Distribution of copy number alterations and impact of chromosome arm call thresholds for meningioma

Ruchit V. Patel, Hia S. Ghosh, David M. Meredith, Scott Ryall, Elizabeth B. Claus, Rameen Beroukhim, Azra H. Ligon, Sandro Santagata and Wenya Linda Bi ()
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Ruchit V. Patel: Harvard Medical School
Hia S. Ghosh: Harvard Medical School
David M. Meredith: Harvard Medical School
Scott Ryall: Harvard Medical School
Elizabeth B. Claus: Harvard Medical School
Rameen Beroukhim: Harvard Medical School
Azra H. Ligon: Harvard Medical School
Sandro Santagata: Harvard Medical School
Wenya Linda Bi: Harvard Medical School

Nature Communications, 2025, vol. 16, issue 1, 1-13

Abstract: Abstract Chromosome-arm copy number alterations (CNAs) are an important component of cancer molecular classifiers. CNAs are often translated into binary chromosome arm calls (arm gain/loss) using an arm call threshold before integration into classification schemes. However, substantial variability exists in thresholds used to define arm calls from CNA data. Here, we analyze 1042 meningiomas with whole-genome microarray data and 13 meningiomas with multifocal sampling to characterize how CNA thresholds influence molecular classification and prognostication. Changing arm call thresholds shifts the association of chromosomal arm calls with meningioma recurrence in an arm-dependent manner and upgrades 21.5% of cases from low-grade to high-grade in a molecularly Integrated Grade (IG) scheme. The impact of threshold differences in IG prediction of recurrence is most evident amongst intermediate grade (IG-2) tumors and CNA call thresholds approaching whole-chromosome arm length (>95%). The designation of chromosome loss or gain remains stable across a majority of thresholds, although this varies in a chromosome-dependent manner. CNAs fluctuate among paired primary-recurrent tumors, mostly growing on recurrence, but cluster in discrete sizes within a tumor. Appreciation of the impact of chromosome arm call thresholds can help ensure robustness of molecular classification paradigms.

Date: 2025
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DOI: 10.1038/s41467-025-60734-0

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