Chapter 1 Introduction

Genomic alterations including single nucleotide substitution (SBS), copy number alteration (CNA), etc. are the major force for cancer initialization and development. Due to the specificity of molecular lesions caused by genomic alterations, we can generate characteristic alteration spectra, called mutational signature.

1.1 Biological Significance of Mutational Signature

To illustrate the biological significance of mutational signatures, we show some well organized figures here.

The illustration of SBS signature, fig source: https://www.nature.com/articles/nrg3729

Figure 1.1: The illustration of SBS signature, fig source: https://www.nature.com/articles/nrg3729

The illustration of SBS signature (2), fig source: https://www.nature.com/articles/s41467-018-05228-y

Figure 1.2: The illustration of SBS signature (2), fig source: https://www.nature.com/articles/s41467-018-05228-y

SBS signature is a famous type of mutational signature. SBS signatures are well studied and related to single-strand changes, typically caused by defective DNA repair. Common etiologies contain aging, defective DNA mismatch repair, smoking, ultraviolet light exposure and APOBEC.

Currently, all SBS signatures are summarized in COSMIC database, including two versions: v2 and v3.

Recently, Alexandrov et al. (2020) extends the concept of mutational signature to three types of alteration: SBS, DBS and INDEL. All reported common signatures are recorded in COSMIC (https://cancer.sanger.ac.uk/cosmic/signatures/), so we usually call them COSMIC signatures.

The illustration of copy number signatures, fig source: https://www.nature.com/articles/s41588-018-0212-y

Figure 1.3: The illustration of copy number signatures, fig source: https://www.nature.com/articles/s41588-018-0212-y

Copy number signatures are less studied and many works are still to be done. The introduction is described in Chapter 3.

1.2 Sigminer

Here, we present an easy-to-use and scalable toolkit for mutational signature analysis and visualization in R. We named it sigminer (signature + miner). This tool can help users to extract, analyze and visualize signatures from genomic alteration records, thus providing new insight into cancer study.

Currently, sigminer supports four types of signature:

  • SBS signature in the form of 96 (6, 24, 384, 1536 and 6144) components.
  • DBS signature in the form of 78 (186) components.
  • ID (INDEL) signature in the form of 83 (28) components.
  • Copy number signature by the method either from Macintyre et al. (2018) or from our group work.

1.3 Installation

The stable release version of sigminer package can be installed from the CRAN:

Set dependencies = TRUE is recommended because many packages are required for full features in sigminer.

The development version of sigminer package can be installed from Github:

1.4 Issues or Suggestions

Any issue or suggestion can be posted on GitHub issue, we will reply ASAP.

Any pull requrest is welcome.

1.6 Overview of Contents

The contents of this manual have been divided into 4 sections:

  • Common workflow.
    • de novo signature discovery.
    • single sample exposure quantification.
    • subtype prediction.
  • Target visualization.
    • copy number profile.
    • copy number distribution.
    • catalogue profile.
    • signature profile.
    • exposure profile.
  • Universal analysis.
    • association analysis.
    • group analysis.
  • Other utilities.

1.7 Citation and LICENSE


The software is made available for non commercial research purposes only under the MIT. However, notwithstanding any provision of the MIT License, the software currently may not be used for commercial purposes without explicit written permission after contacting Shixiang Wang or Xue-Song Liu .

MIT © 2019-2020 Shixiang Wang, Xue-Song Liu

MIT © 2018 Geoffrey Macintyre

MIT © 2018 Anand Mayakonda


Cancer Biology Group @ShanghaiTech

Research group led by Xue-Song Liu in ShanghaiTech University

References

Alexandrov, Ludmil B, Jaegil Kim, Nicholas J Haradhvala, Mi Ni Huang, Alvin Wei Tian Ng, Yang Wu, Arnoud Boot, et al. 2020. “The Repertoire of Mutational Signatures in Human Cancer.” Nature 578 (7793). Nature Publishing Group: 94–101.

Macintyre, Geoff, Teodora E Goranova, Dilrini De Silva, Darren Ennis, Anna M Piskorz, Matthew Eldridge, Daoud Sie, et al. 2018. “Copy Number Signatures and Mutational Processes in Ovarian Carcinoma.” Nature Genetics 50 (9). Nature Publishing Group: 1262–70.