Role of non-coding sequence variants in cancer

Khurana, Ekta; Fu, Yao; Chakravarty, Dimple; Demichelis, Francesca; Rubin, Mark A.; Gerstein, Mark

doi:10.1038/nrg.2015.17

Cited by 425 publications

(341 citation statements)

References 157 publications

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“…Besides the importance of variants in protein-coding regions, the majority of the alterations occur in noncoding portions of the genome [125]. Different noncoding sequence variants, ranging from single-nucleotide variants to small insertions and deletions, to large structural variants have been demonstrated in human cancer [125][126][127].…”

Section: Discussionmentioning

confidence: 99%

“…Different noncoding sequence variants, ranging from single-nucleotide variants to small insertions and deletions, to large structural variants have been demonstrated in human cancer [125][126][127]. Somatic variations can lead to gain of transcription factor-binding sites responsible of tumorigenesis.…”

Section: Discussionmentioning

confidence: 99%

“…Somatic variations can lead to gain of transcription factor-binding sites responsible of tumorigenesis. Fusion events because of genomic rearrangements, variations in noncoding RNAs and their binding sites, and variants in pseudogenes modulating gene expression have been linked to cancer [125]. Like somatic variants, germline noncoding affects gene expression through several mechanisms (e.g., promoter mutations, single-nucleotide polymorphisms in enhancers and noncoding RNAs and their binding sites, and variants in introns) [125].…”

Section: Discussionmentioning

confidence: 99%

“…Fusion events because of genomic rearrangements, variations in noncoding RNAs and their binding sites, and variants in pseudogenes modulating gene expression have been linked to cancer [125]. Like somatic variants, germline noncoding affects gene expression through several mechanisms (e.g., promoter mutations, single-nucleotide polymorphisms in enhancers and noncoding RNAs and their binding sites, and variants in introns) [125]. Recently, recurrent mutations have been found in the promoter of TBC1D12 and WDR74, as well as in the long noncoding RNAs MALAT1 and NEAT1 in breast cancer [108].…”

Section: Discussionmentioning

confidence: 99%

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Deciphering and Targeting Oncogenic Mutations and Pathways in Breast Cancer

et al. 2016

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Advances in DNA and RNA sequencing revealed substantially greater genomic complexity in breast cancer than simple models of a few driver mutations would suggest. Only very few, recurrent mutations or copy-number variations in cancer-causing genes have been identified. The two most common alterations in breast cancer are TP53 (affecting the majority of triple-negative breast cancers) and PIK3CA (affecting almost half of estrogen receptor-positive cancers) mutations, followed by a long tail of individually rare mutations affecting <1%–20% of cases. Each cancer harbors from a few dozen to a few hundred potentially high-functional impact somatic variants, along with a much larger number of potentially high-functional impact germline variants. It is likely that it is the combined effect of all genomic variations that drives the clinical behavior of a given cancer. Furthermore, entirely new classes of oncogenic events are being discovered in the noncoding areas of the genome and in noncoding RNA species driven by errors in RNA editing. In light of this complexity, it is not unexpected that, with the exception of HER2 amplification, no robust molecular predictors of benefit from targeted therapies have been identified. In this review, we summarize the current genomic portrait of breast cancer, focusing on genetic aberrations that are actively being targeted with investigational drugs.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Deciphering and Targeting Oncogenic Mutations and Pathways in Breast Cancer

et al. 2016

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“…However, not all genetic information is translated into proteins, and research into the role of non-coding regions in disease is coming under greater scrutiny. 13 Although non-coding RNA was initially disregarded as being data junk, it is now known to provide insight into a hidden layer of internal signalling pathways that orchestrate highly specific nucleic acid recognition and RNA modifications. 14, 15 Wang et al performed a meta-analysis on the impact of the long non-coding RNA SPRY4-IT1 on cancer prognosis.…”

Section: Sources Of Omics Data Genomicsmentioning

confidence: 99%

Application of omic technologies in cancer research

et al. 2018

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Understanding the biology of health and diseases such as cancer, generating insight into the triggers and potentiators of disease and the development of therapeutic approaches to counter and treat disease requires detailed interrogation of inherited genes, and the dynamic positioning of the transcriptome and proteome. In the last 10 years, significant technological developments and increases in sample throughput capabilities have led to a dramatic increase in the size and complexity of the datasets that can be generated. A key challenge now is to develop robust approaches for analysing and interpreting these, and converting data into biologically-and clinically-relevant information. Herein, we provide an overview of approaches for acquiring, integrating and interpreting complex datasets generated using multiple omic platforms, with a focus on the field of cancer research, and highlight key successful data handling and integration applications.

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cis ‐Regulatory Driver Mutations in Cancer Genomes

Poulos

Wong

2017

Encyclopedia of Life Sciences

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The increasing availability of large‐scale whole cancer genome sequencing data sets has enabled research efforts to focus on cancer driver mutations within the noncoding genome. cis ‐Regulatory elements are sites responsible for the control of gene expression. Mutations at such loci may modify transcription factor binding sites, disrupt enhancer–promoter interactions or affect epigenetic marks. cis ‐Regulatory driver mutations have been found in some cancer types to‐date, with initial discoveries of point mutations in the TERT promoter, small insertions creating an enhancer regulating TAL1 and genomic rearrangements leading to simultaneous enhancer dysregulation of EVI1 and GATA2 . However, recent discoveries have also revealed mechanisms responsible for the accumulation of recurrent, and even functional, somatic single‐nucleotide mutations in regulatory regions, wholly apart from selective pressure. Close scrutiny of candidate cis ‐regulatory mutations is necessary in order to effectively identify mutations that have undergone positive selection and to accurately distinguish driver from passenger mutations. Key Concepts Improvements in DNA sequencing technology, and the commencement of large‐scale cancer genome sequencing projects, have made discoveries in the noncoding genome possible. cis ‐Regulatory elements, such as promoters and enhancers, are responsible for the control of gene expression. Transcription factors bind to cis ‐regulatory elements, influencing the rate of transcription of DNA to mRNA. Mutations in cis ‐regulatory elements can drive cancer development by altering transcription factor binding sites, enhancer–promoter interactions or the epigenetic landscape of the cell. Alterations to cis ‐regulatory elements can lead to gene dysregulation, potentially impacting the expression of oncogenes or tumour suppressor genes. Recurrent somatic driver mutations affecting cis ‐regulatory elements have already been identified in cancer genomes, including point mutations, insertions and deletions (indels) and structural rearrangements. cis ‐Regulatory elements may appear recurrently mutated in cancer due to selective pressures or due to other mechanisms driving the formation of mutation hot spots. Transcription factor binding can inhibit access to nucleotide excision repair (NER) enzymes, leading to increased mutation rates at transcription factor binding sites in some cancers. DNA modifications and nucleotide composition can both influence the propensity for certain sites to become mutated. Close scrutiny of candidate cis ‐regulatory mutations is necessary in order to effectively identify sites under selective pressure and to accurately distinguish driver from passenger mutations.

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Role of non-coding sequence variants in cancer

Cited by 425 publications

References 157 publications

Deciphering and Targeting Oncogenic Mutations and Pathways in Breast Cancer

Deciphering and Targeting Oncogenic Mutations and Pathways in Breast Cancer

Application of omic technologies in cancer research

cis ‐Regulatory Driver Mutations in Cancer Genomes

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