Department of Theoretical Biophysics, Humboldt-University of Berlin, 10115 Berlin, Germany and Therapeutic Research Group Oncology, Bayer Pharma AG, 13353 Berlin, Germany.
Bioinformatics. 2013 Mar 15;29(6):671-7. doi: 10.1093/bioinformatics/btt028. Epub 2013 Jan 22.
Fusion genes result from genomic rearrangements, such as deletions, amplifications and translocations. Such rearrangements can also frequently be observed in cancer and have been postulated as driving event in cancer development. to detect them, one needs to analyze the transition region of two segments with different copy number, the location where fusions are known to occur. Finding fusion genes is essential to understanding cancer development and may lead to new therapeutic approaches.
Here we present a novel method, the Genomic Fusion Detection algorithm, to predict fusion genes on a genomic level based on SNP-array data. This algorithm detects genes at the transition region of segments with copy number variation. With the application of defined constraints, certain properties of the detected genes are evaluated to predict whether they may be fused. We evaluated our prediction by calculating the observed frequency of known fusions in both primary cancers and cell lines. We tested a set of cell lines positive for the BCR-ABL1 fusion and prostate cancers positive for the TMPRSS2-ERG fusion. We could detect the fusions in all positive cell lines, but not in the negative controls.
融合基因是由基因组重排产生的，如缺失、扩增和易位。这种重排也经常在癌症中观察到，并被认为是癌症发展的驱动事件。为了检测它们，需要分析两个具有不同拷贝数的片段的转换区，这是已知融合发生的位置。发现融合基因对于理解癌症的发展至关重要，并可能导致新的治疗方法。
在这里，我们提出了一种新的方法，即基因组融合检测算法，该算法基于 SNP 芯片数据在基因组水平上预测融合基因。该算法检测拷贝数变异片段转换区的基因。通过应用定义的约束条件，评估检测到的基因的某些特性，以预测它们是否可能融合。我们通过计算原发性癌症和细胞系中已知融合的观察频率来评估我们的预测。我们测试了一组 BCR-ABL1 融合阳性的细胞系和 TMPRSS2-ERG 融合阳性的前列腺癌。我们能够在所有阳性细胞系中检测到融合，但在阴性对照中没有检测到。
