scholarly journals Detecting presence of mutational signatures in cancer with confidence

2017 ◽  
Author(s):  
Xiaoqing Huang ◽  
Damian Wojtowicz ◽  
Teresa M. Przytycka
Keyword(s):  
Breast Cancer ◽  
Somatic Mutations ◽  
Decomposition Methods ◽  
R Package ◽  
The Novel ◽  
Cancer Etiology ◽  
Mutational Signatures ◽  
Dna Damage And Repair ◽  
Cancer Genomes ◽  
Additional Support

AbstractCancers arise as the result of somatically acquired changes in the DNA of cancer cells. However, in addition to the mutations that confer a growth advantage, cancer genomes accumulate a large number of somatic mutations resulting from normal DNA damage and repair processes as well as mutations triggered by carcinogenic exposures or cancer related aberrations of DNA mainte-nance machinery. These mutagenic processes often produce characteristic mutational patterns called mutational signatures. Decomposition of cancer’s mutation catalog into mutations consistent with such signatures can provide valuable information about cancer etiology. However, the results from different decomposition methods are not always consistent. Hence, one needs to not only be able to decompose a patient’s mutational profile into signatures but also to establish the accuracy of such decomposition. We proposed two complementary ways of measuring confidence and stability of decomposition results and applied them to analyze mutational signatures in breast cancer genomes. We identified very stable and highly unstable signatures, as well as signatures that have been missed altogether. We also provided additional support for the novel signatures. Our results emphasize the importance of assessing the confidence and stability of inferred signature contributions. All tools developed in this paper have been implemented in an R package, called SignatureEstimation, which is available from https://www.ncbi.nlm.nih.gov/CBBresearch/Przytycka/index.cgi#signatureestimation.

scholarly journals Network-based approaches elucidate differences within APOBEC and clock-like signatures in breast cancer

2019 ◽  
Author(s):  
Yoo-Ah Kim ◽  
Damian Wojtowicz ◽  
Rebecca Sarto Basso ◽  
Itay Sason ◽  
Welles Robinson ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Dna Damage ◽  
Somatic Mutations ◽  
Cancer Genome ◽  
Mutational Signatures ◽  
Dna Damage And Repair ◽  
Cancer Genomes ◽  
Cancer Mutations ◽  
Functional Pathways ◽  
Dna Maintenance

AbstractStudies of cancer mutations typically focus on identifying cancer driving mutations. However, in addition to the mutations that confer a growth advantage, cancer genomes accumulate a large number of passenger somatic mutations resulting from normal DNA damage and repair processes as well as mutations triggered by carcinogenic exposures or cancer related aberrations of DNA maintenance machinery. These mutagenic processes often produce characteristic mutational patterns called mutational signatures. Understanding the etiology of the mutational signatures shaping a cancer genome is an important step towards understanding tumorigenesis. Considering mutational signatures as phenotypes, we asked two complementary questions (i) what are functional pathways whose geneexpressionprofiles are associated with mutational signatures, and (ii) what aremutated pathways(if any) that might underlie specific mutational signatures? We have been able to identify pathways associated with mutational signatures on both expression and mutation levels. In particular, our analysis provides novel insights into mutagenic processes in breast cancer by capturing important differences in the etiology of different APOBEC related signatures and the two clock-like signatures. These results are important for understanding mutagenic processes in cancer and for developing personalized drug therapies.

scholarly journals DNA deaminases induce break-associated mutation showers with implication of APOBEC3B and 3A in breast cancer kataegis

eLife ◽  
2013 ◽  
Vol 2 ◽  
Author(s):  
Benjamin JM Taylor ◽  
Serena Nik-Zainal ◽  
Yee Ling Wu ◽  
Lucy A Stebbings ◽  
Keiran Raine ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Abasic Site ◽  
Dna Breaks ◽  
Mutational Signatures ◽  
Genome Wide ◽  
Cancer Genomes ◽  
Strand Breakage ◽  
Cytidine Deamination ◽  
Apobec Family ◽  
Insight Into

Breast cancer genomes have revealed a novel form of mutation showers (kataegis) in which multiple same-strand substitutions at C:G pairs spaced one to several hundred nucleotides apart are clustered over kilobase-sized regions, often associated with sites of DNA rearrangement. We show kataegis can result from AID/APOBEC-catalysed cytidine deamination in the vicinity of DNA breaks, likely through action on single-stranded DNA exposed during resection. Cancer-like kataegis can be recapitulated by expression of AID/APOBEC family deaminases in yeast where it largely depends on uracil excision, which generates an abasic site for strand breakage. Localized kataegis can also be nucleated by an I-SceI-induced break. Genome-wide patterns of APOBEC3-catalyzed deamination in yeast reveal APOBEC3B and 3A as the deaminases whose mutational signatures are most similar to those of breast cancer kataegic mutations. Together with expression and functional assays, the results implicate APOBEC3B/A in breast cancer hypermutation and give insight into the mechanism of kataegis.

