Mutational Signatures Are Critical for Proper Estimation of Purifying Selection Pressures in Cancer Somatic Mutation Data When Using the dN/dS Metric

ABSTRACTMutation accumulation over time in normal somatic cells contributes to cancer development and is proposed as a cause of ageing. DNA polymerases Pol ε and Pol δ replicate DNA with high fidelity during normal cell divisions. However, in some cancers defective proofreading due to acquired mutations in the exonuclease domains of POLE or POLD1 causes markedly elevated somatic mutation burdens with distinctive mutational signatures. POLE and POLD1 exonuclease domain mutations also cause familial cancer predisposition when inherited through the germline. Here, we sequenced normal tissue DNA from individuals with germline POLE or POLD1 exonuclease domain mutations. Increased mutation burdens with characteristic mutational signatures were found to varying extents in all normal adult somatic cell types examined, during early embryogenesis and in sperm. Mutation burdens were further markedly elevated in neoplasms from these individuals. Thus human physiology is able to tolerate ubiquitously elevated mutation burdens. Indeed, with the exception of early onset cancer, individuals with germline POLE and POLD1 exonuclease domain mutations are not reported to show abnormal phenotypic features, including those of premature ageing. The results, therefore, do not support a simple model in which all features of ageing are attributable to widespread cell malfunction directly resulting from somatic mutation burdens accrued during life.

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Analysis of multi-omics differences in left-side and right-side colon cancer

PeerJ ◽

10.7717/peerj.11433 ◽

2021 ◽

Vol 9 ◽

pp. e11433

Author(s):

Yanyi Huang ◽

Jinzhong Duanmu ◽

Yushu Liu ◽

Mengyun Yan ◽

Taiyuan Li ◽

...

Keyword(s):

Colon Cancer ◽

Cancer Patients ◽

Gene Mutation ◽

Somatic Mutation ◽

Transcriptome Data ◽

Characteristic Analysis ◽

Immune Microenvironment ◽

Mutation Data ◽

Upregulated Genes ◽

Testing Set

Background Colon cancer is one of the most common tumors in the digestive tract. Studies of left-side colon cancer (LCC) and right-side colon cancer (RCC) show that these two subtypes have different prognoses, outcomes, and clinical responses to chemotherapy. Therefore, a better understanding of the importance of the clinical classifications of the anatomic subtypes of colon cancer is needed. Methods We collected colon cancer patients’ transcriptome data, clinical information, and somatic mutation data from the Cancer Genome Atlas (TCGA) database portal. The transcriptome data were taken from 390 colon cancer patients (172 LCC samples and 218 RCC samples); the somatic mutation data included 142 LCC samples and 187 RCC samples. We compared the expression and prognostic differences of LCC and RCC by conducting a multi-omics analysis of each using the clinical characteristics, immune microenvironment, transcriptomic differences, and mutation differences. The prognostic signatures was validated using the internal testing set, complete set, and external testing set (GSE39582). We also verified the independent prognostic value of the signature. Results The results of our clinical characteristic analysis showed that RCC had a significantly worse prognosis than LCC. The analysis of the immune microenvironment showed that immune infiltration was more common in RCC than LCC. The results of differential gene analysis showed that there were 360 differentially expressed genes, with 142 upregulated genes in LCC and 218 upregulated genes in RCC. The mutation frequency of RCC was generally higher than that of LCC. BRAF and KRAS gene mutations were the dominant genes mutations in RCC, and they had a strong mutual exclusion with APC, while APC gene mutation was the dominant gene mutation in LCC. This suggests that the molecular mechanisms of RCC and LCC differed. The 4-mRNA and 6-mRNA in the prognostic signatures of LCC and RCC, respectively, were highly predictive and may be used as independent prognostic factors. Conclusion The clinical classification of the anatomic subtypes of colon cancer is of great significance for early diagnosis and prognostic risk assessment. Our study provides directions for individualized treatment of left and right colon cancer.

