bioRxivCancer BiologyPreprint
Clustered structural variant hotspots enable oncogenic addiction and plasticity in osteosarcoma
Shreya Raman, Thorsten KaltenbacheriD, David MerrelliD, Kavya PrasadiD, Yutaro TanakaiD, Jakob WeissiD, Emily Iannetta, Byron Butaney, Emily Jorswieck, Amr Salem, Lorena Lazo de la VegaiD, Evelina Ceca, Julia MW WongiD, Suzanne J. ForrestiD, Kelly S. KlegaiD, Mohammad TanhaemamiiD, Cora A. RickeriD, Jinyu WangiD, Diane M. Diehl, Jeremy JohnsoniD, Carrie CibulskisiD, Kristy Schlueter-KuckiD, Liam Q. Alley, Natalie Bucheimer CollinsiD, Cheng‐Zhong ZhangiD, , Riaz GillaniiD
Abstract
The genomic landscape of osteosarcoma, the most common bone cancer worldwide, is among the most structurally complex of all human malignancies. The identification of recurrent and functionally consequential patterns has thus remained a challenge. Across 236 whole-genome sequencing osteosarcoma samples, we uncovered five genomic hotspots of clustered structural variation collectively altered in 58% of tumors. Four were associated with amplification of oncogenes ( MYC , CCND3 , CCNE1 , CDK4) , while the fifth mapped largely upstream of TP53 . Hotspot events showed coordinated patterns of co-occurrence and mutual exclusivity with each other and with tumor suppressor alterations, suggesting genomic context-specific selection. We found localized transcriptional dysregulation at hotspot event loci, and single cells harboring these events converged on a neural crest-like program, linking these structural alterations to a less differentiated cell state. These events were also detectable non-invasively through liquid biopsies and displayed ongoing structural evolution throughout disease progression, a finding with potential clinical utility. Our results provide novel insight into how complex rearrangements shape oncogenesis in osteosarcoma, with broader relevance to other cancers characterized by complex genomes.
- organism: human
- assay: whole-genome sequencing
- research focus: variant calling
- research focus: gene regulatory network inference
- research focus: drug response / drug discovery
- approach: statistical model
- approach: experimental assay
- Citations
- 0
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