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

Katherine Dextraze, Abhijoy Saha, Donnie Kim, Shivali Narang, Michael Lehrer, Anita Rao, Saphal Narang, Dinesh Rao, Salmaan Ahmed, Venkatesh Madhugiri, Clifton David Fuller, Michelle M Kim, Sunil Krishnan, Ganesh Rao, Arvind Rao, Katherine Dextraze, Abhijoy Saha, Donnie Kim, Shivali Narang, Michael Lehrer, Anita Rao, Saphal Narang, Dinesh Rao, Salmaan Ahmed, Venkatesh Madhugiri, Clifton David Fuller, Michelle M Kim, Sunil Krishnan, Ganesh Rao, Arvind Rao

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.

Keywords: Dirichlet regression; glioblastoma; image-derived spatial habitat; imaging-genomics analysis; signaling pathway activity.

Conflict of interest statement

CONFLICTS OF INTEREST There are no competing financial interests.

Figures

Figure 1. The process of generating 16…
Figure 1. The process of generating 16 spatial habitats
Based on 4 MR sequences (multi-parametric MRI scans), classify each voxel within the tumor volume into high and low categories via kmeans clustering. With 16 (24) signal combinations across the 4 sequences (i.e. 0000-1111), every voxel in the tumor volume can be identified uniquely. The resultant habitat map shows the spatial heterogeneity within tumor.
Figure 2. The process of finding important…
Figure 2. The process of finding important and significant (designated “Relevant”) habitats
After adjusting for clinical covariates (age, Karnofsky performance score, tumor volume, and IDH1 mutation status), we identified important habitats (positive variable importance) via Random Forest survival analysis. Those habitats were then assessed for significance via Cox Proportional Hazards Regression to determine overall survival (OS). Only habitat 2,7, and 10 are both important and significant (i.e “relevant”) in determining OS.

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