  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
