Top

Published in:

01-09-2018 | Topic Review

Older studies can underestimate prognosis of glioblastoma biomarker in meta-analyses: a meta-epidemiological study for study-level effect in the current literature

Published in: Journal of Neuro-Oncology | Issue 2/2018

Abstract

Introduction

There are many potential biomarkers in glioblastoma (GBM), and meta-analyses represent the highest level of evidence when inferring their prognostic significance. It is possible however, that inherent design properties of the studies included in these meta-analyses may affect the pooled hazard ratio (HR) of the meta-analyses. This meta-epidemiological study aims to investigate the potential bias of three study-level properties in meta-analyses of GBM biomarkers currently published in the literature.

Methods

Seven electronic databases from inception to December 2017 were searched for meta-analyses evaluating different GBM biomarkers, which were screened against specific criteria. Study-level data were extracted from each meta-analysis, and analyzed using logistic regression to yield the ratio of HR (RHR) summary statistic.

Results

Nine meta-analyses investigating different GBM biomarkers were included. Of all the meta-analyses, the HRs of two studies were significantly underestimated by older studies; they investigated biomarkers IDH1 (RHR = 1.145; p = 0.017) and CD133 (RHR = 0.850; p = 0.013). Study-level size and design showed non-significant trends towards affecting the overall HR in all included meta-analyses.

Conclusions

This meta-epidemiological study demonstrated that study-level year can already significantly affect the pooled HR of GBM biomarkers reported by meta-analyses. It is possible that in the future, more study-level properties will exert significant effect. In terms of future GBM biomarker meta-analyses, special consideration of bias should be given to these study-level properties as potential sources of effect on the prognostic pooled HR.

Available only for authorised users

Sterne JA, Juni P, Schulz KF, Altman DG, Bartlett C, Egger M (2002) Statistical methods for assessing the influence of study characteristics on treatment effects in ‘meta-epidemiological’ research. Stat Med 21(11):1513–1524. https://doi.org/10.1002/sim.1184 CrossRefPubMed

Zhang Z, Xu X, Ni H (2013) Small studies may overestimate the effect sizes in critical care meta-analyses: a meta-epidemiological study. Crit Care 17(1):R2. https://doi.org/10.1186/cc11919 CrossRefPubMedPubMedCentral

Savovic J, Harris RJ, Wood L, Beynon R, Altman D, Als-Nielsen B, Balk EM, Deeks J, Gluud LL, Gluud C, Ioannidis JP, Juni P, Moher D, Pildal J, Schulz KF, Sterne JA (2010) Development of a combined database for meta-epidemiological research. Res Synth Methods 1(3–4):212–225. https://doi.org/10.1002/jrsm.18 CrossRefPubMed

Murad MH, Wang Z (2017) Guidelines for reporting meta-epidemiological methodology research. Evid-Based Med 22(4):139–142. https://doi.org/10.1136/ebmed-2017-110713 CrossRefPubMedPubMedCentral

Strimbu K, Tavel JA (2010) What are biomarkers? Curr Opin HIV AIDS 5(6):463–466. https://doi.org/10.1097/COH.0b013e32833ed177 CrossRefPubMedPubMedCentral

Thuy MN, Kam JK, Lee GC, Tao PL, Ling DQ, Cheng M, Goh SK, Papachristos AJ, Shukla L, Wall KL, Smoll NR, Jones JJ, Gikenye N, Soh B, Moffat B, Johnson N, Drummond KJ (2015) A novel literature-based approach to identify genetic and molecular predictors of survival in glioblastoma multiforme: analysis of 14,678 patients using systematic review and meta-analytical tools. J Clin Neurosci 22(5):785–799CrossRefPubMed

Moher D, Liberati A, Tetzlaff J, Althman D (2009) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med 6(7):e1000097. https://doi.org/10.1371/journal.pmed.1000097 CrossRefPubMedPubMedCentral

Zhang Z (2016) Meta-epidemiological study: a step by step approach by using R. J Evid-Based Med. https://doi.org/10.1111/jebm.12191 CrossRefPubMed

Oxman AD, Guyatt GH, Singer J, Goldsmith CH, Hutchison BG, Milner RA, Streiner DL (1991) Agreement among reviewers of review articles. J Clin Epidemiol 44(1):91–98CrossRefPubMed

10.

