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

Erich P Huang,Arvind Rao,Michael Lehrer,Max Wintermark,Visweswaran Ravikumar,Scott N Hwang,Brenda Fevrier-Sullivan,Anindya Bhadra,James Y Chen,Chad A Holder,John B Freymann

doi:10.18632/oncoscience.353

Abstract

Background and PurposeLower 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 MethodsMultiple-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.ResultsThe 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.ConclusionRadioproteomics analysis might enable an insight into the phenotypic consequences of molecular aberrations in LGGs.

Highlights

Lower grade gliomas (LGGs), those lesions graded II and III, comprise 10-15% of primary brain tumors [1]
Radioproteomics analysis might enable an insight into the phenotypic consequences of molecular aberrations in LGGs
LGGs are classified into molecular subtypes based on alterations in TP53, isocitrate dehydrogenase (IDH) 1 or 2, telomerase reverse transcriptase (TERT), and the transcriptional regulator ATRX: the mutational status of these genes correlates with clinical responses and outcomes [9]

Summary

Introduction

Lower grade gliomas (LGGs), those lesions graded II and III, comprise 10-15% of primary brain tumors [1]. LGGs are classified into molecular subtypes based on alterations in TP53, isocitrate dehydrogenase (IDH) 1 or 2, telomerase reverse transcriptase (TERT), and the transcriptional regulator ATRX: the mutational status of these genes correlates with clinical responses and outcomes [9] Despite these findings, challenges remain in the treatment of LGGs. The complementary assessment of gliomas via imaging and molecular pathology approaches, for understanding the response to treatment and poor outcomes, has spurred the investigation of combining imaging data with molecular (genetic/genomic) information. 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.

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Conclusion