Oops, you're using an old version of your browser so some of the features on this page may not be displaying properly.

MINIMAL Requirements: Google Chrome 24+Mozilla Firefox 20+Internet Explorer 11Opera 15–18Apple Safari 7SeaMonkey 2.15-2.23

Poster Display session 3

3290 - Identification of meningioma patients in high risk of tumor recurrence using microRNA profiling

Date

30 Sep 2019

Session

Poster Display session 3

Topics

Translational Research

Tumour Site

Presenters

Josef Srovnal

Citation

Annals of Oncology (2019) 30 (suppl_5): v760-v796. 10.1093/annonc/mdz268

Authors

J. Srovnal1, H. Slavik1, V. Balik1, M. Vaverka2, L. Hrabalek2, K. Staffova1, J. Vrbkova1, M. Hajduch1

Author affiliations

  • 1 Faculty Of Medicine, Palacky University, 77900 - Olomouc/CZ
  • 2 Dpt. Of Neurosurgery, University Hospital Olomouc, Olomouc/CZ

Resources

Login to access the resources on OncologyPRO.

If you do not have an ESMO account, please create one for free.

Abstract 3290

Background

Meningioma growth rates are highly variable, even within benign subgroups, causing some cases to remain stable while others grow rapidly despite radiotherapy. Biomarkers that differentiate meningiomas by aggression and enable prediction of their biological behavior would therefore be clinically beneficial.

Methods

Microarrays were used to identify microRNA (miRNA) expression in primary recurrent, non-recurrent and secondary meningiomas of all grades. miRNAs found to be deregulated in the microarray experiments were validated by quantitative real-time PCR using samples from a cohort of 191 patients (median age 56). Statistical analysis of the resulting dataset revealed miRNA predictors of meningioma recurrence.

Results

miRNAs exhibiting differential expression (independently of histological grade) in primary recurrent, non-recurrent and secondary meningiomas were identified. The most effective predictive model included miR-331-3p, extent of tumor resection and its localization as predictive markers. The model with a recurrence probability cut-off of 28% and small number of the input data (7) had a high area under the curve (AUC) (0.829), sensitivity (75%), specificity (75%), and acceptable leave-one-out cross-validation (LOOCV) test error (23.2%). miR-18a-5p, miR-130b-3p, miR-146a-5p, miR-1271-5p, age at diagnosis, gender and histological grade showed to be supportive but not predictive factors in the tested models.

Conclusions

This model is a novel predictor of meningioma recurrence that could facilitate optimal postoperative management. Moreover, combining this model with information on the molecular processes underpinning recurrence could enable the identification of distinct meningioma subtypes and targeted therapies.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

The authors.

Funding

Ministry of Health of the Czech Republic (15-29021A); Palacky University Olomouc (LF 2019_003); Ministry of Education, Youth and Sports of the Czech Republic (LO1304, LM2015091); European Regional Development Fund (ENOCH CZ.02.1.01/0.0/0.0/16_019/0000868).

Disclosure

All authors have declared no conflicts of interest.

This site uses cookies. Some of these cookies are essential, while others help us improve your experience by providing insights into how the site is being used.

For more detailed information on the cookies we use, please check our Privacy Policy.

Customise settings
  • Necessary cookies enable core functionality. The website cannot function properly without these cookies, and you can only disable them by changing your browser preferences.