Abstract

The timing and fitness effect of somatic copy number alterations (SCNA) in cancer evolution remains poorly understood. Here we present a framework to determine the timing of a clonal SCNA that encompasses multiple gains. This involves calculating the proportion of time from its last gain to the onset of population expansion (lead time) as well as the proportion of time prior to its first gain (initiation time). Our method capitalizes on the observation that a genomic segment, while in a specific copy number (CN) state, accumulates point mutations proportionally to its CN. Analyzing 184 whole genome sequenced samples from 75 patients across five tumor types, we commonly observe late gains following early initiating events, occurring just before the clonal expansion relevant to the sampling. These include gains acquired after genome doubling in more than 60% of cases. Notably, mathematical modeling suggests that late clonal gains may contain final-expansion drivers. Lastly, SCNAs bolster mutational diversification between subpopulations, exacerbating the circle of proliferation and increasing heterogeneity.

Understanding the timing and fitness of somatic copy number alterations (SCNAs) in cancer would shed light on cancer progression and evolution. Here, the authors develop Butte, a computational framework to estimate the timing of clonal SCNAs that encompass multiple gains, and apply it on whole-genome sequencing data from 184 samples.

Details

Title
Evolving copy number gains promote tumor expansion and bolster mutational diversification
Author
Wang, Zicheng 1 ; Xia, Yunong 2 ; Mills, Lauren 3 ; Nikolakopoulos, Athanasios N. 2 ; Maeser, Nicole 2 ; Dehm, Scott M. 4   VIAFID ORCID Logo  ; Sheltzer, Jason M. 5   VIAFID ORCID Logo  ; Sun, Ruping 2   VIAFID ORCID Logo 

 University of Minnesota, Department of Laboratory Medicine and Pathology, Minneapolis, USA (GRID:grid.17635.36) (ISNI:0000 0004 1936 8657); University of Minnesota, Masonic Cancer Center, Minneapolis, USA (GRID:grid.17635.36) (ISNI:0000000419368657); The Chinese University of Hong Kong (CUHK-Shenzhen), School of Data Science, Shenzhen, China (GRID:grid.10784.3a) (ISNI:0000 0004 1937 0482) 
 University of Minnesota, Department of Laboratory Medicine and Pathology, Minneapolis, USA (GRID:grid.17635.36) (ISNI:0000 0004 1936 8657); University of Minnesota, Masonic Cancer Center, Minneapolis, USA (GRID:grid.17635.36) (ISNI:0000000419368657) 
 University of Minnesota, Department of Pediatrics, Minneapolis, USA (GRID:grid.17635.36) (ISNI:0000 0004 1936 8657) 
 University of Minnesota, Department of Laboratory Medicine and Pathology, Minneapolis, USA (GRID:grid.17635.36) (ISNI:0000 0004 1936 8657); University of Minnesota, Masonic Cancer Center, Minneapolis, USA (GRID:grid.17635.36) (ISNI:0000000419368657); University of Minnesota, Department of Urology, Minneapolis, USA (GRID:grid.17635.36) (ISNI:0000 0004 1936 8657) 
 Yale University, School of Medicine, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710) 
Pages
2025
Publication year
2024
Publication date
2024
Publisher
Nature Publishing Group
e-ISSN
20411723
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2938140624
Copyright
© The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.