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. 2018 Feb 15;9(22):16087-16098.
doi: 10.18632/oncotarget.24498. eCollection 2018 Mar 23.

A gene expression signature predicts recurrence-free survival in meningioma

Adriana Olar  1 , Lindsey D Goodman  2 , Khalida M Wani  3 , Nicholas S Boehling  4 , Devi S Sharma  5 , Reema R Mody  5 , Joy Gumin  6 , Elizabeth B Claus  7   8 , Frederick F Lang  6 , Timothy F Cloughesy  5 , Albert Lai  5 , Kenneth D Aldape  9 , Franco DeMonte  6 , Erik P Sulman  3   10
Affiliations

A gene expression signature predicts recurrence-free survival in meningioma

Adriana Olar et al. Oncotarget. .

Abstract

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.

Keywords: affymetrix; gene expression; meningioma; predictor algorithm; recurrence risk.

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Conflict of interest statement

CONFLICTS OF INTEREST Authors have no conflicts of interest.

Figures

Figure 1
Figure 1
Unsupervised hierarchical clustering with the initial filtered 393 probe sets in the training dataset (n=127) separates 2 differentially expressed groups of tumors. Each row represents a probe set and each column represents a sample. Expression values are shown after batch normalization (A). Kaplan–Meier survival analysis illustrates a trend for decreased tumor recurrence time for patients with meningioma from Group 1 and early survival curve separation (B).
Figure 2
Figure 2
The 18-GEP model applied to the training dataset (A) and then to the validation dataset (B) significantly separates risk groups for meningioma recurrence.
Figure 3
Figure 3
Unsupervised hierarchical clustering with the 18 model probe sets in the training dataset (n=127) (A) and in the validation dataset (B) shows similar patterns of gene expression. Each row represents a probe set and each column represents a sample. Expression values are shown after batch normalization.
See this image and copyright information in PMC

References

    1. Ostrom QT, Gittleman H, Liao P, Vecchione-Koval T, Wolinsky Y, Kruchko C, Barnholtz-Sloan JS. CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2010-2014. Neuro-oncol. 2017;19:v1–88. https://doi.org/10.1093/neuonc/nox158. - DOI - PMC - PubMed
    1. Louis DN, Ohgaki H, Wiestler OD, Cavenee WK, Ellison DW, Figarella-Branger D, Perry A, Raifenberger G, von Deimling A. WHO Classification of Tumours of the Central Nervous System. Lyon, France: IARC; 2016. - PubMed
    1. Benz LS, Wrensch MR, Schildkraut JM, Bondy ML, Warren JL, Wiemels JL, Claus EB. Quality of life after surgery for intracranial meningioma. Cancer. 2018;124:161–66. https://doi.org/10.1002/cncr.30975. - DOI - PMC - PubMed
    1. Pranckeviciene A, Tamasauskas S, Deltuva VP, Bunevicius R, Tamasauskas A, Bunevicius A. Suicidal ideation in patients undergoing brain tumor surgery: prevalence and risk factors. Support Care Cancer. 2016;24:2963–70. https://doi.org/10.1007/s00520-016-3117-2. - DOI - PubMed
    1. Brem SS, Bierman PJ, Black P, Blumenthal DT, Brem H, Chamberlain MC, Chiocca EA, DeAngelis LM, Fenstermaker RA, Fine HA, Friedman A, Glass J, Grossman SA, et al. National Comprehensive Cancer Network Central nervous system cancers: Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw. 2005;3:644–90. - PubMed