scholarly journals Detection and benchmarking of somatic mutations in cancer genomes using RNA-seq data

2018 ◽  
Author(s):  
Alexandre Coudray ◽  
Anna M. Battenhouse ◽  
Philipp Bucher ◽  
Vishwanath R. Iyer
Keyword(s):  
Exome Sequencing ◽  
Somatic Mutations ◽  
Low Cost ◽  
Systematic Evaluation ◽  
Rna Seq ◽  
Whole Exome ◽  
Cancer Genomes ◽  
Additional Support ◽  
Coding Variants ◽  
Measure Gene Expression

ABSTRACTTo detect functional somatic mutations in tumor samples, whole-exome sequencing (WES) is often used for its reliability and relative low cost. RNA-seq, while generally used to measure gene expression, can potentially also be used for identification of somatic mutations. However there has been little systematic evaluation of the utility of RNA-seq for identifying somatic mutations. Here, we develop and evaluate a pipeline for processing RNA-seq data from glioblastoma multiforme (GBM) tumors in order to identify somatic mutations. The pipeline entails the use of the STAR aligner 2-pass procedure jointly with MuTect2 from GATK to detect somatic variants. Variants identified from RNA-seq data were evaluated by comparison against the COSMIC and dbSNP databases, and also compared to somatic variants identified by exome sequencing. We also estimated the putative functional impact of coding variants in the most frequently mutated genes in GBM. Interestingly, variants identified by RNA-seq alone showed better representation of GBM-related mutations cataloged by COSMIC. RNA-seq-only data substantially outperformed the ability of WES to reveal potentially new somatic mutations in known GBM-related pathways, and allowed us to build a high-quality set of somatic mutations common to exome and RNA-seq calls. Using RNA-seq data in parallel with WES data to detect somatic mutations in cancer genomes can thus broaden the scope of discoveries and lend additional support to somatic variants identified by exome sequencing alone.

scholarly journals RepairSig: Deconvolution of DNA damage and repair contributions to the mutational landscape of cancer

2020 ◽  
Author(s):  
Damian Wojtowicz ◽  
Jan Hoinka ◽  
Bayarbaatar Amgalan ◽  
Yoo-Ah Kim ◽  
Teresa M. Przytycka
Keyword(s):  
Dna Damage ◽  
Dna Repair ◽  
Mutational Signatures ◽  
Dna Damage And Repair ◽  
Therapeutic Implications ◽  
Mutational Landscape ◽  
Repair Processes ◽  
Cancer Genomes ◽  
Current Paradigm ◽  
Mutational Processes

AbstractMany mutagenic processes leave characteristic imprints on cancer genomes known as mutational signatures. These signatures have been of recent interest regarding their applicability in studying processes shaping the mutational landscape of cancer. In particular, pinpointing the presence of altered DNA repair pathways can have important therapeutic implications. However, mutational signatures of DNA repair deficiencies are often hard to infer. This challenge emerges as a result of deficient DNA repair processes acting by modifying the outcome of other mutagens. Thus, they exhibit non-additive effects that are not depicted by the current paradigm for modeling mutational processes as independent signatures. To close this gap, we present RepairSig, a method that accounts for interactions between DNA damage and repair and is able to uncover unbiased signatures of deficient DNA repair processes. In particular, RepairSig was able to replace three MMR deficiency signatures previously proposed to be active in breast cancer, with just one signature strikingly similar to the experimentally derived signature. As the first method to model interactions between mutagenic processes, RepairSig is an important step towards biologically more realistic modeling of mutational processes in cancer. The source code for RepairSig is publicly available at https://github.com/ncbi/RepairSig.

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

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Damiano Fantini ◽  
Vania Vidimar ◽  
Yanni Yu ◽  
Salvatore Condello ◽  
Joshua J. Meeks
Keyword(s):  
Cancer Biology ◽  
Somatic Mutations ◽  
R Package ◽  
Mutational Signatures ◽  
Computational Framework ◽  
Molecular Features ◽  
Dna Mutations ◽  
Human Genomes ◽  
Nucleotide Mutation ◽  
Random Patterns

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.

scholarly journals The APOBEC3A deaminase drives episodic mutagenesis in cancer cells

2021 ◽  
Author(s):  
John Maciejowski ◽  
Mia Petljak ◽  
Kevan Chu ◽  
Alexandra Dananberg ◽  
Erik Bergstrom ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Cells ◽  
Cell Lines ◽  
Human Cancer ◽  
B Cell Lymphoma ◽  
Lymphoma Cell ◽  
Mutational Signatures ◽  
Cytidine Deaminases ◽  
Human Cancer Cells ◽  
Cancer Genomes

