Bioinformatics Section, Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
Translational Gastrointestinal Oncology Group, Department of Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands.
Bioinformatics. 2024 Sep 1;40(Suppl 2):ii37-ii44. doi: 10.1093/bioinformatics/btae384.
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.
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.
The R package CIBRA is available at https://github.com/AIT4LIFE-UU/CIBRA.
基因组不稳定性是癌症的一个标志，导致许多体细胞改变。确定哪些改变具有系统范围的影响是一项具有挑战性的任务。然而，这是优先考虑潜在生物标志物的重要第一步。我们开发了 CIBRA（计算识别生物学相关改变），这是一种通过整合两种不同的组学数据类型来确定基因组改变对肿瘤生物学的系统范围影响的方法：一种表示基因组改变（例如基因组学），另一种定义系统范围的表达反应（例如转录组学）。CIBRA 使用来自癌症基因组图谱和哈特威格医学基金会的原发性和转移性癌症数据，在 33 种癌症类型的全基因组筛选中进行了评估。
我们通过成功证实实验验证的致癌基因和肿瘤抑制基因中的点突变的影响（0.79 AUC）来证明 CIBRA 的能力。令人惊讶的是，许多受结构变异影响的基因被确定具有很强的系统范围影响（30.3%），这表明它们在癌症发展中的作用迄今为止在很大程度上被低估了。此外，CIBRA 仅用 10 个病例和对照就可以识别影响，为优先考虑在癌症生物学中具有突出作用的基因组改变提供了一种新方法。我们的研究结果表明，CIBRA 可以通过结合基因组和转录组数据来识别癌症驱动基因。此外，我们的工作表明，结构变异在癌症中具有出乎意料的大量系统范围的影响。因此，CIBRA 有可能预选和完善当前对基因组改变的定义，为诊断、疾病进展和治疗反应得出更细致的生物标志物。
CIBRA 的 R 包可在 https://github.com/AIT4LIFE-UU/CIBRA 上获得。
