A gene expression signature predicts recurrence-free survival in meningioma
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Adriana Olar1, Lindsey D. Goodman2, Khalida M. Wani3, Nicholas S. Boehling4, Devi S. Sharma5, Reema R. Mody5, Joy Gumin6, Elizabeth B. Claus7,8, Frederick F. Lang6, Timothy F. Cloughesy5, Albert Lai5, Kenneth D. Aldape9, Franco DeMonte6 and Erik P. Sulman3,10
1Medical University of South Carolina & Hollings Cancer Center, Departments of Pathology and Laboratory Medicine & Neurosurgery, Charleston, SC, USA
2Neurosciences Graduate Group, Perlman School of Medicine, University of Pennsylvania, Department of Biology, Philadelphia, PA, USA
3The University of Texas MD Anderson Cancer Center, Department of Translational Molecular Pathology, Houston, TX, USA
4St. Charles Cancer Center, Department of Radiation Oncology, Bend, OR, USA
5The University of California at Los Angeles, Department of Neurology, David Geffen School of Medicine, Los Angeles, CA, USA
6The University of Texas MD Anderson Cancer Center, Department of Neurosurgery, Houston, TX, USA
7Brigham and Women’s Hospital, Harvard Medical School, Department of Neurosurgery, Boston, MA, USA
8School of Public Health, Yale University, Department of Biostatistics, New Haven, CT, USA
9MacFeeters-Hamilton Brain Tumour Centre, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
10The University of Texas MD Anderson Cancer Center, Departments of Radiation Oncology and Genomic Medicine, Houston, TX, USA
Erik P. Sulman, email: firstname.lastname@example.org
Keywords: meningioma; gene expression; affymetrix; recurrence risk; predictor algorithm
Received: August 02, 2017 Accepted: February 01, 2018 Epub: February 15, 2018 Published: March 23, 2018
BACKGROUND: Meningioma is the most common primary brain tumor and has a variable risk of local recurrence. While World Health Organization (WHO) grade generally correlates with recurrence, there is substantial within-grade variation of recurrence risk. Current risk stratification does not accurately predict which patients are likely to benefit from adjuvant radiation therapy (RT). We hypothesized that tumors at risk for recurrence have unique gene expression profiles (GEP) that could better select patients for adjuvant RT.
METHODS:We developed a recurrence predictor by machine learning modeling using a training/validation approach.
RESULTS: Three publicly available AffymetrixU133 gene expression datasets (GSE9438, GSE16581, GSE43290) combining 127 primary, non-treated meningiomas of all grades served as the training set. Unsupervised variable selection was used to identify an 18-gene GEP model (18-GEP) that separated recurrences. This model was validated on 62 primary, non-treated cases with similar grade and clinical variable distribution as the training set. When applied to the validation set, 18-GEP separated recurrences with a misclassification error rate of 0.25 (log-rank p=0.0003). 18-GEP was predictive for tumor recurrence [p=0.0008, HR=4.61, 95%CI=1.89-11.23)] and was predictive after adjustment for WHO grade, mitotic index, sex, tumor location, and Simpson grade [p=0.0311, HR=9.28, 95%CI=(1.22-70.29)]. The expression signature included genes encoding proteins involved in normal embryonic development, cell proliferation, tumor growth and invasion (FGF9, SEMA3C, EDNRA), angiogenesis (angiopoietin-2), cell cycle regulation (CDKN1A), membrane signaling (tetraspanin-7, caveolin-2), WNT-pathway inhibitors (DKK3), complement system (C1QA) and neurotransmitter regulation (SLC1A3, Secretogranin-II).
CONCLUSIONS: 18-GEP accurately stratifies patients with meningioma by recurrence risk having the potential to guide the use of adjuvant RT.
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