Dataset Information

MutSignatures: an R package for extraction and analysis of cancer mutational signatures.

ABSTRACT: Cancer cells accumulate somatic mutations as result of DNA damage, inaccurate repair and other mechanisms. Different genetic instability processes result in characteristic non-random patterns of DNA mutations, also known as mutational signatures. We developed mutSignatures, an integrated R-based computational framework aimed at deciphering DNA mutational signatures. Our software provides advanced functions for importing DNA variants, computing mutation types, and extracting mutational signatures via non-negative matrix factorization. Specifically, mutSignatures accepts multiple types of input data, is compatible with non-human genomes, and supports the analysis of non-standard mutation types, such as tetra-nucleotide mutation types. We applied mutSignatures to analyze somatic mutations found in smoking-related cancer datasets. We characterized mutational signatures that were consistent with those reported before in independent investigations. Our work demonstrates that selected mutational signatures correlated with specific clinical and molecular features across different cancer types, and revealed complementarity of specific mutational patterns that has not previously been identified. In conclusion, we propose mutSignatures as a powerful open-source tool for detecting the molecular determinants of cancer and gathering insights into cancer biology and treatment.

SUBMITTER: Fantini D

PROVIDER: S-EPMC7589488 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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MutSignatures: an R package for extraction and analysis of cancer mutational signatures.

Fantini Damiano D Vidimar Vania V Yu Yanni Y Condello Salvatore S Meeks Joshua J JJ

Scientific reports 20201026 1

Cancer cells accumulate somatic mutations as result of DNA damage, inaccurate repair and other mechanisms. Different genetic instability processes result in characteristic non-random patterns of DNA mutations, also known as mutational signatures. We developed mutSignatures, an integrated R-based computational framework aimed at deciphering DNA mutational signatures. Our software provides advanced functions for importing DNA variants, computing mutation types, and extracting mutational signatures ...[more]

PMID: 33106540

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Project description:BACKGROUND:Synonymous mutations have been identified to play important roles in cancer development, although they do not modify the protein sequences. However, relatively little research has specifically delineated the functionality of synonymous mutations in cancer. RESULTS:We investigated the nucleotide-based and amino acid-based features of synonymous mutations across 15 cancer types from The Cancer Genome Atlas (TCGA), and revealed novel driver candidates by identifying hotspot mutations. Firstly, synonymous mutations were analyzed between TCGA and 1000 Genomes Project at nucleotide and amino acid levels. We found that C:G → T:A transitions were the most frequent single-base substitutions, and leucine underwent the largest number of synonymous mutations in TCGA due to prevalent C → T transition, which induced the transformation between optimal and non-optimal codons. Next, 97 synonymous hotspot mutations in 86 genes were nominated as candidate drivers with potential cancer risk by considering the mutational rates across different sequence contexts. We observed that non-CpG-island GC transition sequence context was positively selected across most of cancer types, and different sequence contexts under which hotspot mutations occur could be significance for genetic differences and functional features. We also found that the hotspots were more conserved than neutral mutations of hotspot-mutation-containing-genes and frequently happened at leucine. In addition, we mapped hotspots, neutral and non-hotspot mutations of hotspot-mutation-containing-genes to their respective protein domains and found ion transport domain was the most frequent one, which could mediate the cell interaction and had relevant implication for tumor therapy. And the signatures of synonymous hotspots were qualitatively similar with those of harmful missense variants. CONCLUSIONS:We illustrated the preferences of cancer associated synonymous mutations, especially hotspots, and laid the groundwork for understanding the synonymous mutations act as drivers in cancer.

Project description:Background While recent studies have separately explored mutational signatures and the tumor microenvironment (TME), there is limited research on the associations of both factors in a pan-cancer context. Materials and methods We performed a pan-cancer analysis of over 8,000 tumor samples from The Cancer Genome Atlas (TCGA) project. Machine learning methods were employed to systematically explore the relationship between mutational signatures and TME and develop a risk score based on TME-associated mutational signatures to predict patient survival outcomes. We also constructed an interaction model to explore how mutational signatures and TME interact and influence cancer prognosis. Results Our analysis revealed a varied association between mutational signatures and TME, with the Clock-like signature showing the most widespread influence. Risk scores based on mutational signatures mainly induced by Clock-like and AID/APOBEC activity exhibited strong pan-cancer survival stratification ability. We also propose a novel approach to predict transcriptome decomposed infiltration levels using genome-derived mutational signatures as an alternative approach for exploring TME cell types when transcriptome data are unavailable. Our comprehensive analysis revealed that certain mutational signatures and their interaction with immune cells significantly impact clinical outcomes in particular cancer types. For instance, T cell infiltration levels only served as a prognostic biomarker in melanoma patients with high ultraviolet radiation exposure, breast cancer patients with high homologous recombination deficiency signature, and lung adenocarcinoma patients with high tobacco-associated mutational signature. Conclusion Our study comprehensively explains the complex interplay between mutational signatures and immune infiltration in cancer. The results highlight the importance of considering both mutational signatures and immune phenotypes in cancer research and their significant implications for developing personalized cancer treatments and more effective immunotherapy.

Dataset Information

MutSignatures: an R package for extraction and analysis of cancer mutational signatures.

Publications

MutSignatures: an R package for extraction and analysis of cancer mutational signatures.

Similar Datasets

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