scholarly journals Global Autozygosity Is Associated with Cancer Risk, Mutational Signature and Prognosis

Cancers ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3646
Author(s):  
Limin Jiang ◽  
Fei Guo ◽  
Jijun Tang ◽  
Shuguan Leng ◽  
Scott Ness ◽  
...  
Keyword(s):  
Cancer Risk ◽  
Invasive Carcinoma ◽  
Cancer Risk Assessment ◽  
Mutational Signatures ◽  
Survival Analyses ◽  
Mutational Signature ◽  
Genome Wide ◽  
Cancer Types ◽  
Male Skin ◽  
Disease Specific

Global autozygosity quantifies the genome-wide levels of homozygous and heterozygous variants. It is the signature of non-random reproduction, though it can also be driven by other factors, and has been used to assess risk in various diseases. However, the association between global autozygosity and cancer risk has not been studied. From 4057 cancer subjects and 1668 healthy controls, we found strong associations between global autozygosity and risk in ten different cancer types. For example, the heterozygosity ratio was found to be significantly associated with breast invasive carcinoma in Blacks and with male skin cutaneous melanoma in Caucasians. We also discovered eleven associations between global autozygosity and mutational signatures which can explain a portion of the etiology. Furthermore, four significant associations for heterozygosity ratio were revealed in disease-specific survival analyses. This study demonstrates that global autozygosity is effective for cancer risk assessment.

scholarly journals Learning mutational signatures and their multidimensional genomic properties with TensorSignatures

2019 ◽  
Author(s):  
Harald Vöhringer ◽  
Arne van Hoeck ◽  
Edwin Cuppen ◽  
Moritz Gerstung
Keyword(s):  
Somatic Hypermutation ◽  
Translesion Synthesis ◽  
Strand Break ◽  
Signature Analysis ◽  
Transcription Start ◽  
Mutational Signatures ◽  
Transcription Start Sites ◽  
Mutational Signature ◽  
Cancer Genomes ◽  
Cancer Types

AbstractMutational signature analysis is an essential part of the cancer genome analysis toolkit. Conventionally, mutational signature analysis extracts patterns of different mutation types across many cancer genomes. Here we present TensorSignatures, an algorithm to learn mutational signatures jointly across all variant categories and their genomic context. The analysis of 2,778 primary and 3,824 metastatic cancer genomes of the PCAWG consortium and the HMF cohort shows that practically all signatures operate dynamically in response to various genomic and epigenomic states. The analysis pins differential spectra of UV mutagenesis found in active and inactive chromatin to global genome nucleotide excision repair. TensorSignatures accurately characterises transcription-associated mutagenesis, which is detected in 7 different cancer types. The analysis also unmasks replication- and double strand break repair-driven APOBEC mutagenesis, which manifests with differential numbers and length of mutation clusters indicating a differential processivity of the two triggers. As a fourth example, TensorSignatures detects a signature of somatic hypermutation generating highly clustered variants around the transcription start sites of active genes in lymphoid leukaemia, distinct from a more general and less clustered signature of Polη-driven translesion synthesis found in a broad range of cancer types.Key findingsSimultaneous inference of mutational signatures across mutation types and genomic features refines signature spectra and defines their genomic determinants.Analysis of 6,602 cancer genomes reveals pervasive intra-genomic variation of mutational processes.Distinct mutational signatures found in quiescent and active regions of the genome reveal differential repair and mutagenicity of UV- and tobacco-induced DNA damage.APOBEC mutagenesis produces two signatures reflecting highly clustered, double strand break repair-initiated and lowly clustered replication-driven mutagenesis, respectively.Somatic hypermutation in lymphoid cancers produces a strongly clustered mutational signature localised to transcription start sites, which is distinct from a weakly clustered translesion synthesis signature found in multiple tumour types.

scholarly journals Recessive effects in cancer predisposition exposed by genome-wide and proteome-wide association studies

2020 ◽  
Author(s):  
Nadav Brandes ◽  
Nathan Linial ◽  
Michal Linial
Keyword(s):  
Cancer Risk ◽  
Large Scale ◽  
Association Studies ◽  
Cancer Predisposition ◽  
Small Scale ◽  
Cancer Type ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Cancer Types ◽  
Genomic Regions

