Spatial habitats from multiparametric MR imaging are associated with signaling pathway activities and survival in glioblastoma
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Katherine Dextraze1,*, Abhijoy Saha2,*, Donnie Kim3, Shivali Narang3, Michael Lehrer3,4, Anita Rao5,6, Saphal Narang7, Dinesh Rao8, Salmaan Ahmed9, Venkatesh Madhugiri10, Clifton David Fuller11, Michelle M. Kim13, Sunil Krishnan11, Ganesh Rao12 and Arvind Rao3,11
1Department of Medical Physics, The University of Texas Graduate School of Biomedical Sciences, Houston, TX, USA
2Department of Statistics, The Ohio State University, Columbus, OH, USA
3Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
4Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
5Texas Academy of Math and Science, Denton, TX, USA
6School of Engineering and Applied Sciences, Columbia University, New York City, NY, USA
7Debakey High School for Health Professions, Houston, TX, USA
8Radiology, University of Florida, College of Medicine, Jacksonville, FL, USA
9Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
10Neurosurgery, Tata Memorial Hospital, Mumbai, India
11Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
12Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
13Radiation Oncology, The University of Michigan, Ann Arbor, MI, USA
*These authors have contributed equally to this work
Arvind Rao, email: [email protected]
Keywords: imaging-genomics analysis; image-derived spatial habitat; glioblastoma; signaling pathway activity; Dirichlet regression
Received: May 04, 2017 Accepted: November 20, 2017 Published: December 05, 2017
Glioblastoma (GBM) show significant inter- and intra-tumoral heterogeneity, impacting response to treatment and overall survival time of 12-15 months. To study glioblastoma phenotypic heterogeneity, multi-parametric magnetic resonance images (MRI) of 85 glioblastoma patients from The Cancer Genome Atlas were analyzed to characterize tumor-derived spatial habitats for their relationship with outcome (overall survival) and to identify their molecular correlates (i.e., determine associated tumor signaling pathways correlated with imaging-derived habitat measurements). Tumor sub-regions based on four sequences (fluid attenuated inversion recovery, T1-weighted, post-contrast T1-weighted, and T2-weighted) were defined by automated segmentation. From relative intensity of pixels in the 3-dimensional tumor region, “imaging habitats” were identified and analyzed for their association to clinical and genetic data using survival modeling and Dirichlet regression, respectively. Sixteen distinct tumor sub-regions (“spatial imaging habitats”) were derived, and those associated with overall survival (denoted “relevant” habitats) in glioblastoma patients were identified. Dirichlet regression implicated each relevant habitat with unique pathway alterations. Relevant habitats also had some pathways and cellular processes in common, including phosphorylation of STAT-1 and natural killer cell activity, consistent with cancer hallmarks. This work revealed clinical relevance of MRI-derived spatial habitats and their relationship with oncogenic molecular mechanisms in patients with GBM. Characterizing the associations between imaging-derived phenotypic measurements with the genomic and molecular characteristics of tumors can enable insights into tumor biology, further enabling the practice of personalized cancer treatment. The analytical framework and workflow demonstrated in this study are inherently scalable to multiple MR sequences.
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