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  • Gene Expression Analysis of Pseudoprogression and True Progression in Newly Diagnosed GBM

    Final Number:
    826

    Authors:
    Adam M Robin BS MS MD; Laila Poisson; Aditya Raghunathan MD; Lisa Scarpace MS; Charlotte Burmeister MS; Tobias Walbert MD; Denise Leung MD MS; Tom Mikkelsen MD; Ian Yu Lee MD

    Study Design:
    Other

    Subject Category:

    Meeting: Congress of Neurological Surgeons 2015 Annual Meeting

    Introduction: Despite decades of advancement in our understanding of glioblastoma multiforme (GBM), its prognosis remains unacceptably poor. Pseudoprogression describes a post-treatment reaction demonstrating increased edema and contrast enhancement similar to typical tumor progression except that on subsequent imaging, without escalation of antitumor therapy, these changes stabilize or revert [1]. There are immense therapeutic and research implications for patients based on correct identification of pseudoprogression. This study seeks to identify novel indicators of pseudoprogression.

    Methods: Patients were identified using the Hermelin Brain Tumor Center database at Henry Ford Hospital. Retrospective chart review was conducted. Patients were assigned to either pseudoprogression (PP) or true progression (TP) groups based on whether changes suggestive of disease progression on MRI within 2 months of post-operative therapy initiation regressed without additional antitumor therapy during the ensuing 4 months. Tissues from 31 of these patients with newly diagnosed GBM between 1992 and 2011 were among those molecularly profiled in The Cancer Genome Atlas. Probe-level gene expression data from the Agilent G4502A array were screened for differential expression by two-sample t-test then assessed using Qiagen’s Ingenuity Pathway Analysis (IPA).

    Results: Twenty-one of 31 (68%) cases were identified as TP while 10/31 (32%) were found to have PP. Genes involved in Maturity Onset Diabetes of Young (MODY), pregnane X receptor (PXR), retinoid X receptor (RXR) and complement system signaling pathways were significantly upregulated in PP (P<0.05). The top four networks constructed by IPA involved genes which play a role in humoral and cell-mediated immune and inflammatory responses. The top two networks place PI3K and AKT as molecular hub genes potentially important in our dataset of PP.

    Conclusions: Canonical pathways including MODY signaling, PXR, RXR and complement activation and genes important to biological networks involving immune function and inflammation are associated with pseudoprogression. Further evaluation and validation is currently underway.

    Patient Care: By identifying biomarkers of pseudoprogression, we can more appropriately treat patients with standard therapies and more precisely evaluate the efficacy of future therapies in clinical trials.

    Learning Objectives: To understand the phenomenon and clinical significance of pseudoprogression in treating patients with GBM and introduce potential molecular signatures for its identification.

    References: 1. Kruser TJ, Mehta MP, Robins HI (2013) Pseudoprogression after glioma therapy: a comprehensive review. Expert Rev Neurother 13:389–403

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