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Interpretation of the Role of Germline and Somatic Non-coding Mutations in Cancer: Expression and Chromatin Conformation Informed Analysis

Overview
Publisher Biomed Central
Specialty Genetics
Date 2022 Sep 28
PMID 36171609
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Abstract

Background: There has been extensive scrutiny of cancer driving mutations within the exome (especially amino acid altering mutations) as these are more likely to have a clear impact on protein functions, and thus on cell biology. However, this has come at the neglect of systematic identification of regulatory (non-coding) variants, which have recently been identified as putative somatic drivers and key germline risk factors for cancer development. Comprehensive understanding of non-coding mutations requires understanding their role in the disruption of regulatory elements, which then disrupt key biological functions such as gene expression.

Main Body: We describe how advancements in sequencing technologies have led to the identification of a large number of non-coding mutations with uncharacterized biological significance. We summarize the strategies that have been developed to interpret and prioritize the biological mechanisms impacted by non-coding mutations, focusing on recent annotation of cancer non-coding variants utilizing chromatin states, eQTLs, and chromatin conformation data.

Conclusion: We believe that a better understanding of how to apply different regulatory data types into the study of non-coding mutations will enhance the discovery of novel mechanisms driving cancer.

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References
1.
Zhu H, Uuskula-Reimand L, Isaev K, Wadi L, Alizada A, Shuai S . Candidate Cancer Driver Mutations in Distal Regulatory Elements and Long-Range Chromatin Interaction Networks. Mol Cell. 2020; 77(6):1307-1321.e10. DOI: 10.1016/j.molcel.2019.12.027. View

2.
Pickrell J, Marioni J, Pai A, Degner J, Engelhardt B, Nkadori E . Understanding mechanisms underlying human gene expression variation with RNA sequencing. Nature. 2010; 464(7289):768-72. PMC: 3089435. DOI: 10.1038/nature08872. View

3.
Alexander R, Fang G, Rozowsky J, Snyder M, Gerstein M . Annotating non-coding regions of the genome. Nat Rev Genet. 2010; 11(8):559-71. DOI: 10.1038/nrg2814. View

4.
Nishizaki S, Boyle A . Mining the Unknown: Assigning Function to Noncoding Single Nucleotide Polymorphisms. Trends Genet. 2016; 33(1):34-45. PMC: 5553318. DOI: 10.1016/j.tig.2016.10.008. View

5.
Benner C, Spencer C, Havulinna A, Salomaa V, Ripatti S, Pirinen M . FINEMAP: efficient variable selection using summary data from genome-wide association studies. Bioinformatics. 2016; 32(10):1493-501. PMC: 4866522. DOI: 10.1093/bioinformatics/btw018. View