ArticleBMC medical genomics2019
Development of somatic mutation signatures for risk stratification and prognosis in lung and colorectal adenocarcinomas.
Article in BMC medical genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 15 citations in OpenAlex.
- Insights into treatment-specific prognostic somatic mutations in NSCLC from the AACR NSCLC GENIE BPC cohort analysis.BMC pulmonary medicine · 2024Article
- Mutational signatures for breast cancer diagnosis using artificial intelligence.Journal of the Egyptian National Cancer Institute · 2023Article
- Genetic predisposition to lung adenocarcinoma outcome is a feature already present in patients' noninvolved lung tissue.Cancer science · 2023Article
- Identification of mutational signature for lung adenocarcinoma prognosis and immunotherapy prediction.Journal of molecular medicine (Berlin, Germany) · 2022Article
- Computational Tactics for Precision Cancer Network Biology.International journal of molecular sciences · 2022Review
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- Identification of 6 Hub Proteins and Protein Risk Signature of Colorectal Cancer.BioMed research international · 2020Article
- The International Conference on Intelligent Biology and Medicine (ICIBM) 2018: genomics meets medicine.BMC medical genomics · 2019Article
- RNA sequencing identified novel target genes forScience progressArticle
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Authors and funding
6 authors at 4 institutions in 2 countries.
Funding
Abstract
backgroundPrognostic signatures are vital to precision medicine. However, development of somatic mutation prognostic signatures for cancers remains a challenge. In this study we developed a novel method for discovering somatic mutation based prognostic signatures.
resultsSomatic mutation and clinical data for lung adenocarcinoma (LUAD) and colorectal adenocarcinoma (COAD) from The Cancer Genome Atlas (TCGA) were randomly divided into training (n = 328 for LUAD and 286 for COAD) and validation (n = 167 for LUAD and 141 for COAD) datasets. A novel method of using the log2 ratio of the tumor mutation frequency to the paired normal mutation frequency is computed for each patient and missense mutation. The missense mutation ratios were mean aggregated into gene-level somatic mutation profiles. The somatic mutations were assessed using univariate Cox analysis on the LUAD and COAD training sets separately. Stepwise multivariate Cox analysis resulted in a final gene prognostic signature for LUAD and COAD. Performance was compared to gene prognostic signatures generated using the same pipeline but with different somatic mutation profile representations based on tumor mutation frequency, binary calls, and gene-gene network normalization. Signature high-risk LUAD and COAD cases had worse overall survival compared to the signature low-risk cases in the validation set (log-rank test p-value = 0.0101 for LUAD and 0.0314 for COAD) using mutation tumor frequency ratio (MFR) profiles, while all other methods, including gene-gene network normalization, have statistically insignificant stratification (log-rank test p-value ≥0.05). Most of the genes in the final gene signatures using MFR profiles are cancer-related based on network and literature analysis.
conclusionsWe demonstrated the robustness of MFR profiles and its potential to be a powerful prognostic tool in cancer. The results are robust according to validation testing and the selected genes are biologically relevant.
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