Methylation and copy number profiling: emerging tools to differentiate osteoblastoma from malignant mimics?

Baptiste Ameline, Michaela Nathrath, Karolin H. Nord, Felix Haglund de Flon, Judith V.M.G. Bovée, Andreas H. Krieg, Sylvia Höller, Jürgen Hench, Daniel Baumhoer

Research output: Contribution to journalArticlepeer-review

Abstract

Rearrangements of the transcription factors FOS and FOSB have recently been identified as the genetic driver event underlying osteoid osteoma and osteoblastoma. Nuclear overexpression of FOS and FOSB have since then emerged as a reliable surrogate marker despite limitations in specificity and sensitivity. Indeed, osteosarcoma can infrequently show nuclear FOS expression and a small fraction of osteoblastomas seem to arise independent of FOS/FOSB rearrangements. Acid decalcification and tissue preservation are additional factors that can negatively influence immunohistochemical testing and make diagnostic decision-making challenging in individual cases. Particularly aggressive appearing osteoblastomas, also referred to as epithelioid osteoblastomas, and osteoblastoma-like osteosarcoma can be difficult to distinguish, underlining the need for additional markers to support the diagnosis. Methylation and copy number profiling, a technique well established for the classification of brain tumors, might fill this gap. Here, we set out to comprehensively characterize a series of 77 osteoblastomas by immunohistochemistry, fluorescence in-situ hybridization as well as copy number and methylation profiling and compared our findings to histologic mimics. Our results show that osteoblastomas are uniformly characterized by flat copy number profiles that can add certainty in reaching the correct diagnosis. The methylation cluster formed by osteoblastomas, however, so far lacks specificity and can be misleading in individual cases.

Original languageEnglish
Pages (from-to)1204-1211
JournalModern Pathology
Volume35
Issue number9
Early online date2022
DOIs
Publication statusPublished - 2022

Subject classification (UKÄ)

  • Medical Genetics

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