Cancer3D: understanding cancer mutations through protein structures

Porta-Pardo, Eduard; Hrabe, Thomas; Godzik, Adam

doi:10.1093/nar/gku1140

Cited by 49 publications

(51 citation statements)

References 29 publications

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“…They are more limited in scope that Type I algorithms because they can only be applied to proteins whose three-dimensional structure is either known or can be reasonably predicted. While experimentally determined structures are available only for approximately 6100 human proteins, the structural coverage can be extended to over 13000 proteins 29 by aligning proteins to their close homologues with experimental structures (Supplementary Fig. 1).…”

Section: Resultsmentioning

confidence: 99%

Comparison of algorithms for the detection of cancer drivers at subgene resolution

et al. 2017

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Understanding genetic events that lead to cancer initiation and progression remains one of the biggest challenges in cancer biology. Traditionally most algorithms for cancer driver identification look for genes that have more mutations than expected from the average background mutation rate. However, there is now a wide variety of methods that look for non-random distribution of mutations within proteins as a signal they have a driving role in cancer. Here we classify and review the progress of such sub-gene resolution algorithms, compare their findings on four distinct cancer datasets from The Cancer Genome Atlas and discuss how predictions from these algorithms can be interpreted in the emerging paradigms that challenge the simple dichotomy between driver and passenger genes.

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Section: Resultsmentioning

confidence: 99%

Comparison of algorithms for the detection of cancer drivers at subgene resolution

et al. 2017

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“…Thus, such annotation databases will be useful to decipher the biological consequences between protein 3D structures and driver mutations. Cancer3D is a user-friendly database to analyze somatic missense mutations in the context of protein 3D structures [18]. Mosca et al developed dSysMap, a resource for the systematic mapping of disease-related missense mutations on the structurally annotated binary human interactome [19].…”

Section: Ngs Data Resourcesmentioning

confidence: 99%

Advances in computational approaches for prioritizing driver mutations and significantly mutated genes in cancer genomes

Cheng¹,

Zhao²,

Zhao³

2015

Brief Bioinform

133

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Cancer is often driven by the accumulation of genetic alterations, including single nucleotide variants, small insertions or deletions, gene fusions, copy-number variations, and large chromosomal rearrangements. Recent advances in nextgeneration sequencing technologies have helped investigators generate massive amounts of cancer genomic data and catalog somatic mutations in both common and rare cancer types. So far, the somatic mutation landscapes and signatures of >10 major cancer types have been reported; however, pinpointing driver mutations and cancer genes from millions of available cancer somatic mutations remains a monumental challenge. To tackle this important task, many methods and computational tools have been developed during the past several years and, thus, a review of its advances is urgently needed. Here, we first summarize the main features of these methods and tools for whole-exome, whole-genome and wholetranscriptome sequencing data. Then, we discuss major challenges like tumor intra-heterogeneity, tumor sample saturation and functionality of synonymous mutations in cancer, all of which may result in false-positive discoveries. Finally, we highlight new directions in studying regulatory roles of noncoding somatic mutations and quantitatively measuring circulating tumor DNA in cancer. This review may help investigators find an appropriate tool for detecting potential driver or actionable mutations in rapidly emerging precision cancer medicine.

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“…The Network of Cancer Genes (NCG) is a web‐based repository of systems‐level properties of cancer genes and collects information on 518 known (i.e., experimentally supported) and 1053 candidate (i.e., inferred using statistical methods) cancer genes. The Cancer3D database is focusing on the impact of missense somatic mutations on protein structure and helps users analyze distribution patterns of mutations as well as their association with changes in drug activity. It displays mutations from over 14,700 proteins mapped to more than 24,300 protein structures from the Protein Data Bank .…”

Section: Database Sources Of Cancer‐related Genesmentioning

confidence: 99%

Computational approaches for the identification of cancer genes and pathways

Dimitrakopoulos

Beerenwinkel

2016

WIREs Mechanisms of Disease

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High‐throughput DNA sequencing techniques enable large‐scale measurement of somatic mutations in tumors. Cancer genomics research aims at identifying all cancer‐related genes and solid interpretation of their contribution to cancer initiation and development. However, this venture is characterized by various challenges, such as the high number of neutral passenger mutations and the complexity of the biological networks affected by driver mutations. Based on biological pathway and network information, sophisticated computational methods have been developed to facilitate the detection of cancer driver mutations and pathways. They can be categorized into (1) methods using known pathways from public databases, (2) network‐based methods, and (3) methods learning cancer pathways de novo. Methods in the first two categories use and integrate different types of data, such as biological pathways, protein interaction networks, and gene expression measurements. The third category consists of de novo methods that detect combinatorial patterns of somatic mutations across tumor samples, such as mutual exclusivity and co‐occurrence. In this review, we discuss recent advances, current limitations, and future challenges of these approaches for detecting cancer genes and pathways. We also discuss the most important current resources of cancer‐related genes. WIREs Syst Biol Med 2017, 9:e1364. doi: 10.1002/wsbm.1364For further resources related to this article, please visit the WIREs website.

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Cancer3D: understanding cancer mutations through protein structures

Cited by 49 publications

References 29 publications

Comparison of algorithms for the detection of cancer drivers at subgene resolution

Comparison of algorithms for the detection of cancer drivers at subgene resolution

Advances in computational approaches for prioritizing driver mutations and significantly mutated genes in cancer genomes

Computational approaches for the identification of cancer genes and pathways

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