A practical framework and online tool for mutational signature analyses show intertissue variation and driver dependencies

Andrea Degasperi, Tauanne Dias Amarante, Jan Czarnecki, Scott Shooter, Xueqing Zou, Dominik Glodzik, Sandro Morganella, Arjun S. Nanda, Cherif Badja, Gene Koh, Sophie E. Momen, Ilias Georgakopoulos-Soares, João M.L. Dias, Jamie Young, Yasin Memari, Helen Davies, Serena Nik-Zainal

Research output: Contribution to journalArticlepeer-review

143 Scopus citations

Abstract

Mutational signatures are patterns of mutations that arise during tumorigenesis. We present an enhanced, practical framework for mutational signature analyses. Applying these methods to 3,107 whole-genome-sequenced (WGS) primary cancers of 21 organs reveals known signatures and nine previously undescribed rearrangement signatures. We highlight interorgan variability of signatures and present a way of visualizing that diversity, reinforcing our findings in an independent analysis of 3,096 WGS metastatic cancers. Signatures with a high level of genomic instability are dependent on TP53 dysregulation. We illustrate how uncertainty in mutational signature identification and assignment to samples affects tumor classification, reinforcing that using multiple orthogonal mutational signature data is not only beneficial, but is also essential for accurate tumor stratification. Finally, we present a reference web-based tool for cancer and experimentally generated mutational signatures, called Signal (https://signal.mutationalsignatures.com), that also supports performing mutational signature analyses.

Original languageEnglish (US)
Pages (from-to)249-263
Number of pages15
JournalNature Cancer
Volume1
Issue number2
DOIs
StatePublished - Feb 1 2020

All Science Journal Classification (ASJC) codes

  • Oncology
  • Cancer Research

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