论著摘要 |【MR】成像基因组学揭示了胶质母细胞瘤中MRI衍生的体积肿瘤表型特征的驱动途径(双语版)

2017-12-22 12:01:49 admin 0

Imaging-genomics reveals driving pathways of MRI derived volumetric tumor phenotype features in Glioblastoma.

发表日期: 2016.08.08   来源:BMC Cancer. 2016 Aug 8;16:611.

作者:

Patrick Grossmann1,2, David A. Gutman3,4, William D. DunnJr3,4, Chad A. Holder5 and Hugo J. W. L. Aerts6,7,8.

作者介绍:

1. Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.

2. Department of Biostatistics & Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA.

3. Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA.

4. Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA.

5. Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, USA.

6. Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.

7. Department of Biostatistics & Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA.

8. Radiology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.

摘要

Abstact

背景

胶质母细胞瘤(GBM)肿瘤表现出强烈的表型差异,可以使用磁共振成像(MRI)进行定量,但这些成像表型的潜在生物学驱动因素在很大程度上是未知的。进行成像 - 基因组学分析以揭示MRI衍生的定量体积肿瘤表型特征与分子途径之间的机械关联。

Background

Glioblastoma (GBM) tumors exhibit strong phenotypic differences that can be quantified using magnetic resonance imaging (MRI), but the underlying biological drivers of these imaging phenotypes remain largely unknown. An Imaging-Genomics analysis was performed to reveal the mechanistic associations between MRI derived quantitative volumetric tumor phenotype features and molecular pathways.

方法

我们的分析纳入了一百四十名患有术前MRI和生存数据的患者。定义了体积特征,包括坏死核(NE),对比度增强(CE),通过对比后T1w(肿瘤体积或TB)评估的异常肿瘤体积,基于T2-FLAIR(ED)的肿瘤相关性水肿,以及总体积(TV),以及这些肿瘤组分的比例。 基于可用的基因表达(n = 91),使用预先分组的基因集富集分析来评估途径关联。这些结果被放入了GBM和预后分子亚型的上下文中。

Methods

One hundred fourty one patients with presurgery MRI and survival data were included in our analysis. Volumetric features were defined, including the necrotic core (NE), contrast-enhancement (CE), abnormal tumor volume assessed by post-contrast T1w (tumor bulk or TB), tumor-associated edema based on T2-FLAIR (ED), and total tumor volume (TV), as well as ratios of these tumor components. Based on gene expression where available (n = 91), pathway associations were assessed using a preranked gene set enrichment analysis. These results were put into context of molecular subtypes in GBM and prognostication.

结果

体积特征与不同组的生物过程显着相关(FDR<0.05)。虽然NE和TB富集免疫应答途径和凋亡,但CE与信号转导和蛋白折叠过程相关。ED主要丰富了体内平衡和细胞循环途径。ED也是分子GBM亚型的最强预测因子(AUC = 0.61)。CE是总体生存的最强预测指标(C指数= 0.6;Noether检验,p = 4×10-4)。

Results

Volumetric features were significantly associated with diverse sets of biological processes (FDR < 0.05). While NE and TB were enriched for immune response pathways and apoptosis, CE was associated with signal transduction and protein folding processes. ED was mainly enriched for homeostasis and cell cycling pathways. ED was also the strongest predictor of molecular GBM subtypes (AUC = 0.61). CE was the strongest predictor of overall survival (C-index = 0.6; Noether test, p = 4x10−4).

结论

从MRI中提取的GBM体积特征显著丰富了关于影响患者结果的肿瘤生物学状态的信息。临床决策支持系统可以利用这一信息,在非侵入性成像的基础上制定个性化治疗策略。

Conclusions

GBM volumetric features extracted from MRI are significantly enriched for information about the biological state of a tumor that impacts patient outcomes. Clinical decision-support systems could exploit this information to develop personalized treatment strategies on the basis of noninvasive imaging.

关键词:

胶质母细胞瘤,成像-基因组学,神经影像,非入侵,途径,预测,射线肿瘤学,影像组学,体积测定

Keywords:

Glioblastoma; Imaging-genomics; Neuro-imaging; Noninvasive; Pathways; Prediction; Radiation Oncology; Radiomics; Volumetric

阅读原文:PMID: 27502180  PMCID:   PMC4977720DOI: 10.1186/s12885-016-2659-5


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