Correlations between genomic subgroup and clinical features in a cohort of more than 3000 meningiomas

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  • 1 Yale Program in Brain Tumor Research,
  • 2 Department of Neurosurgery,
  • 3 Department of Genetics, and
  • 22 Department of Neuroscience, Yale School of Medicine, New Haven, Connecticut;
  • 4 Department of Neurosurgery, University of Mississippi Medical Center, Jackson, Mississippi
  • 5 Section of Neurosurgery, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire
  • 6 Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
  • 7 The Third Xiangya Hospital, Central South University, Changsha, China
  • 8 Department of Neurosurgery, Acibadem Mehmet Ali Aydınlar University, School of Medicine, Istanbul, Turkey
  • 9 Department of Neurosurgery, Massachusetts General Hospital, Boston, Massachusetts
  • 10 Department of Neurosurgery, Hôpital Universitaire Pitié-Salpêtrière, AP-HP & Sorbonne Université, Paris, France
  • 11 Department of Neurosurgery, Barrow Neurological Institute, St. Joseph’s Hospital and Medical Center, Phoenix, Arizona
  • 12 Yale Center for Genome Analysis, Yale University West Campus, Orange, Connecticut
  • 13 Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
  • 14 Department of Medical Biology, BAU Faculty of Medicine, Istanbul, Turkey
  • 15 Department of Neurosurgery, Bahcesehir University, School of Medicine, Istanbul, Turkey
  • 16 University of Bonn Medical School, Bonn, Germany
  • 17 Center for Neurosurgery, University Hospital of Cologne, Germany
  • 18 Department of Neurosurgery, Marmara University School of Medicine, Istanbul, Turkey
  • 19 Department of Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
  • 20 Department of Pathology, Yale School of Medicine, New Haven, Connecticut and
  • 21 Department of Neurosurgery, Bethel Clinic, Bielefeld, Germany
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OBJECTIVE

Recent large-cohort sequencing studies have investigated the genomic landscape of meningiomas, identifying somatic coding alterations in NF2, SMARCB1, SMARCE1, TRAF7, KLF4, POLR2A, BAP1, and members of the PI3K and Hedgehog signaling pathways. Initial associations between clinical features and genomic subgroups have been described, including location, grade, and histology. However, further investigation using an expanded collection of samples is needed to confirm previous findings, as well as elucidate relationships not evident in smaller discovery cohorts.

METHODS

Targeted sequencing of established meningioma driver genes was performed on a multiinstitution cohort of 3016 meningiomas for classification into mutually exclusive subgroups. Relevant clinical information was collected for all available cases and correlated with genomic subgroup. Nominal variables were analyzed using Fisher’s exact tests, while ordinal and continuous variables were assessed using Kruskal-Wallis and 1-way ANOVA tests, respectively. Machine-learning approaches were used to predict genomic subgroup based on noninvasive clinical features.

RESULTS

Genomic subgroups were strongly associated with tumor locations, including correlation of HH tumors with midline location, and non-NF2 tumors in anterior skull base regions. NF2 meningiomas were significantly enriched in male patients, while KLF4 and POLR2A mutations were associated with female sex. Among histologies, the results confirmed previously identified relationships, and observed enrichment of microcystic features among “mutation unknown” samples. Additionally, KLF4-mutant meningiomas were associated with larger peritumoral brain edema, while SMARCB1 cases exhibited elevated Ki-67 index. Machine-learning methods revealed that observable, noninvasive patient features were largely predictive of each tumor’s underlying driver mutation.

CONCLUSIONS

Using a rigorous and comprehensive approach, this study expands previously described correlations between genomic drivers and clinical features, enhancing our understanding of meningioma pathogenesis, and laying further groundwork for the use of targeted therapies. Importantly, the authors found that noninvasive patient variables exhibited a moderate predictive value of underlying genomic subgroup, which could improve with additional training data. With continued development, this framework may enable selection of appropriate precision medications without the need for invasive sampling procedures.

ABBREVIATIONS OR = odds ratio; PPV = positive predictive value; PTBE = peritumoral brain edema.

Supplementary Materials

    • pdf Supplementary Tables 1–4 (PDF 789 KB)

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Contributor Notes

Correspondence Murat Günel: Yale School of Medicine, New Haven, CT. murat.gunel@yale.edu.

INCLUDE WHEN CITING Published online October 25, 2019; DOI: 10.3171/2019.8.JNS191266.

M.W.Y. and D.D. contributed equally to this work.

Disclosures The authors report no conflict of interest concerning the materials or methods used in this study or the findings specified in this paper.

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