Abstract The APOBEC3 family of cytidine deaminases is widely speculated to be a major source of somatic mutations in cancer1–3. However, causal links between APOBEC3 enzymes and mutations in human cancer cells have not been established. The identity of the APOBEC3 paralog(s) that may act as prime drivers of mutagenesis and the mechanisms underlying different APOBEC3-associated mutational signatures are unknown. To directly investigate the roles of APOBEC3 enzymes in cancer mutagenesis, candidate APOBEC3 genes were deleted from cancer cell lines recently found to naturally generate APOBEC3-associated mutations in episodic bursts4. Deletion of the APOBEC3A paralog severely diminished the acquisition of mutations of speculative APOBEC3 origins in breast cancer and lymphoma cell lines. APOBEC3 mutational burdens were undiminished in APOBEC3B knockout cell lines. APOBEC3A deletion reduced the appearance of the clustered mutation types kataegis and omikli, which are frequently found in cancer genomes. The uracil glycosylase UNG and the translesion polymerase REV1 were found to play critical roles in the generation of mutations induced by APOBEC3A. These data represent the first evidence for a long-postulated hypothesis that APOBEC3 deaminases generate prevalent clustered and non-clustered mutational signatures in human cancer cells, identify APOBEC3A as a driver of episodic mutational bursts, and dissect the roles of the relevant enzymes in generating the associated mutations in breast cancer and B cell lymphoma cell lines.

scholarly journals MutSpot: detection of non-coding mutation hotspots in cancer genomes

2019 ◽  
Author(s):  
Yu Amanda Guo ◽  
Mei Mei Chang ◽  
Anders Jacobsen Skanderup
Keyword(s):  
Somatic Mutations ◽  
R Package ◽  
Supplementary Information ◽  
Patient Specific ◽  
Supplementary Data ◽  
Link Type ◽  
Genome Wide ◽  
Cancer Genomes ◽  
User Friendly ◽  
Regulatory Dna

AbstractSummaryRecurrence and clustering of somatic mutations (hotspots) in cancer genomes may indicate positive selection and involvement in tumorigenesis. MutSpot performs genome-wide inference of mutation hotspots in non-coding and regulatory DNA of cancer genomes. MutSpot performs feature selection across hundreds of epigenetic and sequence features followed by estimation of position and patient-specific background somatic mutation probabilities. MutSpot is user-friendly, works on a standard workstation, and scales to thousands of cancer genomes.Availability and implementationMutSpot is implemented as an R package and is available at https://github.com/skandlab/MutSpot/Supplementary informationSupplementary data are available at https://github.com/skandlab/MutSpot/

scholarly journals The APOBEC3A deaminase drives episodic mutagenesis in cancer cells

2021 ◽  
Author(s):  
Mia Petljak ◽  
Kevan Chu ◽  
Alexandra Dananberg ◽  
Erik N. Bergstrom ◽  
Patrick von Morgen ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Cells ◽  
Cell Lines ◽  
Human Cancer ◽  
B Cell Lymphoma ◽  
Lymphoma Cell ◽  
Mutational Signatures ◽  
Cytidine Deaminases ◽  
Human Cancer Cells ◽  
Cancer Genomes

ABSTRACTThe APOBEC3 family of cytidine deaminases is widely speculated to be a major source of somatic mutations in cancer1–3. However, causal links between APOBEC3 enzymes and mutations in human cancer cells have not been established. The identity of the APOBEC3 paralog(s) that may act as prime drivers of mutagenesis and the mechanisms underlying different APOBEC3-associated mutational signatures are unknown. To directly investigate the roles of APOBEC3 enzymes in cancer mutagenesis, candidate APOBEC3 genes were deleted from cancer cell lines recently found to naturally generate APOBEC3-associated mutations in episodic bursts4. Deletion of the APOBEC3A paralog severely diminished the acquisition of mutations of speculative APOBEC3 origins in breast cancer and lymphoma cell lines. APOBEC3 mutational burdens were undiminished in APOBEC3B knockout cell lines. APOBEC3A deletion reduced the appearance of the clustered mutation types kataegis and omikli, which are frequently found in cancer genomes. The uracil glycosylase UNG and the translesion polymerase REV1 were found to play critical roles in the generation of mutations induced by APOBEC3A. These data represent the first evidence for a long-postulated hypothesis that APOBEC3 deaminases generate prevalent clustered and non-clustered mutational signatures in human cancer cells, identify APOBEC3A as a driver of episodic mutational bursts, and dissect the roles of the relevant enzymes in generating the associated mutations in breast cancer and B cell lymphoma cell lines.

scholarly journals MutSignatures: An R Package for Extraction and Analysis of Cancer Mutational Signatures

2020 ◽  
Author(s):  
Damiano Fantini ◽  
Vania Vidimar ◽  
Yanni Yu ◽  
Salvatore Condello ◽  
Joshua J. Meeks
Keyword(s):  
Cancer Biology ◽  
Somatic Mutations ◽  
R Package ◽  
Mutational Signatures ◽  
Computational Framework ◽  
Molecular Features ◽  
Dna Mutations ◽  
Repair Mechanisms ◽  
Cancer Types ◽  
Random Patterns

ABSTRACTCancer cells accumulate somatic mutations as result of DNA damage and inaccurate repair mechanisms. Different genetic instability processes result in distinct 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. 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.

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