DerSimonian R, Laird N (1986) Meta-analysis in clinical trials. Controll Clin Trials 7(3):177–188CrossRef

11.

Binabaj MM, Bahrami A, ShahidSales S, Joodi M, Joudi Mashhad M, Hassanian SM, Anvari K, Avan A (2018) The prognostic value of MGMT promoter methylation in glioblastoma: a meta-analysis of clinical trials. J Cell Physiol 233(1):378–386CrossRefPubMed

12.

Chen JR, Xu HZ, Yao Y, Qin ZY (2015) Prognostic value of epidermal growth factor receptor amplification and EGFRvIII in glioblastoma: meta-analysis. Acta Neurol Scand 132(5):310–322CrossRefPubMed

13.

Dai Y, Ning X, Han G, Li W (2016) Assessment of the association between isocitrate dehydrogenase 1 mutation and mortality risk of glioblastoma patients. Mol Neurobiol 53(3):1501–1508CrossRefPubMed

14.

Huang T, Li S, Yang Z, Liu J, Han Y (2016) Loss of heterozygosity of 9p is associated with poorer survival in patients with gliomas. Mol Neurobiol 53(9):6407–6412. https://doi.org/10.1007/s12035-015-9523-5 CrossRefPubMed

15.

Levidou G, El-Habr E, Saetta AA, Bamias C, Katsougiannis K, Patsouris E, Korkolopoulou P (2010) P53 immunoexpression as a prognostic marker for human astrocytomas: a meta-analysis and review of the literature. J Neuro-Oncol 100(3):363–371. https://doi.org/10.1007/s11060-010-0204-y CrossRef

16.

Qin G, Li X, Chen Z, Liao G, Su Y, Chen Y, Zhang W (2017) Prognostic value of YKL-40 in patients with glioblastoma: a systematic review and meta-analysis. Mol Neurobiol 54(5):3264–3270. https://doi.org/10.1007/s12035-016-9878-2 CrossRefPubMed

17.

Xue S, Song G, Yu J (2017) The prognostic significance of PD-L1 expression in patients with glioma: a meta-analysis. Sci Rep 7(1):4231. https://doi.org/10.1038/s41598-017-04023-x CrossRefPubMedPubMedCentral

18.

Yuan Y, Qi C, Maling G, Xiang W, Yanhui L, Ruofei L, Yunhe M, Jiewen L, Qing M (2016) TERT mutation in glioma: frequency, prognosis and risk. J Clin Neurosci 26:57–62CrossRefPubMed

19.

Zhang W, Chen H, Lv S, Yang H (2016) High CD133 expression is associated with worse prognosis in patients with glioblastoma. Mol Neurobiol 53(4):2354–2360CrossRefPubMed

20.

Chen JR, Yao Y, Xu HZ, Qin ZY (2016) Isocitrate dehydrogenase (IDH)1/2 mutations as prognostic markers in patients with glioblastomas. Medicine 95(9):e2583CrossRefPubMedPubMedCentral

21.

Yan H, Parsons DW, Jin G, McLendon R, Rasheed BA, Yuan W, Kos I, Batinic-Haberle I, Jones S, Riggins GJ, Friedman H, Friedman A, Reardon D, Herndon J, Kinzler KW, Velculescu VE, Vogelstein B, Bigner DD (2009) IDH1 and IDH2 mutations in gliomas. N Engl J Med 360(8):765–773. https://doi.org/10.1056/NEJMoa0808710 CrossRefPubMedPubMedCentral

22.

Louis D, Ohgak H, Wiestler O, Cavenee W (2016) World Health Organization Histological Classification of Tumours of the Central Nervous System. International Agency for Research on Cancer, Lyon

23.

Arita H, Narita Y, Yoshida A, Hashimoto N, Yoshimine T, Ichimura K (2015) IDH1/2 mutation detection in gliomas. Brain Tumor Pathol 32(2):79–89. https://doi.org/10.1007/s10014-014-0197-x CrossRefPubMed

24.

