CIBRA identifies genomic alterations with a system-wide impact on tumor biology

• Published in: Bioinformatics (Oxford, England) England), 2024-09, Vol.40 (Supplement_2), p.ii37-ii44
• Main Authors: Lakbir, Soufyan, Buranelli, Caterina, Meijer, Gerrit A, Heringa, Jaap, Fijneman, Remond J A, Abeln, Sanne
• Format: Article
• Language: English
• Subjects: Availability; Biology; Biomarkers; Biomarkers, Tumor - genetics; Cancer; Computational Biology - methods; Genes; Genomes; Genomic Instability; Genomics; Genomics - methods; Humans; Metastases; Neoplasms - genetics; Neoplasms - metabolism; Oncogenes; Point mutation; Transcriptomics; Tumor suppressor genes; Tumors
• ISSN: 1367-4803; 1367-4811; 1367-4811
• DOI: 10.1093/bioinformatics/btae384

Abstract Motivation Genomic instability is a hallmark of cancer, leading to many somatic alterations. Identifying which alterations have a system-wide impact is a challenging task. Nevertheless, this is an essential first step for prioritizing potential biomarkers. We developed CIBRA (Computational Identification of Biologically Relevant Alterations), a method that determines the system-wide impact of genomic alterations on tumor biology by integrating two distinct omics data types: one indicating genomic alterations (e.g. genomics), and another defining a system-wide expression response (e.g. transcriptomics). CIBRA was evaluated with genome-wide screens in 33 cancer types using primary and metastatic cancer data from the Cancer Genome Atlas and Hartwig Medical Foundation. Results We demonstrate the capability of CIBRA by successfully confirming the impact of point mutations in experimentally validated oncogenes and tumor suppressor genes (0.79 AUC). Surprisingly, many genes affected by structural variants were identified to have a strong system-wide impact (30.3%), suggesting that their role in cancer development has thus far been largely under-reported. Additionally, CIBRA can identify impact with only 10 cases and controls, providing a novel way to prioritize genomic alterations with a prominent role in cancer biology. Our findings demonstrate that CIBRA can identify cancer drivers by combining genomics and transcriptomics data. Moreover, our work shows an unexpected substantial system-wide impact of structural variants in cancer. Hence, CIBRA has the potential to preselect and refine current definitions of genomic alterations to derive more nuanced biomarkers for diagnostics, disease progression, and treatment response. Availability and implementation The R package CIBRA is available at https://github.com/AIT4LIFE-UU/CIBRA.