MiSTIC, an integrated platform for the analysis of heterogeneity in large tumour transcriptome datasets.
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| Abstract |
Genome-wide transcriptome profiling has enabled non-supervised classification of tumours, revealing different sub-groups characterized by specific gene expression features. However, the biological significance of these subtypes remains for the most part unclear. We describe herein an interactive platform, Minimum Spanning Trees Inferred Clustering (MiSTIC), that integrates the direct visualization and comparison of the gene correlation structure between datasets, the analysis of the molecular causes underlying co-variations in gene expression in cancer samples, and the clinical annotation of tumour sets defined by the combined expression of selected biomarkers. We have used MiSTIC to highlight the roles of specific transcription factors in breast cancer subtype specification, to compare the aspects of tumour heterogeneity targeted by different prognostic signatures, and to highlight biomarker interactions in AML. A version of MiSTIC preloaded with datasets described herein can be accessed through a public web server (http://mistic.iric.ca); in addition, the MiSTIC software package can be obtained (github.com/iric-soft/MiSTIC) for local use with personalized datasets.
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| Authors | Sebastien Lemieux, Tobias Sargeant, David Laperrière, Houssam Ismail, Geneviève Boucher, Marieke Rozendaal, Vincent-Philippe Lavallée, Dariel Ashton-Beaucage, Brian Wilhelm, Josée Hébert, Douglas J Hilton, Sylvie Mader, Guy Sauvageau |
| Journal | Nucleic acids research (Nucleic Acids Res) Vol. 45 Issue 13 Pg. e122 (Jul 27 2017) ISSN: 1362-4962 [Electronic] England |
| PMID | 28472340 (Publication Type: Journal Article) |
| Copyright | © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research. |
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