Abstract

With the increased use of next-generation sequencing generating large amounts of genomic data, gene expression signatures are becoming critically important tools for the interpretation of these data, and are poised to have a substantial effect on diagnosis, management, and prognosis for a number of diseases. It is becoming crucial to establish whether the expression patterns and statistical properties of sets of genes, or gene signatures, are conserved across independent datasets. Conversely, it is necessary to compare established signatures on the same dataset to better understand how they capture different clinical or biological characteristics. Here we describe how to use sigQC, a tool that enables a streamlined, systematic approach for the evaluation of previously obtained gene signatures across multiple gene expression datasets. We implemented sigQC in an R package, making it accessible to users who have knowledge of file input/output and matrix manipulation in R and a moderate grasp of core statistical principles. SigQC has been adopted in basic biology and translational studies, including, but not limited to, the evaluation of multiple gene signatures for potential clinical use as cancer biomarkers. This protocol uses a previously obtained signature for breast cancer metastasis as an example to illustrate the critical quality control steps involved in evaluating its expression, variability, and structure in breast tumor RNA-sequencing data, a different dataset from that in which the signature was originally derived. We demonstrate how the outputs created from sigQC can be used for the evaluation of gene signatures on large-scale gene expression datasets.

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Data availability

All data that have been used in this publication have been made available through Zenodo at https://doi.org/10.5281/zenodo.1319848.

Code availability

All code that constitutes the sigQC R package is available for use under a GPL v3 license and can be downloaded from the CRAN repository at https://CRAN.R-project.org/package=sigQC.

All scripts used to create the figures in this paper can be downloaded through Zenodo at https://doi.org/10.5281/zenodo.1319848.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Key references using this protocol

Dhawan, A. et al. Nat. Commun. 9, 5228 (2018): https://doi.org/10.1038/s41467-018-07657-1

Haider, S. et al. Genome Biol. 17, 140 (2016): https://doi.org/10.1186/s13059-016-0999-8

Buffa, F. et al. Br. J. Cancer 102, 428–435 (2010): https://doi.org/10.1038/sj.bjc.6605450

Masiero, M. et al. Cancer Cell 24, 229–241 (2013): https://doi.org/10.1016/j.ccr.2013.06.004

Key data used in this protocol

van ’t Veer, L. J. et al. Nature 415, 530–536 (2002): https://doi.org/10.1038/415530a

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Acknowledgements

This work was funded by Cancer Research UK grant 23969 to F.M.B. (F.M.B., A.B., W.-C.C., and A.D.), the Oxford Cancer Centre (A.L.H. and A.D.), the Medical Research Council Stratified Medicine Consortium MR/M016587/1 (T.M. and E.D.), and European Research Council Consolidator Grant 772970 to F.M.B. We are also grateful for a Clarendon Scholarship to A.D.

Author information

Affiliations

  1. Computational Biology and Integrative Genomics Lab, MRC/CRUK Oxford Institute and Department of Oncology, University of Oxford, Oxford, UK

    • Andrew Dhawan
    • , Alessandro Barberis
    • , Wei-Chen Cheng
    • , Enric Domingo
    • , Tim Maughan
    • , Adrian L. Harris
    •  & Francesca M. Buffa
  2. Division of Cancer Studies, University of Manchester, Manchester, UK

    • Catharine West
  3. Translational Hematology and Oncology Research, Cleveland Clinic, Cleveland, OH, USA

    • Jacob G. Scott

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Contributions

F.M.B. conceived the idea and designed the study. A.D., A.B., W.-C.C., J.G.S., and F.M.B. contributed to statistics and data visualization. A.D. performed analyses. A.D., A.B., and W.-C.C. wrote and debugged code. A.B. and F.M.B. supervised the implementation. All authors contributed to application cases and interpretation of data. A.D. and F.M.B. wrote the manuscript, with contributions from all other authors.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Francesca M. Buffa.

Integrated supplementary information

  1. Supplementary Figure 1 Measures of expression of signature genes across TCGA breast cancer dataset.

    Expression of signature genes across the TCGA breast cancer RNA-seq dataset for the metastasis gene signature (top) and a random set of genes (bottom), shown as (a) a barplot for the proportion of samples expressing a gene above the median, (b) a density plot showing the same information as the barplots in (a), and (c) a plot of the proportion of samples showing NA expression for each of the genes of the signature.

  2. Supplementary Figure 2 Assessment of standardization of dataset values on gene signature score.

    Comparison of median and z-transformed median of signature gene expression across the RNA-seq breast cancer dataset for the metastasis gene signature (left) and the random set of genes (right).

Supplementary information

  1. Supplementary Text and Figures

    Supplementary Figs. 1 and 2, Supplementary Table 1, and Supplementary Manuals 1 and 2

  2. Reporting Summary

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DOI

https://doi.org/10.1038/s41596-019-0136-8

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