AbstractThe characterization of germline genetic variation affecting cancer risk, known as cancer predisposition, is fundamental to preventive and personalized medicine. Current attempts to detect cancer predisposition genomic regions are typically based on small-scale familial studies or genome-wide association studies (GWAS) over dedicated case-control cohorts. In this study, we utilized the UK Biobank as a large-scale prospective cohort to conduct a comprehensive analysis of cancer predisposition using both GWAS and proteome-wide association study (PWAS), a method that highlights genetic associations mediated by functional alterations to protein-coding genes. We discovered 137 unique genomic loci implicated with cancer risk in the white British population across nine cancer types and pan-cancer. While most of these genomic regions are supported by external evidence, our results highlight novel loci as well. We performed a comparative analysis of cancer predisposition between cancer types, finding that most of the implicated regions are cancer-type specific. We further analyzed the role of recessive genetic effects in cancer predisposition. We found that 30 of the 137 cancer regions were recovered only by a recessive model, highlighting the importance of recessive inheritance outside of familial studies. Finally, we show that many of the cancer associations exert substantial cancer risk in the studied cohort, suggesting their clinical relevance.

scholarly journals Heritable genetic variants in key cancer genes link cancer risk with anthropometric traits

2020 ◽  
pp. jmedgenet-2019-106799
Author(s):  
Matteo Di Giovannantonio ◽  
Benjamin HL Harris ◽  
Ping Zhang ◽  
Isaac Kitchen-Smith ◽  
Lingyun Xiong ◽  
...  
Keyword(s):  
Cancer Risk ◽  
Genetic Variants ◽  
Genome Wide Association Study ◽  
Strong Association ◽  
Tissue Type ◽  
Cancer Genes ◽  
Anthropometric Measures ◽  
Genome Wide ◽  
Increased Risk ◽  
Cancer Types

BackgroundHeight and other anthropometric measures are consistently found to associate with differential cancer risk. However, both genetic and mechanistic insights into these epidemiological associations are notably lacking. Conversely, inherited genetic variants in tumour suppressors and oncogenes increase cancer risk, but little is known about their influence on anthropometric traits.MethodsBy integrating inherited and somatic cancer genetic data from the Genome-Wide Association Study Catalog, expression Quantitative Trait Loci databases and the Cancer Gene Census, we identify SNPs that associate with different cancer types and differential gene expression in at least one tissue type, and explore the potential pleiotropic associations of these SNPs with anthropometric traits through SNP-wise association in a cohort of 500,000 individuals.ResultsWe identify three regulatory SNPs for three important cancer genes, FANCA, MAP3K1 and TP53 that associate with both anthropometric traits and cancer risk. Of particular interest, we identify a previously unrecognised strong association between the rs78378222[C] SNP in the 3' untranslated region (3'-UTR) of TP53 and both increased risk for developing non-melanomatous skin cancer (OR=1.36 (95% 1.31 to 1.41), adjusted p=7.62E−63), brain malignancy (OR=3.12 (2.22 to 4.37), adjusted p=1.43E−12) and increased standing height (adjusted p=2.18E−24, beta=0.073±0.007), lean body mass (adjusted p=8.34E−37, beta=0.073±0.005) and basal metabolic rate (adjusted p=1.13E−31, beta=0.076±0.006), thus offering a novel genetic link between these anthropometric traits and cancer risk.ConclusionOur results clearly demonstrate that heritable variants in key cancer genes can associate with both differential cancer risk and anthropometric traits in the general population, thereby lending support for a genetic basis for linking these human phenotypes.

scholarly journals MetaMutationalSigs: Comparison of mutational signature refitting results made easy

2021 ◽  
Author(s):  
Palash Pandey ◽  
Sanjeevani Arora ◽  
Gail Rosen
Keyword(s):  
Cancer Genetics ◽  
Research Question ◽  
Treatment Decision ◽  
Signature Analysis ◽  
Cancer Evolution ◽  
Mutational Signatures ◽  
Mutational Signature ◽  
Specific Research Question ◽  
Cancer Types ◽  
User Friendly

The analysis of mutational signatures is becoming increasingly common in cancer genetics, with emerging implications in cancer evolution, classification, treatment decision and prognosis. Recently, several packages have been developed for mutational signature analysis, with each using different methodology and yielding significantly different results. Because of the nontrivial differences in tools' refitting results, researchers may desire to survey and compare the available tools, in order to objectively evaluate the results for their specific research question, such as which mutational signatures are prevalent in different cancer types. There is a need for a software that can aggregate results from different refitting packages and present them in a user-friendly way to facilitate effective comparison of mutational signatures.

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