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Predicting Cancer Tissue-of-Origin by a Machine Learning Method Using DNA Somatic Mutation Data

Frontiers in Genetics ◽

10.3389/fgene.2020.00674 ◽

2020 ◽

Vol 11 ◽

Author(s):

Xiaojun Liu ◽

Lianxing Li ◽

Lihong Peng ◽

Bo Wang ◽

Jidong Lang ◽

...

Keyword(s):

Machine Learning ◽

Somatic Mutation ◽

Cancer Tissue ◽

Machine Learning Method ◽

Learning Method ◽

Tissue Of Origin ◽

Mutation Data

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Identification, incidence and clinical outcomes of patients (pts) with hypermutated prostate cancer (PC).

Journal of Clinical Oncology ◽

10.1200/jco.2019.37.15_suppl.5072 ◽

2019 ◽

Vol 37 (15_suppl) ◽

pp. 5072-5072

Author(s):

Simon Yuen Fai Fu ◽

Elie Ritch ◽

Cameron Herberts ◽

Steven Yip ◽

Daniel Khalaf ◽

...

Keyword(s):

Prostate Cancer ◽

Median Time ◽

Somatic Mutation ◽

Clinical Data ◽

Castration Resistant Prostate Cancer ◽

Control Cohort ◽

Mutational Signatures ◽

Whole Exome ◽

Mutation Burden ◽

Homozygous Deletions

5072 Background: A small proportion of metastatic PC exhibit outlier somatic mutation (mut) rates exceeding the average of 4.4 mut/Mb. The incidence, clinical course and treatment response of pts with hypermutation (HM) is poorly characterised. Methods: We performed targeted sequencing from a panel of PC genes using plasma cell-free DNA samples collected from metastatic castration-resistant prostate cancer (mCRPC) pts and calculated somatic mutation burden. HM samples were additionally subjected to whole exome sequencing to determine trinucleotide mutational signatures and microsatellite instability (MSI). Clinical data was retrospectively collected and compared to a control cohort of 199 mCRPC pts. Results: 671 samples from 434 pts had ctDNA > 2% and were evaluable. 32 samples from 24 pts had > 11 mut/Mb and fell above the 95th percentile for mutation burden with a median mutation burden of 34 mut/Mb. 11 pts had deleterious mutations or homozygous deletions in mismatch repair (MMR) genes and 4 further pts had evidence of MMR deficiency (MMRd) from mutational signatures and MSI status. The remaining 9 pts had either BRCA2 mutations (n = 4), Kataegis (localized hypermutation, n = 3), or undefined causes for HM (n = 2). The incidence of MMRd was 3.5% (15/434), and germline MMRd was 0.2% (1/434). For MMRd pts with available clinical data (10/15) at diagnosis, the median age was 73.6 y, 70% had Gleason score ≥8, and 50% presented with M1 disease. Comparing the MMRd with the control cohort, median time from ADT to CRPC was 9.1 m (95% CI 6.9–11.4) vs. 18.2 m (95% CI 15.1–21.3), p = 0.001; median time from CRPC to death was 13.1 m (95% CI 0.3–25.9) vs. 40.1 m (95% CI 32.4–47.8), p < 0.001. Conclusions: HM and MMRd can be identified using liquid biopsy and could help to select pts for immunotherapy.

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Integrating Protein–Protein Interaction Networks and Somatic Mutation Data to Detect Driver Modules in Pan-Cancer

Interdisciplinary Sciences Computational Life Sciences ◽

10.1007/s12539-021-00475-y ◽

2021 ◽

Author(s):

Hao Wu ◽

Zhongli Chen ◽

Yingfu Wu ◽

Hongming Zhang ◽

Quanzhong Liu

Keyword(s):

Somatic Mutation ◽

Protein Interaction ◽

Protein Interaction Networks ◽

Interaction Networks ◽

Protein Protein Interaction ◽

Protein Protein Interaction Networks ◽

Mutation Data ◽

Pan Cancer

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Mutational signatures and heterogeneous host response revealed via large-scale characterization of SARS-CoV-2 genomic diversity

10.1101/2020.07.06.189944 ◽

2020 ◽

Cited By ~ 3

Author(s):

