Copy number signature analysis improves our understanding of chromosomal aberrations in pediatric solid tumors

Abstract

Genomic instability & mutation are two hallmarks of cancer and present differentially across cancer types and patients (Negrini et al., 2010). Single base mutational signature analysis has already improved the understanding of cancer-causing mechanisms by disentangling mutational patterns and their underlying causes (Alexandrov et al., 2020). While genomic copy number data provides an additional view of the cancer genome, little is understood about copy number patterns. Copy number data can also be subject to mutational profiling and signature analysis (Macintyre et al., 2018; Steele et al., 2022; Tao et al., 2023). Several frameworks exist to classify copy number segments into profiles from which signatures are extracted. Of these, CN signatures seem the most promising (Steele et al., 2022). We aimed to investigate mutational mechanisms in pediatric cancer through CN signature analysis, whilst validating the method in pediatric data. We de novo extracted 10 copy number signatures from a cohort of 340 whole genome sequenced (WGS) pediatric solid tumors. These 10 signatures represent varying degrees of amplification, loss of heterozygosity (LOH), and homozygous deletion (HD). The various signatures present in our data can be further explored to allow for improved understanding of fundamental cancer-causing mechanisms and their pathogenicity, ultimately improving patient care. Furthermore, this work demonstrates that pediatric copy number signature analysis is promising but impeded by cohort size. We conclude that advancement of copy number signature frameworks is required to distinguish relevant cellular processes such as genomic and chromosomal instability in pediatric cancer cohorts.