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Abstract Differential gene expression analysis may discover a set of genes too large to easily investigate, so a means of ranking genes by biological interest level is desired. p values are frequently abused for this purpose. As an alternative, we propose a method of ranking by confidence bounds on the log fold change, based on the previously developed TREAT test. These confidence bounds provide guaranteed false discovery rate and false coverage-statement rate control. When applied to a breast cancer dataset, the top-ranked genes by Topconfects emphasize markedly different biological processes compared to the top-ranked genes by p value.

Triple-negative breast cancer (TNBC) represents 10-20% of all human ductal adenocarcinomas and has a poor prognosis relative to other subtypes. Hence, new molecular targets for therapeutic intervention are necessary. Analyses of panels of human or mouse cancer lines derived from the same individual that differ in their cellular phenotypes but not in genetic background have been instrumental in defining the molecular players that drive the various hallmarks of cancer. To determine the molecular regulators of metastasis in TNBC, we completed a rigorous in vitro and in vivo characterisation of four populations of the MDA-MB-231 human breast cancer line ranging in aggressiveness from non-metastatic to spontaneously metastatic to lung, liver, spleen and lymph node. Single nucleotide polymorphism (SNP) array analyses and genome-wide mRNA expression profiles of tumour cells isolated from orthotopic mammary xenografts were compared between the four lines to define both cell autonomous pathways and genes associated with metastatic proclivity. Gene set enrichment analysis (GSEA) demonstrated an unexpected association between both ribosome biogenesis and mRNA metabolism and metastatic capacity. Differentially expressed genes or families of related genes were allocated to one of four categories, associated with either metastatic initiation (e.g. CTSC, ENG, BMP2), metastatic virulence (e.g. ADAMTS1, TIE1), metastatic suppression (e.g. CST1, CST2, CST4, CST6, SCNNA1, BMP4) or metastatic avirulence (e.g. CD74). Collectively, this model system based on MDA-MB-231 cells should be useful for the assessment of gene function in the metastatic cascade and also for the testing of novel experimental therapeutics for the treatment of TNBC. This article has an associated First Person interview with the first author of the paper.