Multiple-response regression analysis links magnetic resonance imaging features to de-regulated protein expression and pathway activity in lower grade glioma

Michael Lehrer1, Anindya Bhadra2, Visweswaran Ravikumar1, James Y. Chen6,7, Max Wintermark3, Scott N. Hwang4, Chad A. Holder5, Erich P. Huang8, Brenda Fevrier-Sullivan9, John B. Freymann9, Arvind Rao1 and TCGA Glioma Phenotype Research Group

1Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, TX, USA

2Department of Statistics, Purdue University, West Lafayette, IN, USA

3Department of Radiology, Neuroradiology Division, Stanford University, Palo Alto, CA, USA

4Diagnostic Imaging, St. Jude Children’s Research Hospital, Memphis, TN, USA

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

6University of California San Diego Health System, San Diego, CA, USA

7Department of Radiology, San Diego VA Medical Center, San Diego, CA, USA

8Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, MD, USA

9Clinical Monitoring Research Program, Leidos Biomedical Research Inc., Frederick National Laboratory for Cancer Research, Frederick, MD, USA

Correspondence to:

Arvind Rao, email: aruppore@mdanderson.org

Keywords: imaging-proteomics analysis, radiomics, lower grade glioma, signaling pathway activity, multiple-response regression

Received: March 30, 2017    Accepted: May 02, 2017    Published: June 23, 2017


Background and Purpose: Lower grade gliomas (LGGs), lesions of WHO grades II and III, comprise 10-15% of primary brain tumors. In this first-of-a-kind study, we aim to carry out a radioproteomic characterization of LGGs using proteomics data from the TCGA and imaging data from the TCIA cohorts, to obtain an association between tumor MRI characteristics and protein measurements.

The availability of linked imaging and molecular data permits the assessment of relationships between tumor genomic/proteomic measurements with phenotypic features.

Materials and Methods: Multiple-response regression of the image-derived, radiologist scored features with reverse-phase protein array (RPPA) expression levels generated correlation coefficients for each combination of image-feature and protein or phospho-protein in the RPPA dataset. Significantly-associated proteins for VASARI features were analyzed with Ingenuity Pathway Analysis software. Hierarchical clustering of the results of the pathway analysis was used to determine which feature groups were most strongly correlated with pathway activity and cellular functions.

Results: The multiple-response regression approach identified multiple proteins associated with each VASARI imaging feature. VASARI features were found to be correlated with expression of IL8, PTEN, PI3K/Akt, Neuregulin, ERK/MAPK, p70S6K and EGF signaling pathways.

Conclusion: Radioproteomics analysis might enable an insight into the phenotypic consequences of molecular aberrations in LGGs.

PII: 353