Evidence map›Paper›PMID 40698865›Full record

ArticleBriefings in bioinformatics2025

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

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

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Yijing ZhangCollege of Bioinformatics Science and Technology, Harbin Medical University, 157 Baojian Road, Nangang District, Harbin, Heilongjiang Province, 150081, China.
Shaobo KangCollege of Bioinformatics Science and Technology, Harbin Medical University, 157 Baojian Road, Nangang District, Harbin, Heilongjiang Province, 150081, China.
Renjie DouCollege of Bioinformatics Science and Technology, Harbin Medical University, 157 Baojian Road, Nangang District, Harbin, Heilongjiang Province, 150081, China.
Wanmei ZhangCollege of Bioinformatics Science and Technology, Harbin Medical University, 157 Baojian Road, Nangang District, Harbin, Heilongjiang Province, 150081, China.
Yuanyuan LiuCollege of Bioinformatics Science and Technology, Harbin Medical University, 157 Baojian Road, Nangang District, Harbin, Heilongjiang Province, 150081, China.
Yang WuCollege of Bioinformatics Science and Technology, Harbin Medical University, 157 Baojian Road, Nangang District, Harbin, Heilongjiang Province, 150081, China.
Dongxue LiCollege of Bioinformatics Science and Technology, Harbin Medical University, 157 Baojian Road, Nangang District, Harbin, Heilongjiang Province, 150081, China.
Fangfang FanCollege of Bioinformatics Science and Technology, Harbin Medical University, 157 Baojian Road, Nangang District, Harbin, Heilongjiang Province, 150081, China.
Yanyan PingCollege of Bioinformatics Science and Technology, Harbin Medical University, 157 Baojian Road, Nangang District, Harbin, Heilongjiang Province, 150081, China.ORCID 0000-0002-1810-1200

Funding

Heilongjiang Postdoctoral Scientific Research Developmental Fund LBH-Q20147National Natural Science Foundation of China 32170675
6 · The paper itself

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.

Indexed as

Computational BiologyMutationNeoplasmsAdenocarcinoma of LungHumansPhenotypePrognosiscancer phenotypesco-occurring mutationsmutation orderpan cancer analysis

Identifiers

PMID40698865
PMCPMC12284763

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.