Refinement of computational identification of somatic copy number alterations using DNA methylation microarrays illustrated in cancers of unknown primary.
Brief Bioinform
; 23(5)2022 09 20.
Article
en En
| MEDLINE
| ID: mdl-35524475
ABSTRACT
High-throughput genomic technologies are increasingly used in personalized cancer medicine. However, computational tools to maximize the use of scarce tissues combining distinct molecular layers are needed. Here we present a refined strategy, based on the R-package 'conumee', to better predict somatic copy number alterations (SCNA) from deoxyribonucleic acid (DNA) methylation arrays. Our approach, termed hereafter as 'conumee-KCN', improves SCNA prediction by incorporating tumor purity and dynamic thresholding. We trained our algorithm using paired DNA methylation and SNP Array 6.0 data from The Cancer Genome Atlas samples and confirmed its performance in cancer cell lines. Most importantly, the application of our approach in cancers of unknown primary identified amplified potentially actionable targets that were experimentally validated by Fluorescence in situ hybridization and immunostaining, reaching 100% specificity and 93.3% sensitivity.
Palabras clave
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Neoplasias Primarias Desconocidas
/
Variaciones en el Número de Copia de ADN
Tipo de estudio:
Diagnostic_studies
Límite:
Humans
Idioma:
En
Revista:
Brief Bioinform
Asunto de la revista:
BIOLOGIA
/
INFORMATICA MEDICA
Año:
2022
Tipo del documento:
Article
País de afiliación:
España