EMu: probabilistic inference of mutational processes and their localization in the cancer genome

Mutational processes constantly shape the cancer genome and defects in DNA repair pathways of tumor cells facilitate the accumulation of genomic alterations [...]

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Integrative analysis of mutated genes and mutational processes reveals seven colorectal cancer subtypes

10.1101/2020.05.18.101022 ◽

2020 ◽

Author(s):

Hamed Dashti ◽

Abdollah Dehzangi ◽

Masroor Bayati ◽

James Breen ◽

Nigel Lovell ◽

...

Keyword(s):

Colorectal Cancer ◽

Biomarker Discovery ◽

Point Mutations ◽

Cancer Genome ◽

Causative Mutation ◽

Cancer Subtypes ◽

Genome Consortium ◽

Novel Concept ◽

Cancer Experience ◽

Mutational Processes

AbstractColorectal cancer (CRC) is one of the leading causes of cancer-related deaths in the world. It has been reported that ∼10%-15% of individuals with colorectal cancer experience a causative mutation in the known susceptibility genes, highlighting the importance of identifying mutations for early detection in high risk individuals. Through extensive sequencing projects such as the International Cancer Genome Consortium (ICGC), a large number of somatic point mutations have been identified that can be used to identify cancer-associated genes, as well as the signature of mutational processes defined by the tri-nucleotide sequence context (motif) of mutated sites. Mutation is the hallmark of cancer genome, and many studies have reported cancer subtyping based on the type of frequently mutated genes, or the proportion of mutational processes, however, none of these cancer subtyping methods consider these features simultaneously. This highlights the need for a better and more inclusive subtype classification approach to enable biomarker discovery and thus inform drug development for CRC. In this study, we developed a statistical pipeline based on a novel concept ‘gene-motif’, which merges mutated gene information with tri-nucleotide motif of mutated sites, to identify cancer subtypes, in this case CRCs. Our analysis identified for the first time, 3,131 gene-motif combinations that were significantly mutated in 536 ICGC colorectal cancer samples compared to other cancer types, identifying seven CRC subtypes with distinguishable phenotypes and biomarkers. Interestingly, we identified several genes that were mutated in multiple subtypes but with unique sequence contexts. Taken together, our results highlight the importance of considering both the mutation type and mutated genes in identification of cancer subtypes and cancer biomarkers.

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SigProfilerMatrixGenerator: a tool for visualizing and exploring patterns of small mutational events

10.1101/653097 ◽

2019 ◽

Author(s):

Erik N. Bergstrom ◽

Mi Ni Huang ◽

Uma Mahto ◽

Mark Barnes ◽

Michael R. Stratton ◽

...

Keyword(s):

Cancer Genome ◽

Single Base ◽

Insertions And Deletions ◽

Link Type ◽

Base Substitutions ◽

Mutational Pattern ◽

Comprehensive Classification ◽

Single Matrix ◽

Mutational Processes

ABSTRACTBackgroundCancer genomes are peppered with somatic mutations imprinted by different mutational processes. The mutational pattern of a cancer genome can be used to identify and understand the etiology of the underlying mutational processes. A plethora of prior research has focused on examining mutational signatures and mutational patterns from single base substitutions and their immediate sequencing context. We recently demonstrated that further classification of small mutational events (including substitutions, insertions, deletions, and doublet substitutions) can be used to provide a deeper understanding of the mutational processes that have molded a cancer genome. However, there has been no standard tool that allows fast, accurate, and comprehensive classification for all types of small mutational eventsResultsHere, we present SigProfilerMatrixGenerator, a computational tool designed for optimized exploration and visualization of mutational patterns for all types of small mutational events. SigProfilerMatrixGenerator is written in Python with an R wrapper package provided for users that prefer working in an R environment. SigProfilerMatrixGenerator produces fourteen distinct matrices by considering transcriptional strand bias of individual events and by incorporating distinct classifications for single base substitutions, doublet base substitutions, and small insertions and deletions. While the tool provides a comprehensive classification of mutations, SigProfilerMatrixGenerator is also faster and more memory efficient than existing tools that generate only a single matrix.ConclusionsSigProfilerMatrixGenerator provides a standardized method for classifying small mutational events that is both efficient and scalable to large datasets. In addition to extending the classification of single base substitutions, the tool is the first to provide support for classifying doublet base substitutions and small insertions and deletions. SigProfilerMatrixGenerator is freely available athttps://github.com/AlexandrovLab/SigProfilerMatrixGeneratorwith an extensive documentation athttps://osf.io/s93d5/wiki/home/.

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