| dc.description.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. | |
