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IMOP-癌症:识别影响癌症表型的突变顺序对。

IMOP-Cancer: identifying mutation order pairs impacting cancer phenotypes.

作者信息

Zhang Yijing, Kang Shaobo, Dou Renjie, Zhang Wanmei, Liu Yuanyuan, Wu Yang, Li Dongxue, Fan Fangfang, Ping Yanyan

机构信息

College of Bioinformatics Science and Technology, Harbin Medical University, 157 Baojian Road, Nangang District, Harbin, Heilongjiang Province, 150081, China.

出版信息

Brief Bioinform. 2025 Jul 2;26(4). doi: 10.1093/bib/bbaf362.

Abstract

Cancer development and progression are driven by the accumulation of somatic genetic alterations, which occur in a specific temporal order. However, how the order of mutations impacts cancer phenotypes of solid tumors remains poorly understood. To address this, we developed a novel computational framework, IMOP-Cancer (Identifying Mutation Order Pairs in Cancer), to identify mutation gene pairs whose order influences cancer phenotypes. We applied IMOP-Cancer to The Cancer Genome Atlas-Lung Adenocarcinoma (TCGA-LUAD) cohort and identified 446 key mutation order pairs, with 34 pairs significantly associated with prognosis. Mutation order impacts cancer phenotypes, as demonstrated by CSMD3 and PTPRD (tumor proliferation) and TP53 and NAV3 (immune modulation), with effects validated in four independent datasets. We further presented the impact of mutation pairs on cancer phenotypes through case studies in the TCGA cohorts of bladder urothelial carcinoma (BLCA), and colon adenocarcinoma (COAD). We extended this analysis to 33 cancer cohorts from TCGA portal, identifying 106 034 critical mutation pairs across 17 cancers, with 3036 pairs co-occurring in multiple cancers. Shared mutation pairs across cancers also showed distinct effects on cancer phenotype. Our study highlights the importance of mutation order in cancer progression and diversity, offering new insights into the temporal dynamics of co-occurring mutations and paving the way for personalized treatment strategies and improved diagnosis.

摘要

癌症的发生和发展是由体细胞基因改变的积累所驱动的,这些改变按特定的时间顺序发生。然而,突变顺序如何影响实体瘤的癌症表型仍知之甚少。为了解决这个问题,我们开发了一种新的计算框架IMOP-Cancer(识别癌症中的突变顺序对),以识别其顺序影响癌症表型的突变基因对。我们将IMOP-Cancer应用于癌症基因组图谱-肺腺癌(TCGA-LUAD)队列,识别出446个关键突变顺序对,其中34对与预后显著相关。突变顺序影响癌症表型,如CSMD3和PTPRD(肿瘤增殖)以及TP53和NAV3(免疫调节)所证明的,其作用在四个独立数据集中得到验证。我们通过对膀胱尿路上皮癌(BLCA)和结肠腺癌(COAD)的TCGA队列进行案例研究,进一步展示了突变对在癌症表型上的影响。我们将这种分析扩展到来自TCGA门户的33个癌症队列,在17种癌症中识别出106034个关键突变对,其中3036对在多种癌症中共同出现。跨癌症的共享突变对也对癌症表型显示出不同的影响。我们的研究强调了突变顺序在癌症进展和多样性中的重要性,为同时发生的突变的时间动态提供了新的见解,并为个性化治疗策略和改进诊断铺平了道路。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c469/12284763/90a212e495e9/bbaf362f1.jpg

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