Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activities.
Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activities.
复制标题
DOI：
10.1126/scitranslmed.aaa7582
复制
发表时间：
2015-09-02
影响因子：
17.1
通讯作者：
Gevaert O
中科院分区：
文献类型：
--
作者：
Itakura H;Achrol AS;Mitchell LA;Loya JJ;Liu T;Westbroek EM;Feroze AH;Rodriguez S;Echegaray S;Azad TD;Yeom KW;Napel S;Rubin DL;Chang SD;Harsh GR 4th;Gevaert O
胶质母细胞瘤（GBM）是成人中最常见且高度致命的原发性恶性脑肿瘤。迫切需要易于获取的非侵入性生物标志物，其能够描绘潜在的分子活动并预测对治疗的反应。为此，我们试图确定仅通过定量磁共振成像特征区分的GBM亚型，以便更好地管理GBM患者。从121例初发、单发、单侧GBM患者的磁共振图像中提取了捕捉每个病变形状、质地和边缘锐度的定量图像特征。在开发队列中，使用对这些图像特征进行10,000次迭代的一致性聚类，出现了三个不同的表型“簇”。这三个簇——预多灶性、球形和边缘强化，名称反映了它们的图像特征——在一个独立队列中得到验证，该队列由144例来自癌症基因组图谱（TCGA）且具有相似肿瘤特征的多机构患者组成。使用通过PARADIGM算法对TCGA肿瘤拷贝数和基因表达数据进行分析得出的通路活性估计值，每个簇映射到一组独特的分子信号通路。不同的通路，如c - Kit和FOXA，在每个簇中富集，表明由图像特征确定的不同分子活动。每个簇还显示出不同的生存概率，表明其预后重要性。我们的成像方法提供了一种对GBM患者进行分层的非侵入性方法，还提供了独特的分子特征集，为GBM的靶向治疗和个性化治疗提供信息。
Glioblastoma (GBM) is the most common and highly lethal primary malignant brain tumor in adults. There is a dire need for easily accessible, noninvasive biomarkers that can delineate underlying molecular activities and predict response to therapy. To this end, we sought to identify subtypes of GBM, differentiated solely by quantitative MR imaging features, that could be used for better management of GBM patients. Quantitative image features capturing the shape, texture, and edge sharpness of each lesion were extracted from MR images of 121 patients with de novo, solitary, unilateral GBM. Three distinct phenotypic “clusters” emerged in the development cohort using consensus clustering with 10,000 iterations on these image features. These three clusters—pre-multifocal, spherical, and rim-enhancing, names reflecting their image features—were validated in an independent cohort consisting of 144 multi-institution patients with similar tumor characteristics from The Cancer Genome Atlas (TCGA). Each cluster mapped to a unique set of molecular signaling pathways using pathway activity estimates derived from analysis of TCGA tumor copy number and gene expression data with the PARADIGM algorithm. Distinct pathways, such as c-Kit and FOXA, were enriched in each cluster, indicating differential molecular activities as determined by image features. Each cluster also demonstrated differential probabilities of survival, indicating prognostic importance. Our imaging method offers a noninvasive approach to stratify GBM patients and also provides unique sets of molecular signatures to inform targeted therapy and personalized treatment of GBM.