tumor region, as well as aggressive biological processes of cell adhesion and angiogenesis to be more enriched in the "high-risk" group of poor OS in the male population. In contrast, higher expressions of Laws energy features (which detect levels and edges) from the necrotic core with significant involvement of immune related signaling pathways was observed in the "low-risk" group of the female population.
Sexually dimorphic radiogenomic models could help risk-stratify GBM patients for personalized treatment decisions.
最近的流行病学研究表明，性别二态性影响胶质母细胞瘤（GBM）的治疗反应和预后结果。为此，我们试图（i）从预处理 MRI 扫描中识别出肿瘤亚区（瘤周水肿、增强肿瘤和坏死核心）中独特的性别特异性放射组学表型，这些表型对 GBM 的总生存期（OS）具有预后意义，以及（ii）研究基于 MRI 的表型与相应转录组数据的放射基因组关联，以确定驱动 GBM 中性别特异性肿瘤生物学和治疗反应的信号通路。
在回顾性研究中，从多个机构中筛选了 313 名 GBM 患者（男性=196 名，女性=117 名）进行放射组学分析，其中 130 名用于训练，并在独立的 183 名患者队列中进行验证。对于放射基因组分析，使用了 147 名 GBM 患者（男性=94 名，女性=53 名），其中 125 名用于训练，22 名用于独立验证。
基于钆增强 T1 加权 MRI 的放射组学特征的 Cox 回归模型允许分别在男性和女性队列中进行训练，从而建立更精确的预后模型。我们的放射基因组分析显示，增强肿瘤区域中 Laws 能量特征（代表更高的异质性）的表达更高，以及细胞黏附和血管生成等侵袭性生物学过程在男性人群中 OS 较差的“高危”组中更为丰富。相比之下，在女性人群的“低危”组中，坏死核心中 Laws 能量特征（检测水平和边缘）的表达更高，并且涉及到免疫相关信号通路。
性别二态性放射基因组模型可以帮助 GBM 患者进行风险分层，以做出个性化的治疗决策。
