RosettaSX: Reliable gene expression signature scoring of cancer models and patients

• Main Authors: Julian Kreis, Boro Nedić, Johanna Mazur, Miriam Urban, Sven-Eric Schelhorn, Thomas Grombacher, Felix Geist, Benedikt Brors, Michael Zühlsdorf, Eike Staub
• Format: Article
• Language: English
• Published: Elsevier 2021-11-01
• Series: Neoplasia: An International Journal for Oncology Research
• Subjects: Gene expression signature; Cancer expression profiling; Cancer subtypes; Multiomics Analyses; Analyses; Web service
• Online Access: http://www.sciencedirect.com/science/article/pii/S1476558621000750

Gene expression signatures have proven their potential to characterize important cancer phenomena like oncogenic signaling pathway activities, cellular origins of tumors, or immune cell infiltration into tumor tissues. Large collections of expression signatures provide the basis for their application to data sets, but the applicability of each signature in a new experimental context must be reassessed. We apply a methodology that utilizes the previously developed concept of coherent expression of genes in signatures to identify translatable signatures before scoring their activity in single tumors. We present a web interface (www.rosettasx.com) that applies our methodology to expression data from the Cancer Cell Line Encyclopaedia and The Cancer Genome Atlas. Configurable heat maps visualize per-cancer signature scores for 293 hand-curated literature-derived gene sets representing a wide range of cancer-relevant transcriptional modules and phenomena. The platform allows users to complement heatmaps of signature scores with molecular information on SNVs, CNVs, gene expression, gene dependency, and protein abundance or to analyze own signatures. Clustered heatmaps and further plots to drill-down results support users in studying oncological processes in cancer subtypes, thereby providing a rich resource to explore how mechanisms of cancer interact with each other as demonstrated by exemplary analyses of 2 cancer types.