Mirzaei Golrokh
Department of Computer Science and Engineering, Ohio State University, Marion, OH 403302, USA.
Cancers (Basel). 2022 Jun 22;14(13):3060. doi: 10.3390/cancers14133060.
Chromosomal rearrangements are generally a consequence of improperly repaired double-strand breaks in DNA. These genomic aberrations can be a driver of cancers. Here, we investigated the use of chromosomal rearrangements for classification of cancer tumors and the effect of inter- and intrachromosomal rearrangements in cancer classification. We used data from the Catalogue of Somatic Mutations in Cancer (COSMIC) for breast, pancreatic, and prostate cancers, for which the COSMIC dataset reports the highest number of chromosomal aberrations. We developed a framework known as GraphChrom for cancer classification. GraphChrom was developed using a graph neural network which models the complex structure of chromosomal aberrations (CA) and provides local connectivity between the aberrations. The proposed framework illustrates three important contributions to the field of cancers. Firstly, it successfully classifies cancer types and subtypes. Secondly, it evolved into a novel data extraction technique which can be used to extract more informative graphs (informative aberrations associated with a sample); and thirdly, it predicts that interCAs (rearrangements between two or more chromosomes) are more effective in cancer prediction than intraCAs (rearrangements within the same chromosome), although intraCAs are three times more likely to occur than intraCAs.
染色体重排通常是DNA中双链断裂修复不当的结果。这些基因组畸变可能是癌症的驱动因素。在这里，我们研究了利用染色体重排对癌症肿瘤进行分类，以及染色体间和染色体内重排在癌症分类中的作用。我们使用了来自癌症体细胞突变目录（COSMIC）的乳腺癌、胰腺癌和前列腺癌数据，COSMIC数据集报告了这些癌症中数量最多的染色体畸变。我们开发了一个名为GraphChrom的癌症分类框架。GraphChrom是使用图神经网络开发的，该网络对染色体畸变（CA）的复杂结构进行建模，并提供畸变之间的局部连通性。所提出的框架展示了对癌症领域的三个重要贡献。首先，它成功地对癌症类型和亚型进行了分类。其次，它演变成了一种新颖的数据提取技术，可用于提取更多信息丰富的图（与样本相关的信息丰富的畸变）；第三，它预测染色体间重排（两条或多条染色体之间的重排）在癌症预测中比染色体内重排（同一染色体内的重排）更有效，尽管染色体内重排比染色体间重排发生的可能性高三倍。