Alex Graudenzi ◽

Davide Maspero ◽

Fabrizio Angaroni ◽

Rocco Piazza ◽

Daniele Ramazzotti

Keyword(s):

Large Scale ◽

Purifying Selection ◽

Genomic Diversity ◽

Nucleotide Substitutions ◽

Mutational Signatures ◽

Mutational Hotspots ◽

Scale Characterization ◽

Definition Of ◽

Mutational Processes

AbstractTo dissect the mechanisms underlying the inflation of variants in the SARS-CoV-2 genome, we present one of the largest up-to-date analyses of intra-host genomic diversity, which reveals that most samples present heterogeneous genomic architectures, due to the interplay between host-related mutational processes and transmission dynamics.The deconvolution of the set of intra-host minor variants unveils the existence of non overlapping mutational signatures related to specific nucleotide substitutions, which prove that distinct hosts respond differently to SARS-CoV-2 infections, and which are likely ruled by APOBEC, Reactive Oxygen Species (ROS) and ADAR.Thanks to a corrected-for-signatures dN/dS analysis we demonstrate that the mutational processes underlying such signatures are affected by purifying selection, with important exceptions. In fact, several mutations linked to low-rate mutational processes appear to transit to clonality in the population, eventually leading to the definition of new clonal genotypes and to a statistically significant increase of overall genomic diversity.Importantly, the analysis of the phylogenetic model shows the presence of multiple homoplasies, due to mutational hotspots, phantom mutations or positive selection, and supports the hypothesis of transmission of minor variants during infections. Overall, the results of this study pave the way for the integrated characterization of intra-host genomic diversity and clinical outcome of SARS-CoV-2 hosts.

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Multivariate association analysis with somatic mutation data

Biometrics ◽

10.1111/biom.12745 ◽

2017 ◽

Vol 74 (1) ◽

pp. 176-184

Author(s):

Qianchuan He ◽

Yang Liu ◽

Ulrike Peters ◽

Li Hsu

Keyword(s):

Association Analysis ◽

Somatic Mutation ◽

Mutation Data

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CANCERSIGN: a user-friendly and robust tool for identification and classification of mutational signatures and patterns in cancer genomes

10.1101/424960 ◽

2018 ◽

Cited By ~ 1

Author(s):

Masroor Bayati ◽

Hamid Reza Rabiee ◽

Mehrdad Mehrbod ◽

Fatemeh Vafaee ◽

Diako Ebrahimi ◽

...

Keyword(s):

Somatic Mutation ◽

Whole Genome ◽

Mutational Signatures ◽

Coding Regions ◽

Whole Exome ◽

Cancer Genomes ◽

Genome Consortium ◽

User Friendly ◽

Pooled Samples

Analyses of large somatic mutation datasets, using advanced computational algorithms, have revealed at least 30 independent mutational signatures in tumor samples. These studies have been instrumental in identification and quantification of responsible endogenous and exogenous molecular processes in cancer. The quantitative approach used to deconvolute mutational signatures is becoming an integral part of cancer research. Therefore, development of a stand-alone tool with a user-friendly graphical interface for analysis of cancer mutational signatures is necessary. In this manuscript, we introduce CANCERSIGN as an open access bioinformatics tool that uses raw mutation data (BED files) as input, and identifies 3-mer and 5-mer mutational signatures. CANCERSIGN enables users to identify signatures within whole genome, whole exome or pooled samples. It can also identify signatures in specific regions of the genome (defined by user). Additionally, this tool enables users to perform clustering on tumor samples based on the raw mutation counts as well as using the proportion of mutational signatures in each sample. Using this tool, we analysed all the whole genome somatic mutation datasets profiled by the International Cancer Genome Consortium (ICGC) and identified a number of novel signatures. By examining signatures found in exonic and non-exonic regions of the genome using WGS and comparing this to signatures found in WES data we observe that WGS can identify additional non-exonic signatures that are enriched in the non-coding regions of the genome while the deeper sequencing of WES may help identify weak signatures that are otherwise missed in shallower WGS data.

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