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

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.

TCGA-TCIA Impact on Radiogenomics Cancer Research: A Systematic Review

“…To search for a relationship between tumor MRI features and proteomics data, Lehrer et al [33] conducted a “radio-proteomic” analysis of LGG on 57 patients. The combination of imaging and protein abundance data showed VASARI imaging features significantly associated with several changes in protein level.…”

Use of Genome-Scale Integrated Analysis to Identify Key Genes and Potential Molecular Mechanisms in Recurrence of Lower-Grade Brain Glioma

“…By means of variable activation of the Wnt/beta-catenin signaling pathway, the (pro)renin receptor (PRR) plays a crucial role in development of glioma [27], with poor prognostic implications for the final outcome of brain glioma via SFRP1 inhibition and Wnt activation reported to contribute to the infiltrative glioma phenotype at early stages of disease progression [28]. Radio-proteomics analysis has shown that the phenotypic consequences of molecular aberrations in LGGs are correlated with expression of PI3K/Akt signaling pathways [29]. Moreover, the tumorigenesis of pleomorphic glioblastoma may increase with the enhancement aggregation of affected genes on specific molecular pathways (e.g., focal adhesion or ECM-receptor interaction) [30].…”

Exploratory Analysis of Qualitative MR Imaging Features for the Differentiation of Glioblastoma and Brain Metastases

“…The VASARI MR feature guide has been developed for primary brain tumors and has mostly been applied to GBMs (13, [18][19][20][21][22][23][24] and in a few studies to lower grade gliomas (26)(27)(28). To the best of our knowledge, this is the first study applying VASARI features to BMs.…”

“…Even though most commonly used to describe GBMs, the VASARI features have also been applied to lower grade gliomas (LGG): Hyare et al tried to predict isocitrate dehydrogenase 1 (IDH1) mutation status (26), Zhou et al aimed at predicting histological grade and tumor progression as well as mutation status (IDH1 and 1p/19q codeletion) (27) and Lehrer et al evaluated the relationship between MR tumor characteristics and protein measurements (28).…”