Carmignani M, Volpe AR, Aldea M, Soritau O, Irimie A, Florian IS, Tomuleasa C, Baritchii A, Petrushev B, Crisan G, Valle G (2014) Glioblastoma stem cells: a new target for metformin and arsenic trioxide. J Biol Regul Homeost Agents 28(1):1–15PubMed

25.

Liu G, Yuan X, Zeng Z, Tunici P, Ng H, Abdulkadir IR, Lu L, Irvin D, Black KL, Yu JS (2006) Analysis of gene expression and chemoresistance of CD133 + cancer stem cells in glioblastoma. Mol Cancer 5:67. https://doi.org/10.1186/1476-4598-5-67 CrossRefPubMedPubMedCentral

26.

Egger M, Smith GD, Schneider M, Minder C (1997) Bias in meta-analysis detected by a simple, graphical test. BMJ 315(7109):629–634. https://doi.org/10.1136/bmj.315.7109.629 CrossRefPubMedPubMedCentral

27.

Sterne JAC, Sutton AJ, Ioannidis JPA, Terrin N, Jones DR, Lau J, Carpenter J, Rücker G, Harbord RM, Schmid CH, Tetzlaff J, Deeks JJ, Peters J, Macaskill P, Schwarzer G, Duval S, Altman DG, Moher D, Higgins JPT (2011) Recommendations for examining and interpreting funnel plot asymmetry in meta-analyses of randomised controlled trials. BMJ. https://doi.org/10.1136/bmj.d4002 PubMedCentralCrossRefPubMed

28.

Fan CH, Liu WL, Cao H, Wen C, Chen L, Jiang G (2013) O6-methylguanine DNA methyltransferase as a promising target for the treatment of temozolomide-resistant gliomas. Cell Death Dis 4:e876. https://doi.org/10.1038/cddis.2013.388 CrossRefPubMedPubMedCentral

29.

Zhu H, Zhang Y, Chen J, Qiu J, Huang K, Wu M, Xia C (2017) IDH1 R132H mutation enhances cell migration by activating AKT-mTOR signaling pathway, but sensitizes cells to 5-FU treatment as NADPH and GSH are reduced. PLoS ONE 12(1):e0169038. https://doi.org/10.1371/journal.pone.0169038 CrossRefPubMedPubMedCentral

30.

Stupp R, Mason WP, van den Bent MJ, Weller M, Fisher B, Taphoorn MJ, Belanger K, Brandes AA, Marosi C, Bogdahn U, Curschmann J, Janzer RC, Ludwin SK, Gorlia T, Allgeier A, Lacombe D, Cairncross JG, Eisenhauer E, Mirimanoff RO (2005) Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med 352(10):987–996. https://doi.org/10.1056/NEJMoa043330 CrossRefPubMed

Title: Older studies can underestimate prognosis of glioblastoma biomarker in meta-analyses: a meta-epidemiological study for study-level effect in the current literature
Publication date: 01-09-2018
Published in: Journal of Neuro-Oncology / Issue 2/2018
Print ISSN: 0167-594X
Electronic ISSN: 1573-7373
DOI: https://doi.org/10.1007/s11060-018-2897-2

Keynote webinar | Spotlight on sleep in brain health

Springer Medicine

Older studies can underestimate prognosis of glioblastoma biomarker in meta-analyses: a meta-epidemiological study for study-level effect in the current literature

Abstract

Introduction

Methods

Results

Conclusions

Keynote webinar | Spotlight on sleep in brain health

Springer Medicine

Abstract

Introduction

Methods

Results

Conclusions

Please log in to get access to this content

Other articles of this Issue 2/2018

Prognostic relevance of mutations and copy number alterations assessed with targeted next generation sequencing in IDH mutant grade II glioma

Management of glioblastoma at safety-net hospitals

Contrast enhancement predicting survival in integrated molecular subtypes of diffuse glioma: an observational cohort study

Virus vector-mediated genetic modification of brain tumor stromal cells after intravenous delivery

Prognostic value of Ki-67 index in adult medulloblastoma after accounting for molecular subgroup: a retrospective clinical and molecular analysis

GOLPH3 promotes glioma progression via facilitating JAK2–STAT3 pathway activation