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

TL;DR

CIBRA is a novel computational method that integrates genomics and transcriptomics data to identify genomic alterations with a system-wide impact on tumor biology, revealing underreported roles of structural variants in cancer.

Contribution

The paper introduces CIBRA, a new approach for prioritizing cancer-related genomic alterations by assessing their system-wide impact using multi-omics data integration.

Findings

CIBRA confirms known oncogene and tumor suppressor impacts.

Structural variants have a larger role in cancer than previously reported.

CIBRA can identify impactful alterations with as few as ten cases.

Abstract

Background: 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. 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 underreported. Additionally, CIBRA can identify impact with only ten cases and controls, providing a novel way to prioritize genomic alterations with a prominent role in cancer biology. Conclusions: 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. CIBRA is available at https://github.com/AIT4LIFE-UU/CIBRA