ReviewFrontiers in genetics2017
Computational Methods for Characterizing Cancer Mutational Heterogeneity.
Review in Frontiers in genetics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Integrative spatial and genomic analysis of tumor heterogeneity with Tumoroscope.Nature communications · 2024Article
- A novel hypergraph model for identifying and prioritizing personalized drivers in cancer.PLoS computational biology · 2024Article
- A Bayesian framework to study tumor subclone-specific expression by combining bulk DNA and single-cell RNA sequencing data.Genome research · 2024Article
- LRT-CLUSTER: A New Clustering Algorithm Based on Likelihood Ratio Test to Identify Driving Genes.Interdisciplinary sciences, computational life sciences · 2023Article
- A network-based method for identifying cancer driver genes based on node control centrality.Experimental biology and medicine (Maywood, N.J.) · 2023Article
- Review
- Computational Approaches for the Investigation of Intra-tumor Heterogeneity and Clonal Evolution from Bulk Sequencing Data in Precision Oncology Applications.Advances in experimental medicine and biology · 2022Review
- The Architecture of a Precision Oncology Platform.Advances in experimental medicine and biology · 2022Review
- Computational methods for cancer driver discovery: A survey.Theranostics · 2021Review
- Power and pitfalls of computational methods for inferring clone phylogenies and mutation orders from bulk sequencing data.Scientific reports · 2020Article
- Modeling and analysis of site-specific mutations in cancer identifies known plus putative novel hotspots and bias due to contextual sequences.Computational and structural biotechnology journal · 2020Article
- HotSpotAnnotations-a database for hotspot mutations and annotations in cancer.Database : the journal of biological databases and curation · 2020Article
- Modelling cancer progression using Mutual Hazard Networks.Bioinformatics (Oxford, England) · 2020Article
- CBNA: A control theory based method for identifying coding and non-coding cancer drivers.PLoS computational biology · 2019Article
- Efficient algorithms to discover alterations with complementary functional association in cancer.PLoS computational biology · 2019Article
- Differentially mutated subnetworks discovery.Algorithms for molecular biology : AMB · 2019Article
- Predicting clone genotypes from tumor bulk sequencing of multiple samples.Bioinformatics (Oxford, England) · 2018Article
- The genomic landscape of cutaneous SCC reveals drivers and a novel azathioprine associated mutational signature.Nature communications · 2018Article
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Abstract
Advances in DNA sequencing technologies have allowed the characterization of somatic mutations in a large number of cancer genomes at an unprecedented level of detail, revealing the extreme genetic heterogeneity of cancer at two different levels: inter-tumor, with different patients of the same cancer type presenting different collections of somatic mutations, and intra-tumor, with different clones coexisting within the same tumor. Both inter-tumor and intra-tumor heterogeneity have crucial implications for clinical practices. Here, we review computational methods that use somatic alterations measured through next-generation DNA sequencing technologies for characterizing tumor heterogeneity and its association with clinical variables. We first review computational methods for studying inter-tumor heterogeneity, focusing on methods that attempt to summarize cancer heterogeneity by discovering pathways that are commonly mutated across different patients of the same cancer type. We then review computational methods for characterizing intra-tumor heterogeneity using information from bulk sequencing data or from single cell sequencing data. Finally, we present some of the recent computational methodologies that have been proposed to identify and assess the association between inter- or intra-tumor heterogeneity with clinical variables.
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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.