Unscrambling cancer genomes via integrated analysis of structural variation and copy number

Charles Shale; Daniel L Cameron; Jonathan Baber; Marie Wong; Mark J Cowley; Anthony T Papenfuss; Edwin Cuppen; Peter Priestley

doi:10.1016/j.xgen.2022.100112

Unscrambling cancer genomes via integrated analysis of structural variation and copy number

Cell Genom. 2022 Mar 22;2(4):100112. doi: 10.1016/j.xgen.2022.100112. eCollection 2022 Apr 13.

Authors

Charles Shale^{1

2}, Daniel L Cameron^{1

3

4}, Jonathan Baber^{1

2}, Marie Wong^{5

6}, Mark J Cowley^{5

6}, Anthony T Papenfuss^{3

4

7

8}, Edwin Cuppen^{2

9}, Peter Priestley^{1

2}

Affiliations

¹ Hartwig Medical Foundation Australia, Sydney, NSW, Australia.
² Hartwig Medical Foundation, Science Park 408, Amsterdam, the Netherlands.
³ Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia.
⁴ Department of Medical Biology, University of Melbourne, Melbourne, VIC, Australia.
⁵ Children's Cancer Institute, Lowy Cancer Centre, UNSW Sydney, Kensington, NSW, Australia.
⁶ School of Women's and Children's Health, UNSW Sydney, Kensington, NSW, Australia.
⁷ Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.
⁸ Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia.
⁹ Center for Molecular Medicine and Oncode Institute, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, the Netherlands.

Abstract

Complex somatic genomic rearrangements and copy number alterations are hallmarks of nearly all cancers. We have developed an algorithm, LINX, to aid interpretation of structural variant and copy number data derived from short-read, whole-genome sequencing. LINX classifies raw structural variant calls into distinct events and predicts their effect on the local structure of the derivative chromosome and the functional impact on affected genes. Visualizations facilitate further investigation of complex rearrangements. LINX allows insights into a diverse range of structural variation events and can reliably detect pathogenic rearrangements, including gene fusions, immunoglobulin enhancer rearrangements, intragenic deletions, and duplications. Uniquely, LINX also predicts chained fusions that we demonstrate account for 13% of clinically relevant oncogenic fusions. LINX also reports a class of inactivation events that we term homozygous disruptions that may be a driver mutation in up to 9% of tumors and may frequently affect PTEN, TP53, and RB1.

Keywords: LINX; cancer genomics; ecDNA; gene fusion; genomic rearrangement; genomic shard; homozygous disruption; mobile element insertion; reciprocal duplication; structural variation.