Small molecule inhibition of FOXM1: How to bring a novel compound into genomic context

Abstract

Deregulation of transcription factor (TF) networks is emerging as a major pathogenic event in many human cancers (Darnell, 2002 [1]; Libermann and Zerbini, 2006 [2]; Laoukili et al., 2007 [3]). Small molecule intervention is an attractive avenue to understand TF regulatory mechanisms in healthy and disease state, as well as for exploiting these targets therapeutically (Koehler et al., 2003 [4]; Berg, 2008 [5]; Koehler, 2010 [6]). However, because of their physico-chemical properties, TF targeting has been proven to be difficult (Verdine and Walensky, 2007 [7]). The TF FOXM1 is an important mitotic player (Wonsey and Follettie, 2005 [8]; Laoukili et al., 2005 [9]; McDonald, 2005 [10]) also implicated in cancer progression (Laoukili et al., 2007 [3]; Teh, 2011 [11]; Koo, 2012 [12]) and drug resistance development (Kwok et al., 2010 [13]; Carr et al., [14]). Therefore, its inhibition is an attractive goal for cancer therapy. Here, we describe a computational biology approach, by giving detailed insights into methodologies and technical results, which was used to analyze the transcriptional RNA-Seq data presented in our previous work (Gormally et al., 2014 [20]). Our Bioinformatics analysis shed light on the cellular effect of a novel FOXM1 inhibitor (FDI-6) newly identified through a biophysical screen. The data for this report is available at the public GEO repository (accession number http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE58626).

Keywords: FOXM1; Genomics; RNA-Seq; Small molecule inhibition; Transcription.

Fig. 1

Reproducibility analysis of the RNA-Seq data. (A) Hierarchical clustering of gene expression profiles, showing the similarity of different treatment times. See Table 2 for details of Euclidian distance between paired libraries. h = hours. (B) Multidimensional scaling plot of different time points. Color legend is reported in the upper left corner of the plot. hrs = hours; dim = dimension; logFC = logarithm of fold change. (C) Differential expression between untreated libraries. Each comparison is run between one library and the other two (e.g. in “Rep 1 vs Rep 2 & 3” replicate 1 indicates first replicate versus the other two (second and third) replicates pulled together). up = up-regulated; down = down-regulated; both = up- and down-regulated; Rep = replicate.

Fig. 2

Differential expression and time clustering analysis. (A) Global differential expression map of RNA-Seq after 3 h treatment versus untreated. Y-axis: logarithm of fold change (logFC); X-axis: logarithm of counts per million of reads (logCPM). Red dots: up-regulated genes (n = 1951); green dots: down-regulated genes (n = 1552). FDR = false discovery rate. (B) Number of genes differentially expressed for each couple of time points. Up = upregulated in second time points compared to first one; down = downregulated in second time point compared to first one; both = some of up and down categories. Abbreviations as in Fig. 1C. (C) Venn diagrams showing the overlap of differentially expressed genes at the different time points. hrs = hours. (D) Temporal cluster analysis grouping genes that show similar changes in expression after FDI-6 treatment, original profiles. (E) Same as D, standardized profiles.

Fig. 3

Cross validation with independent data sets. (A) Barplots showing the percentage of differentially expressed gene sets with a transcription factor occupancy in the promoter region (2 kb upstream of tss). Plots are shown for the closely related forkhead transcription factors FOXM1, FOXA1, FOXA2, FOXP2 as well as the transcriptional activator GATA1. For each plot, from left to right, bars represent down-regulated (down), up-regulated (up), and not differentially expressed (not de) gene sets. (B) Barplot showing the percentage of genes in the different temporal clusters with a FOXM1 peak in the promoter region. (C) Barplot showing the fraction of genes that were up- and down-regulated by FDI-6 and were commonly differentially expressed in an existing microarray data set of FOXM1 siRNA knockdown. Error bars indicate s.e.m.