Spatial habitats from multiparametric MR imaging are associated with signaling pathway activities and survival in glioblastoma

// Katherine Dextraze 1, * , Abhijoy Saha 2, * , Donnie Kim 3 , Shivali Narang 3 , Michael Lehrer 3, 4 , Anita Rao 5, 6 , Saphal Narang 7 , Dinesh Rao 8 , Salmaan Ahmed 9 , Venkatesh Madhugiri 10 , Clifton David Fuller 11 , Michelle M. Kim 13 , Sunil Krishnan 11 , Ganesh Rao 12 and Arvind Rao 3, 11 1 Department of Medical Physics, The University of Texas Graduate School of Biomedical Sciences, Houston, TX, USA 2 Department of Statistics, The Ohio State University, Columbus, OH, USA 3 Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA 4 Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA 5 Texas Academy of Math and Science, Denton, TX, USA 6 School of Engineering and Applied Sciences, Columbia University, New York City, NY, USA 7 Debakey High School for Health Professions, Houston, TX, USA 8 Radiology, University of Florida, College of Medicine, Jacksonville, FL, USA 9 Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA 10 Neurosurgery, Tata Memorial Hospital, Mumbai, India 11 Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA 12 Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA 13 Radiation Oncology, The University of Michigan, Ann Arbor, MI, USA * These authors have contributed equally to this work Correspondence to: Arvind Rao, email: aruppore@mdanderson.org 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 ABSTRACT 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.