Mutations in tumor signaling, metastases, and synthetic lethality establish distinct patterns

Effective identification of oncogenic mutations is essential for diagnosis, forecasting resistance, and metastasis in remission. It is required for an optimal drug regimen. We develop a framework to discover mutations that co-exist in different oncoproteins, and those that are excluded, likely encoding oncogene-induced senescence. First, mapping the proteins onto pathways assists combinatorial drug selections and helps to detect metastases. Second, it provides the molecular basis for synthetic lethality, to date investigated at the genome level. Our pan-cancer profiles of ~60,000 tumor sequences, detect 3424 co-existing tumor-specific mutations. Mapping them onto pathways indicates that they preferentially promote specific primary tumors. We uncover metastatic mutations and provide metastatic breast-cancer markers. This work not only clarifies the mechanistic basis of intratumor mutational diversity but usefully reveals markers for metastasis in patients' genomes and introduces a novel computational framework for detecting metastasis based on tumor mutational profiles. Mapping the mutations onto pathways provides an invaluable metastasis-targeting resource, guiding drug combinations.

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Publication Details

Journal
PLoS Computational Biology
Published
2025-08-04
DOI
https://doi.org/10.1371/journal.pcbi.1013351
Citations
2
Primary Topic
Bioinformatics and Genomic Networks
Type
article
Field-Weighted Citation Impact
1.61

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article

Mutations in tumor signaling, metastases, and synthetic lethality establish distinct patterns

Bengi Ruken Yavuz, Ruth Nussinov, Nurcan Tunçbağ, Uğur Şahin et al.
2 citations
PLoS Computational Biology
Bioinformatics and Genomic Networks
1.61
article

Mutations in tumor signaling, metastases, and synthetic lethality establish distinct patterns

Bengi Ruken Yavuz, Ruth Nussinov, Nurcan Tunçbağ, Uğur Şahin, Hyunbum Jang
article en
2 citations

Abstract

Effective identification of oncogenic mutations is essential for diagnosis, forecasting resistance, and metastasis in remission. It is required for an optimal drug regimen. We develop a framework to discover mutations that co-exist in different oncoproteins, and those that are excluded, likely encoding oncogene-induced senescence. First, mapping the proteins onto pathways assists combinatorial drug selections and helps to detect metastases. Second, it provides the molecular basis for synthetic lethality, to date investigated at the genome level. Our pan-cancer profiles of ~60,000 tumor sequences, detect 3424 co-existing tumor-specific mutations. Mapping them onto pathways indicates that they preferentially promote specific primary tumors. We uncover metastatic mutations and provide metastatic breast-cancer markers. This work not only clarifies the mechanistic basis of intratumor mutational diversity but usefully reveals markers for metastasis in patients' genomes and introduces a novel computational framework for detecting metastasis based on tumor mutational profiles. Mapping the mutations onto pathways provides an invaluable metastasis-targeting resource, guiding drug combinations.

PLoS Computational BiologyVol. 21(8)
Koç University (TR), Tel Aviv University (IL), Frederick National Laboratory for Cancer Research (US), National Cancer Institute (US)
Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, National Cancer Institute
Good health and well-being
Openalex Percentile: Top 16%
Bioinformatics and Genomic Networks
1.61
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Mutations in tumor signaling, metastases, and synthetic lethality establish distinct patterns — Bengi Ruken Yavuz, Ruth Nussinov, et al. · PLoS Computational Biology (2025) | TGRS Research Map | TGRS