ArticleBritish journal of cancer2018
Cancer subtype identification using somatic mutation data.
Article in British journal of cancer, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
- Integrating DNA Mutations and RNA Expression of Cancer Driver Genes in Asian Rare Cancers: A Pan-Cancer Analysis.JCO precision oncology · 2026Article
- Multimodal Impact of Number of Metastases and Genetic Alterations on Survival in Metastatic Non-Small Cell Lung Cancer.JCO precision oncology · 2026Article
- Clinical significance and functional characterization of RRN3 in gastric cancer: insights from pan-cancer analysis and experimental validation.Frontiers in oncology · 2026Article
- A computation-efficient network with feature aggregation for cancer subtype classification on histopathological images.Engineering applications of artificial intelligence · 2025Article
- A set cover algorithm identifies minimal circulating tumour DNA sequencing targets for colorectal cancer detection.Scientific reports · 2025Article
- VICatMix: variational Bayesian clustering and variable selection for discrete biomedical data.Bioinformatics advances · 2025Article
- PartIES: a disease subtyping framework with Partition-level Integration using diffusion-Enhanced Similarities from multi-omics Data.Briefings in bioinformatics · 2024Article
- Integrating somatic mutation profiles with structural deep clustering network for metabolic stratification in pancreatic cancer: a comprehensive analysis of prognostic and genomic landscapes.Briefings in bioinformatics · 2023Article
- Heterogeneity in the gene regulatory landscape of leiomyosarcoma.NAR cancer · 2023Article
- Structural underpinnings of mutation rate variations in the human genome.Nucleic acids research · 2023Article
- Smart Sensors and Microtechnologies in the Precision Medicine Approach against Lung Cancer.Pharmaceuticals (Basel, Switzerland) · 2023Review
- Comparison of cancer subtype identification methods combined with feature selection methods in omics data analysis.BioData mining · 2023Article
- Computational Methods Summarizing Mutational Patterns in Cancer: Promise and Limitations for Clinical Applications.Cancers · 2023Review
- The Network Zoo: a multilingual package for the inference and analysis of gene regulatory networks.Genome biology · 2023Article
- EpICC: A Bayesian neural network model with uncertainty correction for a more accurate classification of cancer.Scientific reports · 2022Article
- Pan-cancer integrative analysis of whole-genome De novo somatic point mutations reveals 17 cancer types.BMC bioinformatics · 2022Article
- Integrative analysis of mutated genes and mutational processes reveals novel mutational biomarkers in colorectal cancer.BMC bioinformatics · 2022Article
- Using an Improved Residual Network to Identify PIK3CA Mutation Status in Breast Cancer on Ultrasound Image.Frontiers in oncology · 2022Article
- Redox Metabolism-Associated Molecular Classification of Clear Cell Renal Cell Carcinoma.Oxidative medicine and cellular longevity · 2022Article
- Molecular Subtyping of Cancer Based on Robust Graph Neural Network and Multi-Omics Data Integration.Frontiers in genetics · 2022Article
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5 authors.
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Abstract
backgroundWith the onset of next-generation sequencing technologies, we have made great progress in identifying recurrent mutational drivers of cancer. As cancer tissues are now frequently screened for specific sets of mutations, a large amount of samples has become available for analysis. Classification of patients with similar mutation profiles may help identifying subgroups of patients who might benefit from specific types of treatment. However, classification based on somatic mutations is challenging due to the sparseness and heterogeneity of the data.
methodsHere we describe a new method to de-sparsify somatic mutation data using biological pathways. We applied this method to 23 cancer types from The Cancer Genome Atlas, including samples from 5805 primary tumours.
resultsWe show that, for most cancer types, de-sparsified mutation data associate with phenotypic data. We identify poor prognostic subtypes in three cancer types, which are associated with mutations in signal transduction pathways for which targeted treatment options are available. We identify subtype-drug associations for 14 additional subtypes. Finally, we perform a pan-cancer subtyping analysis and identify nine pan-cancer subtypes, which associate with mutations in four overarching sets of biological pathways.
conclusionsThis study is an important step toward understanding mutational patterns in cancer.
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