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      Computational Methods for Characterizing Cancer Mutational Heterogeneity

      Frontiers in Genetics

      Frontiers Media S.A.

      cancer heterogeneity, mutations, cancer pathways, mutual exclusivity, clinical association

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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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          Most cited references 133

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          Hallmarks of Cancer: The Next Generation

          The hallmarks of cancer comprise six biological capabilities acquired during the multistep development of human tumors. The hallmarks constitute an organizing principle for rationalizing the complexities of neoplastic disease. They include sustaining proliferative signaling, evading growth suppressors, resisting cell death, enabling replicative immortality, inducing angiogenesis, and activating invasion and metastasis. Underlying these hallmarks are genome instability, which generates the genetic diversity that expedites their acquisition, and inflammation, which fosters multiple hallmark functions. Conceptual progress in the last decade has added two emerging hallmarks of potential generality to this list-reprogramming of energy metabolism and evading immune destruction. In addition to cancer cells, tumors exhibit another dimension of complexity: they contain a repertoire of recruited, ostensibly normal cells that contribute to the acquisition of hallmark traits by creating the "tumor microenvironment." Recognition of the widespread applicability of these concepts will increasingly affect the development of new means to treat human cancer. Copyright © 2011 Elsevier Inc. All rights reserved.
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            Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

            DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.
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              Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

              Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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                Author and article information

                Contributors
                Journal
                Front Genet
                Front Genet
                Front. Genet.
                Frontiers in Genetics
                Frontiers Media S.A.
                1664-8021
                14 June 2017
                2017
                : 8
                Affiliations
                Department of Information Engineering, University of Padova Padova, Italy
                Author notes

                Edited by: Rosalba Giugno, University of Verona, Italy

                Reviewed by: Luciano Cascione, Institute of Oncology Research, Switzerland; Matteo D'Antonio, University of California, San Diego, United States; Faraz Hach, University of British Columbia, Canada

                *Correspondence: Fabio Vandin fabio.vandin@ 123456unipd.it

                This article was submitted to Bioinformatics and Computational Biology, a section of the journal Frontiers in Genetics

                Article
                10.3389/fgene.2017.00083
                5469877
                Copyright © 2017 Vandin.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                Page count
                Figures: 5, Tables: 0, Equations: 0, References: 133, Pages: 12, Words: 10740
                Funding
                Funded by: National Science Foundation 10.13039/100000001
                Award ID: IIS-1247581
                Award ID: MIUR of Italy
                Award ID: AMANDA
                Categories
                Genetics
                Review

                Genetics

                clinical association, mutual exclusivity, cancer pathways, mutations, cancer heterogeneity

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