ArticleBriefings in bioinformatics2025
IMOP-Cancer: identifying mutation order pairs impacting cancer phenotypes.
Article in Briefings in bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
1 citing paper in PubMed.
- Is cancer the result of uncontrolled cellular growth? a glance into the tumorigenic process.Cancer cell international · 2026Review
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Authors and funding
9 authors.
Funding
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.
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Registered trials
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