ArticlePacific Symposium on Biocomputing. Pacific Symposium on Biocomputing2015
Binning somatic mutations based on biological knowledge for predicting survival: an application in renal cell carcinoma.
Article in Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2015. 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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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.
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Who cites it
10 citing papers in PubMed, 17 citations in OpenAlex.
- The Role of Artificial Intelligence in the Diagnosis and Prognosis of Renal Cell Tumors.Diagnostics (Basel, Switzerland) · 2021Review
- Novel features and enhancements in BioBin, a tool for the biologically inspired binning and association analysis of rare variants.Bioinformatics (Oxford, England) · 2018Article
- Pathway Instability Is an Effective New Mutation-Based Type of Cancer Biomarkers.Frontiers in oncology · 2018Article
- Identifying subtype-specific associations between gene expression and DNA methylation profiles in breast cancer.BMC medical genomics · 2017Article
- Identification of epigenetic interactions between miRNA and DNA methylation associated with gene expression as potential prognostic markers in bladder cancer.BMC medical genomics · 2017Article
- Knowledge-driven binning approach for rare variant association analysis: application to neuroimaging biomarkers in Alzheimer's disease.BMC medical informatics and decision making · 2017Article
- Using knowledge-driven genomic interactions for multi-omics data analysis: metadimensional models for predicting clinical outcomes in ovarian carcinoma.Journal of the American Medical Informatics Association : JAMIA · 2017Article
- A biologically informed method for detecting rare variant associations.BioData mining · 2016Article
- miR-372 suppresses tumour proliferation and invasion by targeting IGF2BP1 in renal cell carcinoma.Cell proliferation · 2015Article
- Predicting censored survival data based on the interactions between meta-dimensional omics data in breast cancer.Journal of biomedical informatics · 2015Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Enormous efforts of whole exome and genome sequencing from hundreds to thousands of patients have provided the landscape of somatic genomic alterations in many cancer types to distinguish between driver mutations and passenger mutations. Driver mutations show strong associations with cancer clinical outcomes such as survival. However, due to the heterogeneity of tumors, somatic mutation profiles are exceptionally sparse whereas other types of genomic data such as miRNA or gene expression contain much more complete data for all genomic features with quantitative values measured in each patient. To overcome the extreme sparseness of somatic mutation profiles and allow for the discovery of combinations of somatic mutations that may predict cancer clinical outcomes, here we propose a new approach for binning somatic mutations based on existing biological knowledge. Through the analysis using renal cell carcinoma dataset from The Cancer Genome Atlas (TCGA), we identified combinations of somatic mutation burden based on pathways, protein families, evolutionary conversed regions, and regulatory regions associated with survival. Due to the nature of heterogeneity in cancer, using a binning strategy for somatic mutation profiles based on biological knowledge will be valuable for improved prognostic biomarkers and potentially for tailoring therapeutic strategies by identifying combinations of driver mutations.
Indexed as
Identifiers
25592572PMC4299944W2014090780What OpenQuestion holds
Registered trials
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.