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

Grossmann et al. BMC Cancer (2016) 16:611 DOI 10.1186/s12885-016-2659-5 RESEARCH ARTICLE Open Access Imaging-genomics reveals driving pathways of MRI derived volumetric tumor phenotype features in Glioblastoma Patrick Grossmann1,2*† , David A. Gutman3,4†, William D. Dunn Jr3,4, Chad A. Holder5 and Hugo J. W. L. Aerts1,2,6 Abstract 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. 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. 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). Conclusion: 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. Keywords: Imaging-genomics, Radiomics, Glioblastoma, Volumetric, Pathways, Prediction, Noninvasive, Radiation Oncology, Neuro-imaging Background Glioblastoma (GBM) is a highly invasive and diffuse WHO grade IV tumor and is the most lethal central nervous system malignancy with an annual age-adjusted incidence rate of 3.19/100,000 per population [1]. Despite aggressive surgical therapy, radiation therapy, and temozolomide administration the 2-year survival rate remains * Correspondence: † Equal contributors 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 Full list of author information is available at the end of the article around 27 % [2]. As a result, recent investigations have focused on capitalizing on the high molecular heterogeneity of gliomas to develop personalized treatment strategies [3]. One promising avenue of these investigations involves quantitative analyses of radiographic data, where imaging modalities are used to quantify tumor phenotype noninvasively. In magnetic resonance imaging (MRI), GBM tumors exhibit strong phenotypic features such as Necrosis, Edema, Contrast Enhancement, and Tumor Bulk (Fig. 1). These properties can be captured without and with intravenous administration of gadolinium-based contrast agents including T1-weighted or FLuid-Attenuated Inversion Recovery © 2016 The Author(s). Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Grossmann et al. BMC Cancer (2016) 16:611 Page 2 of 10 Fig. 1 Examples of volumetric tumor phenotype features. Glioblastoma (GBM) tumors show strong phenotypic differences, which can be objectively quantified with volumetrics. This figure shows examples of GBM tumors exhibiting high (top) and low (bottom) volumetric feature values for Necrosis, Contrast Enhancement, Edema, and Tumor Bulk (columns) as they appear on T1 weighted (columns 1,2, and 4) or T2-FLAIR (column 3) magnetic resonance images for different patients (FLAIR) (Fig. 2). In this way, visible tumor phenotype features can be systematically quantified. As the underlying drivers of these phenotypes are biological in nature, recent efforts have been conducted indicating underlying genetic characteristics of imaging features. For example, tumor “Ring Enhancement” was found to be significantly associated with unmethylated MGMT promoter status [4, 5], which is known to be a biomarker for response to temozolomide and survival. Similarly, “Contrast Enhancement” and “Mass Effect” imaging features were found to be strongly correlated with expression of groups of genes involved in hypoxia and proliferation, respectively [6]. However, a systematic classification of tumor phenotype features in terms of their underlying cell biological processes on a genomewide scale in GBM remains absent, although clinical applicability of these image features will depend on knowledge about how these features are driven by tumor biological processes that determine disease progression. In this study, we present an Imaging-Genomics analysis to investigate the associations of a large set of biological processes and presurgical diagnostic MRI derived quantitative volumetric tumor phenotype features, such as Necrosis or Edema, focusing on the publicly available GBM dataset from The Cancer Genome Atlas (TCGA). These analyses were tied to molecular subtypes in GBM and prognostics. Image based volumetric features provide noninvasive tumor phenotype information complementary to genomic technologies and clinical information, potentially allowing advances in patient stratification and clinical decision-making. Methods Magnetic resonance imaging DICOM formatted files of presurgical T1 and T2 sequence magnetic resonance images (MRIs) were accessed and downloaded in November 2014 from TCIA (https://wiki.cancerimagingarchive.net/display/Public/TCGA-GBM), a large archive of medical images of cancer patients who have matched molecular data at The Cancer Genome Atlas (TCGA). Cases that had both T1 and T2-FLAIR images available, were of reasonable quality to perform tumor segmentation, and had presurgical negative status were included. As the (...truncated)