DNA copy number motifs are strong and independent predictors of survival in breast cancer

• Published in: Communications biology Vol. 3; no. 1; p. 153
• Main Authors: Pladsen, Arne V., Nilsen, Gro, Rueda, Oscar M., Aure, Miriam R., Borgan, Ørnulf, Liestøl, Knut, Vitelli, Valeria, Frigessi, Arnoldo, Langerød, Anita, Mathelier, Anthony, Engebråten, Olav, Kristensen, Vessela, Wedge, David C., Van Loo, Peter, Caldas, Carlos, Børresen-Dale, Anne-Lise, Russnes, Hege G., Lingjærde, Ole Christian
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 02-04-2020; Nature Publishing Group
• Subjects: 38/39; 45/61; 631/114/2397; 631/67/1347; 631/67/1857; 631/67/69; 692/4028/67/1857; Algorithms; Biology; Biomarkers, Tumor - genetics; Biomedical and Life Sciences; Breast cancer; Breast Neoplasms - genetics; Breast Neoplasms - mortality; Breast Neoplasms - therapy; Clinical Decision-Making; Copy number; Databases, Genetic; DNA Copy Number Variations; Female; Gene Dosage; Gene Expression Profiling; Gene mapping; Genomes; Genomic Instability; Humans; Life Sciences; Medical prognosis; Middle Aged; Precision medicine; Predictive Value of Tests; Prognosis; Replication; Risk Assessment; Risk Factors; Transcriptome; Tumors
• ISSN: 2399-3642; 2399-3642
• DOI: 10.1038/s42003-020-0884-6

Somatic copy number alterations are a frequent sign of genome instability in cancer. A precise characterization of the genome architecture would reveal underlying instability mechanisms and provide an instrument for outcome prediction and treatment guidance. Here we show that the local spatial behavior of copy number profiles conveys important information about this architecture. Six filters were defined to characterize regional traits in copy number profiles, and the resulting Copy Aberration Regional Mapping Analysis (CARMA) algorithm was applied to tumors in four breast cancer cohorts (
n
 = 2919). The derived motifs represent a layer of information that complements established molecular classifications of breast cancer. A score reflecting presence or absence of motifs provided a highly significant independent prognostic predictor. Results were consistent between cohorts. The nonsite-specific occurrence of the detected patterns suggests that CARMA captures underlying replication and repair defects and could have a future potential in treatment stratification.
Pladsen et al. develop Copy Aberration Regional Mapping Analysis (CARMA), an algorithm that derives motifs for copy number profiles in breast cancers by integrating several features, to predict breast cancer prognosis and stratifications. Their algorithm can detect replication and repair defects and can be used in personalized medicine.